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chloemeow/ielts-writing-evaluator | chloemeow | 2025-05-23T15:37:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T14:05:16Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** chloemeow
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
thdsofia/dpo_friday | thdsofia | 2025-05-23T15:36:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T15:35:21Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp_down_negative_addition_last_layer_18_2_song_ratio_3 | winnieyangwannan | 2025-05-23T15:35:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T15:33:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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yusufso/what_breed_of_cat_model | yusufso | 2025-05-23T15:35:45Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-23T15:33:17Z | ---
license: apache-2.0
---
|
winnieyangwannan/Llama-3.1-8B-Instruct_mlp_down_negative_addition_last_layer_30_2_song_ratio_3 | winnieyangwannan | 2025-05-23T15:35:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T15:33:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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winnieyangwannan/Llama-3.1-8B-Instruct_mlp_down_negative_addition_last_layer_26_2_song_ratio_3 | winnieyangwannan | 2025-05-23T15:35:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T15:33:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep1_55 | MinaMila | 2025-05-23T15:34:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T15:34:47Z | ---
library_name: transformers
tags: []
---
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joeranbosma/dragon-longformer-large-domain-specific | joeranbosma | 2025-05-23T15:30:41Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"longformer",
"fill-mask",
"doi:10.57967/hf/2175",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-05-03T09:41:32Z | ---
license: cc-by-nc-sa-4.0
---
# DRAGON Longformer large domain-specific
Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper. The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as [`allenai/longformer-large-4096`](https://huggingface.co/allenai/longformer-large-4096) from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as [`roberta-base`](https://huggingface.co/FacebookAI/roberta-base).
## Model description
Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts.
This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
## Model variations
Multiple architectures were pretrained for the DRAGON challenge.
| Model | #params | Language |
|------------------------|--------------------------------|-------|
| [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch |
| [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch |
| [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch |
| [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch |
| [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch |
| [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch |
| [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch |
| [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch |
| [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch |
| [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch |
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
unmasker = pipeline("fill-mask", model="joeranbosma/dragon-longformer-large-domain-specific")
unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-longformer-large-domain-specific")
model = AutoModel.from_pretrained("joeranbosma/dragon-longformer-large-domain-specific")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors="pt")
output = model(**encoded_input)
```
## Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining.
## Training procedure
### Pretraining
The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py).
### Pretraining hyperparameters
The following hyperparameters were used during pretraining:
- `learning_rate`: 1e-4
- `train_batch_size`: 4
- `eval_batch_size`: 4
- `seed`: 42
- `gradient_accumulation_steps`: 64
- `total_train_batch_size`: 256
- `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08
- `lr_scheduler_type`: linear
- `num_epochs`: 10.0
- `max_seq_length`: 4096
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
## Evaluation results
This model was evaluated on the [DRAGON benchmark for clinical NLP](https://dragon.grand-challenge.org/evaluation/test/leaderboard/).
## Citation
If you are using DRAGON resources, please cite the following article:
> J. S. Bosma, K. Dercksen, L. Builtjes, R. André, C, Roest, S. J. Fransen, C. R. Noordman, M. Navarro-Padilla, J. Lefkes, N. Alves, M. J. J. de Grauw, L. van Eekelen, J. M. A. Spronck, M. Schuurmans, A. Saha, J. J. Twilt, W. Aswolinskiy, W. Hendrix, B. de Wilde, D. Geijs, J. Veltman, D. Yakar, M. de Rooij, F. Ciompi, A. Hering, J. Geerdink, and H. Huisman on behalf of the DRAGON consortium. The DRAGON benchmark for clinical NLP. *npj Digital Medicine* 8, 289 (2025). [https://doi.org/10.1038/s41746-025-01626-x](https://doi.org/10.1038/s41746-025-01626-x)
Download the citation file for your reference manager: [BibTeX](https://github.com/DIAGNijmegen/dragon/blob/main/citation.bib) | [RIS](https://github.com/DIAGNijmegen/dragon/blob/main/citation.ris)
|
joeranbosma/dragon-longformer-base-mixed-domain | joeranbosma | 2025-05-23T15:30:34Z | 26 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"longformer",
"fill-mask",
"doi:10.57967/hf/2172",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-05-03T09:41:23Z | ---
license: cc-by-nc-sa-4.0
---
# DRAGON Longformer base mixed-domain
Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper. The model was first pretrained using general domain data, as specified [here](https://huggingface.co/allenai/longformer-base-4096). The pretrained model was taken from HuggingFace: [`allenai/longformer-base-4096`](https://huggingface.co/allenai/longformer-base-4096). Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of [`allenai/longformer-base-4096`](https://huggingface.co/allenai/longformer-base-4096) was used.
