modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-27 06:27:46
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 499
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 54
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-06-27 06:26:25
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
eventdata-utd/ConfliBERT-cont-uncased-BBC_News | eventdata-utd | 2024-05-20T21:27:27Z | 0 | 0 | null | [
"en",
"region:us"
] | null | 2024-04-04T20:05:30Z | ---
language:
- en
---
Political news dataset from BBC for relevance classification. |
tali1/LLMNIDS-t5small-1 | tali1 | 2024-05-20T21:26:15Z | 108 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain",
"dataset:LLMNIDS-t5small-1/autotrain-data",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-20T21:25:49Z |
---
tags:
- autotrain
- text2text-generation
widget:
- text: "I love AutoTrain"
datasets:
- LLMNIDS-t5small-1/autotrain-data
---
# Model Trained Using AutoTrain
- Problem type: Seq2Seq
## Validation Metrics
loss: 0.03447870910167694
rouge1: 97.3376
rouge2: 43.622
rougeL: 97.3669
rougeLsum: 97.3596
gen_len: 4.5563
runtime: 31.2263
samples_per_second: 218.918
steps_per_second: 13.706
: 3.0
|
sxg2520/tiny-chatbot-dpo | sxg2520 | 2024-05-20T21:25:59Z | 2 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T21:23:56Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
model-index:
- name: tiny-chatbot-dpo
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. -->
# tiny-chatbot-dpo
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
osrojo/Emotion | osrojo | 2024-05-20T21:24:37Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-05-20T21:24:20Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
yifanxie/jasper-cicada | yifanxie | 2024-05-20T21:19:32Z | 133 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-20T21:17:28Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.40.1
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="yifanxie/jasper-cicada",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<eos><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"yifanxie/jasper-cicada",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"yifanxie/jasper-cicada",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "yifanxie/jasper-cicada" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<eos><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 256
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GemmaForCausalLM(
(model): GemmaModel(
(embed_tokens): Embedding(256000, 2048, padding_idx=0)
(layers): ModuleList(
(0-17): 18 x GemmaDecoderLayer(
(self_attn): GemmaSdpaAttention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=256, bias=False)
(v_proj): Linear(in_features=2048, out_features=256, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(rotary_emb): GemmaRotaryEmbedding()
)
(mlp): GemmaMLP(
(gate_proj): Linear(in_features=2048, out_features=16384, bias=False)
(up_proj): Linear(in_features=2048, out_features=16384, bias=False)
(down_proj): Linear(in_features=16384, out_features=2048, bias=False)
(act_fn): PytorchGELUTanh()
)
(input_layernorm): GemmaRMSNorm()
(post_attention_layernorm): GemmaRMSNorm()
)
)
(norm): GemmaRMSNorm()
)
(lm_head): Linear(in_features=2048, out_features=256000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
ezuryy/gpt-neo-125m__Summ_Dialog_News__qlora | ezuryy | 2024-05-20T21:16:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T21:16:56Z | ---
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] |
matthieuzone/PECORINObis | matthieuzone | 2024-05-20T21:16:23Z | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T21:08:02Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/PECORINObis
<Gallery />
## Model description
These are matthieuzone/PECORINObis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/PECORINObis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
BilalMuftuoglu/beit-base-patch16-224-85-fold4 | BilalMuftuoglu | 2024-05-20T21:09:42Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-20T20:49:03Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-85-fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9545454545454546
---
<!-- 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. -->
# beit-base-patch16-224-85-fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9545
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.6874 | 0.6818 |
| No log | 2.0 | 4 | 0.7083 | 0.7045 |
| No log | 3.0 | 6 | 0.8871 | 0.7045 |
| No log | 4.0 | 8 | 0.7305 | 0.7045 |
| 0.6246 | 5.0 | 10 | 0.5791 | 0.7045 |
| 0.6246 | 6.0 | 12 | 0.5888 | 0.7045 |
| 0.6246 | 7.0 | 14 | 0.6050 | 0.7045 |
| 0.6246 | 8.0 | 16 | 0.5468 | 0.7045 |
| 0.6246 | 9.0 | 18 | 0.5351 | 0.7045 |
| 0.4453 | 10.0 | 20 | 0.4155 | 0.8409 |
| 0.4453 | 11.0 | 22 | 0.8266 | 0.7045 |
| 0.4453 | 12.0 | 24 | 0.3905 | 0.8409 |
| 0.4453 | 13.0 | 26 | 0.3942 | 0.8409 |
| 0.4453 | 14.0 | 28 | 0.4015 | 0.8409 |
| 0.3613 | 15.0 | 30 | 0.3474 | 0.8182 |
| 0.3613 | 16.0 | 32 | 0.4763 | 0.8182 |
| 0.3613 | 17.0 | 34 | 0.3894 | 0.7955 |
| 0.3613 | 18.0 | 36 | 0.4290 | 0.7955 |
| 0.3613 | 19.0 | 38 | 0.3525 | 0.8636 |
| 0.2928 | 20.0 | 40 | 0.3426 | 0.8864 |
| 0.2928 | 21.0 | 42 | 0.4060 | 0.8182 |
| 0.2928 | 22.0 | 44 | 0.6962 | 0.75 |
| 0.2928 | 23.0 | 46 | 0.3514 | 0.8636 |
| 0.2928 | 24.0 | 48 | 0.5302 | 0.8409 |
| 0.2256 | 25.0 | 50 | 0.3094 | 0.8636 |
| 0.2256 | 26.0 | 52 | 0.2977 | 0.8636 |
| 0.2256 | 27.0 | 54 | 0.4883 | 0.8182 |
| 0.2256 | 28.0 | 56 | 0.3008 | 0.8409 |
| 0.2256 | 29.0 | 58 | 0.3226 | 0.8409 |
| 0.2231 | 30.0 | 60 | 0.4101 | 0.8409 |
| 0.2231 | 31.0 | 62 | 0.3197 | 0.8409 |
| 0.2231 | 32.0 | 64 | 0.4133 | 0.7727 |
| 0.2231 | 33.0 | 66 | 0.2923 | 0.8636 |
| 0.2231 | 34.0 | 68 | 0.4391 | 0.8636 |
| 0.1756 | 35.0 | 70 | 0.3016 | 0.8636 |
| 0.1756 | 36.0 | 72 | 0.2749 | 0.9091 |
| 0.1756 | 37.0 | 74 | 0.3146 | 0.8864 |
| 0.1756 | 38.0 | 76 | 0.3095 | 0.8409 |
| 0.1756 | 39.0 | 78 | 0.3017 | 0.8864 |
| 0.1592 | 40.0 | 80 | 0.2762 | 0.8864 |
| 0.1592 | 41.0 | 82 | 0.4054 | 0.8409 |
| 0.1592 | 42.0 | 84 | 0.2787 | 0.8864 |
| 0.1592 | 43.0 | 86 | 0.3193 | 0.8636 |
| 0.1592 | 44.0 | 88 | 0.2783 | 0.9091 |
| 0.1857 | 45.0 | 90 | 0.2934 | 0.9091 |
| 0.1857 | 46.0 | 92 | 0.3579 | 0.8636 |
| 0.1857 | 47.0 | 94 | 0.3501 | 0.8864 |
| 0.1857 | 48.0 | 96 | 0.3358 | 0.8864 |
| 0.1857 | 49.0 | 98 | 0.2981 | 0.9091 |
| 0.1179 | 50.0 | 100 | 0.3364 | 0.8636 |
| 0.1179 | 51.0 | 102 | 0.3324 | 0.8636 |
| 0.1179 | 52.0 | 104 | 0.1725 | 0.9545 |
| 0.1179 | 53.0 | 106 | 0.1222 | 0.9545 |
| 0.1179 | 54.0 | 108 | 0.1500 | 0.9091 |
| 0.1448 | 55.0 | 110 | 0.2358 | 0.9091 |
| 0.1448 | 56.0 | 112 | 0.2224 | 0.9091 |
| 0.1448 | 57.0 | 114 | 0.1457 | 0.9318 |
| 0.1448 | 58.0 | 116 | 0.1745 | 0.9318 |
| 0.1448 | 59.0 | 118 | 0.1990 | 0.9091 |
| 0.1343 | 60.0 | 120 | 0.2905 | 0.8864 |
| 0.1343 | 61.0 | 122 | 0.3842 | 0.8864 |
| 0.1343 | 62.0 | 124 | 0.3031 | 0.8864 |
| 0.1343 | 63.0 | 126 | 0.2642 | 0.8864 |
| 0.1343 | 64.0 | 128 | 0.2412 | 0.9091 |
| 0.1109 | 65.0 | 130 | 0.3347 | 0.8864 |
| 0.1109 | 66.0 | 132 | 0.4005 | 0.8864 |
| 0.1109 | 67.0 | 134 | 0.2905 | 0.8864 |
| 0.1109 | 68.0 | 136 | 0.3168 | 0.9318 |
| 0.1109 | 69.0 | 138 | 0.3845 | 0.8864 |
| 0.1221 | 70.0 | 140 | 0.3178 | 0.9318 |
| 0.1221 | 71.0 | 142 | 0.2690 | 0.9318 |
| 0.1221 | 72.0 | 144 | 0.2516 | 0.8864 |
| 0.1221 | 73.0 | 146 | 0.2347 | 0.9091 |
| 0.1221 | 74.0 | 148 | 0.2376 | 0.9318 |
| 0.1191 | 75.0 | 150 | 0.2480 | 0.9318 |
| 0.1191 | 76.0 | 152 | 0.2597 | 0.9091 |
| 0.1191 | 77.0 | 154 | 0.3071 | 0.9091 |
| 0.1191 | 78.0 | 156 | 0.3354 | 0.9091 |
| 0.1191 | 79.0 | 158 | 0.2988 | 0.8864 |
| 0.1133 | 80.0 | 160 | 0.2760 | 0.9091 |
| 0.1133 | 81.0 | 162 | 0.2832 | 0.9318 |
| 0.1133 | 82.0 | 164 | 0.2793 | 0.9318 |
| 0.1133 | 83.0 | 166 | 0.2779 | 0.9091 |
| 0.1133 | 84.0 | 168 | 0.3004 | 0.8864 |
| 0.098 | 85.0 | 170 | 0.3275 | 0.8864 |
| 0.098 | 86.0 | 172 | 0.3394 | 0.8864 |
| 0.098 | 87.0 | 174 | 0.3257 | 0.8864 |
| 0.098 | 88.0 | 176 | 0.3172 | 0.8864 |
| 0.098 | 89.0 | 178 | 0.3122 | 0.9318 |
| 0.0917 | 90.0 | 180 | 0.3208 | 0.9318 |
| 0.0917 | 91.0 | 182 | 0.3236 | 0.9318 |
| 0.0917 | 92.0 | 184 | 0.3274 | 0.9318 |
| 0.0917 | 93.0 | 186 | 0.3331 | 0.8864 |
| 0.0917 | 94.0 | 188 | 0.3379 | 0.8864 |
| 0.0989 | 95.0 | 190 | 0.3404 | 0.8864 |
| 0.0989 | 96.0 | 192 | 0.3452 | 0.8864 |
| 0.0989 | 97.0 | 194 | 0.3491 | 0.8864 |
| 0.0989 | 98.0 | 196 | 0.3489 | 0.8864 |
| 0.0989 | 99.0 | 198 | 0.3482 | 0.8864 |
| 0.0916 | 100.0 | 200 | 0.3467 | 0.8864 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
beam-searchers/dpo-llama-lora-model | beam-searchers | 2024-05-20T21:08:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-19T20:13:04Z | ---
library_name: transformers
tags:
- unsloth
---
# 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] |
msbayindir/mt5-small-sample | msbayindir | 2024-05-20T21:06:44Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-19T00:29:37Z | ---
library_name: transformers
language:
- tr
metrics:
- bleu
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
<p>
This model(10-epoch) has been trained with the dataset
<a href="https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset">Turkish-Reading-Comprehension-Question-Answering-Dataset </a>for Turkish question-answering
</p>
<h1>Usage</h1>
<h2>install package</h2>
<ul>
<li>!pip install transformers</li>
<li>!pip install evaluate</li>
<li>!pip install rouge</li>
</ul>
<h2>Codes</h2>
<h3>Imports</h3>
```python
import torch
import nltk
import string
import evaluate # Bleu
import pandas as pd
import numpy as np
from transformers import T5Tokenizer, MT5Model, MT5ForConditionalGeneration, MT5TokenizerFast
import warnings
```
```python
TOKENIZER = MT5TokenizerFast.from_pretrained("msbayindir/mt5-small-sample")
MODEL = MT5ForConditionalGeneration.from_pretrained("msbayindir/mt5-small-sample", return_dict=True)
DEVICE = "cuda:0"
MODEL = MODEL.to(device=DEVICE)
Q_LEN = 256 # Question Length
T_LEN = 32
```
```python
def predict_answer(context, question, ref_answer=None):
inputs = TOKENIZER(question, context, max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True)
input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask)
predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True)
if ref_answer:
# Load the Bleu metric
bleu = evaluate.load("google_bleu")
score = bleu.compute(predictions=[predicted_answer],
references=[ref_answer])
print("Context: \n", context)
print("\n")
print("Question: \n", question)
return {
"Reference Answer: ": ref_answer,
"Predicted Answer: ": predicted_answer,
"BLEU Score: ": score
}
else:
return predicted_answer
```
###Samples
```python
context = """Katmandu Büyükşehir Şehri (KMC),
uluslararası ilişkileri teşvik etmek amacıyla Uluslararası İlişkiler Sekreterliği (IRC) kurmuştur.
KMC'nin ilk uluslararası ilişkisi 1975 yılında Eugene, Oregon, Amerika Birleşik Devletleri ile kurulmuştur.
Bu etkinlik, diğer 8 şehirle resmi ilişkiler kurarak daha da geliştirilmiştir:
Motsumoto City of Japan, Rochester, Myanmar Yangon (eski adıyla Rangoon),
Çin Halk Cumhuriyeti'nden Xi'an, Belarus Minsk ve Kore Demokratik Cumhuriyeti'nden Pyongyang. KMC'nin sürekli çabası,
Katmandu için daha iyi kentsel yönetim ve gelişim programları elde etmek için SAARC ülkeleri, diğer Uluslararası ajanslar ve
dünyanın diğer birçok büyük şehirleri ile etkileşimini geliştirmektir."""
answer = "Katmandu ilk uluslararası ilişkisini hangi yılda yarattı?"
predict_answer(context,answer)
'1975'
```
```python
context = """Yapay zeka (YZ), modern dünyada hızla gelişen bir teknoloji alanıdır. YZ, sağlık, finans ve eğitim gibi birçok sektörde kullanılmaktadır.
Sağlık alanında, YZ algoritmaları hastalık teşhislerinde yardımcı olabilir. Finans sektöründe, YZ, piyasa analizleri yaparak yatırım kararlarını destekler.
Eğitimde ise, öğrenci performansını izler ve bireyselleştirilmiş öğrenme programları oluşturur.
Ancak, YZ'nin kullanımı etik ve gizlilik konularında endişeler doğurur.
Veri güvenliği ve algoritmik önyargılar, dikkatle ele alınması gereken önemli meselelerdir."""
answer = "Yapay zeka hangi sektörlerde kullanılmaktadır?"
predict_answer(context,answer)
'sağlık, finans ve eğitim'
``` |
hazyresearch/M2-BERT-128-Retrieval-Encoder-V1 | hazyresearch | 2024-05-20T21:06:23Z | 126 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"custom_code",
"en",
"arxiv:2402.07440",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | fill-mask | 2024-02-10T00:34:16Z | ---
license: apache-2.0
language:
- en
pipeline_tag: fill-mask
inference: false
---
# Monarch Mixer-BERT
The 80M checkpoint for M2-BERT-128 from the paper [Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT](https://arxiv.org/abs/2402.07440).
Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instructions on how to download and fine-tune it!
