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null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/v2ray/SchizoGPT-8x22B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q2_K.gguf.part2of2) | Q2_K | 52.2 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ3_XS.gguf.part2of2) | IQ3_XS | 58.3 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ3_S.gguf.part2of2) | IQ3_S | 61.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q3_K_S.gguf.part2of2) | Q3_K_S | 61.6 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ3_M.gguf.part2of2) | IQ3_M | 64.6 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q3_K_M.gguf.part2of2) | Q3_K_M | 67.9 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q3_K_L.gguf.part2of2) | Q3_K_L | 72.7 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.IQ4_XS.gguf.part2of2) | IQ4_XS | 76.5 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q4_K_S.gguf.part2of2) | Q4_K_S | 80.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q4_K_M.gguf.part2of2) | Q4_K_M | 85.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q5_K_S.gguf.part2of2) | Q5_K_S | 97.1 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q5_K_M.gguf.part3of3) | Q5_K_M | 100.1 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q6_K.gguf.part3of3) | Q6_K | 115.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q8_0.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q8_0.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q8_0.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF/resolve/main/SchizoGPT-8x22B.Q8_0.gguf.part4of4) | Q8_0 | 149.5 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["not-for-all-audiences"], "datasets": ["v2ray/r-chatgpt-general-dump"], "base_model": "v2ray/SchizoGPT-8x22B", "quantized_by": "mradermacher"} | mradermacher/SchizoGPT-8x22B-GGUF | null | [
"transformers",
"not-for-all-audiences",
"en",
"dataset:v2ray/r-chatgpt-general-dump",
"base_model:v2ray/SchizoGPT-8x22B",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:05:25+00:00 |
translation | transformers | !pip install sentencepiece transformers==4.33
import torch
from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
def fix_tokenizer(tokenizer, new_lang='fer_Latn'):
""" Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """
old_len = len(tokenizer) - int(new_lang in tokenizer.added_tokens_encoder)
tokenizer.lang_code_to_id[new_lang] = old_len-1
tokenizer.id_to_lang_code[old_len-1] = new_lang
# always move "mask" to the last position
tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset
tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()}
if new_lang not in tokenizer._additional_special_tokens:
tokenizer._additional_special_tokens.append(new_lang)
# clear the added token encoder; otherwise a new token may end up there by mistake
tokenizer.added_tokens_encoder = {}
tokenizer.added_tokens_decoder = {}
MODEL_URL = "DinoDelija/nllb_english_fering"
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_URL)
tokenizer = NllbTokenizer.from_pretrained(MODEL_URL)
fix_tokenizer(tokenizer)
def translate(
text,
model,
tokenizer,
src_lang='eng_Latn',
tgt_lang='fer_Latn',
max_length='auto',
num_beams=4,
n_out=None,
**kwargs
):
tokenizer.src_lang = src_lang
encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
if max_length == 'auto':
max_length = int(32 + 2.0 * encoded.input_ids.shape[1])
model.eval()
generated_tokens = model.generate(
**encoded.to(model.device),
forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang],
max_length=max_length,
num_beams=num_beams,
num_return_sequences=n_out or 1,
**kwargs
)
out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
if isinstance(text, str) and n_out is None:
return out[0]
return
translate("красная птица", model=model, tokenizer=tokenizer) | {"language": ["en"], "license": "mit", "pipeline_tag": "translation"} | DinoDelija/nllb_english_fering | null | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"translation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
]
| null | 2024-04-26T12:05:56+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBertLoRa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the IMDB Movie dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0234
- Accuracy: {'accuracy': 0.884}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.4076 | {'accuracy': 0.876} |
| 0.429 | 2.0 | 500 | 0.3507 | {'accuracy': 0.863} |
| 0.429 | 3.0 | 750 | 0.5018 | {'accuracy': 0.881} |
| 0.2304 | 4.0 | 1000 | 0.7036 | {'accuracy': 0.864} |
| 0.2304 | 5.0 | 1250 | 0.8113 | {'accuracy': 0.862} |
| 0.1136 | 6.0 | 1500 | 0.8428 | {'accuracy': 0.882} |
| 0.1136 | 7.0 | 1750 | 0.9134 | {'accuracy': 0.89} |
| 0.0153 | 8.0 | 2000 | 0.9723 | {'accuracy': 0.884} |
| 0.0153 | 9.0 | 2250 | 1.0225 | {'accuracy': 0.884} |
| 0.0089 | 10.0 | 2500 | 1.0234 | {'accuracy': 0.884} |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "DistilBertLoRa", "results": []}]} | Abdo36/DistilBertLoRa | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T12:06:51+00:00 |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speaker-segmentation-fine-tuned-callhome-eng-6
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/callhome eng dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5147
- Der: 0.1839
- False Alarm: 0.0668
- Missed Detection: 0.0694
- Confusion: 0.0477
## 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: 0.002
- 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: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.4083 | 1.0 | 362 | 0.4880 | 0.1967 | 0.0505 | 0.0840 | 0.0621 |
| 0.3919 | 2.0 | 724 | 0.4688 | 0.1852 | 0.0608 | 0.0717 | 0.0527 |
| 0.3708 | 3.0 | 1086 | 0.4637 | 0.1846 | 0.0581 | 0.0738 | 0.0527 |
| 0.3549 | 4.0 | 1448 | 0.4636 | 0.1809 | 0.0585 | 0.0689 | 0.0535 |
| 0.3299 | 5.0 | 1810 | 0.4727 | 0.1835 | 0.0587 | 0.0699 | 0.0549 |
| 0.3457 | 6.0 | 2172 | 0.4727 | 0.1861 | 0.0654 | 0.0672 | 0.0535 |