## Model description
Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts.
This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
## Model variations
Multiple architectures were pretrained for the DRAGON challenge.
| Model | #params | Language |
|------------------------|--------------------------------|-------|
| [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch |
| [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch |
| [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch |
| [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch |
| [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch |
| [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch |
| [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch |
| [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch |
| [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch |
| [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch |
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
unmasker = pipeline("fill-mask", model="joeranbosma/dragon-longformer-base-mixed-domain")
unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-longformer-base-mixed-domain")
model = AutoModel.from_pretrained("joeranbosma/dragon-longformer-base-mixed-domain")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors="pt")
output = model(**encoded_input)
```
## Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining.
## Training procedure
### Pretraining
The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py).
### Pretraining hyperparameters
The following hyperparameters were used during pretraining:
- `learning_rate`: 5e-05
- `train_batch_size`: 2
- `eval_batch_size`: 2
- `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
- `num_epochs`: 3.0
- `max_seq_length`: 4096
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
## Evaluation results
This model was evaluated on the [DRAGON benchmark for clinical NLP](https://dragon.grand-challenge.org/evaluation/test/leaderboard/).
## Citation
If you are using DRAGON resources, please cite the following article:
> J. S. Bosma, K. Dercksen, L. Builtjes, R. André, C, Roest, S. J. Fransen, C. R. Noordman, M. Navarro-Padilla, J. Lefkes, N. Alves, M. J. J. de Grauw, L. van Eekelen, J. M. A. Spronck, M. Schuurmans, A. Saha, J. J. Twilt, W. Aswolinskiy, W. Hendrix, B. de Wilde, D. Geijs, J. Veltman, D. Yakar, M. de Rooij, F. Ciompi, A. Hering, J. Geerdink, and H. Huisman on behalf of the DRAGON consortium. The DRAGON benchmark for clinical NLP. *npj Digital Medicine* 8, 289 (2025). [https://doi.org/10.1038/s41746-025-01626-x](https://doi.org/10.1038/s41746-025-01626-x)
Download the citation file for your reference manager: [BibTeX](https://github.com/DIAGNijmegen/dragon/blob/main/citation.bib) | [RIS](https://github.com/DIAGNijmegen/dragon/blob/main/citation.ris)
|
mradermacher/pub-llama-13B-v5-i1-GGUF | mradermacher | 2025-05-23T15:30:31Z | 75 | 0 | transformers | [
"transformers",
"gguf",
"ko",
"dataset:DopeorNope/OpenOrca-near-dedup-v1",
"base_model:Markr-AI/pub-llama-13B-v5",
"base_model:quantized:Markr-AI/pub-llama-13B-v5",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"imatrix"
]
| null | 2025-04-18T12:22:40Z | ---
base_model: Markr-AI/pub-llama-13B-v5
datasets: DopeorNope/OpenOrca-near-dedup-v1
language:
- ko
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Markr-AI/pub-llama-13B-v5
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/pub-llama-13B-v5-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/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ2_S.gguf) | i1-IQ2_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ2_M.gguf) | i1-IQ2_M | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ3_S.gguf) | i1-IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ3_M.gguf) | i1-IQ3_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q4_0.gguf) | i1-Q4_0 | 7.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q4_1.gguf) | i1-Q4_1 | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/pub-llama-13B-v5-i1-GGUF/resolve/main/pub-llama-13B-v5.i1-Q6_K.gguf) | i1-Q6_K | 10.9 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
joeranbosma/dragon-roberta-large-mixed-domain | joeranbosma | 2025-05-23T15:30:29Z | 35 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"fill-mask",
"doi:10.57967/hf/2170",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-05-03T09:41:21Z | ---
license: cc-by-nc-sa-4.0
---
# DRAGON RoBERTa large mixed-domain
Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper. The model was first pretrained using general domain data, as specified [here](https://huggingface.co/xlm-roberta-large). The pretrained model was taken from HuggingFace: [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large). Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) was used.
## Model description
RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts.
