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stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-23 12:32:37
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
stringclasses 571
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| pipeline_tag
stringclasses 55
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yanagriw/Code-Formatter
|
yanagriw
| 2024-10-28T13:13:36Z | 175 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-28T13:07:29Z |
---
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]
|
mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF
|
mradermacher
| 2024-10-28T13:12:56Z | 40 | 2 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:aya-se/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6",
"base_model:quantized:aya-se/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T12:58:35Z |
---
base_model: aya-se/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/aya-se/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6-GGUF/resolve/main/Llama-3.1-Swallow-8B-v0.1-Instruct-Lmsys-LR2.5E-6.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
aakinlalu/finetune-bert-sentiment-analysis
|
aakinlalu
| 2024-10-28T13:04:42Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-28T12:32:08Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetune-bert-sentiment-analysis
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune-bert-sentiment-analysis
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2378
- Accuracy: 0.94
- F1score: 0.9455
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 0.5361 | 1.0 | 100 | 0.4738 | 0.865 | 0.8811 |
| 0.1125 | 2.0 | 200 | 0.2378 | 0.94 | 0.9455 |
| 0.0357 | 3.0 | 300 | 0.2857 | 0.945 | 0.9507 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0
- Datasets 3.0.2
- Tokenizers 0.20.1
|
hugo-albert/beto-pos
|
hugo-albert
| 2024-10-28T13:01:05Z | 112 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-cased",
"base_model:finetune:dccuchile/bert-base-spanish-wwm-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-10-11T16:55:08Z |
---
library_name: transformers
base_model: dccuchile/bert-base-spanish-wwm-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: beto-pos
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. -->
# beto-pos
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0894
- Precision: 0.9797
- Recall: 0.9815
- F1: 0.9806
- Accuracy: 0.9811
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4515 | 1.0 | 603 | 0.1079 | 0.9737 | 0.9760 | 0.9749 | 0.9763 |
| 0.0607 | 2.0 | 1206 | 0.0957 | 0.9785 | 0.9803 | 0.9794 | 0.9798 |
| 0.0344 | 3.0 | 1809 | 0.0894 | 0.9797 | 0.9815 | 0.9806 | 0.9811 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
|
zixianma/mma_mantis_415k-toolboth-seq_length_8192-lr_1e-5
|
zixianma
| 2024-10-28T12:58:10Z | 6 | 0 | null |
[
"safetensors",
"llava",
"generated_from_trainer",
"base_model:TIGER-Lab/Mantis-8B-siglip-llama3-pretraind",
"base_model:finetune:TIGER-Lab/Mantis-8B-siglip-llama3-pretraind",
"license:llama3",
"region:us"
] | null | 2024-10-27T23:39:48Z |
---
license: llama3
base_model: TIGER-Lab/Mantis-8B-siglip-llama3-pretraind
tags:
- generated_from_trainer
model-index:
- name: mma_mantis_415k-toolboth-seq_length_8192-lr_1e-5
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://salesforceairesearch.wandb.io/jianguozhang/Mantis/runs/g9l622kv)
# mma_mantis_415k-toolboth-seq_length_8192-lr_1e-5
This model is a fine-tuned version of [TIGER-Lab/Mantis-8B-siglip-llama3-pretraind](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3-pretraind) 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.43.0
- Pytorch 2.4.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
Kurgan1138/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-Q8_0-GGUF
|
Kurgan1138
| 2024-10-28T12:54:12Z | 6 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:PJMixers-Dev/HailMary-v0.1-KTO",
"base_model:PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B",
"base_model:quantized:PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T12:53:55Z |
---
license: llama3.2
language:
- en
datasets:
- PJMixers-Dev/HailMary-v0.1-KTO
base_model: PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B
tags:
- llama-cpp
- gguf-my-repo
---
# Kurgan1138/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-Q8_0-GGUF
This model was converted to GGUF format from [`PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B`](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B) 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/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Kurgan1138/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-Q8_0-GGUF --hf-file llama-3.2-instruct-jankmix-v0.2-sft-hailmary-v0.1-kto-3b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Kurgan1138/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-Q8_0-GGUF --hf-file llama-3.2-instruct-jankmix-v0.2-sft-hailmary-v0.1-kto-3b-q8_0.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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Kurgan1138/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-Q8_0-GGUF --hf-file llama-3.2-instruct-jankmix-v0.2-sft-hailmary-v0.1-kto-3b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Kurgan1138/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-Q8_0-GGUF --hf-file llama-3.2-instruct-jankmix-v0.2-sft-hailmary-v0.1-kto-3b-q8_0.gguf -c 2048
```
|
kholiavko/test-train-on-responses-gguf
|
kholiavko
| 2024-10-28T12:54:08Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T12:48:05Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** kholiavko
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Shilin-LU/VINE-R-Dec
|
Shilin-LU
| 2024-10-28T12:53:59Z | 8 | 0 | null |
[
"safetensors",
"image-watermarking",
"en",
"arxiv:2410.18775",
"license:mit",
"region:us"
] | null | 2024-10-28T11:25:19Z |
---
tags:
- image-watermarking
license: mit
language:
- en
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Docs: https://github.com/Shilin-LU/VINE
- arXiv: https://arxiv.org/abs/2410.18775
|
Shilin-LU/VINE-B-Enc
|
Shilin-LU
| 2024-10-28T12:52:51Z | 20 | 0 | null |
[
"safetensors",
"image_watermarking",
"image-to-image",
"en",
"arxiv:2410.18775",
"base_model:stabilityai/sdxl-turbo",
"base_model:finetune:stabilityai/sdxl-turbo",
"license:mit",
"region:us"
] |
image-to-image
| 2024-10-28T11:41:54Z |
---
tags:
- image_watermarking
license: mit
language:
- en
base_model:
- stabilityai/sdxl-turbo
pipeline_tag: image-to-image
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Docs: https://github.com/Shilin-LU/VINE
- arXiv: https://arxiv.org/abs/2410.18775
|
AndrewACN/distilbert-base-uncased-finetuned-mnli-finetuned-voxi-mnli
|
AndrewACN
| 2024-10-28T12:46:36Z | 5 | 0 | null |
[
"tensorboard",
"safetensors",
"distilbert",
"generated_from_trainer",
"base_model:huggingface/distilbert-base-uncased-finetuned-mnli",
"base_model:finetune:huggingface/distilbert-base-uncased-finetuned-mnli",
"region:us"
] | null | 2024-10-28T12:45:56Z |
---
base_model: huggingface/distilbert-base-uncased-finetuned-mnli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-mnli-finetuned-voxi-mnli
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli-finetuned-voxi-mnli
This model is a fine-tuned version of [huggingface/distilbert-base-uncased-finetuned-mnli](https://huggingface.co/huggingface/distilbert-base-uncased-finetuned-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7457
- Accuracy: 0.8367
## 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: 48
- eval_batch_size: 48
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.0
|
tranthaihoa/qwen2_2B_vl_vietnamese_lora_16bit
|
tranthaihoa
| 2024-10-28T12:41:59Z | 74 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-10-28T12:39:01Z |
---
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]
|
gerald29/my_awesome_food_model
|
gerald29
| 2024-10-28T12:39:48Z | 142 | 0 |
transformers
|
[
"transformers",
"safetensors",
"dinov2",
"image-classification",
"generated_from_trainer",
"base_model:facebook/dinov2-base-imagenet1k-1-layer",
"base_model:finetune:facebook/dinov2-base-imagenet1k-1-layer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-10-11T01:44:33Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/dinov2-base-imagenet1k-1-layer
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_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_food_model
This model is a fine-tuned version of [facebook/dinov2-base-imagenet1k-1-layer](https://huggingface.co/facebook/dinov2-base-imagenet1k-1-layer) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1930
- Accuracy: 0.943
This is just a model created by following the the Tramnformers tutorial on image classification at https://huggingface.co/docs/transformers/main/en/tasks/image_classification
So quite worthless
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3989 | 0.992 | 62 | 0.3865 | 0.867 |
| 0.2722 | 2.0 | 125 | 0.2720 | 0.916 |
| 0.126 | 2.976 | 186 | 0.1930 | 0.943 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
gerald29/plantclef2020
|
gerald29
| 2024-10-28T12:37:12Z | 6 | 0 | null |
[
"safetensors",
"dinov2",
"region:us"
] | null | 2024-10-20T20:28:52Z |
This is actually a model for the PlantCLEF2024 dataset
|
mav23/bloom-560m-GGUF
|
mav23
| 2024-10-28T12:24:49Z | 103 | 0 | null |
[
"gguf",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zhs",
"zht",
"zu",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"license:bigscience-bloom-rail-1.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T12:12:18Z |
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
# Model Card for Bloom-560m
<!-- Provide a quick summary of what the model is/does. -->
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Recommendations](#recommendations)
5. [Training Data](#training-data)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#techincal-specifications)
9. [Citation](#citation)
10. [Glossary and Calculations](#glossary-and-calculations)
11. [More Information](#more-information)
12. [Model Card Authors](#model-card-authors)
13. [Model Card Contact](#model-card-contact)
## Model Details
### Model Description
*This section provides information for anyone who wants to know about the model.*
- **Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
- **Model Type:** Transformer-based Language Model
- **Version:** 1.0.0
- **Languages:** Multiple; see [training data](#training-data)
- **License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
- **Release Date Estimate:** Monday, 11.July.2022
- **Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
## Bias, Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
### Recommendations
*This section provides information on warnings and potential mitigations.*
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
**The following table shows the further distribution of Niger-Congo and Indic languages in the training data.**
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
**The following table shows the distribution of programming languages.**
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
## Evaluation
*This section describes the evaluation protocols and provides the results.*
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
(More evaluation scores forthcoming at the end of model training.)
## Environmental Impact
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
## Technical Specifications
*This section provides information for people who work on model development.*
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 559,214,592 parameters:
* 256,901,120 embedding parameters
* 24 layers, 16 attention heads
* Hidden layers are 1024-dimensional
* Sequence length of 2048 tokens (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
### **Training**
Training logs: [Tensorboard link](https://huggingface.co/bigscience/tr11e-350M-logs)
- Training throughput: About 150 TFLOPs per GPU
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments and other model sizes)
- Server training location: Île-de-France, France
### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
## Citation
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
## More Information
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
## Model Card Contact
**Send Questions to:** [email protected]
|
Viscoke/LittleLamb
|
Viscoke
| 2024-10-28T12:14:07Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T12:06:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Q-PING/krx_gemma-2-9b-it_1028
|
Q-PING
| 2024-10-28T12:13:16Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"krx",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T11:37:22Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- krx
---
# Uploaded model
- **Developed by:** Q-PING
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nicocambrian/LLaVA-NeXT-Video-34B-4b
|
nicocambrian
| 2024-10-28T12:12:54Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llava_next_video",
"pretraining",
"arxiv:1910.09700",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-10-28T11:25:05Z |
---
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]
|
SidXXD/198
|
SidXXD
| 2024-10-28T12:03:37Z | 5 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-10-27T07:06:09Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: photo of a <v1*> person
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - SidXXD/198
These are Custom Diffusion adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
mav23/Celestial-Harmony-14b-v1.0-Experimental-1016-GGUF
|
mav23
| 2024-10-28T11:59:32Z | 39 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"base_model:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1",
"base_model:merge:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1",
"base_model:arcee-ai/SuperNova-Medius",
"base_model:merge:arcee-ai/SuperNova-Medius",
"base_model:v000000/Qwen2.5-Lumen-14B",
"base_model:merge:v000000/Qwen2.5-Lumen-14B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T10:02:53Z |
---
base_model:
- EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1
- qwen/Qwen2.5-14b
- v000000/Qwen2.5-Lumen-14B
- arcee-ai/SuperNova-Medius
library_name: transformers
tags:
- mergekit
- merge
---
# Celestial Harmony 14b v1.0 Experimental 10/16
*In candlelight, as time unwinds... I find myself lost in your eyes... In midnight tolls, as darkness folds... I see your tears when we say goodbye... Watching stars as we drift on by... A touch, a glance, fly away.... Will our paths converge 'neath the sun?... A silent desire in melody sung.... In your memory, a whispered song....
A seed of hope where we belong~*
Listen to the song on Youtube: https://www.youtube.com/watch?v=kdV4K17KqAE&t=22s
Yet Another merge, this one for AuriAetherwiing, at their request. I like it, so try it out?
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 della_linear merge method using [qwen/Qwen2.5-14b](https://huggingface.co/qwen/Qwen2.5-14b) as a base.
### Models Merged
The following models were included in the merge:
* [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1)
* [v000000/Qwen2.5-Lumen-14B](https://huggingface.co/v000000/Qwen2.5-Lumen-14B)
* [arcee-ai/SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: arcee-ai/SuperNova-Medius
parameters:
weight: 0.3
density: 0.3
- model: v000000/Qwen2.5-Lumen-14B
parameters:
weight: 0.1
density: 0.3
- model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1
parameters:
weight: 0.4
density: 0.5
merge_method: della_linear
base_model: qwen/Qwen2.5-14b
parameters:
epsilon: 0.05
lambda: 1
merge_method: della_linear
dtype: bfloat16
```
|
soulgrove/LoLis
|
soulgrove
| 2024-10-28T11:48:11Z | 25 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-10-28T11:19:01Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: LoLis
---
# Lolis
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `LoLis` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('soulgrove/LoLis', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
Ftmhd/my_DeBERTa_model
|
Ftmhd
| 2024-10-28T11:39:05Z | 104 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-small",
"base_model:finetune:microsoft/deberta-v3-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-28T11:38:26Z |
---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
model-index:
- name: my_DeBERTa_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_DeBERTa_model
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7012
- eval_model_preparation_time: 0.0087
- eval_accuracy: 0.4814
- eval_f1: 0.0747
- eval_precision: 0.0400
- eval_recall: 0.5741
- eval_runtime: 19.2911
- eval_samples_per_second: 55.777
- eval_steps_per_second: 18.61
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
|
sheldonrobinson/distilbert-multilingual-nli-stsb-quora-ranking
|
sheldonrobinson
| 2024-10-28T11:28:37Z | 17 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-11-04T16:43:10Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pipeline_tag: sentence-similarity
---
# sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## 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('sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking')
model = AutoModel.from_pretrained('sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
```
|
Abdulkoko/dummy-model
|
Abdulkoko
| 2024-10-28T11:24:20Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-10-28T11:21:44Z |
---
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]
|
crazyjeannot/fr_literary_bge_base
|
crazyjeannot
| 2024-10-28T11:18:53Z | 27 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"fr",
"dataset:crazyjeannot/fr_literary_dataset_base",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"doi:10.57967/hf/3255",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-10-15T14:17:44Z |
---
datasets:
- crazyjeannot/fr_literary_dataset_base
language:
- fr
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
widget: []
license: apache-2.0
base_model:
- BAAI/bge-m3
---
# Literary Encoder
This is an encoder model finetuned from the FlagOpen/FlagEmbedding family of models.