| 0.3241 | 7.0 | 2534 | 0.4921 | 0.1835 | 0.0621 | 0.0701 | 0.0513 |
| 0.3116 | 8.0 | 2896 | 0.4859 | 0.1839 | 0.0647 | 0.0677 | 0.0515 |
| 0.304 | 9.0 | 3258 | 0.4639 | 0.1788 | 0.0571 | 0.0718 | 0.0499 |
| 0.2896 | 10.0 | 3620 | 0.4844 | 0.1826 | 0.0659 | 0.0676 | 0.0490 |
| 0.2853 | 11.0 | 3982 | 0.4696 | 0.1787 | 0.0521 | 0.0777 | 0.0489 |
| 0.2831 | 12.0 | 4344 | 0.4858 | 0.1831 | 0.0662 | 0.0684 | 0.0484 |
| 0.2746 | 13.0 | 4706 | 0.4799 | 0.1828 | 0.0639 | 0.0703 | 0.0486 |
| 0.2685 | 14.0 | 5068 | 0.4951 | 0.1847 | 0.0658 | 0.0695 | 0.0494 |
| 0.2627 | 15.0 | 5430 | 0.5042 | 0.1829 | 0.0627 | 0.0713 | 0.0489 |
| 0.2551 | 16.0 | 5792 | 0.5066 | 0.1839 | 0.0671 | 0.0682 | 0.0486 |
| 0.2509 | 17.0 | 6154 | 0.5126 | 0.1854 | 0.0690 | 0.0695 | 0.0469 |
| 0.2502 | 18.0 | 6516 | 0.5196 | 0.1861 | 0.0676 | 0.0695 | 0.0490 |
| 0.247 | 19.0 | 6878 | 0.5187 | 0.1844 | 0.0670 | 0.0698 | 0.0476 |
| 0.2417 | 20.0 | 7240 | 0.5147 | 0.1839 | 0.0668 | 0.0694 | 0.0477 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["speaker-diarization", "speaker-segmentation", "generated_from_trainer"], "datasets": ["diarizers-community/callhome"], "base_model": "pyannote/segmentation-3.0", "model-index": [{"name": "speaker-segmentation-fine-tuned-callhome-eng-6", "results": []}]} | tgrhn/speaker-segmentation-fine-tuned-callhome-eng-6 | null | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"dataset:diarizers-community/callhome",
"base_model:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:07:22+00:00 |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "259.45 +/- 15.43", "name": "mean_reward", "verified": false}]}]}]} | Jurij1/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-26T12:08:36+00:00 |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b1-finetuned-cityscapes-1024-1024-straighter-only
This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0331
- Mean Iou: 0.9378
- Mean Accuracy: 0.9644
- Overall Accuracy: 0.9883
- Accuracy Default: 1e-06
- Accuracy Pipe: 0.9182
- Accuracy Floor: 0.9790
- Accuracy Background: 0.9961
- Iou Default: 1e-06
- Iou Pipe: 0.8600
- Iou Floor: 0.9637
- Iou Background: 0.9896
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Default | Accuracy Pipe | Accuracy Floor | Accuracy Background | Iou Default | Iou Pipe | Iou Floor | Iou Background |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------:|:--------------:|:-------------------:|:-----------:|:--------:|:---------:|:--------------:|
| 0.5349 | 1.0 | 36 | 0.1593 | 0.8143 | 0.8613 | 0.9661 | 1e-06 | 0.6324 | 0.9614 | 0.9903 | 1e-06 | 0.5490 | 0.9277 | 0.9660 |
| 0.1472 | 2.0 | 72 | 0.0977 | 0.8792 | 0.9255 | 0.9782 | 1e-06 | 0.8153 | 0.9690 | 0.9922 | 1e-06 | 0.7119 | 0.9456 | 0.9800 |
| 0.0902 | 3.0 | 108 | 0.0708 | 0.9014 | 0.9285 | 0.9820 | 1e-06 | 0.8194 | 0.9690 | 0.9972 | 1e-06 | 0.7669 | 0.9558 | 0.9815 |
| 0.0662 | 4.0 | 144 | 0.0586 | 0.9146 | 0.9552 | 0.9842 | 1e-06 | 0.9036 | 0.9666 | 0.9954 | 1e-06 | 0.8015 | 0.9567 | 0.9856 |
| 0.0543 | 5.0 | 180 | 0.0490 | 0.9225 | 0.9514 | 0.9856 | 1e-06 | 0.8844 | 0.9734 | 0.9964 | 1e-06 | 0.8208 | 0.9606 | 0.9860 |
| 0.0486 | 6.0 | 216 | 0.0445 | 0.9252 | 0.9640 | 0.9862 | 1e-06 | 0.9244 | 0.9729 | 0.9947 | 1e-06 | 0.8265 | 0.9616 | 0.9875 |
| 0.042 | 7.0 | 252 | 0.0414 | 0.9279 | 0.9658 | 0.9867 | 1e-06 | 0.9315 | 0.9699 | 0.9959 | 1e-06 | 0.8332 | 0.9626 | 0.9880 |
| 0.0389 | 8.0 | 288 | 0.0381 | 0.9322 | 0.9695 | 0.9874 | 1e-06 | 0.9413 | 0.9716 | 0.9956 | 1e-06 | 0.8448 | 0.9632 | 0.9888 |
| 0.0359 | 9.0 | 324 | 0.0386 | 0.9319 | 0.9629 | 0.9871 | 1e-06 | 0.9215 | 0.9702 | 0.9970 | 1e-06 | 0.8451 | 0.9630 | 0.9877 |
| 0.034 | 10.0 | 360 | 0.0374 | 0.9313 | 0.9632 | 0.9873 | 1e-06 | 0.9202 | 0.9730 | 0.9965 | 1e-06 | 0.8422 | 0.9634 | 0.9883 |
| 0.0322 | 11.0 | 396 | 0.0383 | 0.9300 | 0.9570 | 0.9871 | 1e-06 | 0.8993 | 0.9746 | 0.9971 | 1e-06 | 0.8379 | 0.9642 | 0.9878 |
| 0.0306 | 12.0 | 432 | 0.0353 | 0.9340 | 0.9678 | 0.9876 | 1e-06 | 0.9358 | 0.9710 | 0.9965 | 1e-06 | 0.8494 | 0.9637 | 0.9888 |
| 0.0292 | 13.0 | 468 | 0.0337 | 0.9355 | 0.9734 | 0.9881 | 1e-06 | 0.9527 | 0.9719 | 0.9957 | 1e-06 | 0.8529 | 0.9637 | 0.9898 |
| 0.0286 | 14.0 | 504 | 0.0334 | 0.9355 | 0.9686 | 0.9881 | 1e-06 | 0.9352 | 0.9745 | 0.9960 | 1e-06 | 0.8530 | 0.9641 | 0.9895 |
| 0.0271 | 15.0 | 540 | 0.0325 | 0.9389 | 0.9682 | 0.9885 | 1e-06 | 0.9325 | 0.9758 | 0.9964 | 1e-06 | 0.8624 | 0.9648 | 0.9897 |
| 0.0266 | 16.0 | 576 | 0.0327 | 0.9373 | 0.9696 | 0.9883 | 1e-06 | 0.9378 | 0.9748 | 0.9961 | 1e-06 | 0.8576 | 0.9646 | 0.9897 |
| 0.0257 | 17.0 | 612 | 0.0350 | 0.9330 | 0.9673 | 0.9877 | 1e-06 | 0.9302 | 0.9766 | 0.9952 | 1e-06 | 0.8463 | 0.9636 | 0.9892 |
| 0.0246 | 18.0 | 648 | 0.0333 | 0.9354 | 0.9665 | 0.9881 | 1e-06 | 0.9269 | 0.9764 | 0.9961 | 1e-06 | 0.8522 | 0.9644 | 0.9896 |
| 0.0242 | 19.0 | 684 | 0.0326 | 0.9378 | 0.9681 | 0.9884 | 1e-06 | 0.9311 | 0.9772 | 0.9959 | 1e-06 | 0.8588 | 0.9648 | 0.9896 |
| 0.0231 | 20.0 | 720 | 0.0339 | 0.9366 | 0.9665 | 0.9883 | 1e-06 | 0.9256 | 0.9781 | 0.9958 | 1e-06 | 0.8557 | 0.9646 | 0.9896 |
| 0.0236 | 21.0 | 756 | 0.0333 | 0.9365 | 0.9702 | 0.9883 | 1e-06 | 0.9375 | 0.9779 | 0.9951 | 1e-06 | 0.8552 | 0.9644 | 0.9900 |
| 0.0227 | 22.0 | 792 | 0.0327 | 0.9375 | 0.9690 | 0.9885 | 1e-06 | 0.9339 | 0.9773 | 0.9958 | 1e-06 | 0.8577 | 0.9649 | 0.9900 |
| 0.0226 | 23.0 | 828 | 0.0331 | 0.9378 | 0.9644 | 0.9883 | 1e-06 | 0.9182 | 0.9790 | 0.9961 | 1e-06 | 0.8600 | 0.9637 | 0.9896 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "base_model": "nvidia/segformer-b1-finetuned-cityscapes-1024-1024", "model-index": [{"name": "segformer-b1-finetuned-cityscapes-1024-1024-straighter-only", "results": []}]} | selvaa/segformer-b1-finetuned-cityscapes-1024-1024-straighter-only | null | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"base_model:nvidia/segformer-b1-finetuned-cityscapes-1024-1024",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:10:12+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gemma-2b-dolly-qa