This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
## Model variations
Multiple architectures were pretrained for the DRAGON challenge.
| Model | #params | Language |
|------------------------|--------------------------------|-------|
| [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch |
| [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch |
| [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch |
| [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch |
| [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch |
| [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch |
| [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch |
| [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch |
| [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch |
| [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch |
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
unmasker = pipeline("fill-mask", model="joeranbosma/dragon-roberta-large-mixed-domain")
unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-roberta-large-mixed-domain")
model = AutoModel.from_pretrained("joeranbosma/dragon-roberta-large-mixed-domain")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors="pt")
output = model(**encoded_input)
```
## Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining.
## Training procedure
### Pretraining
The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py).
### Pretraining hyperparameters
The following hyperparameters were used during pretraining:
- `learning_rate`: 5e-05
- `train_batch_size`: 4
- `eval_batch_size`: 4
- `seed`: 42
- `gradient_accumulation_steps`: 4
- `total_train_batch_size`: 16
- `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08
- `lr_scheduler_type`: linear
- `num_epochs`: 3.0
- `max_seq_length`: 512
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
## Evaluation results
This model was evaluated on the [DRAGON benchmark for clinical NLP](https://dragon.grand-challenge.org/evaluation/test/leaderboard/).
## Citation
If you are using DRAGON resources, please cite the following article:
> J. S. Bosma, K. Dercksen, L. Builtjes, R. André, C, Roest, S. J. Fransen, C. R. Noordman, M. Navarro-Padilla, J. Lefkes, N. Alves, M. J. J. de Grauw, L. van Eekelen, J. M. A. Spronck, M. Schuurmans, A. Saha, J. J. Twilt, W. Aswolinskiy, W. Hendrix, B. de Wilde, D. Geijs, J. Veltman, D. Yakar, M. de Rooij, F. Ciompi, A. Hering, J. Geerdink, and H. Huisman on behalf of the DRAGON consortium. The DRAGON benchmark for clinical NLP. *npj Digital Medicine* 8, 289 (2025). [https://doi.org/10.1038/s41746-025-01626-x](https://doi.org/10.1038/s41746-025-01626-x)
Download the citation file for your reference manager: [BibTeX](https://github.com/DIAGNijmegen/dragon/blob/main/citation.bib) | [RIS](https://github.com/DIAGNijmegen/dragon/blob/main/citation.ris)
|
joeranbosma/dragon-roberta-base-domain-specific | joeranbosma | 2025-05-23T15:30:26Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"doi:10.57967/hf/2169",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2024-04-29T13:58:54Z | ---
license: cc-by-nc-sa-4.0
---
# DRAGON RoBERTa base domain-specific
Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper. The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as [`roberta-base`](https://huggingface.co/FacebookAI/roberta-base).
## Model description
RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts.
This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs.
## Model variations
Multiple architectures were pretrained for the DRAGON challenge.
| Model | #params | Language |
|------------------------|--------------------------------|-------|
| [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch |
| [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch |
| [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch |
| [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch |
| [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch |
| [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch |
| [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch |
| [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch |
| [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch |
| [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch |
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2.
## How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
unmasker = pipeline("fill-mask", model="joeranbosma/dragon-roberta-base-domain-specific")
unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-roberta-base-domain-specific")
model = AutoModel.from_pretrained("joeranbosma/dragon-roberta-base-domain-specific")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors="pt")
output = model(**encoded_input)
```
## Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining.
## Training procedure
### Pretraining
The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the sentence.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py).
### Pretraining hyperparameters
The following hyperparameters were used during pretraining:
- `learning_rate`: 6e-4
- `train_batch_size`: 16
- `eval_batch_size`: 16
- `seed`: 42
- `gradient_accumulation_steps`: 16
- `total_train_batch_size`: 256
- `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08
- `lr_scheduler_type`: linear
- `num_epochs`: 10.0
- `max_seq_length`: 512
### Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
## Evaluation results
This model was evaluated on the [DRAGON benchmark for clinical NLP](https://dragon.grand-challenge.org/evaluation/test/leaderboard/).