## How to use
You can load this model using Hugging Face `AutoModel`:
```python
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True)
```
This model uses the Hugging Face `bert-base-uncased tokenizer`:
```
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
```
## How to use
This model generates embeddings for retrieval. The embeddings have a dimensionality of 768:
```
from transformers import AutoTokenizer, AutoModelForMaskedLM
max_seq_length = 128
testing_string = "Every morning, I make a cup of coffee to start my day."
model = AutoModelForMaskedLM.from_pretrained("jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased", model_max_length=max_seq_length)
input_ids = tokenizer([testing_string], return_tensors="pt", padding="max_length", return_token_type_ids=False, truncation=True, max_length=max_seq_length)
outputs = model(**input_ids)
embeddings = outputs['sentence_embedding']
```
### Remote Code
This model requires `trust_remote_code=True` to be passed to the `from_pretrained` method. This is because we use custom PyTorch code (see our GitHub). You should consider passing a `revision` argument that specifies the exact git commit of the code, for example:
```python
mlm = AutoModelForMaskedLM.from_pretrained(
"jonsaadfalcon/M2-BERT-128-Retrieval-Encoder-V1",
trust_remote_code=True,
)
```
### Configuration
Note `use_flash_mm` is false by default. Using FlashMM is currently not supported. |
yifanxie/jasper-cicada-1 | yifanxie | 2024-05-20T21:03:38Z | 135 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-20T21:01:37Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.40.1
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="yifanxie/jasper-cicada-1",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<eos><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"yifanxie/jasper-cicada-1",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"yifanxie/jasper-cicada-1",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "yifanxie/jasper-cicada-1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<eos><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 256
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GemmaForCausalLM(
(model): GemmaModel(
(embed_tokens): Embedding(256000, 2048, padding_idx=0)
(layers): ModuleList(
(0-17): 18 x GemmaDecoderLayer(
(self_attn): GemmaSdpaAttention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=256, bias=False)
(v_proj): Linear(in_features=2048, out_features=256, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(rotary_emb): GemmaRotaryEmbedding()
)
(mlp): GemmaMLP(
(gate_proj): Linear(in_features=2048, out_features=16384, bias=False)
(up_proj): Linear(in_features=2048, out_features=16384, bias=False)
(down_proj): Linear(in_features=16384, out_features=2048, bias=False)
(act_fn): PytorchGELUTanh()
)
(input_layernorm): GemmaRMSNorm()
(post_attention_layernorm): GemmaRMSNorm()
)
)
(norm): GemmaRMSNorm()
)
(lm_head): Linear(in_features=2048, out_features=256000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
mergekit-community/TopEvolution | mergekit-community | 2024-05-20T21:01:09Z | 12 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:merge:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:mergekit-community/mergekit-slerp-ebgdloh",
"base_model:merge:mergekit-community/mergekit-slerp-ebgdloh",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T20:53:23Z | ---
base_model:
- NousResearch/Hermes-2-Pro-Mistral-7B
- mergekit-community/mergekit-slerp-ebgdloh
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:
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [mergekit-community/mergekit-slerp-ebgdloh](https://huggingface.co/mergekit-community/mergekit-slerp-ebgdloh)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Hermes-2-Pro-Mistral-7B
- model: mergekit-community/mergekit-slerp-ebgdloh
merge_method: slerp
base_model: mergekit-community/mergekit-slerp-ebgdloh
dtype: bfloat16
parameters:
t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
```
|
msbayindir/sample-mt5-small | msbayindir | 2024-05-20T21:00:44Z | 110 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-16T16:01:54Z | ---
library_name: transformers
language:
- tr
metrics:
- bleu
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
<p>
This model has been trained with the dataset
<a href="https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset">Turkish-Reading-Comprehension-Question-Answering-Dataset </a>for Turkish question-answering
</p>
<h1>Usage</h1>
<h2>install package</h2>
<ul>
<li>!pip install transformers</li>
<li>!pip install evaluate</li>
<li>!pip install rouge</li>
</ul>
<h2>Codes</h2>
<h3>Imports</h3>
```python
import torch
import nltk
import string
import evaluate # Bleu
import pandas as pd
import numpy as np
from transformers import T5Tokenizer, MT5Model, MT5ForConditionalGeneration, MT5TokenizerFast
import warnings
```
```python
TOKENIZER = MT5TokenizerFast.from_pretrained("msbayindir/sample-mt5-small")
MODEL = MT5ForConditionalGeneration.from_pretrained("msbayindir/sample-mt5-small", return_dict=True)
DEVICE = "cuda:0"
MODEL = MODEL.to(device=DEVICE)
Q_LEN = 256 # Question Length
T_LEN = 32
```
```python
def predict_answer(context, question, ref_answer=None):
inputs = TOKENIZER(question, context, max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True)
input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask)
predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True)
if ref_answer:
# Load the Bleu metric
bleu = evaluate.load("google_bleu")
score = bleu.compute(predictions=[predicted_answer],
references=[ref_answer])
print("Context: \n", context)
print("\n")
print("Question: \n", question)
return {
"Reference Answer: ": ref_answer,
"Predicted Answer: ": predicted_answer,
"BLEU Score: ": score
}
else:
return predicted_answer
```
###Samples
```python
context = """Katmandu Büyükşehir Şehri (KMC),
uluslararası ilişkileri teşvik etmek amacıyla Uluslararası İlişkiler Sekreterliği (IRC) kurmuştur.
KMC'nin ilk uluslararası ilişkisi 1975 yılında Eugene, Oregon, Amerika Birleşik Devletleri ile kurulmuştur.
Bu etkinlik, diğer 8 şehirle resmi ilişkiler kurarak daha da geliştirilmiştir:
Motsumoto City of Japan, Rochester, Myanmar Yangon (eski adıyla Rangoon),
Çin Halk Cumhuriyeti'nden Xi'an, Belarus Minsk ve Kore Demokratik Cumhuriyeti'nden Pyongyang. KMC'nin sürekli çabası,
Katmandu için daha iyi kentsel yönetim ve gelişim programları elde etmek için SAARC ülkeleri, diğer Uluslararası ajanslar ve
dünyanın diğer birçok büyük şehirleri ile etkileşimini geliştirmektir."""
answer = "Katmandu ilk uluslararası ilişkisini hangi yılda yarattı?"
predict_answer(context,answer)
'1975'
```
```python
context = """Yapay zeka (YZ), modern dünyada hızla gelişen bir teknoloji alanıdır. YZ, sağlık, finans ve eğitim gibi birçok sektörde kullanılmaktadır.
Sağlık alanında, YZ algoritmaları hastalık teşhislerinde yardımcı olabilir. Finans sektöründe, YZ, piyasa analizleri yaparak yatırım kararlarını destekler.
Eğitimde ise, öğrenci performansını izler ve bireyselleştirilmiş öğrenme programları oluşturur.
Ancak, YZ'nin kullanımı etik ve gizlilik konularında endişeler doğurur.
Veri güvenliği ve algoritmik önyargılar, dikkatle ele alınması gereken önemli meselelerdir."""
answer = "Yapay zeka hangi sektörlerde kullanılmaktadır?"
predict_answer(context,answer)
'sağlık, finans ve eğitim'
``` |
elloco123/dqn-SpaceInvadersNoFrameskip-v4 | elloco123 | 2024-05-20T20:58:56Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-20T20:58:21Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 575.00 +/- 128.39
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga elloco123 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga elloco123 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga elloco123
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
matthieuzone/OSSAU-_IRATYbis | matthieuzone | 2024-05-20T20:58:56Z | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T20:50:46Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/OSSAU-_IRATYbis
<Gallery />
## Model description
These are matthieuzone/OSSAU-_IRATYbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/OSSAU-_IRATYbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
OwOpeepeepoopoo/NoSoup4U5 | OwOpeepeepoopoo | 2024-05-20T20:55:06Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T12:09:14Z | ---
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] |
LoneStriker/Yi-1.5-34B-32K-8.0bpw-h8-exl2 | LoneStriker | 2024-05-20T20:49:49Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T20:37:34Z | ---
license: apache-2.0
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
BilalMuftuoglu/beit-base-patch16-224-85-fold3 | BilalMuftuoglu | 2024-05-20T20:48:55Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-20T20:28:24Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-85-fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9090909090909091
---
<!-- 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. -->
# beit-base-patch16-224-85-fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3297
- Accuracy: 0.9091
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.6425 | 0.6136 |
| No log | 2.0 | 4 | 0.6036 | 0.7273 |
| No log | 3.0 | 6 | 0.5549 | 0.7045 |
| No log | 4.0 | 8 | 0.4879 | 0.7273 |
| 0.5424 | 5.0 | 10 | 0.7873 | 0.7045 |
| 0.5424 | 6.0 | 12 | 0.6817 | 0.7045 |
| 0.5424 | 7.0 | 14 | 0.4846 | 0.75 |
| 0.5424 | 8.0 | 16 | 0.5266 | 0.7273 |
| 0.5424 | 9.0 | 18 | 0.4487 | 0.7727 |
| 0.5207 | 10.0 | 20 | 0.3768 | 0.8182 |
| 0.5207 | 11.0 | 22 | 0.6762 | 0.7045 |
| 0.5207 | 12.0 | 24 | 0.3988 | 0.8409 |
| 0.5207 | 13.0 | 26 | 0.3812 | 0.8864 |
| 0.5207 | 14.0 | 28 | 0.4207 | 0.7727 |
| 0.386 | 15.0 | 30 | 0.3801 | 0.8182 |
| 0.386 | 16.0 | 32 | 0.5402 | 0.7045 |
| 0.386 | 17.0 | 34 | 0.3865 | 0.8636 |
| 0.386 | 18.0 | 36 | 0.3635 | 0.8182 |
| 0.386 | 19.0 | 38 | 0.5374 | 0.7273 |
| 0.3232 | 20.0 | 40 | 0.5088 | 0.75 |
| 0.3232 | 21.0 | 42 | 0.3507 | 0.8182 |
| 0.3232 | 22.0 | 44 | 0.2995 | 0.8409 |
| 0.3232 | 23.0 | 46 | 0.3403 | 0.8409 |
| 0.3232 | 24.0 | 48 | 0.3858 | 0.8182 |
| 0.249 | 25.0 | 50 | 0.4126 | 0.7727 |
| 0.249 | 26.0 | 52 | 0.4907 | 0.8182 |
| 0.249 | 27.0 | 54 | 0.3799 | 0.8182 |
| 0.249 | 28.0 | 56 | 0.3528 | 0.8182 |
| 0.249 | 29.0 | 58 | 0.3775 | 0.8182 |
| 0.2064 | 30.0 | 60 | 0.3520 | 0.8182 |
| 0.2064 | 31.0 | 62 | 0.4397 | 0.7727 |
| 0.2064 | 32.0 | 64 | 0.4284 | 0.75 |
| 0.2064 | 33.0 | 66 | 0.3833 | 0.8409 |
| 0.2064 | 34.0 | 68 | 0.3558 | 0.8409 |
| 0.2066 | 35.0 | 70 | 0.4880 | 0.8182 |
| 0.2066 | 36.0 | 72 | 0.3739 | 0.8182 |
| 0.2066 | 37.0 | 74 | 0.5409 | 0.7727 |
| 0.2066 | 38.0 | 76 | 0.4869 | 0.8182 |
| 0.2066 | 39.0 | 78 | 0.8398 | 0.75 |
| 0.1776 | 40.0 | 80 | 0.5410 | 0.7955 |
| 0.1776 | 41.0 | 82 | 0.4740 | 0.8409 |
| 0.1776 | 42.0 | 84 | 0.3428 | 0.8636 |
| 0.1776 | 43.0 | 86 | 0.3135 | 0.8864 |
| 0.1776 | 44.0 | 88 | 0.3297 | 0.9091 |
| 0.1732 | 45.0 | 90 | 0.3982 | 0.9091 |
| 0.1732 | 46.0 | 92 | 0.5961 | 0.7955 |
| 0.1732 | 47.0 | 94 | 0.4798 | 0.8864 |
| 0.1732 | 48.0 | 96 | 0.5187 | 0.8182 |
| 0.1732 | 49.0 | 98 | 0.4430 | 0.9091 |
| 0.1372 | 50.0 | 100 | 0.4522 | 0.9091 |
| 0.1372 | 51.0 | 102 | 0.5617 | 0.8409 |
| 0.1372 | 52.0 | 104 | 0.6568 | 0.8182 |
| 0.1372 | 53.0 | 106 | 0.8141 | 0.7955 |
| 0.1372 | 54.0 | 108 | 0.6189 | 0.8409 |
| 0.1305 | 55.0 | 110 | 0.5124 | 0.8636 |
| 0.1305 | 56.0 | 112 | 0.5095 | 0.8636 |
| 0.1305 | 57.0 | 114 | 0.4101 | 0.8864 |
| 0.1305 | 58.0 | 116 | 0.7712 | 0.7727 |
| 0.1305 | 59.0 | 118 | 0.5073 | 0.7955 |
| 0.1423 | 60.0 | 120 | 0.3890 | 0.8636 |
| 0.1423 | 61.0 | 122 | 0.5701 | 0.8182 |
| 0.1423 | 62.0 | 124 | 0.5482 | 0.8409 |
| 0.1423 | 63.0 | 126 | 0.5508 | 0.8409 |
| 0.1423 | 64.0 | 128 | 0.6589 | 0.7955 |
| 0.13 | 65.0 | 130 | 0.7184 | 0.75 |
| 0.13 | 66.0 | 132 | 0.4702 | 0.8864 |
| 0.13 | 67.0 | 134 | 0.4339 | 0.9091 |
| 0.13 | 68.0 | 136 | 0.4463 | 0.9091 |
| 0.13 | 69.0 | 138 | 0.4887 | 0.8864 |
| 0.1232 | 70.0 | 140 | 0.5121 | 0.8636 |
| 0.1232 | 71.0 | 142 | 0.4944 | 0.9091 |
| 0.1232 | 72.0 | 144 | 0.5208 | 0.8864 |
| 0.1232 | 73.0 | 146 | 0.6074 | 0.8182 |
| 0.1232 | 74.0 | 148 | 0.8013 | 0.75 |
| 0.1241 | 75.0 | 150 | 0.7022 | 0.7727 |
| 0.1241 | 76.0 | 152 | 0.5641 | 0.8864 |
| 0.1241 | 77.0 | 154 | 0.6550 | 0.8409 |
| 0.1241 | 78.0 | 156 | 0.6268 | 0.8409 |
| 0.1241 | 79.0 | 158 | 0.5466 | 0.8864 |
| 0.1254 | 80.0 | 160 | 0.5453 | 0.8864 |
| 0.1254 | 81.0 | 162 | 0.5663 | 0.8636 |
| 0.1254 | 82.0 | 164 | 0.5377 | 0.8409 |
| 0.1254 | 83.0 | 166 | 0.5381 | 0.8864 |
| 0.1254 | 84.0 | 168 | 0.5459 | 0.8636 |
| 0.1061 | 85.0 | 170 | 0.5490 | 0.8636 |
| 0.1061 | 86.0 | 172 | 0.5444 | 0.8636 |
| 0.1061 | 87.0 | 174 | 0.5344 | 0.8636 |
| 0.1061 | 88.0 | 176 | 0.5251 | 0.8636 |
| 0.1061 | 89.0 | 178 | 0.5178 | 0.8864 |
| 0.1084 | 90.0 | 180 | 0.5161 | 0.8864 |
| 0.1084 | 91.0 | 182 | 0.5184 | 0.8864 |
| 0.1084 | 92.0 | 184 | 0.5185 | 0.8864 |
| 0.1084 | 93.0 | 186 | 0.5300 | 0.8636 |
| 0.1084 | 94.0 | 188 | 0.5599 | 0.8636 |
| 0.1025 | 95.0 | 190 | 0.5972 | 0.8182 |
| 0.1025 | 96.0 | 192 | 0.6083 | 0.8182 |
| 0.1025 | 97.0 | 194 | 0.5969 | 0.8409 |
| 0.1025 | 98.0 | 196 | 0.5769 | 0.8636 |
| 0.1025 | 99.0 | 198 | 0.5673 | 0.8636 |
| 0.1184 | 100.0 | 200 | 0.5642 | 0.8636 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ulkaa/gemma-2b-dolly-qa | ulkaa | 2024-05-20T20:46:32Z | 13 | 1 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:other",
"region:us"
] | null | 2024-03-16T19:44:05Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: gemma-2b-dolly-qa
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. -->
# gemma-2b-dolly-qa
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 296
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.0a0+cxx11.abi
- Datasets 2.18.0
- Tokenizers 0.15.2 |
fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-264015 | fine-tuned | 2024-05-20T20:44:59Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"custom_code",
"en",
"dataset:fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-264015",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-20T20:44:44Z | ---
license: apache-2.0
datasets:
- fine-tuned/arguana-c-256-24-gpt-4o-2024-05-13-264015
- allenai/c4
language:
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
---
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:
custom
## 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/arguana-c-256-24-gpt-4o-2024-05-13-264015',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
matthieuzone/MUNSTERbis | matthieuzone | 2024-05-20T20:42:11Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T20:33:59Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/MUNSTERbis
<Gallery />
## Model description
These are matthieuzone/MUNSTERbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/MUNSTERbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
LoneStriker/Yi-1.5-34B-32K-6.0bpw-h6-exl2 | LoneStriker | 2024-05-20T20:37:30Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T20:26:37Z | ---
license: apache-2.0
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
alexx1/llama3-omegle-lora-r128-gguf | alexx1 | 2024-05-20T20:34:52Z | 7 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"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 | 2024-05-20T20:30:53Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** alexx1
- **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)
|
matthieuzone/MOZZARELLAbis | matthieuzone | 2024-05-20T20:33:44Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T20:25:36Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/MOZZARELLAbis
<Gallery />
## Model description
These are matthieuzone/MOZZARELLAbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/MOZZARELLAbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
stanrom/ShareCaptioner | stanrom | 2024-05-20T20:28:14Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"InternLMXComposer",
"feature-extraction",
"custom_code",
"arxiv:2311.12793",
"region:us"
] | feature-extraction | 2024-05-12T19:58:19Z | ---
inference: false
---
<br>
<br>
# ShareCaptioner Model Card
## Model details
**Model type:**
ShareCaptioner is an open-source captioner fine-tuned on GPT4-Vision-assisted [ShareGPT4V](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) detailed caption data with a resolution of 448x448. ShareCaptioner is based on the improved [InternLM-Xcomposer-7B](https://github.com/InternLM/InternLM-XComposer) base model.