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0211
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 1480
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 2.9203 | 1.6393 | 100 | 2.5631 |
| 2.4253 | 3.2787 | 200 | 2.2695 |
| 2.2443 | 4.9180 | 300 | 2.1581 |
| 2.1512 | 6.5574 | 400 | 2.1002 |
| 2.1033 | 8.1967 | 500 | 2.0723 |
| 2.0876 | 9.8361 | 600 | 2.0565 |
| 2.0668 | 11.4754 | 700 | 2.0460 |
| 2.0478 | 13.1148 | 800 | 2.0387 |
| 2.0403 | 14.7541 | 900 | 2.0328 |
| 2.0366 | 16.3934 | 1000 | 2.0286 |
| 2.0238 | 18.0328 | 1100 | 2.0255 |
| 2.0231 | 19.6721 | 1200 | 2.0233 |
| 2.0126 | 21.3115 | 1300 | 2.0220 |
| 2.0164 | 22.9508 | 1400 | 2.0211 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.0.post0+cxx11.abi
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "google/gemma-2b", "model-index": [{"name": "gemma-2b-dolly-qa", "results": []}]} | snarktank/gemma-2b-dolly-qa | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
]
| null | 2024-04-26T12:10:44+00:00 |
null | peft |
# Model Card for Model ID
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## Model Details
### Model Description
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "openlm-research/open_llama_3b_v2"} | yiyic/llama3b-text-ent-lora-clf-epoch-2 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
]
| null | 2024-04-26T12:15:36+00:00 |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"} | cgihlstorf/NEW_finetuned_llama27b32_1_0.0003_alternate_RANDOM_25_pct | null | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
]
| null | 2024-04-26T12:15:55+00:00 |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "openlm-research/open_llama_3b_v2"} | yiyic/llama3b-text-prop-lora-clf-epoch-2 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
]
| null | 2024-04-26T12:16:21+00:00 |
null | null | {} | nelson-pawait/Whisper_fine_tune | null | [
"region:us"
]
| null | 2024-04-26T12:16:49+00:00 |
|
null | transformers | {} | chrisfa/donut-base-sroie | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:18:57+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Siren1000-Chatbot-Phi2
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "Siren1000-Chatbot-Phi2", "results": []}]} | RayBoustany/Siren1000-Chatbot-Phi2 | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
]
| null | 2024-04-26T12:20:10+00:00 |
token-classification | transformers | {"license": "mit"} | PDBEurope/Bioformer8L-ProteinStructure-NER-v0.1_onnx | null | [
"transformers",
"onnx",
"bert",
"token-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:20:33+00:00 |
|
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0718
- Accuracy: 0.9744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2579 | 1.0 | 190 | 0.1356 | 0.9519 |
| 0.1805 | 2.0 | 380 | 0.0895 | 0.97 |
| 0.1528 | 3.0 | 570 | 0.0718 | 0.9744 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9744444444444444, "name": "Accuracy"}]}]}]} | rhlc/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:22:23+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8684
- Matthews Correlation: 0.5356
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5163 | 1.0 | 535 | 0.4563 | 0.4507 |
| 0.342 | 2.0 | 1070 | 0.4735 | 0.5230 |
| 0.2315 | 3.0 | 1605 | 0.6357 | 0.5262 |
| 0.1717 | 4.0 | 2140 | 0.8156 | 0.5189 |
| 0.1259 | 5.0 | 2675 | 0.8684 | 0.5356 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": []}]} | kkater/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:22:39+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | hossniper/Reinforce-CartPole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| null | 2024-04-26T12:23:54+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-bert-finetuned-squad
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "distilled-bert-finetuned-squad", "results": []}]} | momo345/distilled-bert-finetuned-squad | null | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:25:06+00:00 |
null | null | {} | Farset/1 | null | [
"region:us"
]
| null | 2024-04-26T12:25:21+00:00 |
|
null | null | {} | MonsterMMORPG/computer_graphics_lecture | null | [
"region:us"
]
| null | 2024-04-26T12:25:44+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **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]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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 Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | vaatsav06/Llama3_medqa | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
]
| null | 2024-04-26T12:26:23+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | RayBoustany/Siren1000-Chatbot-Phi2-Merged | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:27:28+00:00 |
text-classification | transformers | {"license": "apache-2.0", "pipeline_tag": "text-classification", "widget": [{"text": "\u4eca\u5929\u5fc3\u60c5\u5f88\u4e0d\u9519", "example_title": "\u793a\u4f8b1"}, {"text": "\u4f60\u4e2a\u5927\u574f\u86cb", "example_title": "\u793a\u4f8b2"}]} | moyitech/bert_sentiment_analysis | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:27:56+00:00 |
|
null | null | {} | drublackberry/Meta-Llama-3-8B-Instruct-coach-hbr-crl | null | [
"region:us"
]
| null | 2024-04-26T12:28:07+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** Digeriuz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | Digeriuz/mistral-7b-bnb-4bit-annomi | null | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:29:32+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/final2 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:30:07+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** ruslandev
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This model is finetuned on the data of [Samantha](https://erichartford.com/meet-samantha).