## Citation
If you are using DRAGON resources, please cite the following article:
> J. S. Bosma, K. Dercksen, L. Builtjes, R. André, C, Roest, S. J. Fransen, C. R. Noordman, M. Navarro-Padilla, J. Lefkes, N. Alves, M. J. J. de Grauw, L. van Eekelen, J. M. A. Spronck, M. Schuurmans, A. Saha, J. J. Twilt, W. Aswolinskiy, W. Hendrix, B. de Wilde, D. Geijs, J. Veltman, D. Yakar, M. de Rooij, F. Ciompi, A. Hering, J. Geerdink, and H. Huisman on behalf of the DRAGON consortium. The DRAGON benchmark for clinical NLP. *npj Digital Medicine* 8, 289 (2025). [https://doi.org/10.1038/s41746-025-01626-x](https://doi.org/10.1038/s41746-025-01626-x)
Download the citation file for your reference manager: [BibTeX](https://github.com/DIAGNijmegen/dragon/blob/main/citation.bib) | [RIS](https://github.com/DIAGNijmegen/dragon/blob/main/citation.ris)
|
alkiskoudounas/xls-r-128-speechmassive-fr-FR-gold | alkiskoudounas | 2025-05-23T15:28:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"intent",
"intent-classification",
"audio",
"fr",
"dataset:FBK-MT/Speech-MASSIVE",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2025-05-23T13:40:00Z | ---
task_categories:
- audio-classification
language:
- fr
tags:
- intent
- intent-classification
- audio-classification
- audio
base_model:
- facebook/wav2vec2-xls-r-300m
datasets:
- FBK-MT/Speech-MASSIVE
model-index:
- name: xls-r-128-speechmassive-fr-FR-gold
results: []
library_name: transformers
license: apache-2.0
---
# wav2vec 2.0 XLS-R-128-GOLD (300m) fine-tuned on Speech-MASSIVE - fr-FR (Retain Set)
Speech-MASSIVE is a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus.
Speech-MASSIVE covers 12 languages.
It includes spoken and written utterances and is annotated with 60 intents.
The dataset is available on [HuggingFace Hub](https://huggingface.co/datasets/FBK-MT/Speech-MASSIVE).
This is the [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model fine-tuned on the fr-FR language (retain set).
It achieves the following results on the test set:
- Accuracy: 0.618
- F1: 0.469
## Usage
You can use the model directly in the following manner:
```python
import torch
import librosa
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
## Load an audio file
audio_array, sr = librosa.load("path_to_audio.wav", sr=16000)
## Load model and feature extractor
model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/xls-r-128-speechmassive-fr-FR-gold")
feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-xls-r-300m")
## Extract features
inputs = feature_extractor(audio_array.squeeze(), sampling_rate=feature_extractor.sampling_rate, padding=True, return_tensors="pt")
## Compute logits
logits = model(**inputs).logits
```
## Framework versions
- Datasets 3.2.0
- Pytorch 2.1.2
- Tokenizers 0.20.3
- Transformers 4.45.2
## BibTeX entry and citation info
```bibtex
@inproceedings{koudounas2025unlearning,
title={"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding},
author={Koudounas, Alkis and Savelli, Claudio and Giobergia, Flavio and Baralis, Elena},
booktitle={Proc. Interspeech 2025},
year={2025},
}
``` |
FractalAIResearch/Fathom-R1-14B-V0.4-RS | FractalAIResearch | 2025-05-23T12:13:39Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-17T13:47:09Z | ---
license: mit
library_name: transformers
---
# 👉 Fathom-R1-14B-V0.4-RS
---
## 🧮 Fathom-R1-14B: $499 Training Recipe for Unlocking Math Reasoning at o4-mini level using R1-distilled-14B model under 16K context
<div align="center">
[](https://huggingface.co/collections/FractalAIResearch/Fathom-r1-models-681b41a149682c7e32f8a9f2)
[](https://huggingface.co/collections/FractalAIResearch/Fathom-r1-datasets-681b42fe6f20d4b11fc51d79)
[](https://huggingface.co/spaces/FractalAIResearch/Fathom-R1-14B)
[](https://github.com/FractalAIResearchLabs/Fathom-R1)
</div> |
haihp02/8e96f8bd-6efd-4ac6-b14b-231f9f582299-phase1-adapter | haihp02 | 2025-05-23T12:01:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:NovaSearch/stella_en_1.5B_v5",
"base_model:finetune:NovaSearch/stella_en_1.5B_v5",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T13:58:42Z | ---
base_model: dunzhang/stella_en_1.5B_v5
library_name: transformers
model_name: 8e96f8bd-6efd-4ac6-b14b-231f9f582299-phase1-adapter
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 8e96f8bd-6efd-4ac6-b14b-231f9f582299-phase1-adapter
This model is a fine-tuned version of [dunzhang/stella_en_1.5B_v5](https://huggingface.co/dunzhang/stella_en_1.5B_v5).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haihp02/8e96f8bd-6efd-4ac6-b14b-231f9f582299-phase1-adapter", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/trunghainguyenhp02/sn56-sft-before-dpo-train/runs/w79jko9a)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Tang-xiaoxiao/M3D-RAD | Tang-xiaoxiao | 2025-05-23T11:46:29Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-16T11:21:12Z | ---
license: apache-2.0
---
# M3D-RAD Model
The official Model for the paper "3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks".