**Model date:**
ShareCaptioner was trained in Nov 2023.
**Paper or resources for more information:**
[[Project](https://ShareGPT4V.github.io/)] [[Paper](https://huggingface.co/papers/2311.12793)] [[Code](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V)]
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
## Intended use
**Primary intended uses:**
The primary use of ShareCaptioner is about producing high-quality image captions.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Finetuning dataset
- 100K GPT4-Vision-generated image-text pairs
|
LoneStriker/Yi-1.5-34B-32K-5.0bpw-h6-exl2 | LoneStriker | 2024-05-20T20:26:34Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T20:17:26Z | ---
license: apache-2.0
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
jack8885/flock-submission-qwen-0520 | jack8885 | 2024-05-20T20:22:17Z | 132 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T20:21:38Z | ---
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]
<!-- 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] |
seregadgl101/baii_v12_13ep | seregadgl101 | 2024-05-20T20:21:34Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-20T20:19:50Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# seregadgl101/baii_v12_13ep
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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('seregadgl101/baii_v12_13ep')
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=seregadgl101/baii_v12_13ep)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
alexx1/llama3-omegle-lora-r128-adapter | alexx1 | 2024-05-20T20:17:24Z | 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 | 2024-05-20T20:16:27Z | ---
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:** alexx1
- **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)
|
LoneStriker/Yi-1.5-34B-32K-4.65bpw-h6-exl2 | LoneStriker | 2024-05-20T20:17:22Z | 13 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-20T20:08:51Z | ---
license: apache-2.0
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
alexx1/llama3-omegle-lora-r128-16bit | alexx1 | 2024-05-20T20:16:20Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T20:13:23Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** alexx1
- **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)
|
seregadgl101/baii_v12_12ep | seregadgl101 | 2024-05-20T20:15:00Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-20T20:12:33Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# seregadgl101/baii_v12_12ep
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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('seregadgl101/baii_v12_12ep')
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=seregadgl101/baii_v12_12ep)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
HariprasathSB/whisper-tamil-vulnerable | HariprasathSB | 2024-05-20T20:10:42Z | 93 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:HariprasathSB/whisper-vulnerablee",
"base_model:finetune:HariprasathSB/whisper-vulnerablee",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-20T19:21:30Z | ---
license: apache-2.0
base_model: HariprasathSB/whisper-vulnerablee
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tamil-vulnerable
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tamil-vulnerable
This model is a fine-tuned version of [HariprasathSB/whisper-vulnerablee](https://huggingface.co/HariprasathSB/whisper-vulnerablee) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0500
- Wer: 77.6086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0307 | 1.7544 | 200 | 1.0500 | 77.6086 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
saad177/my_awesome_billsum_model | saad177 | 2024-05-20T20:09:58Z | 8 | 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 | 2024-05-20T16:37:40Z | ---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: my_awesome_billsum_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
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: 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: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
OwOpeepeepoopoo/DancingElaine5 | OwOpeepeepoopoo | 2024-05-20T20:09:42Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T12:09:12Z | ---
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] |
LoneStriker/Yi-1.5-34B-32K-4.0bpw-h6-exl2 | LoneStriker | 2024-05-20T20:08:47Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T20:01:21Z | ---
license: apache-2.0
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
BilalMuftuoglu/beit-base-patch16-224-85-fold1 | BilalMuftuoglu | 2024-05-20T20:07:31Z | 33 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-20T19:46:35Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-85-fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9772727272727273
---
<!-- 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. -->
# beit-base-patch16-224-85-fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1430
- Accuracy: 0.9773
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.7308 | 0.5455 |
| No log | 2.0 | 4 | 0.7927 | 0.7045 |
| No log | 3.0 | 6 | 0.9672 | 0.7045 |
| No log | 4.0 | 8 | 0.6257 | 0.7045 |
| 0.6404 | 5.0 | 10 | 0.4646 | 0.7955 |
| 0.6404 | 6.0 | 12 | 0.5648 | 0.7045 |
| 0.6404 | 7.0 | 14 | 0.4389 | 0.7727 |
| 0.6404 | 8.0 | 16 | 0.4523 | 0.75 |
| 0.6404 | 9.0 | 18 | 0.4698 | 0.75 |
| 0.455 | 10.0 | 20 | 0.3707 | 0.8409 |
| 0.455 | 11.0 | 22 | 0.3594 | 0.8182 |
| 0.455 | 12.0 | 24 | 0.6136 | 0.7273 |
| 0.455 | 13.0 | 26 | 0.3022 | 0.8864 |
| 0.455 | 14.0 | 28 | 0.2919 | 0.8409 |
| 0.3981 | 15.0 | 30 | 0.3612 | 0.8182 |
| 0.3981 | 16.0 | 32 | 0.2492 | 0.8864 |
| 0.3981 | 17.0 | 34 | 0.2460 | 0.9091 |
| 0.3981 | 18.0 | 36 | 0.2931 | 0.8636 |
| 0.3981 | 19.0 | 38 | 0.1822 | 0.9091 |
| 0.3257 | 20.0 | 40 | 0.2060 | 0.9091 |
| 0.3257 | 21.0 | 42 | 0.2195 | 0.8864 |
| 0.3257 | 22.0 | 44 | 0.2624 | 0.9091 |
| 0.3257 | 23.0 | 46 | 0.2384 | 0.9091 |
| 0.3257 | 24.0 | 48 | 0.1767 | 0.9318 |
| 0.2553 | 25.0 | 50 | 0.2040 | 0.9318 |
| 0.2553 | 26.0 | 52 | 0.1981 | 0.9091 |
| 0.2553 | 27.0 | 54 | 0.1835 | 0.9318 |
| 0.2553 | 28.0 | 56 | 0.1820 | 0.9318 |
| 0.2553 | 29.0 | 58 | 0.1466 | 0.9545 |
| 0.2083 | 30.0 | 60 | 0.1668 | 0.9318 |
| 0.2083 | 31.0 | 62 | 0.2229 | 0.9318 |
| 0.2083 | 32.0 | 64 | 0.1783 | 0.9545 |
| 0.2083 | 33.0 | 66 | 0.1944 | 0.8864 |
| 0.2083 | 34.0 | 68 | 0.3025 | 0.9091 |
| 0.2353 | 35.0 | 70 | 0.4457 | 0.8409 |
| 0.2353 | 36.0 | 72 | 0.2759 | 0.9318 |
| 0.2353 | 37.0 | 74 | 0.2179 | 0.9318 |
| 0.2353 | 38.0 | 76 | 0.3911 | 0.9091 |
| 0.2353 | 39.0 | 78 | 0.5785 | 0.8409 |
| 0.1782 | 40.0 | 80 | 0.2339 | 0.9318 |
| 0.1782 | 41.0 | 82 | 0.2302 | 0.9091 |
| 0.1782 | 42.0 | 84 | 0.3967 | 0.8864 |
| 0.1782 | 43.0 | 86 | 0.4447 | 0.8636 |
| 0.1782 | 44.0 | 88 | 0.2020 | 0.9091 |
| 0.2059 | 45.0 | 90 | 0.1911 | 0.9318 |
| 0.2059 | 46.0 | 92 | 0.2609 | 0.9091 |
| 0.2059 | 47.0 | 94 | 0.2925 | 0.9091 |
| 0.2059 | 48.0 | 96 | 0.2079 | 0.9318 |
| 0.2059 | 49.0 | 98 | 0.1853 | 0.9318 |
| 0.1706 | 50.0 | 100 | 0.2860 | 0.9318 |
| 0.1706 | 51.0 | 102 | 0.3735 | 0.8636 |
| 0.1706 | 52.0 | 104 | 0.1968 | 0.9318 |
| 0.1706 | 53.0 | 106 | 0.1722 | 0.9318 |
| 0.1706 | 54.0 | 108 | 0.3123 | 0.8636 |
| 0.1429 | 55.0 | 110 | 0.3297 | 0.8864 |
| 0.1429 | 56.0 | 112 | 0.1430 | 0.9773 |
| 0.1429 | 57.0 | 114 | 0.1134 | 0.9773 |
| 0.1429 | 58.0 | 116 | 0.2312 | 0.9091 |
| 0.1429 | 59.0 | 118 | 0.2826 | 0.9091 |
| 0.1325 | 60.0 | 120 | 0.2417 | 0.9091 |
| 0.1325 | 61.0 | 122 | 0.1393 | 0.9318 |
| 0.1325 | 62.0 | 124 | 0.2178 | 0.9318 |
| 0.1325 | 63.0 | 126 | 0.3991 | 0.9091 |
| 0.1325 | 64.0 | 128 | 0.3325 | 0.9091 |
| 0.1481 | 65.0 | 130 | 0.2327 | 0.9091 |
| 0.1481 | 66.0 | 132 | 0.2885 | 0.9091 |
| 0.1481 | 67.0 | 134 | 0.3576 | 0.9091 |
| 0.1481 | 68.0 | 136 | 0.2686 | 0.9318 |
| 0.1481 | 69.0 | 138 | 0.1717 | 0.9545 |
| 0.1237 | 70.0 | 140 | 0.1493 | 0.9545 |
| 0.1237 | 71.0 | 142 | 0.1429 | 0.9318 |
| 0.1237 | 72.0 | 144 | 0.1790 | 0.9318 |
| 0.1237 | 73.0 | 146 | 0.1590 | 0.9318 |
| 0.1237 | 74.0 | 148 | 0.1971 | 0.8864 |
| 0.105 | 75.0 | 150 | 0.2229 | 0.9318 |
| 0.105 | 76.0 | 152 | 0.1789 | 0.8864 |
| 0.105 | 77.0 | 154 | 0.1671 | 0.9545 |
| 0.105 | 78.0 | 156 | 0.2435 | 0.9318 |
| 0.105 | 79.0 | 158 | 0.2658 | 0.9318 |
| 0.0923 | 80.0 | 160 | 0.2092 | 0.9318 |
| 0.0923 | 81.0 | 162 | 0.1748 | 0.9318 |
| 0.0923 | 82.0 | 164 | 0.1727 | 0.9318 |
| 0.0923 | 83.0 | 166 | 0.1945 | 0.9091 |
| 0.0923 | 84.0 | 168 | 0.2429 | 0.9318 |
| 0.1033 | 85.0 | 170 | 0.2796 | 0.9318 |
| 0.1033 | 86.0 | 172 | 0.2548 | 0.9318 |
| 0.1033 | 87.0 | 174 | 0.2379 | 0.9091 |
| 0.1033 | 88.0 | 176 | 0.2409 | 0.9091 |
| 0.1033 | 89.0 | 178 | 0.2421 | 0.9091 |
| 0.1073 | 90.0 | 180 | 0.2332 | 0.9091 |
| 0.1073 | 91.0 | 182 | 0.2231 | 0.9091 |
| 0.1073 | 92.0 | 184 | 0.2153 | 0.9318 |
| 0.1073 | 93.0 | 186 | 0.2088 | 0.9318 |
| 0.1073 | 94.0 | 188 | 0.2058 | 0.9318 |
| 0.104 | 95.0 | 190 | 0.2040 | 0.9318 |
| 0.104 | 96.0 | 192 | 0.2046 | 0.9318 |
| 0.104 | 97.0 | 194 | 0.2043 | 0.9318 |
| 0.104 | 98.0 | 196 | 0.2056 | 0.9318 |
| 0.104 | 99.0 | 198 | 0.2081 | 0.9318 |
| 0.0896 | 100.0 | 200 | 0.2097 | 0.9318 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
LoneStriker/Yi-1.5-34B-32K-3.0bpw-h6-exl2 | LoneStriker | 2024-05-20T20:01:18Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"3-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T19:55:35Z | ---
license: apache-2.0
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
matthieuzone/MIMOLETTEbis | matthieuzone | 2024-05-20T20:00:03Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T19:51:54Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/MIMOLETTEbis
<Gallery />
## Model description
These are matthieuzone/MIMOLETTEbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/MIMOLETTEbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
baek26/all_2245_bart-all_rl | baek26 | 2024-05-20T19:46:41Z | 50 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2024-05-20T19:46:10Z | ---
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/tmput7n83q2/baek26/all_2245_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/tmput7n83q2/baek26/all_2245_bart-all_rl")
model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmput7n83q2/baek26/all_2245_bart-all_rl")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
matthieuzone/GRUYEREbis | matthieuzone | 2024-05-20T19:43:13Z | 3 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T19:35:01Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/GRUYEREbis
<Gallery />
## Model description
These are matthieuzone/GRUYEREbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/GRUYEREbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
isaaclee/duration_mistral_train_run2 | isaaclee | 2024-05-20T19:43:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T17:39:41Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: duration_mistral_train_run2
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. -->
# duration_mistral_train_run2
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 |
davelotito/donut_experiment_bayesian_trial_3 | davelotito | 2024-05-20T19:42:22Z | 48 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-05-20T18:56:23Z | ---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_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. -->
# donut_experiment_bayesian_trial_3
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5840
- Bleu: 0.0667
- Precisions: [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846]
- Brevity Penalty: 0.0934
- Length Ratio: 0.2967
- Translation Length: 483
- Reference Length: 1628
- Cer: 0.7599
- Wer: 0.8328
## 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: 0.00017060423589132634
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.2012 | 1.0 | 253 | 0.6694 | 0.0547 | [0.7680851063829788, 0.6682808716707022, 0.6067415730337079, 0.5484949832775919] | 0.0851 | 0.2887 | 470 | 1628 | 0.7597 | 0.8411 |
| 0.127 | 2.0 | 506 | 0.6071 | 0.0638 | [0.7818930041152263, 0.6876456876456877, 0.6370967741935484, 0.5841269841269842] | 0.0954 | 0.2985 | 486 | 1628 | 0.7570 | 0.8360 |
| 0.0766 | 3.0 | 759 | 0.5786 | 0.0655 | [0.8125, 0.735224586288416, 0.6885245901639344, 0.6407766990291263] | 0.0915 | 0.2948 | 480 | 1628 | 0.7564 | 0.8319 |
| 0.0259 | 4.0 | 1012 | 0.5840 | 0.0667 | [0.8136645962732919, 0.7347417840375586, 0.6829268292682927, 0.6346153846153846] | 0.0934 | 0.2967 | 483 | 1628 | 0.7599 | 0.8328 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1
|
BilalMuftuoglu/beit-base-patch16-224-75-fold5 | BilalMuftuoglu | 2024-05-20T19:35:52Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-20T19:10:41Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-75-fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9534883720930233
---
<!-- 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. -->
# beit-base-patch16-224-75-fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2664
- Accuracy: 0.9535
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.6862 | 0.5116 |
| No log | 2.0 | 4 | 0.5913 | 0.7209 |
| No log | 3.0 | 6 | 0.7204 | 0.6977 |
| No log | 4.0 | 8 | 0.5995 | 0.6977 |
| 0.6162 | 5.0 | 10 | 0.4235 | 0.8140 |
| 0.6162 | 6.0 | 12 | 0.3975 | 0.8140 |
| 0.6162 | 7.0 | 14 | 0.6029 | 0.7674 |
| 0.6162 | 8.0 | 16 | 0.4670 | 0.8140 |
| 0.6162 | 9.0 | 18 | 0.3448 | 0.8372 |