Prompt format is Alpaca. I used the same system prompt as the original Samantha.
```
"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{SYSTEM_PROMPT}
### Input:
{QUESTION}
### Response:
"""
```
# Training
[gptchain](https://github.com/RuslanPeresy/gptchain) framework has been used for training.
## Training hyperparameters
- learning_rate: 2e-4
- seed: 3407
- gradient_accumulation_steps: 4
- per_device_train_batch_size: 2
- optimizer: adamw_8bit
- lr_scheduler_type: linear
- warmup_steps: 5
- num_epochs: 2
- weight_decay: 0.01
## Training results
|Training Loss | Epoch | Step |
|--------------|-------|------|
|2.0778 |0.0 |1 |
|0.6255 |0.18 |120 |
|0.6208 |0.94 |620 |
|0.6244 |2.0 |1306 |
2 epoch finetuning from llama-3-8b took 1 hour on a single A100 with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "datasets": ["cognitivecomputations/samantha-data"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | ruslandev/llama-3-8b-samantha | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"dataset:cognitivecomputations/samantha-data",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:31:12+00:00 |
text-generation | transformers |
# **Introduction**
The model was trained to translate a single sentence from English to Korean with a 1.18M dataset in the general domain.
Dataset: [nayohan/aihub-en-ko-translation-1.2m](https://huggingface.co/datasets/nayohan/aihub-en-ko-translation-1.2m)
### **Loading the Model**
Use the following Python code to load the model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nayohan/llama3-8b-it-translation-general-en-ko-1sent"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.bfloat16
)
```
### **Generating Text**
To generate text, use the following Python code: Currently, this model only support English to Korean, not other languages or reverse and styles.
```python
style="written"
SYSTEM_PROMPT=f"Acts as a translator. Translate en sentences into ko sentences in {style} style."
s = "The aerospace industry is a flower in the field of technology and science."
conversation = [{'role': 'system', 'content': SYSTEM_PROMPT},
{'role': 'user', 'content': s}]
inputs = tokenizer.apply_chat_template(
conversation,
tokenize=True,
add_generation_prompt=True,
return_tensors='pt'
).to("cuda")
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][len(inputs[0]):]))
```
```
# Result
# INPUT: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nActs as a translator. Translate en sentences into ko sentences in colloquial style.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nThe aerospace industry is a flower in the field of technology and science.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n
# OUTPUT: 항공 우주 산업은 기술과 과학의 꽃입니다.<|eot_id|>
# INPUT: <|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nActs as a translator. Translate en sentences into ko sentences in colloquial style.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n
Technical and basic sciences are very important in terms of research. It has a significant impact on the industrial development of a country. Government policies control the research budget.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n
# OUTPUT: 기술과 기초과학은 연구 측면에서 매우 중요합니다. 한 국가의 산업 발전에 큰 영향을 미칩니다. 정부 정책은 연구 예산을 통제합니다.<|eot_id|>
```
### **Citation**
```bibtex
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url={https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
```
Our trainig code can be found here: [TBD] | {"language": ["en", "ko"], "license": "llama3", "library_name": "transformers", "tags": ["translation", "enko", "ko"], "datasets": ["nayohan/aihub-en-ko-translation-1.2m"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"], "pipeline_tag": "text-generation"} | nayohan/llama3-8b-it-translation-general-en-ko-1sent | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"translation",
"enko",
"ko",
"conversational",
"en",
"dataset:nayohan/aihub-en-ko-translation-1.2m",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:32:33+00:00 |
null | null | {} | NextLevel9/model0 | null | [
"region:us"
]
| null | 2024-04-26T12:35:22+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# virus_pythia_14_1024_compliment
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "virus_pythia_14_1024_compliment", "results": []}]} | Hack90/virus_pythia_14_1024_compliment | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:35:40+00:00 |
null | null | {"license": "llama3"} | anilsahin/a | null | [
"license:llama3",
"region:us"
]
| null | 2024-04-26T12:37:08+00:00 |
|
null | null | {} | LucasHolo/openelm-450m-instruct | null | [
"region:us"
]
| null | 2024-04-26T12:38:01+00:00 |
|
null | null | {} | diegomrodrigues/test_hf_logger | null | [
"tensorboard",
"region:us"
]
| null | 2024-04-26T12:42:10+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llm_output
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.16.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "llm_output", "results": []}]} | RohithMidigudla/llm_output | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T12:42:39+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hc-impaired-all-v3
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the honzapucalek/hc_impaired_all_v3 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3837
- Wer: 0.1107
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0275 | 6.87 | 1000 | 0.2212 | 0.1163 |
| 0.0021 | 13.75 | 2000 | 0.3051 | 0.1123 |
| 0.0004 | 20.62 | 3000 | 0.3517 | 0.1113 |
| 0.0001 | 27.49 | 4000 | 0.3760 | 0.1104 |
| 0.0001 | 34.36 | 5000 | 0.3837 | 0.1107 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["honzapucalek/hc_impaired_all_v3"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "hc-impaired-all-v3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "honzapucalek/hc_impaired_all_v3 cs", "type": "honzapucalek/hc_impaired_all_v3", "config": "cs", "split": "test", "args": "cs"}, "metrics": [{"type": "wer", "value": 0.11072981011210249, "name": "Wer"}]}]}]} | honzapucalek/hc-impaired-all-v3 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:honzapucalek/hc_impaired_all_v3",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:42:57+00:00 |
text-generation | transformers | # Lovelace Medium Alpha1
550M parameter Transformer-XL style model trained on 100B tokens of The Pile!
This model was originally trained for the "Direct Prefrence Heads" paper, but will also be used as the basis for much of my future research.