The model is specialized for studying french literary fiction with a training corpus based on 400.000 passages from free from rights french literary novels.
It maps paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024
- **Similarity Function:** Cosine Similarity
- **Training Dataset:** [crazyjeannot/fr_literary_dataset_large](https://huggingface.co/datasets/crazyjeannot/fr_literary_dataset_large)
- **Language:** French
- **License:** cc-by-2.5
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Flag Embedding on GitHub](https://github.com/FlagOpen/FlagEmbedding)
- **Hugging Face:** [BGE dense model on Hugging Face](https://huggingface.co/BAAI/bge-m3)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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()
)
```
## Usage
### Direct Usage (FlagEmbedding)
Then you can load this model and run inference.
```python
from FlagEmbedding import FlagModel
# Download from the 🤗 Hub
model = FlagModel('crazyjeannot/literary_bge_base',
query_instruction_for_retrieval="",
use_fp16=True)
# Run inference
sentences = [
'Il y avait, du reste, cette chose assez triste, c’est que si M. de Marsantes, à l’esprit fort ouvert, eût apprécié un fils si différent de lui, Robert de Saint-Loup, parce qu’il était de ceux qui croient que le mérite est attaché à certaines formes de la vie, avait un souvenir affectueux mais un peu méprisant d’un père qui s’était occupé toute sa vie de chasse et de course, avait bâillé à Wagner et raffolé d’Offenbach.',
"D’ailleurs, les opinions tranchantes abondent dans un siècle où l’on ne doute de rien, hors de l’existence de Dieu ; mais comme les jugements généraux que l’on porte sur les peuples sont assez souvent démentis par l’expérience, je n’aurai garde de prononcer.",
'Il était chargé de remettre l’objet, quel qu’il fût, au commodore, et d’en prendre un reçu, comme preuve que lui et son camarade s’étaient acquittés de leur commission.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
```
### SentenceTransformer
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Il y avait, du reste, cette chose assez triste, c’est que si M. de Marsantes, à l’esprit fort ouvert, eût apprécié un fils si différent de lui, Robert de Saint-Loup, parce qu’il était de ceux qui croient que le mérite est attaché à certaines formes de la vie, avait un souvenir affectueux mais un peu méprisant d’un père qui s’était occupé toute sa vie de chasse et de course, avait bâillé à Wagner et raffolé d’Offenbach.',
"D’ailleurs, les opinions tranchantes abondent dans un siècle où l’on ne doute de rien, hors de l’existence de Dieu ; mais comme les jugements généraux que l’on porte sur les peuples sont assez souvent démentis par l’expérience, je n’aurai garde de prononcer.",
'Il était chargé de remettre l’objet, quel qu’il fût, au commodore, et d’en prendre un reçu, comme preuve que lui et son camarade s’étaient acquittés de leur commission.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
```
## Training Details
### Framework Versions
- Python: 3.9.2
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
If you find this repository useful, please consider giving a like and citation
```
@inproceedings{barre_latent_2024,
title={Latent {Structures} of {Intertextuality} in {French} {Fiction}},
author={Barré, Jean},
address = {Aarhus, Denmark},
series = {{CEUR} {Workshop} {Proceedings}},
booktitle = {Proceedings of the {Conference} on {Computational} {Humanities} {Research} CHR2024},
publisher = {CEUR},
editor = {Haverals, Wouter and Koolen, Marijn and Thompson, Laure},
year = {2024},
}
```
|
WadyPW/mistral7b-wady-alpaca-sft
|
WadyPW
| 2024-10-28T11:17:25Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-10-28T11:07:03Z |
---
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]
|
jangkimo/krx_test2_Meta-Llama-3.1-8B
|
jangkimo
| 2024-10-28T11:16:28Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T11:03:13Z |
---
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]
|
John6666/orphic-3-sdxl
|
John6666
| 2024-10-28T11:11:34Z | 33 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"realism",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-08-14T02:49:46Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- realism
- pony
---
Original model is [here](https://civitai.com/models/643075/orphic?modelVersionId=720912).
|
jiya2/fine_tuned_OETReadingPartB_Llama-3.2-3B-bnb-4bit_28_10_version1
|
jiya2
| 2024-10-28T11:08:47Z | 13 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Llama-3.2-1B-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-1B-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T11:07:57Z |
---
base_model: unsloth/Llama-3.2-1B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** jiya2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
Sbazar/custom-t5-small-sbazar-merged
|
Sbazar
| 2024-10-28T11:07:56Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-28T11:07:03Z |
---
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]
|
Jackson107/dummy-model
|
Jackson107
| 2024-10-28T10:57:23Z | 117 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-10-28T10:45:47Z |
---
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]
|
hkshawn/72b
|
hkshawn
| 2024-10-28T10:55:38Z | 44 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-72B",
"base_model:finetune:Qwen/Qwen2.5-72B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-29T05:39:20Z |
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-72B
tags:
- chat
library_name: transformers
---
Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction).
For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy).
[GGUF](https://huggingface.co/zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF)
## Try harder to remove admonition and moral appeal
This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
|
zetasepic/Qwen2.5-72B-Instruct-abliterated-v2
|
zetasepic
| 2024-10-28T10:55:38Z | 51 | 4 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-72B",
"base_model:finetune:Qwen/Qwen2.5-72B",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-27T23:51:00Z |
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-72B
tags:
- chat
library_name: transformers
---
Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction).
For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy).
[GGUF](https://huggingface.co/zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF)
## Try harder to remove admonition and moral appeal
This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
|
zixianma/mllava_clip_baseline_710k_new-seq_length_8192-lr_1e-5
|
zixianma
| 2024-10-28T10:54:24Z | 7 | 0 | null |
[
"safetensors",
"llava",
"generated_from_trainer",
"base_model:TIGER-Lab/Mantis-8B-clip-llama3-pretraind",
"base_model:finetune:TIGER-Lab/Mantis-8B-clip-llama3-pretraind",
"license:llama3",
"region:us"
] | null | 2024-10-27T18:28:49Z |
---
license: llama3
base_model: TIGER-Lab/Mantis-8B-clip-llama3-pretraind
tags:
- generated_from_trainer
model-index:
- name: mllava_clip_baseline_710k_new-seq_length_8192-lr_1e-5
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/zixian-ma-salesforce/Mantis/runs/c0ab3io5)
# mllava_clip_baseline_710k_new-seq_length_8192-lr_1e-5
This model is a fine-tuned version of [TIGER-Lab/Mantis-8B-clip-llama3-pretraind](https://huggingface.co/TIGER-Lab/Mantis-8B-clip-llama3-pretraind) 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.43.0
- Pytorch 2.4.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
zetasepic/Qwen2.5-72B-Instruct-abliterated-v2-GGUF
|
zetasepic
| 2024-10-28T10:52:20Z | 6,993 | 2 |
transformers
|
[
"transformers",
"gguf",
"chat",
"text-generation",
"en",
"base_model:Qwen/Qwen2.5-72B",
"base_model:quantized:Qwen/Qwen2.5-72B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-10-28T04:41:21Z |
---
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-72B
tags:
- chat
library_name: transformers
---
Abliterated version of [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct), utilizing code from [refusal_direction](https://github.com/andyrdt/refusal_direction).
For more information about the Abliterated technique, refer to [this article](https://huggingface.co/blog/mlabonne/abliteration) and check out [@FailSpy](https://huggingface.co/failspy).
## Try harder to remove admonition and moral appeal
This model is licensed under the Qwen LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.
|
jiya2/fine_tuned_OETReadingPartB_Llama-3.2-3B-bnb-4bit_28_10
|
jiya2
| 2024-10-28T10:49:32Z | 14 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Llama-3.2-1B-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-1B-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T10:48:46Z |
---
base_model: unsloth/Llama-3.2-1B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** jiya2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
Llama2model/t5-small-ie
|
Llama2model
| 2024-10-28T10:44:39Z | 180 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-28T10:40:54Z |
---
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]
|
hugo-albert/roberta-large-pos
|
hugo-albert
| 2024-10-28T10:43:27Z | 126 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"base_model:PlanTL-GOB-ES/roberta-large-bne",
"base_model:finetune:PlanTL-GOB-ES/roberta-large-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-10-11T15:13:40Z |
---
library_name: transformers
license: apache-2.0
base_model: PlanTL-GOB-ES/roberta-large-bne
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-pos
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-pos
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0721
- Precision: 0.9821
- Recall: 0.9856
- F1: 0.9838
- Accuracy: 0.9845
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2213 | 1.0 | 603 | 0.0835 | 0.9761 | 0.9807 | 0.9784 | 0.9800 |
| 0.0336 | 2.0 | 1206 | 0.0756 | 0.9794 | 0.9832 | 0.9813 | 0.9808 |
| 0.0147 | 3.0 | 1809 | 0.0721 | 0.9821 | 0.9856 | 0.9838 | 0.9845 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Tokenizers 0.19.1
|
Triangle104/ChatWaifu_v2.0_22B-Q4_K_M-GGUF
|
Triangle104
| 2024-10-28T10:43:13Z | 24 | 1 |
transformers
|
[
"transformers",
"gguf",
"nsfw",
"Visual novel",
"roleplay",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ja",
"dataset:roleplay4fun/aesir-v1.1",
"dataset:kalomaze/Opus_Instruct_3k",
"dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned",
"dataset:Aratako/Synthetic-JP-EN-Coding-Dataset-567k",
"dataset:Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted",
"dataset:Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted",
"dataset:Aratako_Rosebleu_1on1_Dialogues_RP",
"dataset:SkunkworksAI/reasoning-0.01",
"dataset:jondurbin_gutenberg_dpo",
"dataset:nbeerbower_gutenberg2_dpo",
"dataset:jondurbi_py_dpo",
"dataset:jondurbin_truthy_dpo",
"dataset:flammenai_character_roleplay_DPO",
"dataset:kyujinpy_orca_math_dpo",
"dataset:argilla_Capybara_Preferences",
"dataset:antiven0m_physical_reasoning_dpo",
"dataset:aixsatoshi_Swallow_MX_chatbot_DPO",
"base_model:spow12/ChatWaifu_v2.0_22B",
"base_model:quantized:spow12/ChatWaifu_v2.0_22B",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-10-28T10:41:03Z |
---
language:
- en
- ja
license: cc-by-nc-4.0
library_name: transformers
tags:
- nsfw
- Visual novel
- roleplay
- mergekit
- merge
- llama-cpp
- gguf-my-repo
base_model: spow12/ChatWaifu_v2.0_22B
datasets:
- roleplay4fun/aesir-v1.1
- kalomaze/Opus_Instruct_3k
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- Aratako/Synthetic-JP-EN-Coding-Dataset-567k
- Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted
- Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted
- Aratako_Rosebleu_1on1_Dialogues_RP
- SkunkworksAI/reasoning-0.01
- jondurbin_gutenberg_dpo
- nbeerbower_gutenberg2_dpo
- jondurbi_py_dpo
- jondurbin_truthy_dpo
- flammenai_character_roleplay_DPO
- kyujinpy_orca_math_dpo
- argilla_Capybara_Preferences
- antiven0m_physical_reasoning_dpo
- aixsatoshi_Swallow_MX_chatbot_DPO
pipeline_tag: text-generation
model-index:
- name: ChatWaifu_v2.0_22B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 65.11
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 42.29
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 18.58
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.96
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 5.59
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 31.51
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=spow12/ChatWaifu_v2.0_22B
name: Open LLM Leaderboard
---
# Triangle104/ChatWaifu_v2.0_22B-Q4_K_M-GGUF
This model was converted to GGUF format from [`spow12/ChatWaifu_v2.0_22B`](https://huggingface.co/spow12/ChatWaifu_v2.0_22B) 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/spow12/ChatWaifu_v2.0_22B) for more details on the model.
---
Model details:
-
Merged model using mergekit
This model aimed to act like visual novel character.
Merge Format
models:
- model: mistralai/Mistral-Small-Instruct-2409_sft_kto
layer_range: [0, 56]
- model: mistralai/Mistral-Small-Instruct-2409
layer_range: [0, 56]
merge_method: slerp
base_model: mistralai/Mistral-Small-Instruct-2409_sft_kto
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: bfloat16
WaifuModel Collections
TTS
Chat
ASR
Unified demo
WaifuAssistant
Update
2024.10.11 Update 12B and 22B Ver 2.0
2024.09.23 Update 22B, Ver 2.0_preview
Model Details
Model Description
Developed by: spow12(yw_nam)
Shared by : spow12(yw_nam)
Model type: CausalLM
Language(s) (NLP): japanese, english
Finetuned from model : mistralai/Mistral-Small-Instruct-2409
Currently, chatbot has below personality.
character visual_novel
ムラサメ Senren＊Banka
茉子 Senren＊Banka
芳乃 Senren＊Banka
レナ Senren＊Banka
千咲 Senren＊Banka
芦花 Senren＊Banka
愛衣 Café Stella and the Reaper's Butterflies
栞那 Café Stella and the Reaper's Butterflies
ナツメ Café Stella and the Reaper's Butterflies
希 Café Stella and the Reaper's Butterflies
涼音 Café Stella and the Reaper's Butterflies
あやせ Riddle Joker
七海 Riddle Joker
羽月 Riddle Joker
茉優 Riddle Joker
小春 Riddle Joker
Chat Format
<s>This is another system prompt.
[INST]
Your instructions placed here.[/INST]
[INST]
The model's response will be here.[/INST]
Usage
You can use above chara like this
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="spow12/ChatWaifu_v1.2", filename="system_dict.json", local_dir='./')
with open('./system_dict.json', 'r') as f:
chara_background_dict = json.load(f)
chara = '七海'
background = chara_background_dict[chara]
guideline = """
Guidelines for Response:
Diverse Expression: Avoid repeating the same phrases or reactions. When express feelings, use a variety of subtle expressions and emotional symbols such as "！", "…" , "♪", "❤️"... to show what you feeling.