| 0.4312 | 10.0 | 20 | 0.4464 | 0.8372 |
| 0.4312 | 11.0 | 22 | 0.3396 | 0.8605 |
| 0.4312 | 12.0 | 24 | 0.4007 | 0.8372 |
| 0.4312 | 13.0 | 26 | 0.3398 | 0.8140 |
| 0.4312 | 14.0 | 28 | 0.4276 | 0.8605 |
| 0.3453 | 15.0 | 30 | 0.4336 | 0.8605 |
| 0.3453 | 16.0 | 32 | 0.3777 | 0.8140 |
| 0.3453 | 17.0 | 34 | 0.5910 | 0.8140 |
| 0.3453 | 18.0 | 36 | 0.6095 | 0.8140 |
| 0.3453 | 19.0 | 38 | 0.3570 | 0.8140 |
| 0.3288 | 20.0 | 40 | 0.5202 | 0.8140 |
| 0.3288 | 21.0 | 42 | 0.5604 | 0.8140 |
| 0.3288 | 22.0 | 44 | 0.2949 | 0.8372 |
| 0.3288 | 23.0 | 46 | 0.3442 | 0.8837 |
| 0.3288 | 24.0 | 48 | 0.2820 | 0.8372 |
| 0.2571 | 25.0 | 50 | 0.3240 | 0.8605 |
| 0.2571 | 26.0 | 52 | 0.2909 | 0.8837 |
| 0.2571 | 27.0 | 54 | 0.2429 | 0.8837 |
| 0.2571 | 28.0 | 56 | 0.2280 | 0.9302 |
| 0.2571 | 29.0 | 58 | 0.3984 | 0.8605 |
| 0.2012 | 30.0 | 60 | 0.2905 | 0.8605 |
| 0.2012 | 31.0 | 62 | 0.2509 | 0.9070 |
| 0.2012 | 32.0 | 64 | 0.2888 | 0.8605 |
| 0.2012 | 33.0 | 66 | 0.2689 | 0.8605 |
| 0.2012 | 34.0 | 68 | 0.2417 | 0.8837 |
| 0.1814 | 35.0 | 70 | 0.2418 | 0.9070 |
| 0.1814 | 36.0 | 72 | 0.2491 | 0.9070 |
| 0.1814 | 37.0 | 74 | 0.2998 | 0.9070 |
| 0.1814 | 38.0 | 76 | 0.2744 | 0.9302 |
| 0.1814 | 39.0 | 78 | 0.2664 | 0.9535 |
| 0.1555 | 40.0 | 80 | 0.2160 | 0.9302 |
| 0.1555 | 41.0 | 82 | 0.3875 | 0.9070 |
| 0.1555 | 42.0 | 84 | 0.4608 | 0.9070 |
| 0.1555 | 43.0 | 86 | 0.2978 | 0.9302 |
| 0.1555 | 44.0 | 88 | 0.4461 | 0.8837 |
| 0.1459 | 45.0 | 90 | 0.3603 | 0.9070 |
| 0.1459 | 46.0 | 92 | 0.2973 | 0.9302 |
| 0.1459 | 47.0 | 94 | 0.3385 | 0.8837 |
| 0.1459 | 48.0 | 96 | 0.3239 | 0.8837 |
| 0.1459 | 49.0 | 98 | 0.4315 | 0.8837 |
| 0.1372 | 50.0 | 100 | 0.3519 | 0.8837 |
| 0.1372 | 51.0 | 102 | 0.4148 | 0.8837 |
| 0.1372 | 52.0 | 104 | 0.4687 | 0.8837 |
| 0.1372 | 53.0 | 106 | 0.3287 | 0.8837 |
| 0.1372 | 54.0 | 108 | 0.3194 | 0.9070 |
| 0.1049 | 55.0 | 110 | 0.3703 | 0.8837 |
| 0.1049 | 56.0 | 112 | 0.3522 | 0.9070 |
| 0.1049 | 57.0 | 114 | 0.2572 | 0.9070 |
| 0.1049 | 58.0 | 116 | 0.2523 | 0.9070 |
| 0.1049 | 59.0 | 118 | 0.3136 | 0.9070 |
| 0.1143 | 60.0 | 120 | 0.3638 | 0.9070 |
| 0.1143 | 61.0 | 122 | 0.2916 | 0.9535 |
| 0.1143 | 62.0 | 124 | 0.2521 | 0.9302 |
| 0.1143 | 63.0 | 126 | 0.2735 | 0.9302 |
| 0.1143 | 64.0 | 128 | 0.3112 | 0.9302 |
| 0.0885 | 65.0 | 130 | 0.3246 | 0.9302 |
| 0.0885 | 66.0 | 132 | 0.3264 | 0.9070 |
| 0.0885 | 67.0 | 134 | 0.3351 | 0.9302 |
| 0.0885 | 68.0 | 136 | 0.3455 | 0.9302 |
| 0.0885 | 69.0 | 138 | 0.3579 | 0.9302 |
| 0.1064 | 70.0 | 140 | 0.3926 | 0.9302 |
| 0.1064 | 71.0 | 142 | 0.4370 | 0.9070 |
| 0.1064 | 72.0 | 144 | 0.4149 | 0.9302 |
| 0.1064 | 73.0 | 146 | 0.3315 | 0.9535 |
| 0.1064 | 74.0 | 148 | 0.2704 | 0.9302 |
| 0.1047 | 75.0 | 150 | 0.2600 | 0.9302 |
| 0.1047 | 76.0 | 152 | 0.3215 | 0.9535 |
| 0.1047 | 77.0 | 154 | 0.4110 | 0.9302 |
| 0.1047 | 78.0 | 156 | 0.4414 | 0.8837 |
| 0.1047 | 79.0 | 158 | 0.3589 | 0.9302 |
| 0.0937 | 80.0 | 160 | 0.3085 | 0.9535 |
| 0.0937 | 81.0 | 162 | 0.2889 | 0.9535 |
| 0.0937 | 82.0 | 164 | 0.2787 | 0.9535 |
| 0.0937 | 83.0 | 166 | 0.3251 | 0.9535 |
| 0.0937 | 84.0 | 168 | 0.4483 | 0.9070 |
| 0.0748 | 85.0 | 170 | 0.5490 | 0.8605 |
| 0.0748 | 86.0 | 172 | 0.5422 | 0.8605 |
| 0.0748 | 87.0 | 174 | 0.5282 | 0.8837 |
| 0.0748 | 88.0 | 176 | 0.5733 | 0.8605 |
| 0.0748 | 89.0 | 178 | 0.5978 | 0.8605 |
| 0.0834 | 90.0 | 180 | 0.5763 | 0.8605 |
| 0.0834 | 91.0 | 182 | 0.5270 | 0.8605 |
| 0.0834 | 92.0 | 184 | 0.4946 | 0.8837 |
| 0.0834 | 93.0 | 186 | 0.4881 | 0.9070 |
| 0.0834 | 94.0 | 188 | 0.5115 | 0.8605 |
| 0.1016 | 95.0 | 190 | 0.5445 | 0.8605 |
| 0.1016 | 96.0 | 192 | 0.5537 | 0.8605 |
| 0.1016 | 97.0 | 194 | 0.5451 | 0.8605 |
| 0.1016 | 98.0 | 196 | 0.5323 | 0.8605 |
| 0.1016 | 99.0 | 198 | 0.5190 | 0.8837 |
| 0.0657 | 100.0 | 200 | 0.5155 | 0.8837 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
rdsmaia/my_awesome_mind_model | rdsmaia | 2024-05-20T19:29:47Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-02-17T21:09:04Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: my_awesome_mind_model
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.07964601769911504
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6607
- Accuracy: 0.0796
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6546 | 0.0708 |
| No log | 1.8667 | 7 | 2.6484 | 0.0796 |
| 2.5954 | 2.9333 | 11 | 2.6503 | 0.0619 |
| 2.5954 | 4.0 | 15 | 2.6522 | 0.0619 |
| 2.5954 | 4.8 | 18 | 2.6549 | 0.0796 |
| 2.5798 | 5.8667 | 22 | 2.6577 | 0.0796 |
| 2.5798 | 6.9333 | 26 | 2.6597 | 0.0796 |
| 2.57 | 8.0 | 30 | 2.6607 | 0.0796 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ssmits/Falcon2-5.5B-Swedish-GGUF | ssmits | 2024-05-20T19:28:18Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"sv",
"base_model:tiiuae/falcon-11B",
"base_model:quantized:tiiuae/falcon-11B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T18:43:10Z | ---
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
base_model:
- tiiuae/falcon-11B
license: apache-2.0
language:
- sv
---
# ssmits/Falcon2-5.5B-Swedish-Q5_K_M-GGUF
This model was converted to GGUF format from [`ssmits/Falcon2-5.5B-Swedish`](https://huggingface.co/ssmits/Falcon2-5.5B-Swedish) 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/ssmits/Falcon2-5.5B-Swedish) 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 ssmits/Falcon2-5.5B-Swedish-Q5_K_M-GGUF --model falcon2-5.5b-swedish.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo ssmits/Falcon2-5.5B-Swedish-Q5_K_M-GGUF --model falcon2-5.5b-swedish.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m falcon2-5.5b-swedish.Q5_K_M.gguf -n 128
``` |
ssmits/Falcon2-5.5B-German-GGUF | ssmits | 2024-05-20T19:28:02Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"lazymergekit",
"llama-cpp",
"gguf-my-repo",
"de",
"base_model:tiiuae/falcon-11B",
"base_model:quantized:tiiuae/falcon-11B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T18:35:26Z | ---
language:
- de
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
- lazymergekit
- llama-cpp
- gguf-my-repo
base_model:
- tiiuae/falcon-11B
---
# ssmits/Falcon2-5.5B-German-Q5_K_M-GGUF
This model was converted to GGUF format from [`ssmits/Falcon2-5.5B-German`](https://huggingface.co/ssmits/Falcon2-5.5B-German) 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/ssmits/Falcon2-5.5B-German) 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 ssmits/Falcon2-5.5B-German-Q5_K_M-GGUF --model falcon2-5.5b-german.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo ssmits/Falcon2-5.5B-German-Q5_K_M-GGUF --model falcon2-5.5b-german.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m falcon2-5.5b-german.Q5_K_M.gguf -n 128
``` |
Arshia-HZ/NLP-AriaBert-Digimag | Arshia-HZ | 2024-05-20T19:26:20Z | 121 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T18:33:40Z | ---
license: apache-2.0
language:
- fa
widget:
- text: "دختری در قطار؛ پرفروشترین کتاب نیویورکتایمز را امروز رایگان بخوانید کتاب دختری در قطار هدیه امروز فیدیبو است."
- text: "استرینگکست: با ترسناکترین بیماری جهان آشنا شوید با گذر زمان و پیشرفت امکانات، سن انسانها روز بهروز بیشتر میشود. ولی با این بالا رفتن سن، بیماریهای جدید و خطرناکی خودشون را به ما نشان میدهند."
---
## Persian Text Classification [DigiMag, Persian News]
The task target is labeling texts in a supervised manner in both existing datasets `DigiMag` and `Persian News`.
### DigiMag
A total of 8,515 articles scraped from [Digikala Online Magazine](https://www.digikala.com/mag/). This dataset includes seven different classes.
1. Video Games
2. Shopping Guide
3. Health Beauty
4. Science Technology
5. General
6. Art Cinema
7. Books Literature
| Label | # |
|:------------------:|:----:|
| Video Games | 1967 |
| Shopping Guide | 125 |
| Health Beauty | 1610 |
| Science Technology | 2772 |
| General | 120 |
| Art Cinema | 1667 |
| Books Literature | 254 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=1YgrCYY-Z0h2z0-PfWVfOGt1Tv0JDI-qz)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ParsBERT v2 | ParsBERT v1 | mBERT |
|:-----------------:|:-----------:|:-----------:|:-----:|
| Digikala Magazine | 93.65* | 93.59 | 90.72 | |
konstaya/qa_model_study_1 | konstaya | 2024-05-20T19:19:59Z | 131 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:sberquad",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-05-20T17:12:23Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- sberquad
model-index:
- name: qa_model_study_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. -->
# qa_model_study_1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sberquad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1351 | 1.0 | 750 | 2.6338 |
| 2.5385 | 2.0 | 1500 | 2.4813 |
| 2.3433 | 3.0 | 2250 | 2.4337 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
redponike/Yi-1.5-34B-32K-GGUF | redponike | 2024-05-20T19:13:16Z | 1 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T17:03:48Z | GGUF quants of [01-ai/Yi-1.5-34B-32K](https://huggingface.co/01-ai/Yi-1.5-34B-32K) |
ssmits/Falcon2-5.5B-Italian-GGUF | ssmits | 2024-05-20T19:12:36Z | 9 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"lazymergekit",
"llama-cpp",
"gguf-my-repo",
"it",
"base_model:tiiuae/falcon-11B",
"base_model:quantized:tiiuae/falcon-11B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T18:45:17Z | ---
language:
- it
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
- lazymergekit
- llama-cpp
- gguf-my-repo
base_model:
- tiiuae/falcon-11B
---
# ssmits/Falcon2-5.5B-Italian-Q5_K_M-GGUF
This model was converted to GGUF format from [`ssmits/Falcon2-5.5B-Italian`](https://huggingface.co/ssmits/Falcon2-5.5B-Italian) 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/ssmits/Falcon2-5.5B-Italian) 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 ssmits/Falcon2-5.5B-Italian-Q5_K_M-GGUF --model falcon2-5.5b-italian.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo ssmits/Falcon2-5.5B-Italian-Q5_K_M-GGUF --model falcon2-5.5b-italian.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m falcon2-5.5b-italian.Q5_K_M.gguf -n 128
```
|
HariprasathSB/whisper-vulnerablee | HariprasathSB | 2024-05-20T19:11:16Z | 93 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:HariprasathSB/whisper-vulnerable",
"base_model:finetune:HariprasathSB/whisper-vulnerable",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-20T17:05:07Z | ---
license: apache-2.0
base_model: HariprasathSB/whisper-vulnerable
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-vulnerablee
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-vulnerablee
This model is a fine-tuned version of [HariprasathSB/whisper-vulnerable](https://huggingface.co/HariprasathSB/whisper-vulnerable) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0136
- Wer: 77.9557
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0637 | 1.7621 | 200 | 1.0136 | 77.9557 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ggorg03/my_awesome_mind_model | ggorg03 | 2024-05-20T19:10:18Z | 160 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-05-20T19:03:47Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: my_awesome_mind_model
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.05309734513274336
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_mind_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6542
- Accuracy: 0.0531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6407 | 0.0265 |
| No log | 1.8667 | 7 | 2.6425 | 0.0708 |
| 2.6382 | 2.9333 | 11 | 2.6468 | 0.0531 |
| 2.6382 | 4.0 | 15 | 2.6550 | 0.0354 |
| 2.6382 | 4.8 | 18 | 2.6535 | 0.0619 |
| 2.6214 | 5.8667 | 22 | 2.6540 | 0.0531 |
| 2.6214 | 6.9333 | 26 | 2.6538 | 0.0531 |
| 2.6137 | 8.0 | 30 | 2.6542 | 0.0531 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
fine-tuned/jina-embeddings-v2-base-en-5202024-6tkj-webapp | fine-tuned | 2024-05-20T19:10:10Z | 4 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Marketing",
"Analytics",
"CRM",
"Data",
"Insights",
"custom_code",
"en",
"dataset:fine-tuned/jina-embeddings-v2-base-en-5202024-6tkj-webapp",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-20T19:09:16Z | ---
license: apache-2.0
datasets:
- fine-tuned/jina-embeddings-v2-base-en-5202024-6tkj-webapp
- allenai/c4
language:
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Marketing
- Analytics
- CRM
- Data
- Insights
---
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:
educational content for customer insights and marketing strategies
## 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/jina-embeddings-v2-base-en-5202024-6tkj-webapp',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
matthieuzone/EPOISSESbis | matthieuzone | 2024-05-20T19:09:21Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T19:01:09Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/EPOISSESbis
<Gallery />
## Model description
These are matthieuzone/EPOISSESbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/EPOISSESbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
UsmanGhias/IceAge | UsmanGhias | 2024-05-20T19:02:29Z | 0 | 1 | null | [
"tensorflow",
"gradio",
"image-processing",
"glaciers",
"en",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T18:07:22Z | ---
language: en
tags:
- tensorflow
- gradio
- image-processing
- glaciers
license: apache-2.0
datasets:
- custom
metrics:
- accuracy
widget:
- text: "Upload an image of a glacier to predict boundaries."