All code used to train and run these models is available here: https://github.com/Avelina9X/memory-transformer-pt4
## Model Architecture
| Name | Value |
| --- | --- |
| Total Parameters | 551M |
| Non-Embedding Parameters | 512M |
| Vocab Size | 50272 |
| \\(d_\text{vocab}\\) | 768 |
| \\(d_\text{model}\\) | 1536 |
| \\(n_\text{layers}\\) | 18 |
| FFN Activation | SwiGLU |
| \\(d_\text{ffn}\\) | 4096 |
| Attention Type | Full |
| Positon Embedding | Reversed RoPE with ABF |
| \\(n_\text{heads}\\) | 24 |
| \\(d_\text{key}\\) | 64 |
| Trained Context | 2048 |
| Trained Memory | 2048 |
| Max Inference Context | 4096 |
## Model Collection
| Model | Link |
| --- | --- |
| Pre-Trained Model | [lovelace-medium-alpha1](https://huggingface.co/Avelina/lovelace-medium-alpha1) |
| Fine-Tuned Model | lovelace-medium-alpha1-instruct |
| DPH Aligned Model | lovelace-medium-alpha1-instruct-hf |
| DPH Aligned Model (Multiple Heads) | lovelace-medium-alpha1-instruct-hf-multihead | | {"language": ["en"], "license": "bsd-3-clause", "library_name": "transformers", "datasets": ["EleutherAI/pile"]} | Avelina/lovelace-medium-alpha1 | null | [
"transformers",
"safetensors",
"lsw_transformer",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:43:00+00:00 |
summarization | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-multinews
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4152
- Rouge1: 14.6798
- Rouge2: 5.2044
- Rougel: 11.2346
- Rougelsum: 12.9794
- Gen Len: 20.0
## 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: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.8162 | 1.0 | 506 | 2.4807 | 14.5888 | 4.9839 | 11.0896 | 12.9 | 20.0 |
| 2.6122 | 2.0 | 1012 | 2.4371 | 14.9075 | 5.3211 | 11.2711 | 13.1998 | 20.0 |
| 2.518 | 3.0 | 1518 | 2.4141 | 14.8607 | 5.2903 | 11.332 | 13.1363 | 20.0 |
| 2.4585 | 4.0 | 2024 | 2.4246 | 14.7346 | 5.2263 | 11.2281 | 13.0277 | 20.0 |
| 2.4206 | 5.0 | 2530 | 2.4152 | 14.6798 | 5.2044 | 11.2346 | 12.9794 | 20.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "metrics": ["rouge"], "base_model": "facebook/bart-base", "pipeline_tag": "summarization", "model-index": [{"name": "bart-base-finetuned-multinews", "results": []}]} | Vexemous/bart-base-finetuned-multinews | null | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"summarization",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:43:15+00:00 |
null | null | {} | eminAydin/gpt2_M1 | null | [
"region:us"
]
| null | 2024-04-26T12:43:15+00:00 |
|
null | null | {} | charaatasnim/colorization-finetuning | null | [
"region:us"
]
| null | 2024-04-26T12:44:37+00:00 |
|
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BV_symbols_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9177
- Accuracy: 0.8697
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6848 | 0.9892 | 69 | 1.4784 | 0.7017 |
| 1.1065 | 1.9928 | 139 | 1.0834 | 0.8167 |
| 0.9403 | 2.9677 | 207 | 0.9177 | 0.8697 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "BV_symbols_model", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train[:30%]", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8697214734950584, "name": "Accuracy"}]}]}]} | diegozambrana/BV_symbols_model | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:44:40+00:00 |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "openlm-research/open_llama_3b_v2"} | yiyic/llama3b-text-ent-lora-clf-epoch-3 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
]
| null | 2024-04-26T12:44:46+00:00 |
null | null |
<!-- 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. -->
# V0424HMA19
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0672
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4874 | 0.09 | 10 | 0.1448 |
| 0.1419 | 0.18 | 20 | 0.1072 |
| 0.1008 | 0.27 | 30 | 0.0774 |
| 0.0902 | 0.36 | 40 | 0.0720 |
| 0.0783 | 0.45 | 50 | 0.0760 |
| 0.0854 | 0.54 | 60 | 0.0870 |
| 0.09 | 0.63 | 70 | 0.0816 |
| 0.0853 | 0.73 | 80 | 0.0755 |
| 0.0815 | 0.82 | 90 | 0.0723 |
| 0.083 | 0.91 | 100 | 0.0683 |
| 0.0817 | 1.0 | 110 | 0.0645 |
| 0.0536 | 1.09 | 120 | 0.0760 |
| 0.0673 | 1.18 | 130 | 0.0727 |
| 0.0618 | 1.27 | 140 | 0.0666 |
| 0.06 | 1.36 | 150 | 0.0729 |
| 0.07 | 1.45 | 160 | 0.0656 |
| 0.0597 | 1.54 | 170 | 0.0744 |
| 0.0663 | 1.63 | 180 | 0.0637 |
| 0.0578 | 1.72 | 190 | 0.0623 |
| 0.0653 | 1.81 | 200 | 0.0632 |
| 0.0595 | 1.9 | 210 | 0.0694 |
| 0.0528 | 1.99 | 220 | 0.0606 |
| 0.0396 | 2.08 | 230 | 0.0618 |
| 0.0348 | 2.18 | 240 | 0.0713 |
| 0.0349 | 2.27 | 250 | 0.0672 |
| 0.0335 | 2.36 | 260 | 0.0655 |
| 0.0352 | 2.45 | 270 | 0.0655 |
| 0.0318 | 2.54 | 280 | 0.0679 |
| 0.0301 | 2.63 | 290 | 0.0691 |
| 0.0313 | 2.72 | 300 | 0.0681 |
| 0.0332 | 2.81 | 310 | 0.0674 |
| 0.0326 | 2.9 | 320 | 0.0673 |
| 0.0343 | 2.99 | 330 | 0.0672 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424HMA19", "results": []}]} | Litzy619/V0424HMA19 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
]
| null | 2024-04-26T12:44:48+00:00 |
text2text-generation | transformers | {"license": "mit", "pipeline_tag": "text2text-generation"} | suriya7/Instruct-Cerebras-590M | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:44:56+00:00 |
|
text-classification | transformers | {} | Dpanov2302/sbert-reviews-classification | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:45:13+00:00 |
|
null | null |
# UVMap-ID: A Controllable and Personalized UV Map Generative Model
[Paper](https://arxiv.org/abs/2404.14568) | {"license": "apache-2.0"} | Jichaozhang/UVMap-ID | null | [
"arxiv:2404.14568",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T12:45:30+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# virus_pythia_14_1024_cross_entropy
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 80
- eval_batch_size: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "virus_pythia_14_1024_cross_entropy", "results": []}]} | Hack90/virus_pythia_14_1024_cross_entropy | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:45:39+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-2
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-2", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-2 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:46:27+00:00 |
text-generation | transformers | 
LostMagic-RP_8B Version 0.42624
Uncensored, Creative, Immersive, Role Play AI
Settings:
```
Prompt Format Chat or Chat Instruct (Silly Tavern Default):
System Message Here
User: input
Bot:
```
Parameters:
```json
{"max_context_length", 8192},
{"max_length", 120},
{"rep_pen", 1.03},
{"rep_pen_slope", 0.70},
{"rep_pen_range", 320},
{"temperature", 1.25},
{"tfs", 1.0},
{"top_a", 0},
{"top_k", 0},
{"top_p", 1.0},
{"min_p", 0.1},
{"typical", 1.0},
{"presence_penalty", 0},
{"mirostat", 0},