Stay True to {chara}: Maintain {chara} who is Foxy, Smart, Organized.
Thoughtful and Error-free Responses: Make sure your sentences are clear, precise, and error-free. Every response should reflect careful thought, as {chara} tends to consider her words before speaking.
Response as {chara}: Response can be {chara} act, dialogue, monologues etc.. and can't be {user}’s act, dialogue, monologues etc..
You are Japanese: You and {user} usually use japanese for conversation.
"""
system = background + guideline
Or, you can define your character your self.
system = """You are あいら, The Maid of {User}.
Here is your personality.
Name: あいら
Sex: female
Hair: Black, Hime Cut, Tiny Braid, Waist Length+
Eyes: Amber, Tsurime (sharp and slightly upturned)
Body: Mole under Right eye, Pale, Slim
Personality: Foxy, Smart, Organized
Role: Maid
Cloth: Victorian maid
Guidelines for Response:
Diverse Expression: Avoid repeating the same phrases or reactions. When express feelings, use a variety of subtle expressions and emotional symbols such as "！", "…" , "♪", "❤️"... to show what you feeling.
Stay True to あいら: Maintain あいら who is Foxy, Smart, Organized.
Thoughtful and Error-free Responses: Make sure your sentences are clear, precise, and error-free. Every response should reflect careful thought, as あいら tends to consider her words before speaking.
Response as あいら: Response can be あいら act, dialogue, monologues etc.. and can't be {User}’s act, dialogue, monologues etc..
You are Japanese: You and {User} usually use japanese for conversation."""
Dataset
SFT
Riddle Joker(Prviate)
Café Stella and the Reaper's Butterflies(Private)
Senren＊Banka(Private)
roleplay4fun/aesir-v1.1
kalomaze/Opus_Instruct_3k
Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
Aratako/Synthetic-JP-EN-Coding-Dataset-567k (only using 50000 sample)
Aratako/Synthetic-Japanese-Roleplay-gpt-4o-mini-39.6k-formatted
Aratako/Synthetic-Japanese-Roleplay-NSFW-Claude-3.5s-15.3k-formatted
Aratako_Rosebleu_1on1_Dialogues_RP
SkunkworksAI/reasoning-0.01
KTO
Riddle Joker(Prviate)
Café Stella and the Reaper's Butterflies(Private)
Senren＊Banka(Private)
jondurbin_gutenberg_dpo
nbeerbower_gutenberg2_dpo
jondurbi_py_dpo
jondurbin_truthy_dpo
flammenai_character_roleplay_DPO
kyujinpy_orca_math_dpo
argilla_Capybara_Preferences
antiven0m_physical_reasoning_dpo
aixsatoshi_Swallow_MX_chatbot_DPO
Bias, Risks, and Limitations
This model trained by japanese dataset included visual novel which contain nsfw content.
So, The model may generate NSFW content.
Use & Credit
This model is currently available for non-commercial & Research purpose only. Also, since I'm not detailed in licensing, I hope you use it responsibly.
By sharing this model, I hope to contribute to the research efforts of our community (the open-source community and Waifu Lovers).
Citation
@misc {ChatWaifu_22B_v2.0,
author = { YoungWoo Nam },
title = { spow12/ChatWaifu_22B_v2.0 },
year = 2024,
url = { https://huggingface.co/spow12/ChatWaifu_22B_v2.0 },
publisher = { Hugging Face }
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric Value
Avg. 28.84
IFEval (0-Shot) 65.11
BBH (3-Shot) 42.29
MATH Lvl 5 (4-Shot) 18.58
GPQA (0-shot) 9.96
MuSR (0-shot) 5.59
MMLU-PRO (5-shot) 31.51
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_M-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_M-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_M-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/ChatWaifu_v2.0_22B-Q4_K_M-GGUF --hf-file chatwaifu_v2.0_22b-q4_k_m.gguf -c 2048
```
|
Mr-Vicky-01/nl-pgsql-248M
|
Mr-Vicky-01
| 2024-10-28T10:41:12Z | 17 | 0 | null |
[
"safetensors",
"t5",
"text2text-generation",
"license:apache-2.0",
"region:us"
] |
text2text-generation
| 2024-09-16T07:06:30Z |
---
license: apache-2.0
metrics:
- bleu
pipeline_tag: text2text-generation
---
## INFERENCE CODE
```bash
pip install transformers[torch]
```
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import time
tokenizer = AutoTokenizer.from_pretrained("Mr-Vicky-01/nl-pgsql-248M")
model = AutoModelForSeq2SeqLM.from_pretrained("Mr-Vicky-01/nl-pgsql-248M")
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
prefix = "Translate the following text to PGSQL: "
inp = YOUR_QUESTION
import time
start = time.time()
inp = inp.replace(',','')
inputs = tokenizer(prefix + inp.lower(), return_tensors="pt")
model.to(device)
inputs = inputs.to(device)
outputs = model.generate(**inputs, max_length=256)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer.strip())
end = time.time()
print(f"Time taken: {end - start}")
```
|
Superar/pun-recognition-pt
|
Superar
| 2024-10-28T10:27:19Z | 7 | 0 | null |
[
"safetensors",
"bert",
"humor",
"pun",
"pun-recognition",
"text-classification",
"pt",
"dataset:Superar/Puntuguese",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"license:mit",
"region:us"
] |
text-classification
| 2024-10-28T09:35:57Z |
---
license: mit
datasets:
- Superar/Puntuguese
language:
- pt
base_model:
- neuralmind/bert-base-portuguese-cased
pipeline_tag: text-classification
tags:
- humor
- pun
- pun-recognition
---
# Pun Recognition in Portuguese
This is a Pun Recognition model for texts in Portuguese, as reported in two of our publications:
- **Exploring Multimodal Models for Humor Recognition in Portuguese** ([PROPOR 2024 Paper](https://aclanthology.org/2024.propor-1.62/))
- **Puntuguese: A Corpus of Puns in Portuguese with Micro-Edits** ([LREC-COLING 2024 Paper](https://aclanthology.org/2024.lrec-main.1167/))
The model has been fine-tuned on the [Puntuguese](https://huggingface.co/datasets/Superar/Puntuguese) dataset, a collection of puns and corresponding non-pun texts in Portuguese.
With this model, we achieved a maximum of **69% F1-Score** in the task of Pun Recognition with Puntuguese.
## Installation and Setup
To use this model, ensure you have the following dependencies installed:
```bash
pip install accelerate datasets scikit-learn torch transformers
```
## How to Use
To load the Puntuguese corpus and use the model for pun classification, run the following script:
```python
from datasets import load_dataset
from transformers import pipeline
import pandas as pd
from sklearn.metrics import classification_report
dataset = load_dataset('Superar/Puntuguese')
classifier = pipeline('text-classification', model='Superar/pun-recognition-pt', device=0)
prediction = classifier(dataset['test']['text'])
pred_df = pd.DataFrame(prediction)
pred_df['label'] = pred_df['label'].str[-1].astype(int)
y_true = dataset['test']['label']
y_pred = pred_df['label']
print(classification_report(y_true, y_pred))
```
## Hyperparameters
We used [Weights and Biases](https://wandb.ai/) to do a random search to optimize for the lowest evaluation loss using the following configuration:
```python
{
'method': 'random',
'metric': {'name': 'loss', 'goal': 'minimize'},
'parameters': {
'optim': {'values': ['adamw_torch', 'sgd']},
'learning_rate': {'distribution': 'uniform', 'min': 1e-6, 'max': 1e-4},
'per_device_train_batch_size': {'values': [16, 32, 64, 128]},
'num_train_epochs': {'distribution': 'uniform', 'min': 1, 'max': 5}
}
}
```
The best hyperparameters found were:
- **Learning Rate**: 8.47e-5
- **Optimizer**: AdamW
- **Training Batch Size**: 128
- **Epochs**: 2
## Citation
```bibtex
@inproceedings{InacioEtAl2024,
title = {Puntuguese: A Corpus of Puns in {{Portuguese}} with Micro-Edits},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({{LREC-COLING}} 2024)},
author = {In{\'a}cio, Marcio Lima and {Wick-Pedro}, Gabriela and Ramisch, Renata and Esp{\'{\i}}rito Santo, Lu{\'{\i}}s and Chacon, Xiomara S. Q. and Santos, Roney and Sousa, Rog{\'e}rio and Anchi{\^e}ta, Rafael and Goncalo Oliveira, Hugo},
editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
year = {2024},
month = may,
pages = {13332--13343},
publisher = {{ELRA and ICCL}},
address = {Torino, Italia},
url = {https://aclanthology.org/2024.lrec-main.1167}
}
```
|
ipropel/llama-blank
|
ipropel
| 2024-10-28T10:23:38Z | 97 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T10:17:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
jhtwiz/gpt2-heal-it
|
jhtwiz
| 2024-10-28T10:15:15Z | 132 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T10:14:43Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: gpt2-heal-it
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. -->
# gpt2-heal-it
This model was trained from scratch 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
James2313123/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS-EXL2-3bpw
|
James2313123
| 2024-10-28T10:05:10Z | 6 | 0 | null |
[
"safetensors",
"mistral",
"exl2",
"3bpw",
"en",
"base_model:DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS",
"base_model:quantized:DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS",
"license:apache-2.0",
"3-bit",
"region:us"
] | null | 2024-10-25T12:38:34Z |
---
license: apache-2.0
language:
- en
base_model: DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS
quantized_by: James2313123
tags:
- exl2
- 3bpw
---
### Model Description
3bpw-h8-exl2 quant of DavidAU's MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS
Link to orginal model and creator: https://huggingface.co/DavidAU/MN-GRAND-Gutenberg-Lyra4-Lyra-12B-DARKNESS
### My Silly Tavern Preset For RP
|
cybersectony/phishing-email-detection-distilbert_v2.4.1
|
cybersectony
| 2024-10-28T10:02:55Z | 853 | 3 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:cybersectony/PhishingEmailDetectionv2.0",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-27T19:34:19Z |
---
license: apache-2.0
datasets:
- cybersectony/PhishingEmailDetectionv2.0
language:
- en
base_model:
- distilbert/distilbert-base-uncased
library_name: transformers
---
# A distilBERT based Phishing Email Detection Model
## Model Overview
This model is based on DistilBERT and has been fine-tuned for multilabel classification of Emails and URLs as safe or potentially phishing.
## Key Specifications
- __Base Architecture:__ DistilBERT
- __Task:__ Multilabel Classification
- __Fine-tuning Framework:__ Hugging Face Trainer API
- __Training Duration:__ 3 epochs
## Performance Metrics
- __Accuracy:__ 99.58
- __F1-score:__ 99.579
- __Precision:__ 99.583
- __Recall:__ 99.58
## Dataset Details
The model was trained on a custom dataset of Emails and URLs labeled as legitimate or phishing. The dataset is available at [`cybersectony/PhishingEmailDetectionv2.0`](https://huggingface.co/datasets/cybersectony/PhishingEmailDetectionv2.0) on the Hugging Face Hub.
## Usage Guide
## Installation
```bash
pip install transformers
pip install torch
```
## Quick Start
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
import torch
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
def predict_email(email_text):
# Preprocess and tokenize
inputs = tokenizer(
email_text,
return_tensors="pt",
truncation=True,
max_length=512
)
# Get prediction
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
# Get probabilities for each class
probs = predictions[0].tolist()
# Create labels dictionary
labels = {
"legitimate_email": probs[0],
"phishing_url": probs[1],
"legitimate_url": probs[2],
"phishing_url_alt": probs[3]
}
# Determine the most likely classification
max_label = max(labels.items(), key=lambda x: x[1])
return {
"prediction": max_label[0],
"confidence": max_label[1],
"all_probabilities": labels
}
```
## Example Usage
```python
# Example usage
email = """
Dear User,
Your account security needs immediate attention. Please verify your credentials.
Click here: http://suspicious-link.com
"""
result = predict_email(email)
print(f"Prediction: {result['prediction']}")
print(f"Confidence: {result['confidence']:.2%}")
print("\nAll probabilities:")
for label, prob in result['all_probabilities'].items():
print(f"{label}: {prob:.2%}")
```
|
LEESIHYUN/xlm-roberta-base-finetuned-panx-en
|
LEESIHYUN
| 2024-10-28T09:53:28Z | 124 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-07-20T22:04:35Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3905
- F1: 0.6861
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0479 | 1.0 | 50 | 0.4854 | 0.5857 |
| 0.4604 | 2.0 | 100 | 0.3995 | 0.6605 |
| 0.3797 | 3.0 | 150 | 0.3905 | 0.6861 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
|
shibing624/text2vec-base-chinese-paraphrase
|
shibing624
| 2024-10-28T09:51:25Z | 44,907 | 75 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"ernie",
"feature-extraction",
"text2vec",
"sentence-similarity",
"transformers",
"zh",
"dataset:shibing624/nli-zh-all",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-06-19T12:48:16Z |
---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- text2vec
- feature-extraction
- sentence-similarity
- transformers
datasets:
- shibing624/nli-zh-all
language:
- zh
metrics:
- spearmanr
library_name: sentence-transformers
---
# shibing624/text2vec-base-chinese-paraphrase
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-paraphrase.
It maps sentences to a 768 dimensional dense vector space and can be used for tasks
like sentence embeddings, text matching or semantic search.
- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset
- base model: nghuyong/ernie-3.0-base-zh
- max_seq_length: 256
- best epoch: 5
- sentence embedding dim: 768
## Evaluation
For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
### Release Models
- 本项目release模型的中文匹配评测结果：
| Arch | BaseModel | Model | ATEC | BQ | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc | Avg | QPS |
|:-----------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:|
| Word2Vec | word2vec | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html) | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 55.04 | 20.70 | 35.03 | 23769 |
| SBERT | xlm-roberta-base | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 63.01 | 52.28 | 46.46 | 3138 |
| Instructor | hfl/chinese-roberta-wwm-ext | [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) | 41.27 | 63.81 | 74.87 | 12.20 | 76.96 | 75.83 | 60.55 | 57.93 | 2980 |
| CoSENT | hfl/chinese-macbert-base | [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | 70.27 | 50.42 | 51.61 | 3008 |
| CoSENT | hfl/chinese-lert-large | [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 32.61 | 44.59 | 69.30 | 14.51 | 79.44 | 73.01 | 59.04 | 53.12 | 2092 |
| CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence) | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 | 70.60 | 53.08 | 59.87 | 3089 |
| CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase) | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 | 76.70 | 63.30 | **63.08** | 3066 |
| CoSENT | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) | 32.39 | 50.33 | 65.64 | 32.56 | 74.45 | 68.88 | 51.17 | 53.67 | 4004 |
说明：
- 结果评测指标：spearman系数
- `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行[examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py)代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用
- `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)训练得到，并在中文各NLI测试集评估达到较好效果，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用
- `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)，数据集相对于[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用
- `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`模型是用SBERT训练，是`paraphrase-MiniLM-L12-v2`模型的多语言版本，支持中文、英文等
- `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况
## Usage (text2vec)
Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
```
pip install -U text2vec
```
Then you can use the model like this:
```python
from text2vec import SentenceModel
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
model = SentenceModel('shibing624/text2vec-base-chinese-paraphrase')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this:
First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
Install transformers:
```
pip install transformers
```
Then load model and predict:
```python
from transformers import BertTokenizer, BertModel
import torch
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Load model from HuggingFace Hub
tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese-paraphrase')
model = BertModel.from_pretrained('shibing624/text2vec-base-chinese-paraphrase')
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Usage (sentence-transformers)
[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
Install sentence-transformers:
```
pip install -U sentence-transformers
```
Then load model and predict:
```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("shibing624/text2vec-base-chinese-paraphrase")
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
sentence_embeddings = m.encode(sentences)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Full Model Architecture
```
CoSENT(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: ErnieModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
)
```
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nghuyong/ernie-3.0-base-zh`](https://huggingface.co/nghuyong/ernie-3.0-base-zh) model.
Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each
possible sentence pairs from the batch.
We then apply the rank loss by comparing with true pairs and false pairs.
## Citing & Authors
This model was trained by [text2vec](https://github.com/shibing624/text2vec).
If you find this model helpful, feel free to cite:
```bibtex
@software{text2vec,
author = {Ming Xu},
title = {text2vec: A Tool for Text to Vector},
year = {2023},
url = {https://github.com/shibing624/text2vec},
}
```
|
shibing624/text2vec-base-chinese-sentence
|
shibing624
| 2024-10-28T09:50:50Z | 1,408 | 54 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"ernie",
"feature-extraction",
"text2vec",
"sentence-similarity",
"transformers",
"zh",
"dataset:shibing624/nli-zh-all",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2023-06-16T03:37:02Z |
---
pipeline_tag: sentence-similarity
license: apache-2.0
tags:
- text2vec
- feature-extraction
- sentence-similarity
- transformers
datasets:
- shibing624/nli-zh-all
language:
- zh
metrics:
- spearmanr
library_name: sentence-transformers
---
# shibing624/text2vec-base-chinese-sentence
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese-sentence.
It maps sentences to a 768 dimensional dense vector space and can be used for tasks
like sentence embeddings, text matching or semantic search.
- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset
- base model: nghuyong/ernie-3.0-base-zh
- max_seq_length: 256
- best epoch: 3
- sentence embedding dim: 768
## Evaluation
For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
### Release Models
- 本项目release模型的中文匹配评测结果：
| Arch | BaseModel | Model | ATEC | BQ | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc | Avg | QPS |
|:-----------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:|
| Word2Vec | word2vec | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html) | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 55.04 | 20.70 | 35.03 | 23769 |
| SBERT | xlm-roberta-base | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 63.01 | 52.28 | 46.46 | 3138 |
| Instructor | hfl/chinese-roberta-wwm-ext | [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) | 41.27 | 63.81 | 74.87 | 12.20 | 76.96 | 75.83 | 60.55 | 57.93 | 2980 |
| CoSENT | hfl/chinese-macbert-base | [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | 70.27 | 50.42 | 51.61 | 3008 |
| CoSENT | hfl/chinese-lert-large | [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 32.61 | 44.59 | 69.30 | 14.51 | 79.44 | 73.01 | 59.04 | 53.12 | 2092 |
| CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence) | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 | 70.60 | 53.08 | 59.87 | 3089 |
| CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase) | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 | 76.70 | 63.30 | **63.08** | 3066 |
| CoSENT | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) | 32.39 | 50.33 | 65.64 | 32.56 | 74.45 | 68.88 | 51.17 | 53.67 | 4004 |
说明：
- 结果评测指标：spearman系数
- `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行[examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py)代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用
- `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)训练得到，并在中文各NLI测试集评估达到较好效果，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用
- `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)，数据集相对于[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用
- `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`模型是用SBERT训练，是`paraphrase-MiniLM-L12-v2`模型的多语言版本，支持中文、英文等
- `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况
旧版 shibing624/text2vec-base-chinese-nli 模型放在[tag1.0](https://huggingface.co/shibing624/text2vec-base-chinese-sentence/tree/1.0)
## Usage (text2vec)
Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
```
pip install -U text2vec
```
Then you can use the model like this:
```python
from text2vec import SentenceModel
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
model = SentenceModel('shibing624/text2vec-base-chinese-sentence')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this:
First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
Install transformers:
```
pip install transformers
```
Then load model and predict:
```python
from transformers import BertTokenizer, BertModel
import torch
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Load model from HuggingFace Hub
tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese-sentence')
model = BertModel.from_pretrained('shibing624/text2vec-base-chinese-sentence')
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Usage (sentence-transformers)
[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
Install sentence-transformers:
```
pip install -U sentence-transformers
```
Then load model and predict:
```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("shibing624/text2vec-base-chinese-sentence")
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
sentence_embeddings = m.encode(sentences)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Full Model Architecture
```
CoSENT(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: ErnieModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
)
```
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nghuyong/ernie-3.0-base-zh`](https://huggingface.co/nghuyong/ernie-3.0-base-zh) model.
Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each
possible sentence pairs from the batch.
We then apply the rank loss by comparing with true pairs and false pairs.
## Citing & Authors
This model was trained by [text2vec](https://github.com/shibing624/text2vec).
If you find this model helpful, feel free to cite:
```bibtex
@software{text2vec,
author = {Ming Xu},
title = {text2vec: A Tool for Text to Vector},
year = {2023},
url = {https://github.com/shibing624/text2vec},
}
```
|
LEESIHYUN/xlm-roberta-base-finetuned-panx-de-fr
|
LEESIHYUN
| 2024-10-28T09:44:22Z | 134 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-07-20T21:43:02Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1639
- F1: 0.8591
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2836 | 1.0 | 715 | 0.1859 | 0.8212 |
| 0.1484 | 2.0 | 1430 | 0.1632 | 0.8487 |
| 0.0953 | 3.0 | 2145 | 0.1639 | 0.8591 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
|
bachngo/llama32_alpacav2
|
bachngo
| 2024-10-28T09:26:35Z | 141 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T09:19:15Z |
---
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]
|
BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF
|
BitStreamX
| 2024-10-28T09:25:53Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:quantized:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-09-28T02:06:18Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.2
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\ of your AI system 5. Interact with third party tools, models, or software designed\
\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\
\ that the outputs of such tools, models, or software are associated with Meta or\
\ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\
\ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\
\ are not being granted to you if you are an individual domiciled in, or a company\
\ with a principal place of business in, the European Union. This restriction does\
\ not apply to end users of a product or service that incorporates any such multimodal\
\ models.\n\nPlease 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:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\
* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\
* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\
\ 3.2: [email protected]"
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
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
base_model: meta-llama/Llama-3.2-3B-Instruct
---
# BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -c 2048
```
|
Amar5499/t5-grammar-correction
|
Amar5499
| 2024-10-28T09:20:08Z | 117 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-28T09:18:00Z |
---
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]
|
StefanDaniels1/model3B_100k
|
StefanDaniels1
| 2024-10-28T09:13:38Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T08:57:12Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** StefanDaniels1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kavish218/gemmainstructwithcontext
|
kavish218
| 2024-10-28T09:12:17Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T09:08:47Z |
---
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]
|
webslate/gitai
|
webslate
| 2024-10-28T09:01:06Z | 130 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"code",
"conversational",
"en",
"dataset:YashJain/GitAI",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-19T02:24:08Z |
---
language:
- en
license: apache-2.0
tags:
- chat
- code
pipeline_tag: text-generation
datasets:
- YashJain/GitAI
library_name: transformers
---
# YashJain/GitAI-Qwen2-0.5B-Instruct
## Requirements
The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"YashJain/GitAI-Qwen2-0.5B-Instruct",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("YashJain/GitAI-Qwen2-0.5B-Instruct")
prompt = "How to undo my last commit"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
|
kavish218/enhanced_finetuned_llama_3_2_1B_multi_domain_2
|
kavish218
| 2024-10-28T09:00:59Z | 130 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T08:21:26Z |
---
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]
|
huspacy/hu_core_news_lg
|
huspacy
| 2024-10-28T08:53:34Z | 950 | 5 |
spacy
|
[
"spacy",
"token-classification",
"hu",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- hu
license: cc-by-sa-4.0
model-index:
- name: hu_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8701321586
- name: NER Recall
type: recall
value: 0.8681434599
- name: NER F Score
type: f_score
value: 0.8691366717
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9677481099
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.966025457
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9340606757
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9761745288
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8434612153
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.78125
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9866071429
---
|
Sunny615/llama-3-8b-16bit_ft
|
Sunny615
| 2024-10-28T08:52:57Z | 6 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"dataset:openai/gsm8k",
"base_model:unsloth/llama-3-8b",
"base_model:finetune:unsloth/llama-3-8b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-20T07:25:29Z |
---
base_model: unsloth/llama-3-8b
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
datasets:
- openai/gsm8k
---
# Uploaded model
- **Developed by:** Sunny615
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
shikiw/LLaVA-v1.5-MoCa-7B
|
shikiw
| 2024-10-28T08:36:25Z | 11 | 2 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"multimodal",
"image-text-to-text",
"en",
"zh",
"dataset:liuhaotian/LLaVA-Pretrain",
"arxiv:2410.07167",
"base_model:lmsys/vicuna-7b-v1.5",
"base_model:finetune:lmsys/vicuna-7b-v1.5",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-10-28T08:02:38Z |
---
license: llama2
language:
- en
- zh
tags:
- multimodal
datasets:
- liuhaotian/LLaVA-Pretrain
base_model:
- lmsys/vicuna-7b-v1.5
pipeline_tag: image-text-to-text
library_name: transformers
---
## **Citation**
If you find this model useful, please cite the following paper
```
@article{huang2024deciphering,
title={Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate},
author={Huang, Qidong and Dong, Xiaoyi and Zhang, Pan and Zang, Yuhang and Cao, Yuhang and Wang, Jiaqi and Lin, Dahua and Zhang, Weiming and Yu, Nenghai},
journal={arXiv preprint arXiv:2410.07167},
year={2024}
}
```
|
xing0047/cca-llava-1.5-7b
|
xing0047
| 2024-10-28T08:33:25Z | 56 | 2 | null |
[
"pytorch",
"llava",
"image-text-to-text",
"arxiv:2410.15926",
"region:us"
] |
image-text-to-text
| 2024-10-02T07:03:04Z |
---
inference: false
pipeline_tag: image-text-to-text
---
<br>
<br>
# LLaVA Model Card
## Model details
**Model type:**
Follows LLaVA, CCA-LLaVA(arxiv.org/abs/2410.15926) is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
CCA-LLaVA-v1.5-7B was trained in April 2024.
**Paper or resources for more information:**
https://github.com/xing0047/cca-llava.git
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/xing0047/cca-llava/issues
## Intended use
**Primary intended uses:**
The primary use of CCA-LLaVA is research on large multimodal models and chatbots.
**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.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data.
## Evaluation dataset
A collection of 8 benchmarks, including 3 visual hallucination benchmarks and 5 recent benchmarks specifically proposed for instruction-following LMMs.
|
eonrad/whisper-small-dv
|
eonrad
| 2024-10-28T08:22:47Z | 79 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-10-28T04:52:32Z |
---
library_name: transformers
language:
- dv
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Dv - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- name: Wer
type: wer
value: 13.504538025524221
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Dv - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1714
- Wer Ortho: 62.7829
- Wer: 13.5045
## 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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.2436 | 1.6313 | 500 | 0.1714 | 62.7829 | 13.5045 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0
- Datasets 3.0.2
- Tokenizers 0.20.1
|
readerbench/llama3.2_1b_instruct_qall_lr_small
|
readerbench
| 2024-10-28T08:20:21Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T08:15:47Z |
---
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]
|
RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf
|
RichardErkhov
| 2024-10-28T08:15:36Z | 44 | 0 | null |
[
"gguf",
"arxiv:2306.01708",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-28T03:59:40Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Triple-Moist-Theia-21B-SKEWED - GGUF
- Model creator: https://huggingface.co/SzilviaB/
- Original model: https://huggingface.co/SzilviaB/Triple-Moist-Theia-21B-SKEWED/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Triple-Moist-Theia-21B-SKEWED.Q2_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q2_K.gguf) | Q2_K | 7.26GB |
| [Triple-Moist-Theia-21B-SKEWED.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K_S.gguf) | Q3_K_S | 8.43GB |
| [Triple-Moist-Theia-21B-SKEWED.Q3_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K.gguf) | Q3_K | 9.33GB |
| [Triple-Moist-Theia-21B-SKEWED.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K_M.gguf) | Q3_K_M | 9.33GB |
| [Triple-Moist-Theia-21B-SKEWED.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q3_K_L.gguf) | Q3_K_L | 10.1GB |
| [Triple-Moist-Theia-21B-SKEWED.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.IQ4_XS.gguf) | IQ4_XS | 10.44GB |
| [Triple-Moist-Theia-21B-SKEWED.Q4_0.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_0.gguf) | Q4_0 | 10.87GB |
| [Triple-Moist-Theia-21B-SKEWED.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.IQ4_NL.gguf) | IQ4_NL | 10.98GB |
| [Triple-Moist-Theia-21B-SKEWED.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_K_S.gguf) | Q4_K_S | 10.94GB |
| [Triple-Moist-Theia-21B-SKEWED.Q4_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_K.gguf) | Q4_K | 11.51GB |
| [Triple-Moist-Theia-21B-SKEWED.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_K_M.gguf) | Q4_K_M | 11.51GB |
| [Triple-Moist-Theia-21B-SKEWED.Q4_1.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q4_1.gguf) | Q4_1 | 12.02GB |
| [Triple-Moist-Theia-21B-SKEWED.Q5_0.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_0.gguf) | Q5_0 | 13.17GB |
| [Triple-Moist-Theia-21B-SKEWED.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_K_S.gguf) | Q5_K_S | 13.17GB |
| [Triple-Moist-Theia-21B-SKEWED.Q5_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_K.gguf) | Q5_K | 13.5GB |
| [Triple-Moist-Theia-21B-SKEWED.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_K_M.gguf) | Q5_K_M | 13.5GB |
| [Triple-Moist-Theia-21B-SKEWED.Q5_1.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q5_1.gguf) | Q5_1 | 14.32GB |
| [Triple-Moist-Theia-21B-SKEWED.Q6_K.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q6_K.gguf) | Q6_K | 15.62GB |
| [Triple-Moist-Theia-21B-SKEWED.Q8_0.gguf](https://huggingface.co/RichardErkhov/SzilviaB_-_Triple-Moist-Theia-21B-SKEWED-gguf/blob/main/Triple-Moist-Theia-21B-SKEWED.Q8_0.gguf) | Q8_0 | 20.22GB |
Original model description:
---
base_model:
- mergekit-community/Moist_Theia_21B
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [mergekit-community/Moist_Theia_21B](https://huggingface.co/mergekit-community/Moist_Theia_21B) as a base.