---
Here's a complete and enhanced version of your Gradio interface documentation for the SGDNet model. This documentation can be part of your model card on Hugging Face or included as a `README.md` in your project repository. It provides clear instructions on setup, usage, and how to interact with the model through Gradio.
---
# SGDNet Gradio Interface
This is a Gradio interface for the SGDNet model, designed to extract glacier boundaries from multisource remote sensing data. The interface provides a user-friendly method to upload satellite images and visualize the predicted glacier boundaries.
## Setup Instructions
Follow these steps to get the Gradio interface up and running on your local machine:
### Prerequisites
Ensure you have Python installed on your system. The interface is built using Gradio, and the model is implemented in TensorFlow.
### Installation
1. **Clone the repository:**
Ensure you have git installed and then clone the repository containing the SGDNet model and the Gradio interface code.
```bash
git clone https://huggingface.co/your_username/SGDNet-gradio
cd SGDNet-gradio
```
2. **Install the required packages:**
Use pip to install the required Python packages from the `requirements.txt` file.
```bash
pip install -r requirements.txt
```
### Running the Interface
1. **Start the Gradio app:**
Run the Gradio interface using the command below. This command executes the Python script that launches the Gradio interface.
```bash
python gradio_app.py
```
2. **Access the Interface:**
Open your web browser and navigate to the URL provided in the command line output (typically `http://127.0.0.1:7860`). This URL hosts your interactive Gradio interface.
## How to Use the Interface
- **Upload Image**: Click on the upload area or drag and drop an image file to upload a satellite image of a glacier.
- **Submit Image**: After uploading the image, click the "Predict" button to process the image through the SGDNet model.
- **View Results**: The interface will display the original image alongside the glacier boundary predictions, allowing you to compare and analyze the results.
## Features
- **Interactive Uploads**: Users can easily upload images through a simple web interface.
- **Real-time Predictions**: The model processes images and provides predictions in real-time.
- **Visual Comparisons**: Directly compare the uploaded images with their prediction outputs.
## Further Help
If you encounter any issues or have questions about using the interface, please refer to the documentation on Hugging Face or submit an issue in the repository.
---
|
matthieuzone/EMMENTALbis | matthieuzone | 2024-05-20T19:00:46Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T18:52:36Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/EMMENTALbis
<Gallery />
## Model description
These are matthieuzone/EMMENTALbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/EMMENTALbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
alexx1/llama3-omegle-lora-r16-adapter | alexx1 | 2024-05-20T18:52:27Z | 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 | 2024-05-20T18:52:14Z | ---
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:** alexx1
- **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)
|
matthieuzone/CHEVREbis | matthieuzone | 2024-05-20T18:43:49Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T18:35:41Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/CHEVREbis
<Gallery />
## Model description
These are matthieuzone/CHEVREbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/CHEVREbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
ssmits/Falcon2-5.5B-French-GGUF | ssmits | 2024-05-20T18:43:30Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"lazymergekit",
"llama-cpp",
"gguf-my-repo",
"fr",
"base_model:tiiuae/falcon-11B",
"base_model:quantized:tiiuae/falcon-11B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T18:24:32Z | ---
language:
- fr
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
- lazymergekit
- llama-cpp
- gguf-my-repo
base_model:
- tiiuae/falcon-11B
---
# ssmits/Falcon2-5.5B-French-Q5_K_M-GGUF
This model was converted to GGUF format from [`ssmits/Falcon2-5.5B-French`](https://huggingface.co/ssmits/Falcon2-5.5B-French) 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/ssmits/Falcon2-5.5B-French) 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 ssmits/Falcon2-5.5B-French-Q5_K_M-GGUF --model falcon2-5.5b-french.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo ssmits/Falcon2-5.5B-French-Q5_K_M-GGUF --model falcon2-5.5b-french.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m falcon2-5.5b-french.Q5_K_M.gguf -n 128
```
|
mscheny/sn6-101 | mscheny | 2024-05-20T18:42:56Z | 179 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-11T01:04:33Z | ---
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] |
bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF | bartowski | 2024-05-20T18:39:55Z | 175 | 5 | null | [
"gguf",
"generated_from_trainer",
"axolotl",
"text-generation",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:01-ai/Yi-1.5-34B",
"base_model:quantized:01-ai/Yi-1.5-34B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2024-05-20T16:58:39Z | ---
license: apache-2.0
base_model: 01-ai/Yi-1.5-34B
tags:
- generated_from_trainer
- axolotl
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of dolphin-2.9.1-yi-1.5-34b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2940">b2940</a> for quantization.
Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.1-yi-1.5-34b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a)
## Prompt format
```
<|im_start|> system
{system_prompt}<|im_end|>
<|im_start|> user
{prompt}<|im_end|>
<|im_start|> assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [dolphin-2.9.1-yi-1.5-34b-Q8_0.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q8_0.gguf) | Q8_0 | 36.54GB | Extremely high quality, generally unneeded but max available quant. |
| [dolphin-2.9.1-yi-1.5-34b-Q6_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q6_K.gguf) | Q6_K | 28.21GB | Very high quality, near perfect, *recommended*. |
| [dolphin-2.9.1-yi-1.5-34b-Q5_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q5_K_M.gguf) | Q5_K_M | 24.32GB | High quality, *recommended*. |
| [dolphin-2.9.1-yi-1.5-34b-Q5_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q5_K_S.gguf) | Q5_K_S | 23.70GB | High quality, *recommended*. |
| [dolphin-2.9.1-yi-1.5-34b-Q4_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q4_K_M.gguf) | Q4_K_M | 20.65GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [dolphin-2.9.1-yi-1.5-34b-Q4_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q4_K_S.gguf) | Q4_K_S | 19.59GB | Slightly lower quality with more space savings, *recommended*. |
| [dolphin-2.9.1-yi-1.5-34b-IQ4_NL.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ4_NL.gguf) | IQ4_NL | 19.52GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [dolphin-2.9.1-yi-1.5-34b-IQ4_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ4_XS.gguf) | IQ4_XS | 18.47GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [dolphin-2.9.1-yi-1.5-34b-Q3_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q3_K_L.gguf) | Q3_K_L | 18.13GB | Lower quality but usable, good for low RAM availability. |
| [dolphin-2.9.1-yi-1.5-34b-Q3_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q3_K_M.gguf) | Q3_K_M | 16.65GB | Even lower quality. |
| [dolphin-2.9.1-yi-1.5-34b-IQ3_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ3_M.gguf) | IQ3_M | 15.56GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [dolphin-2.9.1-yi-1.5-34b-IQ3_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ3_S.gguf) | IQ3_S | 15.01GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [dolphin-2.9.1-yi-1.5-34b-Q3_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q3_K_S.gguf) | Q3_K_S | 14.96GB | Low quality, not recommended. |
| [dolphin-2.9.1-yi-1.5-34b-IQ3_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ3_XS.gguf) | IQ3_XS | 14.23GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [dolphin-2.9.1-yi-1.5-34b-IQ3_XXS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ3_XXS.gguf) | IQ3_XXS | 13.33GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [dolphin-2.9.1-yi-1.5-34b-Q2_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-Q2_K.gguf) | Q2_K | 12.82GB | Very low quality but surprisingly usable. |
| [dolphin-2.9.1-yi-1.5-34b-IQ2_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ2_M.gguf) | IQ2_M | 11.79GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [dolphin-2.9.1-yi-1.5-34b-IQ2_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ2_S.gguf) | IQ2_S | 10.89GB | Very low quality, uses SOTA techniques to be usable. |
| [dolphin-2.9.1-yi-1.5-34b-IQ2_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ2_XS.gguf) | IQ2_XS | 10.30GB | Very low quality, uses SOTA techniques to be usable. |
| [dolphin-2.9.1-yi-1.5-34b-IQ2_XXS.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ2_XXS.gguf) | IQ2_XXS | 9.30GB | Lower quality, uses SOTA techniques to be usable. |
| [dolphin-2.9.1-yi-1.5-34b-IQ1_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ1_M.gguf) | IQ1_M | 8.17GB | Extremely low quality, *not* recommended. |
| [dolphin-2.9.1-yi-1.5-34b-IQ1_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF/blob/main/dolphin-2.9.1-yi-1.5-34b-IQ1_S.gguf) | IQ1_S | 7.49GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF --include "dolphin-2.9.1-yi-1.5-34b-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/dolphin-2.9.1-yi-1.5-34b-GGUF --include "dolphin-2.9.1-yi-1.5-34b-Q8_0.gguf/*" --local-dir dolphin-2.9.1-yi-1.5-34b-Q8_0 --local-dir-use-symlinks False
```
You can either specify a new local-dir (dolphin-2.9.1-yi-1.5-34b-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
1aurent/vit_small_patch14_224.dinobloom | 1aurent | 2024-05-20T18:38:08Z | 32 | 0 | timm | [
"timm",
"safetensors",
"feature-extraction",
"image-classification",
"arxiv:2404.05022",
"license:apache-2.0",
"region:us"
] | feature-extraction | 2024-05-20T17:47:52Z | ---
tags:
- timm
- feature-extraction
- image-classification
library_name: timm
license: apache-2.0
---
# Model card for vit_small_patch14_224.dinobloom
## Model Details
- **Model Type:** Feature backbone
- **Model Stats:**
- Params: 22M (small)
- Image size: 224 x 224 x 3
- Patch size: 14 x 14 x 3
- **Repository:** [github.com:marrlab/DinoBloom](https://github.com/marrlab/DinoBloom)
- **Original Weights:** [Zenodo](https://zenodo.org/records/10908163)
- **License:** [Apache License 2.0](https://github.com/marrlab/DinoBloom/blob/main/LICENSE)
- **Papers:**
- [DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology](https://arxiv.org/abs/2404.05022)
## Model Usage
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
# get example histology image
img = Image.open(
urlopen(
"https://raw.githubusercontent.com/zxaoyou/segmentation_WBC/master/Dataset%201/001.bmp"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/vit_small_patch14_224.dinobloom",
pretrained=True,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@misc{koch2024dinobloom,
title = {DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology},
author = {Valentin Koch and Sophia J. Wagner and Salome Kazeminia and Ece Sancar and Matthias Hehr and Julia Schnabel and Tingying Peng and Carsten Marr},
year = {2024},
eprint = {2404.05022},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
``` |
matthieuzone/CHEDDARbis | matthieuzone | 2024-05-20T18:35:26Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T18:27:17Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/CHEDDARbis
<Gallery />
## Model description
These are matthieuzone/CHEDDARbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/CHEDDARbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
DUAL-GPO-2/phi-2-irepo-chatml-v11-i1 | DUAL-GPO-2 | 2024-05-20T18:31:03Z | 5 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"phi",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"custom_code",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:DUAL-GPO/phi-2-irepo-chatml-merged-i0",
"base_model:adapter:DUAL-GPO/phi-2-irepo-chatml-merged-i0",
"region:us"
] | null | 2024-05-20T15:09:36Z | ---
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: DUAL-GPO/phi-2-irepo-chatml-merged-i0
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: phi-2-irepo-chatml-v11-i1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-2-irepo-chatml-v11-i1
This model is a fine-tuned version of [DUAL-GPO/phi-2-irepo-chatml-merged-i0](https://huggingface.co/DUAL-GPO/phi-2-irepo-chatml-merged-i0) on the HuggingFaceH4/ultrafeedback_binarized 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: 5e-06
- train_batch_size: 6
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
mii-llm/minerva-chat-v0.1-alpha-sft | mii-llm | 2024-05-20T18:30:09Z | 5,641 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"minerva",
"sft",
"conversational",
"it",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T17:52:59Z | ---
license: cc-by-nc-4.0
language:
- it
tags:
- minerva
- sft
---
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft
Minerva sft |
matthieuzone/CHABICHOUbis | matthieuzone | 2024-05-20T18:27:02Z | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T18:18:53Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/CHABICHOUbis
<Gallery />
## Model description
These are matthieuzone/CHABICHOUbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/CHABICHOUbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
Andres2024a/ppo-LunarLander-v2 | Andres2024a | 2024-05-20T18:26:09Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-20T18:25:50Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.24 +/- 13.75
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
DukeNLP/Prob-Gen-70B | DukeNLP | 2024-05-20T18:22:36Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-12T22:03:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
This model has been fine-tuned using 4-bit QLORA, based on [Llama-3-70B from Meta](https://huggingface.co/meta-llama/Meta-Llama-3-8B), and utilizes 3,644 GPT-4-generated grade school math word problems. It generates math word problems with multiple choices within specified contexts.
<!--
## Model Details
### Model Description
- **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]
- **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. -->
The model can be loaded with HuggingFace's Transformers library:
``` python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "DukeNLP/Prob-Gen-70B"
model = AutoModelForCausalLM.from_pretrained(model_id,device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompt = "Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:\nProblem context: <specified-context>\nTested knowledge: <specified-knowledge>"
model_input = tokenizer(prompt, return_tensors="pt").to("cuda")
model_output = model.generate(model_input['input_ids'], max_new_tokens=256)
print(tokenizer.batch_decode(model_output))
```
<!-- ## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
<!-- [More Information Needed]
<!-- ### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-->
<!-- ## Training Details -->
### Training Data
<!-- This should link to a 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. -->
The model is finetuned on 3,644 GPT-4 generated 8th-grade problems, which are also annotated and evaluated by humans, an example of our data point is shown below:
``` json
"options": [
{
"optionText": "Multiply 500 by 3/5 to get 300 tons.",
"correct": true
},
{
"optionText": "Divide 500 by 3 to get 166.67 tons.",
"correct": false
}
],
"problemContext": "Environmental issues",
"evaluated_problem": "A town's recycling plant recycles plastic and glass in a ratio of 3:2. If the plant processes 500 tons of recyclables, how much of it is plastic?",
"unitTitle": "Solving Multi-Step Problems with Proportional Relationships"
```
### Prompting
The model can be evaluated by using the following prompt:
``` python
"""Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
Problem context: <specified-context>
Tested knowledge: <specified-knowledge>"""
```
The contexts used in the dataset are:
```
"Video Games",
"Fashion",
"Influencers/YouTubers",
"Apps and Technology",
"Movies/TV shows",
"Sports",
"Music and Concerts",
"Social Media",
"Environmental issues"
```
The tested knowledge in the dataset are:
```
"Operations with Rational Numbers",
"Expressions and Equations",
"Surface Area and Volume",
"Arithmetic in Base Ten",
"Evaluating Numeric Expressions",
"Properties and Theorems of Angles",
"Data Sets",
"Rational Number Arithmetic",
"Functions and Volume",
"Linear Equations and Linear Systems",
"Representing Data and Distributions",
"Algebraic Expressions",
"Ratios and Rates",
"Solving Equations and Systems of Equations",
"Operations with Integers",
"Scatter Plots",
"Solving Percentage Problems with Proportional Relationships",
"Associations in Data",
"Expressions, Equations, and Inequalities",
"Linear Relationships",
"Representing Data",
"Solving Multi-Step Problems with Proportional Relationships",
"Dividing Fractions",
"Area, Surface Area, and Volume",
"Equivalent Algebraic Expressions",
"Key Features of Linear Equations",
"Proportional Relationships and Percentages",
"Transformations",
"Representing Proportional Relationships"
```
### Sample Generation
Here is an example passage from the training data:
```
Please generate a math problem and options for 8th graders with the following requirements:
Problem context: Movies/TV shows
Tested knowledge: Representing Data and Distributions
Question: Lucas counted the number of episodes in 12 seasons of a TV show. He recorded: 48, 51, 52, 55, 56, 58, 59, 60, 61, 62, 65, 67. How should he create a frequency table for the number of episodes?
Option 1: Group the data into intervals of 10, then count the number of seasons in each interval.
Is correct: False
Option 2: Group the data into intervals of 5 starting from 45, then count the number of seasons in each interval.
Is correct: True
```
And here is an example passage generated from the fine-tuned model:
```
Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
Problem context: Video Games
Tested knowledge: Expressions and Equations
Question: In a video game, the power of a character's weapon is calculated by the formula \(a^b\). If the weapon's power is \(2^{4}\), what is the value of \(a\) and \(b\)?