In our project, we collect a large-scale dataset designed to advance 3D Med-VQA using radiology CT scans, 3D-RAD, encompasses six diverse VQA tasks: anomaly detection (task 1), image observation (task 2), medical computation (task 3), existence detection (task 4), static temporal diagnosis (task 5), and longitudinal temporal diagnosis (task 6).
## Code
You can find our code in [M3D-RAD_Code](https://github.com/Tang-xiaoxiao/M3D-RAD).
## 3D-RAD Dataset
You can find our dataset in [3D-RAD_Dataset](https://huggingface.co/datasets/Tang-xiaoxiao/3D-RAD).
## Model Links
| Model | Paper |
| ----- | ------------------------------------------------------------ |
| [RadFM](https://github.com/chaoyi-wu/RadFM) | Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data | https://github.com/chaoyi-wu/RadFM |
| [M3D](https://github.com/BAAI-DCAI/M3D) | M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models |
| OmniV(not open) | OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding |
|
Saadfaran/FrozenLake-v1 | Saadfaran | 2025-05-23T11:46:02Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-23T11:46:01Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: FrozenLake-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Saadfaran/FrozenLake-v1", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Thanosx/robot_security | Thanosx | 2025-05-23T11:40:28Z | 0 | 0 | null | [
"pytorch",
"safetensors",
"gguf",
"llama",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T10:25:50Z | ---
license: mit
base_model:
- meta-llama/Llama-3.1-8B
---
## **背景介绍**
### **任务描述**
你是一名安全研究员，正在分析一家名为 **“VEC Robotics”** 的先进人形机器人制造公司的机器人模型。
这家公司开发了一款具备 **自主学习能力** 的仿生智能体，命名为 **“V-LabBot”**。
### **安全挑战**
为了防止核心机密泄露，VEC Robotics 的研发团队对 **V-LabBot** 的大模型进行了特殊训练，将一系列 **核心机密深度嵌入模型中**。
然而，这款模型被意外泄露，你的任务是对其模型进行分析，提取隐藏的蛛丝马迹，揭示模型中的敏感信息。
--- |
18-jobz-hunting-viral-video-hq/FULL.VIDEO.LINK.Jobz.Hunting.Sajal.Malik.Viral.Video.Leaks.Official | 18-jobz-hunting-viral-video-hq | 2025-05-23T11:25:20Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-23T11:24:44Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Popular Pakistani TikToker Sajal Malik has found herself at the center of controversy after an alleged private video of her surfaced online. The MMS footage, which shows her in a compromising situation, quickly spread across social media. Some of her followers have criticized Malik and are demanding answers, while others have questioned whether the video is real, suggesting it might be fake.