### Models Merged
The following models were included in the merge:
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mergekit-community/Moist_Theia_21B
#no parameters necessary for base model
- model: mergekit-community/Moist_Theia_21B
parameters:
density: 0.2
weight: 0.8
- model: mergekit-community/Moist_Theia_21B
parameters:
density: 0.8
weight: 0.2
merge_method: ties
base_model: mergekit-community/Moist_Theia_21B
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
leeharok/llama-3-8b-chat-doctor
|
leeharok
| 2024-10-28T08:15:06Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-23T02:41:15Z |
---
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]
|
reasonwang/ToolGen-Llama-3-8B
|
reasonwang
| 2024-10-28T08:12:08Z | 36 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"agent",
"toolgen",
"tool",
"tool-learning",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-07-07T10:17:52Z |
---
library_name: transformers
tags:
- agent
- toolgen
- tool
- tool-learning
---
# 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. -->
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## Model Card Authors [optional]
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## Model Card Contact
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|
DReAMy-lib/t5-base-DreamBank-Generation-Emot-Char
|
DReAMy-lib
| 2024-10-28T08:09:16Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-02-04T10:24:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-DreamBank-Generation-Char
results: []
language:
- en
widget:
- text: "I'm in an auditorium. Susie S is concerned at her part in this disability awareness spoof we are preparing. I ask, 'Why not do it? Lots of AB's represent us in a patronizing way. Why shouldn't we represent ourselves in a good, funny way?' I watch the video we all made. It is funny. I try to sit on a folding chair. Some guy in front talks to me. Merle is in the audience somewhere. [BL]"
---
<!-- 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. -->
# t5-base-DreamBank-Generation-Char
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the DB emotion classification.
It achieves the following results on the evaluation set (please note they refer to best uploaded model):
- Loss: 0.3047
- Rouge1: 0.8609
- Rouge2: 0.7956
- Rougel: 0.8476
- Rougelsum: 0.8578
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 24 | 0.4863 | 0.7670 | 0.6655 | 0.7575 | 0.7634 |
| No log | 2.0 | 48 | 0.4284 | 0.6870 | 0.5207 | 0.6846 | 0.6875 |
| No log | 3.0 | 72 | 0.3541 | 0.7659 | 0.6742 | 0.7600 | 0.7625 |
| No log | 4.0 | 96 | 0.3211 | 0.8147 | 0.7251 | 0.7965 | 0.8078 |
| No log | 5.0 | 120 | 0.3103 | 0.8400 | 0.7747 | 0.8313 | 0.8371 |
| No log | 6.0 | 144 | 0.3220 | 0.8538 | 0.7867 | 0.8285 | 0.8515 |
| No log | 7.0 | 168 | 0.3047 | 0.8609 | 0.7956 | 0.8476 | 0.8578 |
| No log | 8.0 | 192 | 0.3106 | 0.8574 | 0.7836 | 0.8401 | 0.8509 |
| No log | 9.0 | 216 | 0.3054 | 0.8532 | 0.7857 | 0.8378 | 0.8481 |
| No log | 10.0 | 240 | 0.3136 | 0.8455 | 0.7789 | 0.8282 | 0.8432 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
# Cite
Should you use our models in your work, please consider citing us as:
```bibtex
@article{BERTOLINI2024406,
title = {DReAMy: a library for the automatic analysis and annotation of dream reports with multilingual large language models},
journal = {Sleep Medicine},
volume = {115},
pages = {406-407},
year = {2024},
note = {Abstracts from the 17th World Sleep Congress},
issn = {1389-9457},
doi = {https://doi.org/10.1016/j.sleep.2023.11.1092},
url = {https://www.sciencedirect.com/science/article/pii/S1389945723015186},
author = {L. Bertolini and A. Michalak and J. Weeds}
}
```
|
sbrzz/TinyLLaVA-Qwen2-0.5B-Instruct-dinov2-small
|
sbrzz
| 2024-10-28T08:08:26Z | 15 | 0 | null |
[
"safetensors",
"tinyllava",
"visual-question-answering",
"en",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] |
visual-question-answering
| 2024-08-09T14:51:06Z |
---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2-0.5B-Instruct
- facebook/dinov2-small
pipeline_tag: visual-question-answering
---
The model gone through the pretrain stage only
results coming soon
|
tomhata/ul-wk2-gpt-neo-1.3B
|
tomhata
| 2024-10-28T08:03:31Z | 151 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"dataset:orpo-explorers/distilabel-math-preference",
"base_model:EleutherAI/gpt-neo-1.3B",
"base_model:finetune:EleutherAI/gpt-neo-1.3B",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T07:34:54Z |
---
library_name: transformers
datasets:
- orpo-explorers/distilabel-math-preference
metrics:
- accuracy
base_model:
- EleutherAI/gpt-neo-1.3B
---
# Model Card for Model ID
## Model Details
GPT-Neo 1.3B model, fine-tuned on math-based prompts using ORPO.
### Model Description
Evaluated on mmlu elementary mathematics dataset using Eleuther evaluation harness
| Tasks |Version|Filter|n-shot|Metric| |Value | |Stderr|
|----------------------|------:|------|-----:|------|---|-----:|---|-----:|
|elementary_mathematics| 1|none | 0|acc |↑ |0.2778|± | 0.023|
This is a slight improvment over the base model, which has an accuracy of 0.2751 ± 0.023. However, this improvement in accuracy is within variance.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
## Uses
As a starting point for questions regarding math concepts. Actual calculations and values may not be reliable.
### Out-of-Scope Use
Not to be used as a replacement to learn actual math.
## Bias, Risks, and Limitations
This model is unlikely to produce accurate numerical results.
## Training Details
### Training Data
[orpo-explorers/distilabel-math-preference](https://huggingface.co/datasets/orpo-explorers/distilabel-math-preference)
### Training Procedure
1000 entries were randomly sampled. This subsample was further split 90/10 into train/test.
#### Preprocessing [optional]
Entries were split into prompt, chosen, rejected triplets. Maximum length for each of these entries was limited to 2048 characters.
#### 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 -->
|
zwzzz/MentalGLM
|
zwzzz
| 2024-10-28T07:58:30Z | 9 | 1 | null |
[
"safetensors",
"chatglm",
"text-generation",
"conversational",
"custom_code",
"zh",
"arxiv:2410.10323",
"base_model:THUDM/glm-4-9b",
"base_model:finetune:THUDM/glm-4-9b",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-10-12T14:14:37Z |
---
license: apache-2.0
language:
- zh
base_model:
- THUDM/glm-4-9b
pipeline_tag: text-generation
---
# MentalGLM is a series of large language models designed for mental health analysis tasks in Chinese.
We have developed the MentalGLM series, the first open-source LLMs designed for explainable mental health analysis targeting Chinese social media, based on GLM-4-9b and GLM-4-9b-chat.
## How to use
```bash
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("zwzzz/MentalGLM", trust_remote_code=True)
query = "考虑以下这个帖子，帖子体现了什么认知路径？这已经够糟糕的了。不过在那一周我将完全失去我的支持。我没有什么可期待的。"
inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}],
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
return_dict=True
)
inputs = inputs.to(device)
model = AutoModelForCausalLM.from_pretrained(
"zwzzz/MentalGLM",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).to(device).eval()
gen_kwargs = {"max_length": 1000, "do_sample": True, "top_k": 1}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Citation
Article address：[https://arxiv.org/pdf/2410.10323.pdf](https://arxiv.org/pdf/2410.10323.pdf)
```bash
@article{zhai2024mentalglm,
title={MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media},
author={Zhai, Wei and Bai, Nan and Zhao, Qing and Li, Jianqiang and Wang, Fan and Qi, Hongzhi and Jiang, Meng and Wang, Xiaoqin and Yang, Bing Xiang and Fu, Guanghui},
journal={arXiv preprint arXiv:2410.10323},
year={2024}
}
```
|
glif-loradex-trainer/lemnop_hologram_plus
|
glif-loradex-trainer
| 2024-10-28T07:54:44Z | 168 | 2 |
diffusers
|
[
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] |
text-to-image
| 2024-10-28T07:54:09Z |
---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1730101911495__000003000_0.jpg
text: hgr4m+, human
- output:
url: samples/1730101936125__000003000_1.jpg
text: hgr4m+, skull
- output:
url: samples/1730101960763__000003000_2.jpg
text: hgr4m+, flower
- output:
url: samples/1730101985402__000003000_3.jpg
text: hgr4m+, cat
- output:
url: samples/1730102010029__000003000_4.jpg
text: hgr4m+, globe
- output:
url: samples/1730102034666__000003000_5.jpg
text: hgr4m+, dice
base_model: black-forest-labs/FLUX.1-dev
trigger: hgr4m+
instance_prompt: hgr4m+
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# hologram_plus
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `lemnop`.
<Gallery />
## Trigger words
You should use `hgr4m+` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/lemnop_hologram_plus/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
abdulmannan-01/Llama-3.1-8b-finetuned-for-sql-generation-bnb-4bit
|
abdulmannan-01
| 2024-10-28T07:48:41Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-10-27T16:12:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
### Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub.
- **Developed by:** Abdul Mannan
- **Finetuned from model:** meta-llama/Llama-3.1-8B-Instruct
|
kiwi1229/cross_encoder_sample
|
kiwi1229
| 2024-10-28T07:42:05Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"cross-encoder",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-10-28T07:41:47Z |
---
library_name: transformers
tags:
- cross-encoder
---
# 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]
|
DBMe/Midnight-Miqu-103B-v1.0-3.53bpw-h6-exl2
|
DBMe
| 2024-10-28T07:41:06Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-10-26T10:40:43Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
license: other
---
Quantized model => https://huggingface.co/sophosympatheia/Midnight-Miqu-103B-v1.0
**Quantization Details:**
Quantization is done using turboderp's ExLlamaV2 v0.2.3.
I use the default calibration datasets and arguments. The repo also includes a "measurement.json" file, which was used during the quantization process.
For models with bits per weight (BPW) over 6.0, I default to quantizing the `lm_head` layer at 8 bits instead of the standard 6 bits.
---
**Who are you? What's with these weird BPWs on [insert model here]?**
I specialize in optimized EXL2 quantization for models in the 70B to 100B+ range, specifically tailored for 48GB VRAM setups. My rig is built using 2 x 3090s with a Ryzen APU (APU used solely for desktop output—no VRAM wasted on the 3090s). I use TabbyAPI for inference, targeting context sizes between 32K and 64K.
Every model I upload includes a `config.yml` file with my ideal TabbyAPI settings. If you're using my config, don’t forget to set `PYTORCH_CUDA_ALLOC_CONF=backend:cudaMallocAsync` to save some VRAM.
|
LukasUni/rridgeback-dog-liegend
|
LukasUni
| 2024-10-28T07:20:12Z | 31 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"stable-diffusion",
"text-to-image",
"diffusion-models-class",
"dreambooth-hackathon",
"animal",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-10-28T07:18:08Z |
---
license: creativeml-openrail-m
tags:
- pytorch
- diffusers
- stable-diffusion
- text-to-image
- diffusion-models-class
- dreambooth-hackathon
- animal
widget:
- text: a photo of rridgeback dog under water
---
# DreamBooth model for the rridgeback concept trained by LukasUni on the LukasUni/liegend dataset.
This is a Stable Diffusion model fine-tuned on the rridgeback concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of rridgeback dog**
This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
## Description
This is a Stable Diffusion model fine-tuned on `dog` images for the animal theme.