Option 1: \(a = 2\) and \(b = 4\)
Is correct: True
Option 2: \(a = 4\) and \(b = 2\)
Is correct: False
Option 3: \(a = 2\) and \(b = 2\)
Is correct: False
Option 4: \(a = 2\) and \(b = 8\)
Is correct: False
```
|
armaniii/llama-argument-classification | armaniii | 2024-05-20T18:21:19Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-classification",
"arxiv:2405.00828",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-04-13T18:57:13Z | ---
library_name: transformers
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("armaniii/llama-argument-classification")
tokenizer = AutoTokenizer.from_pretrained("armaniii/lllama-argument-classification")
model.to(device)
model.eval()
for batch in tqdm.tqdm(data):
with torch.no_grad():
input_text = tokenizer(batch, padding=True, truncation=True,max_length=2048,return_tensors="pt").to(device)
output = model(**input_text)
logits = output.logits
predicted_class = torch.argmax(logits, dim=1)
# Convert logits to a list of predicted labels
predictions.extend(predicted_class.cpu().tolist())
# Get the ground truth labels
df["predictions"] = predictions
num2label = {
0:"NoArgument",
1:"Argument"
}
```
### 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
- **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]
(https://arxiv.org/abs/2405.00828)
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
DukeNLP/Prob-Gen-8B | DukeNLP | 2024-05-20T18:21:14Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-12T16:17:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
This model has been fine-tuned using 4-bit QLORA, based on [Llama-3-8B from Meta](https://huggingface.co/meta-llama/Meta-Llama-3-8B), and utilizes 3,644 GPT-4-generated grade school math word problems. It generates math word problems with multiple choices within specified contexts.
<!--
## Model Details
### Model Description
- **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]
- **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. -->
The model can be loaded with HuggingFace's Transformers library:
``` python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "DukeNLP/Prob-Gen-8B"
model = AutoModelForCausalLM.from_pretrained(model_id,device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompt = "Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:\nProblem context: <specified-context>\nTested knowledge: <specified-knowledge>"
model_input = tokenizer(prompt, return_tensors="pt").to("cuda")
model_output = model.generate(model_input['input_ids'], max_new_tokens=256)
print(tokenizer.batch_decode(model_output))
```
<!-- ## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
<!-- [More Information Needed]
<!-- ### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-->
<!-- ## Training Details -->
### Training Data
<!-- This should link to a 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. -->
The model is finetuned on 3,644 GPT-4 generated 8th-grade problems, which are also annotated and evaluated by humans, an example of our data point is shown below:
``` json
"options": [
{
"optionText": "Multiply 500 by 3/5 to get 300 tons.",
"correct": true
},
{
"optionText": "Divide 500 by 3 to get 166.67 tons.",
"correct": false
}
],
"problemContext": "Environmental issues",
"evaluated_problem": "A town's recycling plant recycles plastic and glass in a ratio of 3:2. If the plant processes 500 tons of recyclables, how much of it is plastic?",
"unitTitle": "Solving Multi-Step Problems with Proportional Relationships"
```
### Prompting
The model can be evaluated by using the following prompt:
``` python
"""Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
Problem context: <specified-context>
Tested knowledge: <specified-knowledge>"""
```
The contexts used in the dataset are:
```
"Video Games",
"Fashion",
"Influencers/YouTubers",
"Apps and Technology",
"Movies/TV shows",
"Sports",
"Music and Concerts",
"Social Media",
"Environmental issues"
```
The tested knowledge in the dataset are:
```
"Operations with Rational Numbers",
"Expressions and Equations",
"Surface Area and Volume",
"Arithmetic in Base Ten",
"Evaluating Numeric Expressions",
"Properties and Theorems of Angles",
"Data Sets",
"Rational Number Arithmetic",
"Functions and Volume",
"Linear Equations and Linear Systems",
"Representing Data and Distributions",
"Algebraic Expressions",
"Ratios and Rates",
"Solving Equations and Systems of Equations",
"Operations with Integers",
"Scatter Plots",
"Solving Percentage Problems with Proportional Relationships",
"Associations in Data",
"Expressions, Equations, and Inequalities",
"Linear Relationships",
"Representing Data",
"Solving Multi-Step Problems with Proportional Relationships",
"Dividing Fractions",
"Area, Surface Area, and Volume",
"Equivalent Algebraic Expressions",
"Key Features of Linear Equations",
"Proportional Relationships and Percentages",
"Transformations",
"Representing Proportional Relationships"
```
### Sample Generation
Here is an example passage from the training data:
```
Please generate a math problem and options for 8th graders with the following requirements:
Problem context: Movies/TV shows
Tested knowledge: Representing Data and Distributions
Question: Lucas counted the number of episodes in 12 seasons of a TV show. He recorded: 48, 51, 52, 55, 56, 58, 59, 60, 61, 62, 65, 67. How should he create a frequency table for the number of episodes?
Option 1: Group the data into intervals of 10, then count the number of seasons in each interval.
Is correct: False
Option 2: Group the data into intervals of 5 starting from 45, then count the number of seasons in each interval.
Is correct: True
```
And here is an example passage generated from the fine-tuned model:
```
Please generate a math problem and 2 to 4 options for 8th graders with the following requirements:
Problem context: Video Games
Tested knowledge: Expressions and Equations
Question: In a video game, the power of a character's weapon is calculated by the formula \(a^b\). If the weapon's power is \(2^{4}\), what is the value of \(a\) and \(b\)?
Option 1: \(a = 2\) and \(b = 4\)
Is correct: True
Option 2: \(a = 4\) and \(b = 2\)
Is correct: False
Option 3: \(a = 2\) and \(b = 2\)
Is correct: False
Option 4: \(a = 2\) and \(b = 8\)
Is correct: False
```
|
matthieuzone/CAMEMBERTbis | matthieuzone | 2024-05-20T18:18:38Z | 3 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T18:10:36Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks cheese
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - matthieuzone/CAMEMBERTbis
<Gallery />
## Model description
These are matthieuzone/CAMEMBERTbis LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks cheese to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](matthieuzone/CAMEMBERTbis/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
thesven/Llama-3-70B-Instruct-GGUF | thesven | 2024-05-20T18:18:26Z | 0 | 0 | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"en",
"license:llama3",
"region:us"
] | text-generation | 2024-05-19T10:16:53Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3
extra_gated_prompt: >-
### META LLAMA 3 COMMUNITY LICENSE AGREEMENT
Meta Llama 3 Version Release Date: April 18, 2024
"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3
distributed by Meta at https://llama.meta.com/get-started/.
"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into
this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or
regulations to provide legal consent and that has legal authority to bind your employer or such other
person or entity if you are entering in this Agreement on their behalf.
"Meta Llama 3" means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://llama.meta.com/llama-downloads.
"Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any
portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your
principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located
outside of the EEA or Switzerland).
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free
limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama
Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the
Llama Materials.
b. Redistribution and Use.
i. If you distribute or make available the Llama Materials (or any derivative works
thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide
a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta
Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you
use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is
distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model
name.
ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part
of an integrated end user product, then Section 2 of this Agreement will not apply to you.
iii. You must retain in all copies of the Llama Materials that you distribute the following
attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is
licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights
Reserved.”
iv. Your use of the Llama Materials must comply with applicable laws and regulations
(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama
Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by
reference into this Agreement.
v. You will not use the Llama Materials or any output or results of the Llama Materials to
improve any other large language model (excluding Meta Llama 3 or derivative works thereof).
2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users
of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700
million monthly active users in the preceding calendar month, you must request a license from Meta,
which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the
rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY
OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF
ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED,
INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT,
MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR
DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND
ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND
RESULTS.
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING
OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,
INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED
OF THE POSSIBILITY OF ANY OF THE FOREGOING.
5. Intellectual Property.
a. No trademark licenses are granted under this Agreement, and in connection with the Llama
Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other
or any of its affiliates, except as required for reasonable and customary use in describing and
redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to
use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will
comply with Meta’s brand guidelines (currently accessible at
https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use
of the Mark will inure to the benefit of Meta.
b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with
respect to any derivative works and modifications of the Llama Materials that are made by you, as
between you and Meta, you are and will be the owner of such derivative works and modifications.
c. If you institute litigation or other proceedings against Meta or any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or
results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other
rights owned or licensable by you, then any licenses granted to you under this Agreement shall
terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold
harmless Meta from and against any claim by any third party arising out of or related to your use or
distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your acceptance of this
Agreement or access to the Llama Materials and will continue in full force and effect until terminated in
accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in
breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete
and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this
Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of
the State of California without regard to choice of law principles, and the UN Convention on Contracts
for the International Sale of Goods does not apply to this Agreement. The courts of California shall have
exclusive jurisdiction of any dispute arising out of this Agreement.
### Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
#### Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
others to use, Meta Llama 3 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Meta Llama 3 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
widget:
- 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|>
---
## Quantization Details
This repo contains GGUF quantized versions of the Meta Llama 3 70B Instruct model.
The model is supplied in different quantizations so that you can see what works best on the hardware you would like to run it on.
The repo contains quantizations in the following types:
Q4_0
Q4_1
Q4_K
Q4_K_S
Q4_K_M
Q2_K
Q3_K
Q3_K_S
Q3_K_XS
IQ2_K
IQ3_S
IQ3_XXS
IQ4_NL
IQ4_XS
IQ2_S
IQ2_XS
IQ1_S
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
BilalMuftuoglu/beit-base-patch16-224-75-fold2 | BilalMuftuoglu | 2024-05-20T18:16:38Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-20T17:56:18Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-75-fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9534883720930233
---
<!-- 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. -->
# beit-base-patch16-224-75-fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2685
- Accuracy: 0.9535
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 0.7091 | 0.5349 |
| No log | 2.0 | 4 | 0.6502 | 0.7209 |
| No log | 3.0 | 6 | 0.9193 | 0.6977 |
| No log | 4.0 | 8 | 0.7499 | 0.7442 |
| 0.6436 | 5.0 | 10 | 0.4527 | 0.8140 |
| 0.6436 | 6.0 | 12 | 0.4169 | 0.8372 |
| 0.6436 | 7.0 | 14 | 0.5773 | 0.7442 |
| 0.6436 | 8.0 | 16 | 0.4076 | 0.8605 |
| 0.6436 | 9.0 | 18 | 0.3939 | 0.8605 |
| 0.3863 | 10.0 | 20 | 0.4017 | 0.8605 |
| 0.3863 | 11.0 | 22 | 0.4918 | 0.8140 |
| 0.3863 | 12.0 | 24 | 0.2688 | 0.8372 |
| 0.3863 | 13.0 | 26 | 0.3884 | 0.8140 |
| 0.3863 | 14.0 | 28 | 0.3679 | 0.8140 |
| 0.2925 | 15.0 | 30 | 0.2802 | 0.8837 |
| 0.2925 | 16.0 | 32 | 0.2436 | 0.9070 |
| 0.2925 | 17.0 | 34 | 0.2337 | 0.9302 |
| 0.2925 | 18.0 | 36 | 0.3711 | 0.8140 |
| 0.2925 | 19.0 | 38 | 0.2372 | 0.9302 |
| 0.2289 | 20.0 | 40 | 0.2685 | 0.9535 |
| 0.2289 | 21.0 | 42 | 0.2610 | 0.9070 |
| 0.2289 | 22.0 | 44 | 0.3328 | 0.8372 |
| 0.2289 | 23.0 | 46 | 0.3479 | 0.8372 |
| 0.2289 | 24.0 | 48 | 0.2855 | 0.8837 |
| 0.219 | 25.0 | 50 | 0.2962 | 0.9070 |
| 0.219 | 26.0 | 52 | 0.4038 | 0.9070 |
| 0.219 | 27.0 | 54 | 0.3149 | 0.9070 |
| 0.219 | 28.0 | 56 | 0.3212 | 0.9070 |
| 0.219 | 29.0 | 58 | 0.4895 | 0.8605 |
| 0.1933 | 30.0 | 60 | 0.4335 | 0.8837 |
| 0.1933 | 31.0 | 62 | 0.3521 | 0.8372 |
| 0.1933 | 32.0 | 64 | 0.2960 | 0.8837 |
| 0.1933 | 33.0 | 66 | 0.4037 | 0.8372 |
| 0.1933 | 34.0 | 68 | 0.2913 | 0.8837 |
| 0.1892 | 35.0 | 70 | 0.3043 | 0.8837 |
| 0.1892 | 36.0 | 72 | 0.3602 | 0.9302 |
| 0.1892 | 37.0 | 74 | 0.3315 | 0.9302 |
| 0.1892 | 38.0 | 76 | 0.2674 | 0.9302 |
| 0.1892 | 39.0 | 78 | 0.2970 | 0.9535 |
| 0.15 | 40.0 | 80 | 0.2661 | 0.9535 |
| 0.15 | 41.0 | 82 | 0.2551 | 0.8837 |
| 0.15 | 42.0 | 84 | 0.2467 | 0.9302 |
| 0.15 | 43.0 | 86 | 0.3008 | 0.9535 |
| 0.15 | 44.0 | 88 | 0.3265 | 0.9302 |
| 0.1238 | 45.0 | 90 | 0.2668 | 0.9302 |
| 0.1238 | 46.0 | 92 | 0.2574 | 0.9302 |
| 0.1238 | 47.0 | 94 | 0.2498 | 0.9535 |
| 0.1238 | 48.0 | 96 | 0.3319 | 0.8837 |
| 0.1238 | 49.0 | 98 | 0.2358 | 0.9302 |
| 0.1063 | 50.0 | 100 | 0.2015 | 0.9302 |
| 0.1063 | 51.0 | 102 | 0.2171 | 0.9302 |