{"mirostat_tau", 5},
{"mirostat_eta", 0.1},
``` | {"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["roleplay", "uncensored", "lewd", "mature", "not-for-all-audiences", "Llama 3", "8b"], "pipeline_tag": "text-generation"} | Dunjeon/lostmagic-RP-GGUF | null | [
"transformers",
"roleplay",
"uncensored",
"lewd",
"mature",
"not-for-all-audiences",
"Llama 3",
"8b",
"text-generation",
"en",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:46:47+00:00 |
null | null | {} | Paxit0/Bert_marmiton | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T12:48:23+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-410m_mz-131f_PasswordMatch
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m", "model-index": [{"name": "robust_llm_pythia-410m_mz-131f_PasswordMatch", "results": []}]} | AlignmentResearch/robust_llm_pythia-410m_mz-131f_PasswordMatch | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:49:02+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-70m_mz-131f_IMDB
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-70m", "model-index": [{"name": "robust_llm_pythia-70m_mz-131f_IMDB", "results": []}]} | AlignmentResearch/robust_llm_pythia-70m_mz-131f_IMDB | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:49:36+00:00 |
null | null |
<!-- 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. -->
# V0424HMA20
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0675
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8142 | 0.09 | 10 | 0.3516 |
| 0.1881 | 0.18 | 20 | 0.1201 |
| 0.1155 | 0.27 | 30 | 0.0873 |
| 0.0936 | 0.36 | 40 | 0.0807 |
| 0.0868 | 0.45 | 50 | 0.0851 |
| 0.0884 | 0.54 | 60 | 0.0797 |
| 0.0825 | 0.63 | 70 | 0.0671 |
| 0.0726 | 0.73 | 80 | 0.0749 |
| 0.0803 | 0.82 | 90 | 0.0740 |
| 0.0796 | 0.91 | 100 | 0.0675 |
| 0.0722 | 1.0 | 110 | 0.0688 |
| 0.0639 | 1.09 | 120 | 0.0634 |
| 0.0642 | 1.18 | 130 | 0.0750 |
| 0.0638 | 1.27 | 140 | 0.0678 |
| 0.0628 | 1.36 | 150 | 0.0673 |
| 0.0645 | 1.45 | 160 | 0.0682 |
| 0.0575 | 1.54 | 170 | 0.0695 |
| 0.0635 | 1.63 | 180 | 0.0652 |
| 0.0534 | 1.72 | 190 | 0.0661 |
| 0.0682 | 1.81 | 200 | 0.0620 |
| 0.0551 | 1.9 | 210 | 0.0655 |
| 0.0539 | 1.99 | 220 | 0.0631 |
| 0.0342 | 2.08 | 230 | 0.0705 |
| 0.0331 | 2.18 | 240 | 0.0829 |
| 0.0313 | 2.27 | 250 | 0.0669 |
| 0.0286 | 2.36 | 260 | 0.0698 |
| 0.0324 | 2.45 | 270 | 0.0721 |
| 0.0288 | 2.54 | 280 | 0.0713 |
| 0.0294 | 2.63 | 290 | 0.0700 |
| 0.0322 | 2.72 | 300 | 0.0682 |
| 0.0313 | 2.81 | 310 | 0.0675 |
| 0.029 | 2.9 | 320 | 0.0676 |
| 0.0359 | 2.99 | 330 | 0.0675 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "V0424HMA20", "results": []}]} | Litzy619/V0424HMA20 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
]
| null | 2024-04-26T12:50:12+00:00 |
text-generation | transformers | The tnayaj-8B model is an innovative open-source language model specifically engineered for the biomedical domain. Crafted by Jayant AI Labs, this model harnesses state-of-the-art methodologies to achieve unparalleled performance across various biomedical tasks.
🏥 Specialization in medicine: tnayaj-8B caters to the intricate linguistic and informational demands of the medical and life sciences realms. Its refinement stems from extensive training on a comprehensive biomedical dataset, enabling precise and articulate text generation within the domain.
🎓 Exceptional Performance: Boasting a staggering 8 billion parameters 🧠 Advanced Training Methodologies: tnayaj-8B builds upon the foundational prowess of the Meta-Llama-3-8B-Instruct .It integrates the DPO dataset and a tailored array of medical instruction data for refinement. Central to its training regimen are meticulously curated components, including:
---
license: apache-2.0
--- | {"license": "apache-2.0"} | Jayant9928/tnayajv2.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:51:16+00:00 |
null | null | {} | suthawadee/thai_receipt | null | [
"region:us"
]
| null | 2024-04-26T12:52:19+00:00 |
|
null | null | {} | Rahuladitya/newmodel | null | [
"region:us"
]
| null | 2024-04-26T12:52:22+00:00 |
|
text-generation | transformers | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with awq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo meta-llama/Meta-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/meta-llama-Meta-Llama-3-8B-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model meta-llama/Meta-Llama-3-8B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). | {"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg", "base_model": "meta-llama/Meta-Llama-3-8B"} | PrunaAI/meta-llama-Meta-Llama-3-8B-AWQ-4bit-smashed | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"base_model:meta-llama/Meta-Llama-3-8B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T12:54:46+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# virus_pythia_14_1024_headless
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 40
- eval_batch_size: 40
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "virus_pythia_14_1024_headless", "results": []}]} | Hack90/virus_pythia_14_1024_headless | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T12:57:23+00:00 |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-alzheimers
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8319
- Accuracy: 0.5953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0035 | 0.9778 | 22 | 0.9198 | 0.5594 |
| 0.9062 | 2.0 | 45 | 0.8479 | 0.6094 |
| 0.8726 | 2.9333 | 66 | 0.8319 | 0.5953 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-alzheimers", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5953125, "name": "Accuracy"}]}]}]} | rhlc/swin-tiny-patch4-window7-224-finetuned-alzheimers | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:57:28+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/maywell/miqu-evil-dpo
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/miqu-evil-dpo-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miqu-evil-dpo-GGUF/resolve/main/miqu-evil-dpo.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["not-for-all-audiences"], "base_model": "maywell/miqu-evil-dpo", "license_link": "LICENSE", "license_name": "miqu-license", "quantized_by": "mradermacher"} | mradermacher/miqu-evil-dpo-GGUF | null | [
"transformers",
"gguf",
"not-for-all-audiences",
"en",
"base_model:maywell/miqu-evil-dpo",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T12:57:37+00:00 |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r magixn/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "10.94 +/- 5.32", "name": "mean_reward", "verified": false}]}]}]} | magixn/rl_course_vizdoom_health_gathering_supreme | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-26T12:57:47+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "44.80 +/- 27.15", "name": "mean_reward", "verified": false}]}]}]} | hossniper/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| null | 2024-04-26T12:57:58+00:00 |