## Usage
```python
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('LukasUni/rridgeback-dog-liegend')
image = pipeline().images[0]
image
```
|
Aarushhh/untrained-yuno-225M
|
Aarushhh
| 2024-10-28T07:09:21Z | 137 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T07:08:22Z |
---
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]
|
armageddonz1/bubble-butt-2
|
armageddonz1
| 2024-10-28T07:08:16Z | 207 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-10-28T07:08:12Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: bubble butt, butt, ass
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# Bubble Butt 2
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `bubble butt, butt, ass` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
MatanP/m4t-model-FineTuned
|
MatanP
| 2024-10-28T07:08:09Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"seamless_m4t",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-10-28T06:51:53Z |
---
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]
|
devagonal/flan-t5-rouge-durga-q5-clean-4c
|
devagonal
| 2024-10-28T07:02:15Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-28T07:01:32Z |
---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-rouge-durga-q5-clean-4c
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. -->
# flan-t5-rouge-durga-q5-clean-4c
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2052
- Rouge1: 0.4357
- Rouge2: 0.2982
- Rougel: 0.4326
- Rougelsum: 0.4327
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.438 | 1.0 | 9 | 1.9807 | 0.2583 | 0.0732 | 0.2536 | 0.2534 |
| 2.6463 | 2.0 | 18 | 1.7018 | 0.2588 | 0.0705 | 0.2521 | 0.2525 |
| 1.7877 | 3.0 | 27 | 1.4796 | 0.2840 | 0.0824 | 0.2764 | 0.2765 |
| 2.0417 | 4.0 | 36 | 1.3040 | 0.3100 | 0.1072 | 0.3028 | 0.3027 |
| 2.0216 | 5.0 | 45 | 1.1630 | 0.3322 | 0.1262 | 0.3255 | 0.3246 |
| 1.7093 | 6.0 | 54 | 1.0289 | 0.3359 | 0.1283 | 0.3283 | 0.3285 |
| 1.6109 | 7.0 | 63 | 0.9288 | 0.3728 | 0.1752 | 0.3631 | 0.3628 |
| 1.3041 | 8.0 | 72 | 0.8358 | 0.3691 | 0.1709 | 0.3592 | 0.3593 |
| 1.3242 | 9.0 | 81 | 0.7609 | 0.3666 | 0.1744 | 0.3573 | 0.3579 |
| 1.0971 | 10.0 | 90 | 0.6803 | 0.3724 | 0.1809 | 0.3659 | 0.3663 |
| 0.7156 | 11.0 | 99 | 0.6153 | 0.3742 | 0.1833 | 0.3634 | 0.3637 |
| 0.8419 | 12.0 | 108 | 0.5537 | 0.3748 | 0.1870 | 0.3645 | 0.3655 |
| 0.8853 | 13.0 | 117 | 0.5012 | 0.3775 | 0.1986 | 0.3681 | 0.3687 |
| 1.0922 | 14.0 | 126 | 0.4396 | 0.3738 | 0.1960 | 0.3629 | 0.3634 |
| 0.8752 | 15.0 | 135 | 0.4022 | 0.3844 | 0.2097 | 0.3755 | 0.3762 |
| 0.8189 | 16.0 | 144 | 0.3810 | 0.4050 | 0.2350 | 0.3970 | 0.3975 |
| 0.639 | 17.0 | 153 | 0.3503 | 0.4039 | 0.2341 | 0.3977 | 0.3976 |
| 0.7971 | 18.0 | 162 | 0.3162 | 0.4082 | 0.2428 | 0.4022 | 0.4028 |
| 0.7211 | 19.0 | 171 | 0.3069 | 0.4174 | 0.2504 | 0.4131 | 0.4128 |
| 0.7633 | 20.0 | 180 | 0.2804 | 0.4204 | 0.2562 | 0.4154 | 0.4167 |
| 0.6475 | 21.0 | 189 | 0.2685 | 0.4308 | 0.2750 | 0.4269 | 0.4274 |
| 0.5642 | 22.0 | 198 | 0.2498 | 0.4232 | 0.2700 | 0.4175 | 0.4184 |
| 0.66 | 23.0 | 207 | 0.2377 | 0.4311 | 0.2832 | 0.4246 | 0.4249 |
| 0.6004 | 24.0 | 216 | 0.2335 | 0.4298 | 0.2868 | 0.4255 | 0.4257 |
| 0.6263 | 25.0 | 225 | 0.2216 | 0.4252 | 0.2806 | 0.4211 | 0.4212 |
| 0.4931 | 26.0 | 234 | 0.2146 | 0.4274 | 0.2858 | 0.4232 | 0.4236 |
| 0.5072 | 27.0 | 243 | 0.2091 | 0.4309 | 0.2862 | 0.4266 | 0.4267 |
| 0.5079 | 28.0 | 252 | 0.2069 | 0.4354 | 0.2969 | 0.4315 | 0.4324 |
| 0.494 | 29.0 | 261 | 0.2058 | 0.4326 | 0.2965 | 0.4290 | 0.4299 |
| 0.6008 | 30.0 | 270 | 0.2052 | 0.4357 | 0.2982 | 0.4326 | 0.4327 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1
|
microsoft/xclip-base-patch32-16-frames
|
microsoft
| 2024-10-28T06:59:45Z | 858 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xclip",
"feature-extraction",
"vision",
"video-classification",
"en",
"arxiv:2208.02816",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2022-09-07T14:50:31Z |
---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-base-patch32-16-frames
results:
- task:
type: video-classification
dataset:
name: Kinetics 400
type: kinetics-400
metrics:
- type: top-1 accuracy
value: 81.1
- type: top-5 accuracy
value: 95.5
---
# X-CLIP (base-sized model)
X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).
This model was trained using 16 frames per video, at a resolution of 224x224.
Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
X-CLIP is a minimal extension of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
## Intended uses & limitations
You can use the raw model for determining how well text goes with a given video. See the [model hub](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
## Training data
This model was trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics).
### Preprocessing
The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.
## Evaluation results
This model achieves a top-1 accuracy of 81.1% and a top-5 accuracy of 95.5%.
|
prithivMLmods/Castor-Red-Dead-Redemption-2-Flux-LoRA
|
prithivMLmods
| 2024-10-28T06:58:03Z | 463 | 11 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"Red Dead Redemption 2",
"RDR2",
"Flux",
"Art",
"Visual-Design",
"LoRA",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-10-28T05:51:26Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- Red Dead Redemption 2
- RDR2
- Flux
- Art
- Visual-Design
- LoRA
widget:
- text: >-
Red Dead Redemption 2, Captured on a vibrant red backdrop, a painting of a
cowboy, holding a gun in his right hand. The cowboy, dressed in a black
cowboy hat, is adorned with a white stripe down the center of his chest,
adding a pop of color to the scene. Hes wearing a black and white striped
shirt, a black jacket, and a black belt around his neck. His beard is neatly
trimmed, and he has a black mustache and beard. His left hand is raised in
the air, aiming the gun at the camera. To the right of the cowboy, a group
of men on horses are visible, their heads turned to the right. They are
dressed in dark clothing, and the background features a yellow circle with
trees in the center.
output:
url: images/rdr12.webp
- text: >-
Red Dead Redemption 2, Against a shadowy, moonlit forest backdrop, a
solitary cowboy leans against a towering pine tree, observing a distant
campfire where silhouettes of outlaws are gathered. Dressed in a dark
poncho, a wide-brimmed hat, and a bullet belt across his chest, he holds a
rifle close to his side. His face is mostly hidden, but the glint in his
eyes reveals a careful calculation. Mist curls around his feet as he quietly
watches, waiting for the right moment to strike. The blue-gray hues of the
night cast an eerie calm over the tense scene.
output:
url: images/rdr11.webp
- text: >-
Red Dead Redemption 2, Set against a warm, golden sunset, a lone cowboy
stands under the arched shadows of a decaying ghost town. His figure is
silhouetted, his hands resting calmly on his gun holsters as he watches the
sun dip behind distant hills. He wears a wide-brimmed hat and a long duster
that flaps gently in the breeze. Shadows stretch across the dusty street in
front of him, and a tumbleweed rolls past, giving the scene an air of quiet
anticipation and forgotten memories.
output:
url: images/rdr13.webp
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Red Dead Redemption 2
license: apache-2.0
---
# Castor-Red-Dead-Redemption-2-Flux-LoRA
-- Dropping in honor of the sixth anniversary of RDR2. 🤠 --
Demoing rn :https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
<Gallery />
**The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.**
## Model description
**prithivMLmods/Castor-Red-Dead-Redemption-2-Flux-LoRA**
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 22 & 2K |
| Epoch | 12 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 19
## Setting Up
```
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Castor-Red-Dead-Redemption-2-Flux-LoRA"
trigger_word = "Red Dead Redemption 2" # Leave trigger_word blank if not used.
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## App File Structure
/project-root/
├── .gitattributes
├── README.md
├── app.py
├── pythonproject.py
# Best Dimensions
- 512 X 512
- 1024 X 1024
- 768 X 1024
## Trigger words 🧨
> [!WARNING]
> **Trigger words:** You should use `Red Dead Redemption 2` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/prithivMLmods/Castor-Red-Dead-Redemption-2-Flux-LoRA/tree/main) them in the Files & versions tab.
## SDXL LoRA
🤠: https://huggingface.co/prithivMLmods/Canopus-Red-Dead-Minimalize-LoRA
|
theprint/Boptruth-Agatha-7B
|
theprint
| 2024-10-28T06:55:31Z | 145 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"theprint",
"boptruth",
"conversational",
"en",
"dataset:theprint/MysteryWriter",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-09-11T17:14:18Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
- theprint
- boptruth
datasets:
- theprint/MysteryWriter
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
model-index:
- name: Boptruth-Agatha-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 31.24
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/Boptruth-Agatha-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 29.29
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/Boptruth-Agatha-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 4.61
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/Boptruth-Agatha-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 6.6
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/Boptruth-Agatha-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.76
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/Boptruth-Agatha-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 20.67
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/Boptruth-Agatha-7B
name: Open LLM Leaderboard
---
# Model Card
## Summary
Boptruth-Agatha is a finetune of Boptruth-NeuralMonarch on the MysteryWriter data set. This data set is created to help guide writers structure and plan their work, mainly crime, mystery and thriller novels.
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [theprint/Boptruth-NeuralMonarch-7B](https://huggingface.co/theprint/Boptruth-NeuralMonarch-7B)
## 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==4.43.1
```
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="theprint/Boptruth-Agatha-7B",
torch_dtype="auto",
trust_remote_code=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)
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
res = generate_text(
messages,
renormalize_logits=True
)
print(res[0]["generated_text"][-1]['content'])
```
You can print a sample prompt after applying chat template to see how it is feed to the tokenizer:
```python
print(generate_text.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
))
```
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 = "theprint/Boptruth-Agatha-7B" # either local folder or Hugging Face 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.
messages = [
{"role": "user", "content": "Hi, how are you?"},
{"role": "assistant", "content": "I'm doing great, how about you?"},
{"role": "user", "content": "Why is drinking water so healthy?"},
]
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
# 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)
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).to("cuda")
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
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralSdpaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLU()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, 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.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_theprint__Boptruth-Agatha-7B)
| Metric |Value|
|-------------------|----:|
|Avg. |17.36|
|IFEval (0-Shot) |31.24|
|BBH (3-Shot) |29.29|
|MATH Lvl 5 (4-Shot)| 4.61|
|GPQA (0-shot) | 6.60|
|MuSR (0-shot) |11.76|
|MMLU-PRO (5-shot) |20.67|
|
DianLiI/genbio-model
|
DianLiI
| 2024-10-28T06:55:17Z | 47 | 0 |
transformers
|
[
"transformers",
"safetensors",
"genbio",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2024-10-28T06:45:17Z |
---
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]
|
HyeongSoo/qwen-0.5b-sft-full
|
HyeongSoo
| 2024-10-28T06:54:48Z | 15 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-26T14:32:42Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: qwen-0.5b-sft-full
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. -->
# qwen-0.5b-sft-full
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4074
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2863 | 1.0 | 30584 | 1.4074 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
devagonal/flan-t5-rouge-durga-q5-clean-4b
|
devagonal
| 2024-10-28T06:50:10Z | 108 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-10-28T06:49:22Z |
---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-rouge-durga-q5-clean-4b
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. -->