| 0.1063 | 52.0 | 104 | 0.3119 | 0.9302 |
| 0.1063 | 53.0 | 106 | 0.2674 | 0.9070 |
| 0.1063 | 54.0 | 108 | 0.3076 | 0.8837 |
| 0.1112 | 55.0 | 110 | 0.3182 | 0.8837 |
| 0.1112 | 56.0 | 112 | 0.3371 | 0.9070 |
| 0.1112 | 57.0 | 114 | 0.3540 | 0.9070 |
| 0.1112 | 58.0 | 116 | 0.4058 | 0.9070 |
| 0.1112 | 59.0 | 118 | 0.4013 | 0.9070 |
| 0.1128 | 60.0 | 120 | 0.3309 | 0.9302 |
| 0.1128 | 61.0 | 122 | 0.3272 | 0.9302 |
| 0.1128 | 62.0 | 124 | 0.4012 | 0.9070 |
| 0.1128 | 63.0 | 126 | 0.5794 | 0.8605 |
| 0.1128 | 64.0 | 128 | 0.3881 | 0.9070 |
| 0.1168 | 65.0 | 130 | 0.2990 | 0.9070 |
| 0.1168 | 66.0 | 132 | 0.3018 | 0.8837 |
| 0.1168 | 67.0 | 134 | 0.2561 | 0.9302 |
| 0.1168 | 68.0 | 136 | 0.2921 | 0.9302 |
| 0.1168 | 69.0 | 138 | 0.3258 | 0.9070 |
| 0.0846 | 70.0 | 140 | 0.2925 | 0.9302 |
| 0.0846 | 71.0 | 142 | 0.3073 | 0.9302 |
| 0.0846 | 72.0 | 144 | 0.3318 | 0.9302 |
| 0.0846 | 73.0 | 146 | 0.3427 | 0.9302 |
| 0.0846 | 74.0 | 148 | 0.3588 | 0.9070 |
| 0.0845 | 75.0 | 150 | 0.3939 | 0.9070 |
| 0.0845 | 76.0 | 152 | 0.3774 | 0.9070 |
| 0.0845 | 77.0 | 154 | 0.3746 | 0.9070 |
| 0.0845 | 78.0 | 156 | 0.4073 | 0.8837 |
| 0.0845 | 79.0 | 158 | 0.3886 | 0.9070 |
| 0.0885 | 80.0 | 160 | 0.3765 | 0.9070 |
| 0.0885 | 81.0 | 162 | 0.3977 | 0.9070 |
| 0.0885 | 82.0 | 164 | 0.3864 | 0.9070 |
| 0.0885 | 83.0 | 166 | 0.3809 | 0.9070 |
| 0.0885 | 84.0 | 168 | 0.4492 | 0.8605 |
| 0.0859 | 85.0 | 170 | 0.5479 | 0.8605 |
| 0.0859 | 86.0 | 172 | 0.5372 | 0.8605 |
| 0.0859 | 87.0 | 174 | 0.4512 | 0.8605 |
| 0.0859 | 88.0 | 176 | 0.3930 | 0.9070 |
| 0.0859 | 89.0 | 178 | 0.3842 | 0.9302 |
| 0.0764 | 90.0 | 180 | 0.3808 | 0.9302 |
| 0.0764 | 91.0 | 182 | 0.3787 | 0.9302 |
| 0.0764 | 92.0 | 184 | 0.3833 | 0.9070 |
| 0.0764 | 93.0 | 186 | 0.3912 | 0.9070 |
| 0.0764 | 94.0 | 188 | 0.3888 | 0.8837 |
| 0.0727 | 95.0 | 190 | 0.3817 | 0.8837 |
| 0.0727 | 96.0 | 192 | 0.3708 | 0.9070 |
| 0.0727 | 97.0 | 194 | 0.3640 | 0.9070 |
| 0.0727 | 98.0 | 196 | 0.3613 | 0.9302 |
| 0.0727 | 99.0 | 198 | 0.3607 | 0.9302 |
| 0.069 | 100.0 | 200 | 0.3605 | 0.9302 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Felladrin/gguf-sharded-openhermes-tinyllama-sft-qlora | Felladrin | 2024-05-20T18:13:29Z | 14 | 0 | null | [
"gguf",
"base_model:Ritvik19/openhermes-tinyllama-sft-qlora",
"base_model:quantized:Ritvik19/openhermes-tinyllama-sft-qlora",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T18:08:25Z | ---
license: apache-2.0
base_model: Ritvik19/openhermes-tinyllama-sft-qlora
---
Sharded GGUF version of [Ritvik19/openhermes-tinyllama-sft-qlora](https://huggingface.co/Ritvik19/openhermes-tinyllama-sft-qlora). |
Zoyd/01-ai_Yi-1.5-34B-32K-8_0bpw_exl2 | Zoyd | 2024-05-20T18:09:39Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T17:54:43Z | ---
license: apache-2.0
---
**Exllamav2** quant (**exl2** / **8.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_2bpw_exl2)**</center> | <center>10049 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_5bpw_exl2)**</center> | <center>11199 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_0bpw_exl2)**</center> | <center>13186 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_5bpw_exl2)**</center> | <center>15178 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_75bpw_exl2)**</center> | <center>16182 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_0bpw_exl2)**</center> | <center>17170 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_25bpw_exl2)**</center> | <center>18176 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-5_0bpw_exl2)**</center> | <center>21147 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_0bpw_exl2)**</center> | <center>25182 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_5bpw_exl2)**</center> | <center>27230 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-8_0bpw_exl2)**</center> | <center>29577 MB</center> | <center>8</center> |
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
ronenh24/bert-finetuned-ner | ronenh24 | 2024-05-20T18:08:12Z | 118 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-05-20T17:52:29Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9337628014535844
- name: Recall
type: recall
value: 0.9513631773813531
- name: F1
type: f1
value: 0.9424808269423142
- name: Accuracy
type: accuracy
value: 0.9862689115205746
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0629
- Precision: 0.9338
- Recall: 0.9514
- F1: 0.9425
- Accuracy: 0.9863
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0738 | 1.0 | 1756 | 0.0695 | 0.8962 | 0.9286 | 0.9121 | 0.9807 |
| 0.0354 | 2.0 | 3512 | 0.0628 | 0.9358 | 0.9487 | 0.9422 | 0.9860 |
| 0.0224 | 3.0 | 5268 | 0.0629 | 0.9338 | 0.9514 | 0.9425 | 0.9863 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Zoyd/01-ai_Yi-1.5-34B-32K-6_0bpw_exl2 | Zoyd | 2024-05-20T17:58:14Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T16:48:57Z | ---
license: apache-2.0
---
**Exllamav2** quant (**exl2** / **6.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_2bpw_exl2)**</center> | <center>10049 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_5bpw_exl2)**</center> | <center>11199 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_0bpw_exl2)**</center> | <center>13186 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_5bpw_exl2)**</center> | <center>15178 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_75bpw_exl2)**</center> | <center>16182 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_0bpw_exl2)**</center> | <center>17170 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_25bpw_exl2)**</center> | <center>18176 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-5_0bpw_exl2)**</center> | <center>21147 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_0bpw_exl2)**</center> | <center>25182 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_5bpw_exl2)**</center> | <center>27230 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-8_0bpw_exl2)**</center> | <center>29577 MB</center> | <center>8</center> |
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
Zoyd/01-ai_Yi-1.5-34B-32K-5_0bpw_exl2 | Zoyd | 2024-05-20T17:58:04Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T16:16:02Z | ---
license: apache-2.0
---
**Exllamav2** quant (**exl2** / **5.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_2bpw_exl2)**</center> | <center>10049 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_5bpw_exl2)**</center> | <center>11199 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_0bpw_exl2)**</center> | <center>13186 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_5bpw_exl2)**</center> | <center>15178 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_75bpw_exl2)**</center> | <center>16182 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_0bpw_exl2)**</center> | <center>17170 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_25bpw_exl2)**</center> | <center>18176 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-5_0bpw_exl2)**</center> | <center>21147 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_0bpw_exl2)**</center> | <center>25182 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_5bpw_exl2)**</center> | <center>27230 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-8_0bpw_exl2)**</center> | <center>29577 MB</center> | <center>8</center> |
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
ryandono/fine-tune-paligema | ryandono | 2024-05-20T17:58:03Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T17:55:45Z | ---
license: apache-2.0
---
|
Zoyd/01-ai_Yi-1.5-34B-32K-3_5bpw_exl2 | Zoyd | 2024-05-20T17:57:46Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-20T14:05:39Z | ---
license: apache-2.0
---
**Exllamav2** quant (**exl2** / **3.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_2bpw_exl2)**</center> | <center>10049 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_5bpw_exl2)**</center> | <center>11199 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_0bpw_exl2)**</center> | <center>13186 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_5bpw_exl2)**</center> | <center>15178 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_75bpw_exl2)**</center> | <center>16182 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_0bpw_exl2)**</center> | <center>17170 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_25bpw_exl2)**</center> | <center>18176 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-5_0bpw_exl2)**</center> | <center>21147 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_0bpw_exl2)**</center> | <center>25182 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_5bpw_exl2)**</center> | <center>27230 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-8_0bpw_exl2)**</center> | <center>29577 MB</center> | <center>8</center> |
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
0xlexor/genesys | 0xlexor | 2024-05-20T17:57:34Z | 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 | 2024-05-20T17:53:16Z | ---
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
### 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 0.8.2 |
Zoyd/01-ai_Yi-1.5-34B-32K-3_0bpw_exl2 | Zoyd | 2024-05-20T17:57:33Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"3-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T13:33:15Z | ---
license: apache-2.0
---
**Exllamav2** quant (**exl2** / **3.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_2bpw_exl2)**</center> | <center>10049 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_5bpw_exl2)**</center> | <center>11199 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_0bpw_exl2)**</center> | <center>13186 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_5bpw_exl2)**</center> | <center>15178 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_75bpw_exl2)**</center> | <center>16182 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_0bpw_exl2)**</center> | <center>17170 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_25bpw_exl2)**</center> | <center>18176 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-5_0bpw_exl2)**</center> | <center>21147 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_0bpw_exl2)**</center> | <center>25182 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_5bpw_exl2)**</center> | <center>27230 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-8_0bpw_exl2)**</center> | <center>29577 MB</center> | <center>8</center> |
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
Zoyd/01-ai_Yi-1.5-34B-32K-2_5bpw_exl2 | Zoyd | 2024-05-20T17:57:20Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2403.04652",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-20T13:00:57Z | ---
license: apache-2.0
---
**Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_2bpw_exl2)**</center> | <center>10049 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-2_5bpw_exl2)**</center> | <center>11199 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_0bpw_exl2)**</center> | <center>13186 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_5bpw_exl2)**</center> | <center>15178 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-3_75bpw_exl2)**</center> | <center>16182 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_0bpw_exl2)**</center> | <center>17170 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-4_25bpw_exl2)**</center> | <center>18176 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-5_0bpw_exl2)**</center> | <center>21147 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_0bpw_exl2)**</center> | <center>25182 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-6_5bpw_exl2)**</center> | <center>27230 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/01-ai_Yi-1.5-34B-32K-8_0bpw_exl2)**</center> | <center>29577 MB</center> | <center>8</center> |
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples.
Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension.
<div align="center">
Model | Context Length | Pre-trained Tokens
| :------------: | :------------: | :------------: |
| Yi-1.5 | 4K, 16K, 32K | 3.6T
</div>
# Models
- Chat models
<div align="center">
| Name | Download |
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-Chat-16K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B-Chat | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
- Base models
<div align="center">
| Name | Download |
| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Yi-1.5-34B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-34B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-9B-32K | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
| Yi-1.5-6B | • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) |
</div>
# Benchmarks
- Chat models
Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks.
Yi-1.5-9B-Chat is the top performer among similarly sized open-source models.
- Base models
Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks.
Yi-1.5-9B is the top performer among similarly sized open-source models.
# Quick Start
For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
|
BilalMuftuoglu/beit-base-patch16-224-75-fold1 | BilalMuftuoglu | 2024-05-20T17:56:11Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-20T17:35:46Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-75-fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9302325581395349
---
<!-- 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. -->
# beit-base-patch16-224-75-fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2641
- Accuracy: 0.9302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 2 | 1.0987 | 0.3023 |
| No log | 2.0 | 4 | 0.6630 | 0.6977 |
| No log | 3.0 | 6 | 0.8342 | 0.6977 |
| No log | 4.0 | 8 | 0.6752 | 0.6977 |
| 0.7768 | 5.0 | 10 | 0.5408 | 0.7209 |
| 0.7768 | 6.0 | 12 | 0.7252 | 0.6977 |
| 0.7768 | 7.0 | 14 | 0.5609 | 0.7209 |
| 0.7768 | 8.0 | 16 | 0.7345 | 0.6977 |
| 0.7768 | 9.0 | 18 | 0.4614 | 0.7674 |
| 0.4249 | 10.0 | 20 | 0.4434 | 0.8372 |
| 0.4249 | 11.0 | 22 | 0.7552 | 0.7442 |
| 0.4249 | 12.0 | 24 | 0.4142 | 0.7674 |
| 0.4249 | 13.0 | 26 | 0.7183 | 0.7442 |
| 0.4249 | 14.0 | 28 | 0.5591 | 0.7907 |
| 0.3506 | 15.0 | 30 | 0.4363 | 0.6977 |
| 0.3506 | 16.0 | 32 | 0.5738 | 0.7907 |
| 0.3506 | 17.0 | 34 | 0.4286 | 0.8140 |
| 0.3506 | 18.0 | 36 | 0.4200 | 0.8140 |
| 0.3506 | 19.0 | 38 | 0.6514 | 0.7442 |
| 0.3434 | 20.0 | 40 | 0.4190 | 0.7907 |
| 0.3434 | 21.0 | 42 | 0.6220 | 0.8140 |
| 0.3434 | 22.0 | 44 | 0.6334 | 0.7907 |
| 0.3434 | 23.0 | 46 | 0.4487 | 0.8372 |
| 0.3434 | 24.0 | 48 | 0.4960 | 0.8605 |
| 0.2498 | 25.0 | 50 | 0.4179 | 0.8605 |
| 0.2498 | 26.0 | 52 | 0.3221 | 0.8605 |
| 0.2498 | 27.0 | 54 | 0.4776 | 0.8372 |
| 0.2498 | 28.0 | 56 | 0.5756 | 0.8605 |
| 0.2498 | 29.0 | 58 | 0.5444 | 0.8372 |
| 0.2461 | 30.0 | 60 | 0.3973 | 0.8605 |
| 0.2461 | 31.0 | 62 | 0.3672 | 0.8605 |
| 0.2461 | 32.0 | 64 | 0.4071 | 0.8837 |
| 0.2461 | 33.0 | 66 | 0.4678 | 0.7674 |
| 0.2461 | 34.0 | 68 | 0.2641 | 0.9302 |
| 0.2279 | 35.0 | 70 | 0.5551 | 0.8372 |
| 0.2279 | 36.0 | 72 | 0.2727 | 0.9302 |
| 0.2279 | 37.0 | 74 | 0.3312 | 0.8837 |
| 0.2279 | 38.0 | 76 | 0.7485 | 0.7907 |
| 0.2279 | 39.0 | 78 | 0.6407 | 0.8605 |
| 0.183 | 40.0 | 80 | 0.5420 | 0.8372 |
| 0.183 | 41.0 | 82 | 0.7364 | 0.8605 |
| 0.183 | 42.0 | 84 | 0.4141 | 0.8605 |
| 0.183 | 43.0 | 86 | 0.5461 | 0.7907 |
| 0.183 | 44.0 | 88 | 0.3438 | 0.8605 |
| 0.1658 | 45.0 | 90 | 0.3322 | 0.9302 |
| 0.1658 | 46.0 | 92 | 0.3463 | 0.9302 |
| 0.1658 | 47.0 | 94 | 0.6066 | 0.8605 |
| 0.1658 | 48.0 | 96 | 0.6259 | 0.8605 |
| 0.1658 | 49.0 | 98 | 0.4909 | 0.8372 |