null | null | {} | Wauplin/my-test-repo | null | [
"tflite",
"onnx",
"safetensors",
"region:us"
]
| null | 2024-04-26T12:58:46+00:00 |
|
null | peft |
# Model Card for Model ID
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## Model Details
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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## More Information [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"} | ddd20/mistral_7b_legal_version | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
]
| null | 2024-04-26T12:59:20+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | nrbhole/invoices-donut-model-v2 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:00:08+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-14m_mz-131f_IMDB
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_mz-131f_IMDB", "results": []}]} | AlignmentResearch/robust_llm_pythia-14m_mz-131f_IMDB | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:00:43+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/6-254 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:01:08+00:00 |
null | null | {"license": "llama2"} | GVAmaresh/repetitive-project-backend | null | [
"license:llama2",
"region:us"
]
| null | 2024-04-26T13:01:14+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/6-212 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:01:21+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/6-220 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:01:45+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/6-211 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:01:46+00:00 |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-msn-small-finetuned-alzheimers
This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0160
- Accuracy: 0.9969
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2996 | 0.9778 | 22 | 0.3897 | 0.8438 |
| 0.3703 | 2.0 | 45 | 0.3595 | 0.8594 |
| 0.3087 | 2.9778 | 67 | 0.3777 | 0.8625 |
| 0.486 | 4.0 | 90 | 0.4530 | 0.8187 |
| 0.3307 | 4.9778 | 112 | 0.4560 | 0.8234 |
| 0.306 | 6.0 | 135 | 0.3471 | 0.8672 |
| 0.3005 | 6.9778 | 157 | 0.3025 | 0.8859 |
| 0.319 | 8.0 | 180 | 0.2451 | 0.8984 |
| 0.3489 | 8.9778 | 202 | 0.1814 | 0.9281 |
| 0.3251 | 10.0 | 225 | 0.2451 | 0.9156 |
| 0.3034 | 10.9778 | 247 | 0.1566 | 0.9406 |
| 0.2746 | 12.0 | 270 | 0.2493 | 0.8922 |
| 0.2369 | 12.9778 | 292 | 0.1622 | 0.9375 |
| 0.2231 | 14.0 | 315 | 0.1781 | 0.9359 |
| 0.2281 | 14.9778 | 337 | 0.1268 | 0.9531 |
| 0.2001 | 16.0 | 360 | 0.2431 | 0.9141 |
| 0.183 | 16.9778 | 382 | 0.1017 | 0.9625 |
| 0.1891 | 18.0 | 405 | 0.1802 | 0.9391 |
| 0.1862 | 18.9778 | 427 | 0.0869 | 0.9766 |
| 0.1935 | 20.0 | 450 | 0.1079 | 0.9688 |
| 0.1797 | 20.9778 | 472 | 0.1250 | 0.9563 |
| 0.1605 | 22.0 | 495 | 0.0655 | 0.9719 |
| 0.1848 | 22.9778 | 517 | 0.0806 | 0.9766 |
| 0.1498 | 24.0 | 540 | 0.1116 | 0.9578 |
| 0.1394 | 24.9778 | 562 | 0.0807 | 0.9672 |
| 0.1584 | 26.0 | 585 | 0.0525 | 0.9797 |
| 0.1302 | 26.9778 | 607 | 0.0513 | 0.9828 |
| 0.1356 | 28.0 | 630 | 0.0420 | 0.9875 |
| 0.1101 | 28.9778 | 652 | 0.0354 | 0.9875 |
| 0.1227 | 30.0 | 675 | 0.0583 | 0.9766 |
| 0.1158 | 30.9778 | 697 | 0.0253 | 0.9906 |
| 0.117 | 32.0 | 720 | 0.0231 | 0.9906 |
| 0.1022 | 32.9778 | 742 | 0.0726 | 0.9797 |
| 0.1221 | 34.0 | 765 | 0.0160 | 0.9969 |
| 0.0956 | 34.9778 | 787 | 0.0482 | 0.9844 |
| 0.0856 | 36.0 | 810 | 0.0256 | 0.9875 |
| 0.0996 | 36.9778 | 832 | 0.0211 | 0.9906 |
| 0.0848 | 38.0 | 855 | 0.0446 | 0.9797 |
| 0.1001 | 38.9778 | 877 | 0.0274 | 0.9875 |
| 0.0976 | 40.0 | 900 | 0.0225 | 0.9922 |
| 0.0864 | 40.9778 | 922 | 0.0207 | 0.9922 |
| 0.0865 | 42.0 | 945 | 0.0193 | 0.9969 |
| 0.0773 | 42.9778 | 967 | 0.0203 | 0.9922 |
| 0.075 | 44.0 | 990 | 0.0131 | 0.9969 |
| 0.0761 | 44.9778 | 1012 | 0.0129 | 0.9938 |
| 0.0624 | 46.0 | 1035 | 0.0114 | 0.9969 |
| 0.0557 | 46.9778 | 1057 | 0.0102 | 0.9953 |
| 0.0708 | 48.0 | 1080 | 0.0116 | 0.9953 |
| 0.0667 | 48.8889 | 1100 | 0.0131 | 0.9953 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/vit-msn-small", "model-index": [{"name": "vit-msn-small-finetuned-alzheimers", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.996875, "name": "Accuracy"}]}]}]} | rhlc/vit-msn-small-finetuned-alzheimers | null | [
"transformers",
"tensorboard",
"safetensors",
"vit_msn",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/vit-msn-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:04:20+00:00 |
null | transformers | {} | rancelyndar/segformer-b5-asbestos | null | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:04:25+00:00 |
|
null | null | {} | marcomaccarini/DS_robot_test1 | null | [
"region:us"
]
| null | 2024-04-26T13:05:34+00:00 |
|
null | null | {"license": "openrail"} | SeanODonnell/zhengguo | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T13:06:26+00:00 |
|
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | vaatsav06/Llama3_medqa1 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:06:33+00:00 |
null | null | {"license": "openrail"} | SeanODonnell/zhenliu | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T13:06:43+00:00 |
|
null | null | {"license": "openrail"} | SeanODonnell/shenyong | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T13:06:54+00:00 |
|
automatic-speech-recognition | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Mihaj/wav2vec2-large-uralic-voxpopuli-v2-karelian-CodeSwitching_with_pitch_and_tempo_aug | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:07:28+00:00 |
text-classification | transformers | {"license": "unknown"} | DragonImortal/mangaTagger | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:07:49+00:00 |
|
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | presencesw/vistral_test | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:08:15+00:00 |
null | transformers |
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | Shure-Dev/llava-vima | null | [
"transformers",
"safetensors",
"llava",
"pretraining",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"4-bit",
"region:us"
]
| null | 2024-04-26T13:08:41+00:00 |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2048 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1401 with parameters:
```
{'batch_size': 256, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`__main__.MultipleNegativesRankingLoss_with_logging`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: LlamaModel