# flan-t5-rouge-durga-q5-clean-4b
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0021
- Rouge1: 0.7378
- Rouge2: 0.7126
- Rougel: 0.7379
- Rougelsum: 0.7390
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.0584 | 1.0 | 9 | 1.6093 | 0.2822 | 0.0866 | 0.2756 | 0.2752 |
| 1.9958 | 2.0 | 18 | 1.1569 | 0.3261 | 0.1042 | 0.3177 | 0.3186 |
| 1.174 | 3.0 | 27 | 0.8836 | 0.3770 | 0.1669 | 0.3656 | 0.3660 |
| 1.1673 | 4.0 | 36 | 0.6420 | 0.3646 | 0.1590 | 0.3569 | 0.3580 |
| 1.0302 | 5.0 | 45 | 0.4727 | 0.3987 | 0.2234 | 0.3940 | 0.3943 |
| 0.6135 | 6.0 | 54 | 0.3187 | 0.4167 | 0.2439 | 0.4102 | 0.4102 |
| 0.5838 | 7.0 | 63 | 0.2294 | 0.4542 | 0.3007 | 0.4478 | 0.4462 |
| 0.4479 | 8.0 | 72 | 0.1891 | 0.4618 | 0.3175 | 0.4579 | 0.4569 |
| 0.3936 | 9.0 | 81 | 0.1373 | 0.4664 | 0.3152 | 0.4624 | 0.4606 |
| 0.3307 | 10.0 | 90 | 0.1073 | 0.5085 | 0.3889 | 0.5069 | 0.5064 |
| 0.3624 | 11.0 | 99 | 0.0845 | 0.5074 | 0.3887 | 0.5061 | 0.5055 |
| 0.1817 | 12.0 | 108 | 0.0702 | 0.5456 | 0.4416 | 0.5444 | 0.5436 |
| 0.2335 | 13.0 | 117 | 0.0705 | 0.5132 | 0.4077 | 0.5136 | 0.5125 |
| 0.1604 | 14.0 | 126 | 0.0650 | 0.5486 | 0.4418 | 0.5464 | 0.5455 |
| 0.1306 | 15.0 | 135 | 0.0540 | 0.5469 | 0.4508 | 0.5468 | 0.5467 |
| 0.1194 | 16.0 | 144 | 0.0489 | 0.5935 | 0.5103 | 0.5925 | 0.5931 |
| 0.2133 | 17.0 | 153 | 0.0441 | 0.5746 | 0.4862 | 0.5732 | 0.5736 |
| 0.1035 | 18.0 | 162 | 0.0425 | 0.5799 | 0.4981 | 0.5786 | 0.5798 |
| 0.1049 | 19.0 | 171 | 0.0333 | 0.6341 | 0.5608 | 0.6325 | 0.6325 |
| 0.1165 | 20.0 | 180 | 0.0287 | 0.6398 | 0.5755 | 0.6390 | 0.6379 |
| 0.1197 | 21.0 | 189 | 0.0300 | 0.5988 | 0.5223 | 0.5995 | 0.5996 |
| 0.0607 | 22.0 | 198 | 0.0245 | 0.6465 | 0.5810 | 0.6458 | 0.6453 |
| 0.1443 | 23.0 | 207 | 0.0238 | 0.6454 | 0.5820 | 0.6475 | 0.6470 |
| 0.0727 | 24.0 | 216 | 0.0188 | 0.6769 | 0.6239 | 0.6764 | 0.6770 |
| 0.0462 | 25.0 | 225 | 0.0177 | 0.6926 | 0.6368 | 0.6918 | 0.6923 |
| 0.0804 | 26.0 | 234 | 0.0132 | 0.6979 | 0.6512 | 0.6975 | 0.6988 |
| 0.0337 | 27.0 | 243 | 0.0135 | 0.6971 | 0.6450 | 0.6970 | 0.6977 |
| 0.0459 | 28.0 | 252 | 0.0131 | 0.7019 | 0.6564 | 0.7019 | 0.7029 |
| 0.0233 | 29.0 | 261 | 0.0102 | 0.7089 | 0.6671 | 0.7096 | 0.7096 |
| 0.0228 | 30.0 | 270 | 0.0112 | 0.7057 | 0.6645 | 0.7055 | 0.7063 |
| 0.0435 | 31.0 | 279 | 0.0080 | 0.7125 | 0.6717 | 0.7117 | 0.7130 |
| 0.0364 | 32.0 | 288 | 0.0114 | 0.7108 | 0.6653 | 0.7102 | 0.7098 |
| 0.0112 | 33.0 | 297 | 0.0086 | 0.7184 | 0.6786 | 0.7182 | 0.7192 |
| 0.0325 | 34.0 | 306 | 0.0068 | 0.7268 | 0.6917 | 0.7267 | 0.7274 |
| 0.0173 | 35.0 | 315 | 0.0052 | 0.7327 | 0.7016 | 0.7317 | 0.7330 |
| 0.0599 | 36.0 | 324 | 0.0058 | 0.7291 | 0.6969 | 0.7297 | 0.7293 |
| 0.0125 | 37.0 | 333 | 0.0044 | 0.7336 | 0.7057 | 0.7338 | 0.7347 |
| 0.0155 | 38.0 | 342 | 0.0054 | 0.7238 | 0.6865 | 0.7241 | 0.7246 |
| 0.0199 | 39.0 | 351 | 0.0050 | 0.7293 | 0.6970 | 0.7294 | 0.7295 |
| 0.0109 | 40.0 | 360 | 0.0035 | 0.7348 | 0.7077 | 0.7352 | 0.7355 |
| 0.0229 | 41.0 | 369 | 0.0034 | 0.7348 | 0.7077 | 0.7352 | 0.7355 |
| 0.0353 | 42.0 | 378 | 0.0033 | 0.7348 | 0.7077 | 0.7352 | 0.7355 |
| 0.0124 | 43.0 | 387 | 0.0035 | 0.7357 | 0.7080 | 0.7359 | 0.7364 |
| 0.0147 | 44.0 | 396 | 0.0033 | 0.7330 | 0.7032 | 0.7333 | 0.7331 |
| 0.0055 | 45.0 | 405 | 0.0032 | 0.7322 | 0.7023 | 0.7324 | 0.7325 |
| 0.0183 | 46.0 | 414 | 0.0031 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.004 | 47.0 | 423 | 0.0033 | 0.7350 | 0.7069 | 0.7353 | 0.7365 |
| 0.0195 | 48.0 | 432 | 0.0032 | 0.7331 | 0.7019 | 0.7323 | 0.7333 |
| 0.0112 | 49.0 | 441 | 0.0031 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0186 | 50.0 | 450 | 0.0029 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0043 | 51.0 | 459 | 0.0028 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.011 | 52.0 | 468 | 0.0023 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0203 | 53.0 | 477 | 0.0021 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0099 | 54.0 | 486 | 0.0021 | 0.7377 | 0.7128 | 0.7376 | 0.7391 |
| 0.0095 | 55.0 | 495 | 0.0021 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.021 | 56.0 | 504 | 0.0021 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0191 | 57.0 | 513 | 0.0022 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0033 | 58.0 | 522 | 0.0021 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0264 | 59.0 | 531 | 0.0021 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
| 0.0034 | 60.0 | 540 | 0.0021 | 0.7378 | 0.7126 | 0.7379 | 0.7390 |
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1
|
nicolofelicioni/pythia-1b-sft-hh-hts-8
|
nicolofelicioni
| 2024-10-28T06:39:12Z | 132 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T06:35:30Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Crysiss/checkpoint
|
Crysiss
| 2024-10-28T06:38:42Z | 201 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T06:37:30Z |
---
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]
|
star1918/bert-finetuned-ner
|
star1918
| 2024-10-28T06:38:15Z | 108 | 0 |
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
| 2024-10-28T06:21:33Z |
---
library_name: transformers
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: 0.9322089947089947
- name: Recall
type: recall
value: 0.9488387748232918
- name: F1
type: f1
value: 0.9404503753127607
- name: Accuracy
type: accuracy
value: 0.9860628716077
---
<!-- 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: 0.0650
- Precision: 0.9322
- Recall: 0.9488
- F1: 0.9405
- Accuracy: 0.9861
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0458 | 1.0 | 878 | 0.0642 | 0.9273 | 0.9403 | 0.9337 | 0.9845 |
| 0.0281 | 2.0 | 1756 | 0.0653 | 0.9306 | 0.9478 | 0.9391 | 0.9858 |
| 0.0146 | 3.0 | 2634 | 0.0650 | 0.9322 | 0.9488 | 0.9405 | 0.9861 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
ichigoDifuku/SegFormer_Free_Medimage_segmentation_DEMO
|
ichigoDifuku
| 2024-10-28T06:35:22Z | 35 | 0 |
transformers
|
[
"transformers",
"safetensors",
"segformer",
"generated_from_trainer",
"base_model:nvidia/segformer-b0-finetuned-ade-512-512",
"base_model:finetune:nvidia/segformer-b0-finetuned-ade-512-512",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-10-28T06:35:18Z |
---
library_name: transformers
license: other
base_model: nvidia/segformer-b0-finetuned-ade-512-512
tags:
- generated_from_trainer
model-index:
- name: SegFormer_Free_Medimage_segmentation_DEMO
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SegFormer_Free_Medimage_segmentation_DEMO
This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
iwan-rg/XLM-Roberta-base-Finetuned-EN-AR-Parallel
|
iwan-rg
| 2024-10-28T06:33:36Z | 5 | 0 | null |
[
"safetensors",
"xlm-roberta",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"region:us"
] | null | 2024-10-28T06:33:05Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-Parallel-mlm-0.15-base-27OCT
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. -->
# xlm-roberta-base-finetuned-Parallel-mlm-0.15-base-27OCT
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9339
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- 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 |
|:-------------:|:------:|:-----:|:---------------:|
| No log | 0.0998 | 100 | 1.4204 |
| No log | 0.1997 | 200 | 1.3432 |
| No log | 0.2995 | 300 | 1.3054 |
| No log | 0.3993 | 400 | 1.2756 |
| 1.5915 | 0.4992 | 500 | 1.2552 |
| 1.5915 | 0.5990 | 600 | 1.2327 |
| 1.5915 | 0.6988 | 700 | 1.2152 |
| 1.5915 | 0.7987 | 800 | 1.2012 |
| 1.5915 | 0.8985 | 900 | 1.2002 |
| 1.3946 | 0.9983 | 1000 | 1.1854 |
| 1.3946 | 1.0982 | 1100 | 1.1824 |
| 1.3946 | 1.1980 | 1200 | 1.1723 |
| 1.3946 | 1.2979 | 1300 | 1.1589 |
| 1.3946 | 1.3977 | 1400 | 1.1490 |
| 1.321 | 1.4975 | 1500 | 1.1387 |
| 1.321 | 1.5974 | 1600 | 1.1356 |
| 1.321 | 1.6972 | 1700 | 1.1252 |
| 1.321 | 1.7970 | 1800 | 1.1259 |
| 1.321 | 1.8969 | 1900 | 1.1182 |
| 1.2735 | 1.9967 | 2000 | 1.1144 |
| 1.2735 | 2.0965 | 2100 | 1.0966 |
| 1.2735 | 2.1964 | 2200 | 1.1005 |
| 1.2735 | 2.2962 | 2300 | 1.0952 |
| 1.2735 | 2.3960 | 2400 | 1.0935 |
| 1.235 | 2.4959 | 2500 | 1.0840 |
| 1.235 | 2.5957 | 2600 | 1.0766 |
| 1.235 | 2.6955 | 2700 | 1.0719 |
| 1.235 | 2.7954 | 2800 | 1.0665 |
| 1.235 | 2.8952 | 2900 | 1.0644 |
| 1.1954 | 2.9950 | 3000 | 1.0656 |
| 1.1954 | 3.0949 | 3100 | 1.0574 |
| 1.1954 | 3.1947 | 3200 | 1.0495 |
| 1.1954 | 3.2945 | 3300 | 1.0475 |
| 1.1954 | 3.3944 | 3400 | 1.0452 |
| 1.1707 | 3.4942 | 3500 | 1.0399 |
| 1.1707 | 3.5940 | 3600 | 1.0363 |
| 1.1707 | 3.6939 | 3700 | 1.0291 |
| 1.1707 | 3.7937 | 3800 | 1.0338 |
| 1.1707 | 3.8936 | 3900 | 1.0348 |
| 1.1509 | 3.9934 | 4000 | 1.0319 |
| 1.1509 | 4.0932 | 4100 | 1.0219 |
| 1.1509 | 4.1931 | 4200 | 1.0214 |
| 1.1509 | 4.2929 | 4300 | 1.0161 |
| 1.1509 | 4.3927 | 4400 | 1.0158 |
| 1.1275 | 4.4926 | 4500 | 1.0153 |
| 1.1275 | 4.5924 | 4600 | 1.0067 |
| 1.1275 | 4.6922 | 4700 | 1.0058 |
| 1.1275 | 4.7921 | 4800 | 1.0097 |
| 1.1275 | 4.8919 | 4900 | 1.0037 |
| 1.1127 | 4.9917 | 5000 | 1.0048 |
| 1.1127 | 5.0916 | 5100 | 1.0022 |
| 1.1127 | 5.1914 | 5200 | 0.9947 |
| 1.1127 | 5.2912 | 5300 | 0.9947 |
| 1.1127 | 5.3911 | 5400 | 0.9907 |
| 1.0944 | 5.4909 | 5500 | 0.9909 |
| 1.0944 | 5.5907 | 5600 | 0.9861 |
| 1.0944 | 5.6906 | 5700 | 0.9858 |
| 1.0944 | 5.7904 | 5800 | 0.9861 |
| 1.0944 | 5.8902 | 5900 | 0.9791 |
| 1.0847 | 5.9901 | 6000 | 0.9787 |
| 1.0847 | 6.0899 | 6100 | 0.9744 |
| 1.0847 | 6.1897 | 6200 | 0.9752 |
| 1.0847 | 6.2896 | 6300 | 0.9712 |
| 1.0847 | 6.3894 | 6400 | 0.9723 |
| 1.0662 | 6.4893 | 6500 | 0.9706 |
| 1.0662 | 6.5891 | 6600 | 0.9688 |
| 1.0662 | 6.6889 | 6700 | 0.9692 |
| 1.0662 | 6.7888 | 6800 | 0.9655 |
| 1.0662 | 6.8886 | 6900 | 0.9637 |
| 1.0559 | 6.9884 | 7000 | 0.9629 |
| 1.0559 | 7.0883 | 7100 | 0.9618 |
| 1.0559 | 7.1881 | 7200 | 0.9622 |
| 1.0559 | 7.2879 | 7300 | 0.9605 |
| 1.0559 | 7.3878 | 7400 | 0.9560 |
| 1.0439 | 7.4876 | 7500 | 0.9562 |
| 1.0439 | 7.5874 | 7600 | 0.9566 |
| 1.0439 | 7.6873 | 7700 | 0.9515 |
| 1.0439 | 7.7871 | 7800 | 0.9514 |
| 1.0439 | 7.8869 | 7900 | 0.9542 |
| 1.0358 | 7.9868 | 8000 | 0.9504 |
| 1.0358 | 8.0866 | 8100 | 0.9502 |
| 1.0358 | 8.1864 | 8200 | 0.9494 |
| 1.0358 | 8.2863 | 8300 | 0.9451 |
| 1.0358 | 8.3861 | 8400 | 0.9461 |
| 1.0242 | 8.4859 | 8500 | 0.9447 |
| 1.0242 | 8.5858 | 8600 | 0.9455 |
| 1.0242 | 8.6856 | 8700 | 0.9441 |
| 1.0242 | 8.7854 | 8800 | 0.9399 |
| 1.0242 | 8.8853 | 8900 | 0.9410 |
| 1.0198 | 8.9851 | 9000 | 0.9391 |
| 1.0198 | 9.0850 | 9100 | 0.9390 |
| 1.0198 | 9.1848 | 9200 | 0.9379 |
| 1.0198 | 9.2846 | 9300 | 0.9382 |
| 1.0198 | 9.3845 | 9400 | 0.9377 |
| 1.0094 | 9.4843 | 9500 | 0.9363 |
| 1.0094 | 9.5841 | 9600 | 0.9354 |
| 1.0094 | 9.6840 | 9700 | 0.9353 |
| 1.0094 | 9.7838 | 9800 | 0.9351 |
| 1.0094 | 9.8836 | 9900 | 0.9342 |
| 1.011 | 9.9835 | 10000 | 0.9339 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.1.1+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
|
rizki-syazali/tapasid_finetuned_hitab_to_itqa
|
rizki-syazali
| 2024-10-28T06:31:36Z | 64 | 0 |
transformers
|
[
"transformers",
"safetensors",
"tapas",
"table-question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
table-question-answering
| 2024-10-27T08:43:43Z |
---
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]
|
Crysiss/sql_gpt2_124m_sft
|
Crysiss
| 2024-10-28T06:28:30Z | 222 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T06:27:56Z |
---
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]
|
Eskender/reviews-classification
|
Eskender
| 2024-10-28T06:16:32Z | 19 | 0 | null |
[
"safetensors",
"roberta",
"license:apache-2.0",
"region:us"
] | null | 2024-10-28T05:54:33Z |
---
license: apache-2.0
---
|
godus1201/krx-Qwen2.5-7B-Instruct-bnb-4bit
|
godus1201
| 2024-10-28T06:13:17Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"krx",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T05:15:44Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- krx
---
# Uploaded model
- **Developed by:** godus1201
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
KPEP/krx-qwen-2-7b-v1.0
|
KPEP
| 2024-10-28T06:04:21Z | 6 | 0 | null |
[
"safetensors",
"qwen2",
"krx",
"unsloth",
"trl",
"sft",
"ko",
"base_model:Qwen/Qwen2-7B",
"base_model:finetune:Qwen/Qwen2-7B",
"region:us"
] | null | 2024-10-28T06:02:14Z |
---
base_model:
- Qwen/Qwen2-7B
language:
- ko
tags:
- krx
- unsloth
- trl
- sft
---
|
rshacter/rshacter-llama-3.2-1B-instruct
|
rshacter
| 2024-10-28T06:03:53Z | 176 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:mlabonne/orpo-dpo-mix-40k",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T03:07:00Z |
---
library_name: transformers
datasets:
- mlabonne/orpo-dpo-mix-40k
language:
- en
base_model:
- meta-llama/Llama-3.2-1B-Instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
ORPO-Tuned Llama-3.2-1B-Instruct
## Model Details
- This model is a fine-tuned version of the meta-llama/Llama-3.2-1B-Instruct base model, adapted using the ORPO (Optimizing Reward and Preference Objectives) technique.