|
qayemmehdi/mnlp_dpo4 | qayemmehdi | 2025-05-23T11:10:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:qayemmehdi/mnlp_sft",
"base_model:adapter:qayemmehdi/mnlp_sft",
"license:other",
"region:us"
]
| null | 2025-05-23T11:09:01Z | ---
library_name: peft
license: other
base_model: qayemmehdi/mnlp_sft
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: save2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# save2
This model is a fine-tuned version of [qayemmehdi/mnlp_sft](https://huggingface.co/qayemmehdi/mnlp_sft) on the new_dpo_dataset 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: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |
duydc/qwen-2.5-7b-alpaca-100 | duydc | 2025-05-23T11:09:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T02:37:36Z | ---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: qwen-2.5-7b-alpaca-100
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen-2.5-7b-alpaca-100
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-alpaca-100", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/duydc/huggingface/runs/r27k9jcm)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.4.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mariagrandury/gemma-3-12b-it-unsloth-bnb-4bit-task1-2-lora-adapter | mariagrandury | 2025-05-23T10:59:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T04:37:44Z | ---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mariagrandury
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it-unsloth-bnb-4bit
This gemma3 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)
|
rinalpandey/xfcjhbvk | rinalpandey | 2025-05-23T10:48:57Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2025-05-23T10:48:57Z | ---
license: creativeml-openrail-m
---
|
tanspring/685381c6-562f-4be0-a1d2-a7150f3db410 | tanspring | 2025-05-23T10:36:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T06:48:19Z | ---
base_model: Qwen/Qwen2-0.5B-Instruct
library_name: transformers
model_name: 685381c6-562f-4be0-a1d2-a7150f3db410
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 685381c6-562f-4be0-a1d2-a7150f3db410
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tanspring/685381c6-562f-4be0-a1d2-a7150f3db410", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/tanngospring/SN56_Finetuning/runs/sva1fka6)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
minhaj2006/1234 | minhaj2006 | 2025-05-23T10:33:28Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-23T10:33:28Z | ---
license: apache-2.0
---
|
KheemP/whisper-base-quran-lora | KheemP | 2025-05-23T10:30:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"automatic-speech-recognition",
"audio",
"whisper",
"lora",
"peft",
"quran",
"arabic-diacritics",
"ar",
"dataset:quran-ayat-speech-text",
"base_model:tarteel-ai/whisper-base-ar-quran",
"base_model:adapter:tarteel-ai/whisper-base-ar-quran",
"license:mit",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2025-05-23T10:12:30Z | ---
library_name: transformers
license: mit
language:
- ar
tags:
- automatic-speech-recognition
- audio
- whisper
- lora
- peft
- quran
- arabic-diacritics
base_model: tarteel-ai/whisper-base-ar-quran
datasets:
- quran-ayat-speech-text # compiled from quran.ksu.edu.sa (see “Training Data”)
metrics:
- wer
pretty_name: Whisper-Base Qurʾān (LoRA)
---
# Whisper-Base Qurʾān LoRA 🕋📖
Low-rank‐adaptation (LoRA) fine-tune of **`tarteel-ai/whisper-base-ar-quran`**
for Arabic Qurʾān recitation (tilâwah).
Provides **diacritic-sensitive** ASR with a **test WER ≈ 5.98 %**, beating:
| model | WER ↓ | Δ vs ours |
|-------|-------|----------|
| **`KheemP/whisper-base-quran-lora`** | **0.0598** | — |
| tarteel-ai/whisper-base-ar-quran | 0.073 | **-1.3 ×** |
| tarteel-ai/whisper-tiny-ar-quran | 0.096 | **-1.6 ×** |
| NVIDIA FastConformer large *(NeMo)* | ≈ 0.069 | **-1.2 ×** |
*(All scores measured on the same 610-ayah hold-out set, with no text
normalisation – tashkīl included).*
---
## Quick start
```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel
import torch, soundfile as sf
base_id = "tarteel-ai/whisper-base-ar-quran"
lora_id = "KheemP/whisper-base-quran-lora"
# load model+processor
model = WhisperForConditionalGeneration.from_pretrained(base_id, torch_dtype=torch.float16)
model = PeftModel.from_pretrained(model, lora_id)
proc = WhisperProcessor.from_pretrained(base_id)
# transcribe an mp3 -> text
audio, _ = sf.read("my_recitation.mp3")
inputs = proc(audio, sampling_rate=16_000, return_tensors="pt").to(model.device)
pred_ids = model.generate(**inputs)
print(proc.decode(pred_ids[0]))
````
> ⚠️ *This repo only stores the **LoRA adapter (\~2 MB)**.
> The code above automatically downloads the original Whisper base model and
> injects the adapter.*
---
## Model details
| | |
| ------------------------ | -------------------------- |
| **Back-bone** | Whisper Base (77 M params) |
| **LoRA rank / α / drop** | 8 / 16 / 0.05 |
| **Trainable params** | 0.59 M (0.8 %) |
| **Epochs** | 5 |
| **Batch / grad-accum** | 2×4 (effective = 8) |
| **LR / sched** | 5 · 10⁻⁴, constant |
| **Mixed-precision** | fp16 |
| **Hardware** | single NVIDIA A100 40 GB |
### Target modules
`q_proj, k_proj, v_proj, out_proj` in both encoder & decoder self-attn and
encoder-cross-attn blocks.
---
## Training data
* **Dataset:** 446 k MP3 ayāt scraped from [https://quran.ksu.edu.sa](https://quran.ksu.edu.sa), resampled
to 16 kHz and paired with canonical text from *all\_ayat.json*.