| 0.1555 | 50.0 | 100 | 0.6022 | 0.7907 |
| 0.1555 | 51.0 | 102 | 0.5234 | 0.8372 |
| 0.1555 | 52.0 | 104 | 0.4164 | 0.8837 |
| 0.1555 | 53.0 | 106 | 0.3893 | 0.8605 |
| 0.1555 | 54.0 | 108 | 0.3774 | 0.8837 |
| 0.1487 | 55.0 | 110 | 0.7532 | 0.8372 |
| 0.1487 | 56.0 | 112 | 0.7141 | 0.8605 |
| 0.1487 | 57.0 | 114 | 0.4197 | 0.9070 |
| 0.1487 | 58.0 | 116 | 0.6816 | 0.7442 |
| 0.1487 | 59.0 | 118 | 0.5384 | 0.8140 |
| 0.1349 | 60.0 | 120 | 0.4971 | 0.8605 |
| 0.1349 | 61.0 | 122 | 0.4601 | 0.8837 |
| 0.1349 | 62.0 | 124 | 0.4740 | 0.8372 |
| 0.1349 | 63.0 | 126 | 0.5386 | 0.8140 |
| 0.1349 | 64.0 | 128 | 0.3376 | 0.9070 |
| 0.128 | 65.0 | 130 | 0.3905 | 0.9070 |
| 0.128 | 66.0 | 132 | 0.3841 | 0.9302 |
| 0.128 | 67.0 | 134 | 0.3567 | 0.8605 |
| 0.128 | 68.0 | 136 | 0.3985 | 0.8372 |
| 0.128 | 69.0 | 138 | 0.4165 | 0.8372 |
| 0.0875 | 70.0 | 140 | 0.4346 | 0.8605 |
| 0.0875 | 71.0 | 142 | 0.4497 | 0.8372 |
| 0.0875 | 72.0 | 144 | 0.4353 | 0.8837 |
| 0.0875 | 73.0 | 146 | 0.4276 | 0.8837 |
| 0.0875 | 74.0 | 148 | 0.4010 | 0.8837 |
| 0.0932 | 75.0 | 150 | 0.3958 | 0.9070 |
| 0.0932 | 76.0 | 152 | 0.3604 | 0.9070 |
| 0.0932 | 77.0 | 154 | 0.3427 | 0.8837 |
| 0.0932 | 78.0 | 156 | 0.3417 | 0.8837 |
| 0.0932 | 79.0 | 158 | 0.3438 | 0.9070 |
| 0.0943 | 80.0 | 160 | 0.3756 | 0.9302 |
| 0.0943 | 81.0 | 162 | 0.4077 | 0.9302 |
| 0.0943 | 82.0 | 164 | 0.4129 | 0.9302 |
| 0.0943 | 83.0 | 166 | 0.4304 | 0.9302 |
| 0.0943 | 84.0 | 168 | 0.4156 | 0.9302 |
| 0.0753 | 85.0 | 170 | 0.4088 | 0.9070 |
| 0.0753 | 86.0 | 172 | 0.4090 | 0.8837 |
| 0.0753 | 87.0 | 174 | 0.4076 | 0.9070 |
| 0.0753 | 88.0 | 176 | 0.4273 | 0.9070 |
| 0.0753 | 89.0 | 178 | 0.4367 | 0.9070 |
| 0.0846 | 90.0 | 180 | 0.4490 | 0.9070 |
| 0.0846 | 91.0 | 182 | 0.4448 | 0.8837 |
| 0.0846 | 92.0 | 184 | 0.4406 | 0.8837 |
| 0.0846 | 93.0 | 186 | 0.4393 | 0.8837 |
| 0.0846 | 94.0 | 188 | 0.4370 | 0.8837 |
| 0.0865 | 95.0 | 190 | 0.4330 | 0.8837 |
| 0.0865 | 96.0 | 192 | 0.4293 | 0.8837 |
| 0.0865 | 97.0 | 194 | 0.4240 | 0.8837 |
| 0.0865 | 98.0 | 196 | 0.4177 | 0.8837 |
| 0.0865 | 99.0 | 198 | 0.4144 | 0.8837 |
| 0.1019 | 100.0 | 200 | 0.4135 | 0.8837 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
LoML/distilbert-base-uncased-finetuned-emotion | LoML | 2024-05-20T17:48:30Z | 120 | 0 | transformers | [
"transformers",
"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 | 2024-05-20T16:43:03Z | ---
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: 0.9257130045399095
---
<!-- 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: 0.2177
- Accuracy: 0.9255
- F1: 0.9257
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8177 | 1.0 | 250 | 0.3034 | 0.9075 | 0.9067 |
| 0.2404 | 2.0 | 500 | 0.2177 | 0.9255 | 0.9257 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
binitt/hwars-buttons-model | binitt | 2024-05-20T17:46:56Z | 213 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-02-20T12:25:56Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: hwars-buttons-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hwars-buttons-model
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Nielzac/T52Graph2024_05_20_16_48_31_epoch | Nielzac | 2024-05-20T17:46:21Z | 1 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:adapter:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T17:46:16Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google/flan-t5-base
model-index:
- name: T52Graph2024_05_20_16_48_31_epoch
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zengdar/huggingface/runs/6t8qf6pr)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zengdar/huggingface/runs/6t8qf6pr)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zengdar/huggingface/runs/6t8qf6pr)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zengdar/huggingface/runs/6t8qf6pr)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zengdar/huggingface/runs/6t8qf6pr)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zengdar/huggingface/runs/6t8qf6pr)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zengdar/huggingface/runs/6t8qf6pr)
# T52Graph2024_05_20_16_48_31_epoch
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3041
## 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: 0.0005
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.2215 | 0.1434 | 1000 | 0.5359 |
| 0.3327 | 0.2869 | 2000 | 0.3991 |
| 0.2579 | 0.4303 | 3000 | 0.3523 |
| 0.2307 | 0.5737 | 4000 | 0.3326 |
| 0.2178 | 0.7172 | 5000 | 0.3131 |
| 0.1919 | 0.8606 | 6000 | 0.3041 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
MSParkDev/SingSeqBERT-Katchers | MSParkDev | 2024-05-20T17:44:51Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-14T03:46:40Z | ---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: SingSeqBERT-Katchers
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. -->
# SingSeqBERT-Katchers
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5238
- Accuracy: 0.7898
- F1: 0.7893
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4927 | 1.0 | 2522 | 0.5391 | 0.7693 | 0.7687 |
| 0.4503 | 2.0 | 5044 | 0.5258 | 0.7911 | 0.7904 |
| 0.4146 | 3.0 | 7566 | 0.5238 | 0.7898 | 0.7893 |
| 0.3882 | 4.0 | 10088 | 0.5512 | 0.7950 | 0.7944 |
| 0.3633 | 5.0 | 12610 | 0.6592 | 0.7892 | 0.7884 |
| 0.3638 | 6.0 | 15132 | 0.8374 | 0.7811 | 0.7796 |
| 0.3212 | 7.0 | 17654 | 0.8621 | 0.7841 | 0.7833 |
| 0.2878 | 8.0 | 20176 | 0.9864 | 0.7779 | 0.7767 |
| 0.2407 | 9.0 | 22698 | 1.0765 | 0.7832 | 0.7824 |
| 0.2051 | 10.0 | 25220 | 1.1017 | 0.7869 | 0.7864 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
ZovutVanya/ruT5-EmotionNeutralization | ZovutVanya | 2024-05-20T17:37:20Z | 116 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"ru",
"base_model:ai-forever/ruT5-base",
"base_model:finetune:ai-forever/ruT5-base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-14T12:51:28Z | ---
base_model: ai-forever/ruT5-base
tags:
- generated_from_trainer
model-index:
- name: ruT5-EmotionNeutralization
results: []
language:
- ru
license: mit
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [ai-forever/ruT5-base](https://huggingface.co/ai-forever/ruT5-base) on an anonymized Emergency calls dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3848
- ParaScore: 0.8265
## Model description
More information needed
## Intended uses & limitations
Neutralization of emotional speech
## 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: 8
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1 |
BilalMuftuoglu/beit-base-patch16-224-65-fold5 | BilalMuftuoglu | 2024-05-20T17:32:01Z | 9 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-20T17:02:53Z | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-65-fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9014084507042254
---
<!-- 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. -->
# beit-base-patch16-224-65-fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4937
- Accuracy: 0.9014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9231 | 3 | 0.7742 | 0.4507 |
| No log | 1.8462 | 6 | 0.7185 | 0.4930 |
| No log | 2.7692 | 9 | 0.6625 | 0.5634 |
| 0.7338 | 4.0 | 13 | 0.6136 | 0.7183 |
| 0.7338 | 4.9231 | 16 | 0.5974 | 0.6479 |
| 0.7338 | 5.8462 | 19 | 0.5771 | 0.6338 |
| 0.6191 | 6.7692 | 22 | 0.5400 | 0.7042 |
| 0.6191 | 8.0 | 26 | 0.5127 | 0.7183 |
| 0.6191 | 8.9231 | 29 | 0.5341 | 0.7324 |
| 0.5723 | 9.8462 | 32 | 0.4877 | 0.7887 |
| 0.5723 | 10.7692 | 35 | 0.6659 | 0.6197 |
| 0.5723 | 12.0 | 39 | 0.5790 | 0.6761 |
| 0.5161 | 12.9231 | 42 | 0.5001 | 0.7606 |
| 0.5161 | 13.8462 | 45 | 0.4195 | 0.8310 |
| 0.5161 | 14.7692 | 48 | 0.4806 | 0.7746 |
| 0.4982 | 16.0 | 52 | 0.4013 | 0.8028 |
| 0.4982 | 16.9231 | 55 | 0.4189 | 0.8028 |
| 0.4982 | 17.8462 | 58 | 0.4018 | 0.8310 |
| 0.438 | 18.7692 | 61 | 0.5230 | 0.7183 |
| 0.438 | 20.0 | 65 | 0.4768 | 0.7465 |
| 0.438 | 20.9231 | 68 | 0.4428 | 0.7887 |
| 0.4641 | 21.8462 | 71 | 0.4122 | 0.8169 |
| 0.4641 | 22.7692 | 74 | 0.4537 | 0.7746 |
| 0.4641 | 24.0 | 78 | 0.3838 | 0.8310 |
| 0.308 | 24.9231 | 81 | 0.4586 | 0.8028 |
| 0.308 | 25.8462 | 84 | 0.5623 | 0.8028 |
| 0.308 | 26.7692 | 87 | 0.4050 | 0.8310 |
| 0.2766 | 28.0 | 91 | 0.3860 | 0.8169 |
| 0.2766 | 28.9231 | 94 | 0.4062 | 0.8169 |
| 0.2766 | 29.8462 | 97 | 0.6191 | 0.8169 |
| 0.288 | 30.7692 | 100 | 0.6076 | 0.7746 |
| 0.288 | 32.0 | 104 | 0.5300 | 0.8169 |
| 0.288 | 32.9231 | 107 | 0.6178 | 0.7606 |
| 0.2676 | 33.8462 | 110 | 0.4465 | 0.8451 |
| 0.2676 | 34.7692 | 113 | 0.5893 | 0.7606 |
| 0.2676 | 36.0 | 117 | 0.4782 | 0.8169 |
| 0.2306 | 36.9231 | 120 | 0.4946 | 0.8310 |
| 0.2306 | 37.8462 | 123 | 0.4534 | 0.8451 |
| 0.2306 | 38.7692 | 126 | 0.4603 | 0.8451 |
| 0.2095 | 40.0 | 130 | 0.5839 | 0.8028 |
| 0.2095 | 40.9231 | 133 | 0.4536 | 0.8310 |
| 0.2095 | 41.8462 | 136 | 0.4617 | 0.8592 |
| 0.2095 | 42.7692 | 139 | 0.4531 | 0.8592 |
| 0.2171 | 44.0 | 143 | 0.4325 | 0.8732 |
| 0.2171 | 44.9231 | 146 | 0.4732 | 0.8592 |
| 0.2171 | 45.8462 | 149 | 0.4779 | 0.8592 |
| 0.1686 | 46.7692 | 152 | 0.4841 | 0.8451 |
| 0.1686 | 48.0 | 156 | 0.5690 | 0.8310 |
| 0.1686 | 48.9231 | 159 | 0.5477 | 0.8451 |
| 0.1644 | 49.8462 | 162 | 0.5844 | 0.8310 |
| 0.1644 | 50.7692 | 165 | 0.5818 | 0.8310 |
| 0.1644 | 52.0 | 169 | 0.4674 | 0.8451 |
| 0.1915 | 52.9231 | 172 | 0.5320 | 0.8732 |
| 0.1915 | 53.8462 | 175 | 0.4933 | 0.8451 |
| 0.1915 | 54.7692 | 178 | 0.5090 | 0.8592 |
| 0.1561 | 56.0 | 182 | 0.4864 | 0.8451 |
| 0.1561 | 56.9231 | 185 | 0.4652 | 0.8732 |
| 0.1561 | 57.8462 | 188 | 0.5113 | 0.8592 |
| 0.1298 | 58.7692 | 191 | 0.4803 | 0.8732 |
| 0.1298 | 60.0 | 195 | 0.4794 | 0.8451 |
| 0.1298 | 60.9231 | 198 | 0.4743 | 0.8451 |
| 0.1467 | 61.8462 | 201 | 0.4739 | 0.8592 |
| 0.1467 | 62.7692 | 204 | 0.5211 | 0.8451 |
| 0.1467 | 64.0 | 208 | 0.5315 | 0.8592 |
| 0.1363 | 64.9231 | 211 | 0.5182 | 0.8592 |
| 0.1363 | 65.8462 | 214 | 0.5160 | 0.8451 |
| 0.1363 | 66.7692 | 217 | 0.6170 | 0.8169 |
| 0.154 | 68.0 | 221 | 0.4857 | 0.8592 |
| 0.154 | 68.9231 | 224 | 0.4763 | 0.8592 |
| 0.154 | 69.8462 | 227 | 0.4937 | 0.9014 |
| 0.141 | 70.7692 | 230 | 0.5038 | 0.8873 |
| 0.141 | 72.0 | 234 | 0.5026 | 0.8592 |
| 0.141 | 72.9231 | 237 | 0.5019 | 0.8592 |
| 0.1166 | 73.8462 | 240 | 0.5028 | 0.8592 |
| 0.1166 | 74.7692 | 243 | 0.5226 | 0.8592 |
| 0.1166 | 76.0 | 247 | 0.5295 | 0.8732 |
| 0.117 | 76.9231 | 250 | 0.5073 | 0.8732 |
| 0.117 | 77.8462 | 253 | 0.5081 | 0.8732 |
| 0.117 | 78.7692 | 256 | 0.5036 | 0.8592 |
| 0.1037 | 80.0 | 260 | 0.5038 | 0.8451 |
| 0.1037 | 80.9231 | 263 | 0.5072 | 0.8451 |
| 0.1037 | 81.8462 | 266 | 0.5081 | 0.8451 |
| 0.1037 | 82.7692 | 269 | 0.5062 | 0.8310 |
| 0.1085 | 84.0 | 273 | 0.5144 | 0.8451 |
| 0.1085 | 84.9231 | 276 | 0.5208 | 0.8592 |
| 0.1085 | 85.8462 | 279 | 0.5248 | 0.8592 |
| 0.0939 | 86.7692 | 282 | 0.5301 | 0.8592 |
| 0.0939 | 88.0 | 286 | 0.5357 | 0.8451 |
| 0.0939 | 88.9231 | 289 | 0.5398 | 0.8451 |
| 0.0962 | 89.8462 | 292 | 0.5434 | 0.8451 |
| 0.0962 | 90.7692 | 295 | 0.5455 | 0.8451 |
| 0.0962 | 92.0 | 299 | 0.5448 | 0.8451 |
| 0.1131 | 92.3077 | 300 | 0.5446 | 0.8451 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
uiyong/kospi_report_model_0517 | uiyong | 2024-05-20T17:31:22Z | 79 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-20T17:10:51Z | ---
library_name: transformers
tags:
- 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
### 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] |
sgarrett/test_4 | sgarrett | 2024-05-20T17:30:21Z | 134 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:nferruz/ProtGPT2",
"base_model:finetune:nferruz/ProtGPT2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T17:22:16Z | ---
license: apache-2.0
base_model: nferruz/ProtGPT2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: model_output_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. -->
# model_output_2
This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 11.1877
- Accuracy: 0.4684
## 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: 0.001
- 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: 200.0
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
HackerMonica/nllb-200-distilled-600M-en-zh_CN | HackerMonica | 2024-05-20T17:26:30Z | 133 | 2 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"translation",
"en",
"zh",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2024-05-20T15:41:44Z | ---
license: cc-by-nc-4.0
language:
- en
- zh
metrics:
- bleu
pipeline_tag: translation
---
# Model Documentation: English to Simplified Chinese Translation with NLLB-200-distilled-600M
## Model Overview
This document describes a machine translation model fine-tuned from Meta's NLLB-200-distilled-600M for translating from English to Simplified Chinese. The model, hosted at `HackerMonica/nllb-200-distilled-600M-en-zh_CN`, utilizes a distilled version of the NLLB-200 model which has been specifically optimized for translation tasks between the English and Simplified Chinese languages.
## Dependencies
The model requires the `transformers` library by Hugging Face. Ensure that you have the library installed:
```bash
pip install transformers
```
## Setup
Import necessary classes from the `transformers` library:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
```
Initialize the model and tokenizer:
```python
model = AutoModelForSeq2SeqLM.from_pretrained('HackerMonica/nllb-200-distilled-600M-en-zh_CN')
tokenizer = AutoTokenizer.from_pretrained('HackerMonica/nllb-200-distilled-600M-en-zh_CN')
```
## Usage
To use the model for translating text, use python code below to translate text from English to Simplified Chinese:
```python
def translate(text):
inputs = tokenizer(text, return_tensors="pt").to("cuda")
translated_tokens = model.generate(
**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["zho_Hans"], max_length=300
)
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
return translation
```
|
MrBlackSheep/BOOBS-REVpruned | MrBlackSheep | 2024-05-20T17:26:20Z | 6 | 0 | diffusers | [
"diffusers",
"checkpoint",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-02-06T12:20:41Z | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- checkpoint
---
### Model Description
A pruned merge of BOOBS MIX checkpoint and RevAnimated v1.2.2-EOL https://huggingface.co/s6yx/ReV_Animated
- **Developed by:** MrBlackSheep
- **Model type:** Checkpoint **(Pruned version)**
- **License:** creativeml-openrail-m
 |
feysahin/Reinforce-CartPole-v1 | feysahin | 2024-05-20T17:24:22Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-20T17:24:11Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
maneln/tiny-llama | maneln | 2024-05-20T17:22:53Z | 138 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T17:00:25Z | ---
license: apache-2.0
---
|
MrBlackSheep/BOOBS_MIX_Pruned.inpainting | MrBlackSheep | 2024-05-20T17:20:34Z | 9 | 0 | diffusers | [
"diffusers",
"checkpoint",
"image-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] | image-to-image | 2024-04-10T12:13:46Z | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: image-to-image
tags:
- checkpoint
---
### Model Description
Pruned **Inpaint model** for BOOBS MIX checkpoint, made for realistic style and celebrity models.
- **Developed by:** MrBlackSheep
- **Model type:** Checkpoint **Inpaint model** **(Pruned version)**
- **License:** creativeml-openrail-m
 |
SafeVLLMs/SD-GPR1200-dup-pruned | SafeVLLMs | 2024-05-20T17:15:49Z | 43 | 0 | diffusers | [
"diffusers",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-20T17:14:35Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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]
|
Subsets and Splits