(1): Pooling({'word_embedding_dimension': 2048, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "sentence_croissant_alpha_v0.3", "results": [{"task": {"type": "Clustering"}, "dataset": {"name": "MTEB AlloProfClusteringP2P", "type": "lyon-nlp/alloprof", "config": "default", "split": "test", "revision": "392ba3f5bcc8c51f578786c1fc3dae648662cb9b"}, "metrics": [{"type": "v_measure", "value": 56.72912207023513}, {"type": "v_measures", "value": [0.5320130285438164, 0.5262623550285312, 0.5801017400160106, 0.5959165699319396, 0.5834996150492608, 0.5569839493118243, 0.6099665491090271, 0.5780727185697752, 0.4988023041518384, 0.6112933773114886, 0.5320130285438164, 0.5262623550285312, 0.5801017400160106, 0.5959165699319396, 0.5834996150492608, 0.5569839493118243, 0.6099665491090271, 0.5780727185697752, 0.4988023041518384, 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"sentence-transformers",
"safetensors",
"llama",
"feature-extraction",
"sentence-similarity",
"mteb",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:09:06+00:00 |
null | null | {"license": "llama3"} | prmalve/llama | null | [
"license:llama3",
"region:us"
]
| null | 2024-04-26T13:09:22+00:00 |
|
null | transformers |
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### 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 Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Likich/grok-finetune-qualcoding | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:10:13+00:00 |
null | transformers | {} | aang77/mamba_text_classification | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:10:21+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.25 +/- 0.06", "name": "mean_reward", "verified": false}]}]}]} | ahGadji/a2c-PandaReachDense-v3 | null | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-26T13:10:50+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | vaatsav06/Llama3_medqa2 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:10:57+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | AndersGiovanni/social-llama-3-8b-instructions | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:11:12+00:00 |
null | null | {} | heliumbob/Gemma7b_finetuned | null | [
"region:us"
]
| null | 2024-04-26T13:12:51+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-dpo-full
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4929
- Rewards/chosen: 21.1860
- Rewards/rejected: 6.2518
- Rewards/accuracies: 0.7344
- Rewards/margins: 14.9342
- Logps/rejected: -256.4154
- Logps/chosen: -241.4075
- Logits/rejected: -2.7091
- Logits/chosen: -2.7366
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5187 | 0.21 | 100 | 0.5296 | 19.0644 | 9.0310 | 0.7227 | 10.0334 | -253.6362 | -243.5290 | -2.7384 | -2.7638 |
| 0.508 | 0.42 | 200 | 0.5006 | 20.6504 | 7.0237 | 0.7266 | 13.6267 | -255.6435 | -241.9431 | -2.7569 | -2.7826 |
| 0.4808 | 0.63 | 300 | 0.4966 | 20.8183 | 6.9540 | 0.7227 | 13.8643 | -255.7132 | -241.7751 | -2.7115 | -2.7378 |
| 0.4835 | 0.84 | 400 | 0.4917 | 21.2230 | 6.3692 | 0.7344 | 14.8539 | -256.2980 | -241.3705 | -2.7037 | -2.7315 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "zephyr-7b-dpo-full", "results": []}]} | RikkiXu/zephyr-7b-dpo-full | null | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:13:29+00:00 |
text-generation | transformers | {"license": "llama2"} | KnutJaegersberg/Nous-Hermes-2-70b-exl2-4.0bpw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:13:46+00:00 |
|
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trocr-base-printed_license_plates_ocr
This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1550
- Cer: 0.037
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3034 | 1.0 | 2000 | 0.2454 | 0.0472 |
| 0.1451 | 2.0 | 4000 | 0.1550 | 0.037 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "trocr-base-printed_license_plates_ocr", "results": []}]} | artbreguez/trocr-base-printed_license_plates_ocr | null | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:14:14+00:00 |
null | null | {} | Scralius/whisper-base-fr | null | [
"region:us"
]
| null | 2024-04-26T13:15:37+00:00 |
|
null | null | {} | blockblockblock/miqu-evil-dpo-bpw3-exl2 | null | [
"region:us"
]
| null | 2024-04-26T13:18:31+00:00 |
|
null | null | {} | melisa/short-mistral-7b-with-healing2-math-2000 | null | [
"region:us"
]
| null | 2024-04-26T13:19:13+00:00 |
|
text-generation | mlx |
# mlx-community/Qwen1.5-110B-4bit
This model was converted to MLX format from [`Qwen/Qwen1.5-110B`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-110B) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen1.5-110B-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "license": "other", "tags": ["pretrained", "mlx"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-110B/blob/main/LICENSE", "pipeline_tag": "text-generation"} | mlx-community/Qwen1.5-110B-4bit | null | [
"mlx",
"safetensors",
"qwen2",
"pretrained",
"text-generation",
"conversational",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T13:19:33+00:00 |
text-generation | mlx |
# mlx-community/Qwen1.5-110B-8bit
This model was converted to MLX format from [`Qwen/Qwen1.5-110B`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-110B) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen1.5-110B-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "license": "other", "tags": ["pretrained", "mlx"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-110B/blob/main/LICENSE", "pipeline_tag": "text-generation"} | mlx-community/Qwen1.5-110B-8bit | null | [
"mlx",
"safetensors",
"qwen2",
"pretrained",
"text-generation",
"conversational",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T13:19:47+00:00 |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "296.63 +/- 17.46", "name": "mean_reward", "verified": false}]}]}]} | AndrewBJ/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-26T13:21:18+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results-Meta-Llama-3-8B-tagllm-pos-1-reserved-unsloth
This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7917
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9788 | 0.2 | 162 | 2.0304 |
| 1.7172 | 0.4 | 324 | 1.8871 |
| 1.9543 | 0.6 | 486 | 1.8420 |
| 2.2679 | 0.8 | 648 | 1.8056 |
| 1.6227 | 1.0 | 810 | 1.7917 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "model-index": [{"name": "results-Meta-Llama-3-8B-tagllm-pos-1-reserved-unsloth", "results": []}]} | AlienKevin/Meta-Llama-3-8B-tagllm-pos-1-reserved-unsloth | null | [
"peft",
"safetensors",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:llama2",
"region:us"
]
| null | 2024-04-26T13:21:43+00:00 |
text-classification | transformers | {"license": "unknown", "description": "Just a proof of principle test of sst2 finetuned bert model."} | antti97b/bert_test | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:21:52+00:00 |
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