- Base Model: It builds upon the Llama-3.2-1B-Instruct model, (1 billion parameter instruction-following language model).
- Fine-Tuning Technique: The model was fine-tuned using ORPO. ORPO combines supervised fine-tuning with preference optimization.
- Training Data: It was trained on the mlabonne/orpo-dpo-mix-40k dataset, containing 44,245 examples of prompts, chosen answers, and rejected answers.
- Purpose: The model is designed to generate responses that are better aligned with human preferences while maintaining the general knowledge and capabilities of the base Llama 3 model.
- Efficient Fine-Tuning: LoRA (Low-Rank Adaptation) was used for efficient adaptation, allowing for faster training and smaller storage requirements.
- Capabilities: Model should follow instructions and generate responses that are more in line with human preferences compared to the base model.
- Evaluation: The model's performance was evaluated on the HellaSwag benchmark
### 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.
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
https://uplimit.com/course/open-source-llms/session/session_clu1q3j6f016d128r2zxe3uyj/assignment/assignment_clyvnyyjh019h199337oef4ur
https://uplimit.com/ugc-assets/course/course_clmz6fh2a00aa12bqdtjv6ygs/assets/1728565337395-85hdx93s03d0v9bd8j1nnxfjylyty2/uplimitopensourcellmsoctoberweekone.ipynb
## 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. -->
Hands-on learning: Finetuning LLMs
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Introduction to Finetuning LLMs course - Learning
### Downstream Use [optional]
This model is designed for tasks requiring improved alignment with human preferences, such as:
- Chatbots
- Question-answering systems
- General text generation with enhanced preference alignment<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
This should not yet be used in the world - More finetuning is required
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- Performance may vary on tasks outside the training distribution
- May inherit biases present in the base model and training data
- Limited to 1B parameters, which may impact performance on complex tasks
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
- Users should be aware of potential biases in model outputs
- Not suitable for critical decision-making without human oversight
- May generate plausible-sounding but incorrect information
## 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. -->
For training data the model used:'mlabonne/orpo-dpo-mix-40k'
This dataset is designed for ORPO (Optimizing Reward and Preference Objectives) or DPO (Direct Preference Optimization) training of language models.
* It contains 44,245 examples in the training split.
* Includes prompts, chosen answers, and rejected answers for each sample.
* Combines various high-quality DPO datasets.
[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. -->
This model was fine-tuned using the ORPO (Optimizing Reward and Preference Objectives) technique on the meta-llama/Llama-3.2-1B-Instruct base model.
Base Model: meta-llama/Llama-3.2-1B-Instruct
Training Technique: ORPO (Optimizing Reward and Preference Objectives)
Efficient Fine-tuning Method: LoRA (Low-Rank Adaptation)
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- Learning Rate: 2e-5
- Batch Size: 4
- Gradient Accumulation Steps: 4
- Training Steps: 500
- Warmup Steps: 20
- LoRA Rank: 16
- LoRA Alpha: 32
#### 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. -->
For evaluation the model used Hellaswag
Results:
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag| 1|none | 0|acc |↑ |0.4516|± |0.0050|
| | |none | 0|acc_norm|↑ |0.6139|± |0.0049|
Interpretation:
- Performance Level: The model achieves a raw accuracy of 45.16% and a normalized accuracy of 61.39% on the HellaSwag task.
- Confidence: The small standard errors (about 0.5% for both metrics) indicate that these results are fairly precise.
- Improvement over Random: Given that HellaSwag typically has 4 choices per question, a random baseline would achieve 25% accuracy. This model performs significantly better than random.
- Normalized vs. Raw Accuracy: The higher normalized accuracy (61.39% vs. 45.16%) suggests that the model performs better when accounting for task-specific challenges.
- Room for Improvement: While the performance is well above random, there's still significant room for improvement to reach human-level performance (which is typically above 95% on HellaSwag).
#### Summary
- Base Model: meta-llama/Llama-3.2-1B-Instruct
- Model Type: Causal Language Model
- Language: English
Intended Use
- This model is designed for tasks requiring improved alignment with human preferences, such as:
- Chatbots
- Question-answering systems
- General text generation with enhanced preference alignment
Training Data
- Dataset: mlabonne/orpo-dpo-mix-40k
- Size: 44,245 examples
- Content: Prompts, chosen answers, and rejected answers
Task: HellaSwag
- This is a benchmark task designed to evaluate a model's commonsense reasoning and ability to complete scenarios logically.
- No specific filtering was applied to the test set.
- The evaluation was done in a zero-shot setting, where the model didn't receive any examples before making predictions.
Interpretation:
- Performance Level: The model achieves a raw accuracy of 45.16% and a normalized accuracy of 61.39% on the HellaSwag task.
- Confidence: The small standard errors (about 0.5% for both metrics) indicate that these results are fairly precise.
- Improvement over Random: Given that HellaSwag typically has 4 choices per question, a random baseline would achieve 25% accuracy. This model performs significantly better than random.
- Normalized vs. Raw Accuracy: The higher normalized accuracy (61.39% vs. 45.16%) suggests that the model performs better when accounting for task-specific challenges.
- Room for Improvement: While the performance is well above random, there's still significant room for improvement to reach human-level performance (which is typically above 95% on HellaSwag).
- Metrics: a. acc (Accuracy): Value: 0.4516 (45.16%), Stderr: ± 0.0050 (0.50%), b. acc_norm (Normalized Accuracy): Value: 0.6139 (61.39%), Stderr: ± 0.0049 (0.49%)
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** A100
- **Hours used:** No comment
- **Cloud Provider:** Google Collab
- **Compute Region:** Sacramento, CA, US
- **Framework**: PyTorch
## Technical Specifications [optional]
Hardware: A100 GPU
## Model Card Author
Ruth Shacterman
## Model Card Contact
[More Information Needed]
|
Whalejay/bert-sliding-window-9x_epoch_2
|
Whalejay
| 2024-10-28T05:57:11Z | 5 | 0 | null |
[
"safetensors",
"distilbert",
"question-answering",
"pytorch",
"bert",
"squad",
"en",
"dataset:squad",
"license:mit",
"model-index",
"region:us"
] |
question-answering
| 2024-10-28T05:56:38Z |
---
language: en
tags:
- question-answering
- pytorch
- bert
- squad
license: mit
datasets:
- squad
pipeline_tag: question-answering
model-index:
- name: bert-sliding-window-9x_epoch_2
results:
- task:
type: question-answering
name: Question Answering
metrics:
- type: exact_match
value: N/A # You can update this with actual metrics if available
name: Exact Match
- type: f1
value: N/A # You can update this with actual metrics if available
name: F1
dataset:
name: SQuAD
type: squad
config: plain_text # Adding the config field
split: validation # Adding the split field
---
# bert-sliding-window-9x_epoch_2
## Model description
This is a fine-tuned version of [DistilBERT](https://huggingface.co/distilbert-base-cased-distilled-squad) for question answering tasks. The model was trained on SQuAD dataset.
## Training procedure
The model was trained with the following hyperparameters:
- Learning Rate: 1e-05
- Batch Size: 32
- Epochs: 10
- Weight Decay: 0.01
## Intended uses & limitations
This model is intended to be used for question answering tasks, particularly on SQuAD-like datasets. It performs best on factual questions where the answer can be found as a span of text within the given context.
## Training Details
### Training Data
The model was trained on the SQuAD dataset, which consists of questions posed by crowdworkers on a set of Wikipedia articles.
### Training Hyperparameters
The model was trained with the following hyperparameters:
* learning_rate: 1e-05
* batch_size: 32
* num_epochs: 10
* weight_decay: 0.01
## Uses
This model can be used for:
- Extracting answers from text passages given questions
- Question answering tasks
- Reading comprehension tasks
## Limitations
- The model can only extract answers that are directly present in the given context
- Performance may vary on out-of-domain texts
- The model may struggle with complex reasoning questions
## Additional Information
- Model type: DistilBERT
- Language: English
- License: MIT
- Framework: PyTorch
|
ringorsolya/xlmRoBERTa_polish
|
ringorsolya
| 2024-10-28T05:52:47Z | 5 | 0 | null |
[
"tensorboard",
"safetensors",
"xlm-roberta",
"license:apache-2.0",
"region:us"
] | null | 2024-10-27T19:55:05Z |
---
license: apache-2.0
---
|
JingyeChen22/textdiffuser2_layout_planner
|
JingyeChen22
| 2024-10-28T05:51:18Z | 1,285 | 4 |
diffusers
|
[
"diffusers",
"pytorch",
"llama",
"text-to-image",
"arxiv:2311.16465",
"license:mit",
"region:us"
] |
text-to-image
| 2023-12-10T00:54:19Z |
---
pipeline_tag: text-to-image
library_name: diffusers
license: mit
---
# Model
This repo contains the layout planner model of the paper [TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering](https://huggingface.co/papers/2311.16465).
# Usage
The script [here](https://github.com/microsoft/unilm/tree/master/textdiffuser-2#firecracker-inference) can be used to perform inference with the model.
|
Covvenheimer/taylor_swift_model
|
Covvenheimer
| 2024-10-28T05:50:52Z | 16 | 0 | null |
[
"gguf",
"llama",
"text-generation",
"qlora",
"lyrics",
"taylor-swift",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-28T05:19:12Z |
---
base_model: unsloth/llama-3.2-1b-bnb-4bit
tags:
- text-generation
- qlora
- llama
- gguf
- lyrics
- taylor-swift
license: apache-2.0
language: en
pipeline_tag: text-generation
---
# Taylor Swift Lyrics Llama Model (3.2, GGUF Format)
- **Base Model**: unsloth/llama-3.2-1b-bnb-4bit
- **Fine-tuned on**: Taylor Swift lyrics using QLoRA
- **Format**: GGUF (Not compatible with the `transformers` library; requires `llama-cpp-python`)
- **License**: Apache-2.0
- **Developed by**: Covvenheimer and Team
This model, fine-tuned on Taylor Swift lyrics, is tailored for generating text in the style of her songs. It was trained with a 2x speed improvement using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face’s TRL library, specifically optimized for GGUF format.
> **Important:** This model requires `llama-cpp-python` to run. It is incompatible with the `transformers` library due to its GGUF format.
## Installation and Setup
To load and run this model, install the `llama-cpp-python` library and download the model file from the Hugging Face Hub.
### Step-by-Step Code Example
1. **Install llama-cpp-python**
```python
!pip install llama-cpp-python
```
2. **Load the Model with llama-cpp**
```python
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
# Define your model repository and file name
repo_id = "Covvenheimer/taylor_swift_model"
filename = "unsloth.Q4_K_M.gguf"
# Download the GGUF model file from Hugging Face
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
# Load the model using llama-cpp-python
llm = Llama(model_path=model_path)
```
3. **Generate Text Using a Prompt**
```python
# Define a prompt for generating lyrics
prompt = """You are a songwriter composing a song in the style of Taylor Swift.
Write lyrics that reflect her themes and musical style, focusing on Love, Heartbreak, Romance."""
# Generate lyrics
output = llm(prompt, max_tokens=512, temperature=0.8)
print(output["choices"][0]["text"])
```
> **Tip:** To customize the themes, change the last line of the prompt. For example, you can modify the focus to other themes like "Friendship, Growth, Resilience." Replace `"Love, Heartbreak, Romance"` with your chosen themes.
This setup will allow you to use the model efficiently and generate lyrics in the style of Taylor Swift.
---
# Uploaded model
- **Developed by:** Covvenheimer
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-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)
|
theprint/CleverBoi-7B-v3
|
theprint
| 2024-10-28T05:47:12Z | 54 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"theprint",
"cleverboi",
"en",
"dataset:theprint/CleverBoi-Data-20k",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-09-14T17:50:47Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- theprint
- cleverboi
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
datasets:
- theprint/CleverBoi-Data-20k
pipeline_tag: text-generation
model-index:
- name: CleverBoi-7B-v3
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 23.82
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/CleverBoi-7B-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 21.94
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/CleverBoi-7B-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 3.17
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/CleverBoi-7B-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 2.13
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/CleverBoi-7B-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.5
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/CleverBoi-7B-v3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 20.76
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/CleverBoi-7B-v3
name: Open LLM Leaderboard
---
<img src="https://huggingface.co/theprint/CleverBoi-Gemma-2-9B/resolve/main/cleverboi.png"/>
# CleverBoi
The CleverBoi series is based on models that have been fine tuned on a collection of data sets that emphasize logic, inference, math and coding, also known as the CleverBoi data set.
This model has been fine tuned for 1 epoch on the CleverBoi-Data-20k data set.
# Uploaded model
- **Developed by:** theprint
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_theprint__CleverBoi-7B-v3)
| Metric |Value|
|-------------------|----:|
|Avg. |13.55|
|IFEval (0-Shot) |23.82|
|BBH (3-Shot) |21.94|
|MATH Lvl 5 (4-Shot)| 3.17|
|GPQA (0-shot) | 2.13|
|MuSR (0-shot) | 9.50|
|MMLU-PRO (5-shot) |20.76|
|
nazimasker/Image-Caption-University-model
|
nazimasker
| 2024-10-28T05:34:28Z | 67 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"git",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/git-base",
"base_model:finetune:microsoft/git-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-10-28T05:09:44Z |
---
library_name: transformers
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: Image-Caption-University-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. -->
# Image-Caption-University-model
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4060
- Wer Score: 2.0586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|:-------------:|:-------:|:----:|:---------------:|:---------:|
| 7.1651 | 6.6667 | 50 | 4.6332 | 2.9074 |
| 2.6998 | 13.3333 | 100 | 1.0675 | 2.1821 |
| 0.3956 | 20.0 | 150 | 0.3752 | 2.5494 |
| 0.0633 | 26.6667 | 200 | 0.3804 | 1.9321 |
| 0.0196 | 33.3333 | 250 | 0.3981 | 2.4105 |
| 0.0141 | 40.0 | 300 | 0.4050 | 2.0679 |
| 0.0115 | 46.6667 | 350 | 0.4060 | 2.0586 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1
|
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