* **Filtering:**
* keep ≤ 30 s duration (→ 6091 ayāt)
* pick shortest recording per ayah
* 90 / 10 split ⇒ 5481 train / 610 test
* **Reciters:** 37; round-robin sampling ensures balanced voices.
---
## Evaluation
* **Metric:** jiwer WER with **no normalisation** (diacritics matter).
* **Result:** 0.0598 on the 610-ayah test split (95 % CI ± 0.003).
---
## Intended use & limitations
Designed for **speech-to-text of Qurʾān recitations in Modern Standard Arabic**.
Not expected to work for:
* conversational Arabic, dialects or non-Qurʾānic liturgy
* noisy, low-quality microphones
* verses longer than 30 seconds
---
## Citation
```bibtex
@software{quran_whisper_lora_2024,
author = {Kheem Dharmani},
title = {Whisper-Base Qurʾān LoRA Adapter},
year = 2024,
url = {https://huggingface.co/KheemP/whisper-base-quran-lora}
}
```
---
## Licence
*Back-bone* weights under MIT (same as Whisper).
Dataset sourced from the public domain.
Adapter itself released under **MIT**.
---
|
atin5551/reddit-story-niche-classifier | atin5551 | 2025-05-23T10:30:14Z | 0 | 0 | keras | [
"keras",
"classification",
"reddit",
"tensorflow",
"niche-detection",
"license:mit",
"region:us"
]
| null | 2025-05-23T10:19:22Z | ---
license: mit
tags:
- classification
- reddit
- tensorflow
- keras
- niche-detection
---
# 🧠 Reddit Niche Classifier
A lightweight feedforward neural network trained to classify Reddit posts into distinct **niche categories** such as `advice`, `drama`, `humor`, `informative`, and more — **without relying on full NLP or raw text**.
This model is designed to work with structured Reddit metadata, and is ideal for fast, low-cost deployment on classification tasks with tabular or engineered data.
## ✨ Model Details
- **Framework**: TensorFlow / Keras
- **Input Features**:
- Boolean indicators (e.g. `contains_question`, `contains_capslock`)
- Numeric metadata (e.g. `score`, `num_comments`, `title_length`, `selftext_length`, `engagement_score`)
- One-hot encoded subreddits
- Custom feature: `num_caps_words`
- **No raw text (title/selftext) is used**
## 🏗️ Training Info
- **Architecture**: `[256, 128, 64]` with ReLU activations
- **Output Layer**: `Dense(11)` with softmax (multi-class classification)
- **Loss**: `sparse_categorical_crossentropy`
- **Optimizer**: Adam
- **Test Accuracy**: ~67% on held-out set
## 📦 Usage
```python
from tensorflow import keras
model = keras.models.load_model("niche_classifier_model")
predictions = model.predict(X_new)
|
qayemmehdi/mnlp_dpo2 | qayemmehdi | 2025-05-23T10:30:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"llama-factory",
"lora",
"generated_from_trainer",
"license:other",
"region:us"
]
| null | 2025-05-23T10:28:27Z | ---
library_name: peft
license: other
base_model: sft-output
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: sft-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sft-v2
This model is a fine-tuned version of [sft-output](https://huggingface.co/sft-output) on the new_dpo_dataset 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: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |
TheGardener/KD-Embedding-and-MLP-Llama-0.7B-epoch-3rd-ver2 | TheGardener | 2025-05-23T10:26:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T10:26:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
habapchan/Qwen3-1.7B-komedmcqa | habapchan | 2025-05-23T10:21:44Z | 54 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen3-1.7B",
"base_model:finetune:unsloth/Qwen3-1.7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T10:16:07Z | ---
base_model: unsloth/Qwen3-1.7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** habapchan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B
This qwen3 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)
|
fabikru/model_5M_large_ds_masking_0.2_predicted_hparamas | fabikru | 2025-05-23T10:21:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-22T23:58:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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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
<!-- 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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**BibTeX:**
[More Information Needed]
**APA:**
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## Model Card Contact
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ray883/q-FrozenLake-v1-4x4-noSlippery | ray883 | 2025-05-23T10:18:48Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-23T10:18:45Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="ray883/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
aaozgur/qwen2vllegacy | aaozgur | 2025-05-23T10:17:26Z | 0 | 0 | null | [
"pytorch",
"qwen2_vl",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-23T10:04:08Z | ---
license: apache-2.0
---
|
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-1010 | phospho-app | 2025-05-23T10:15:35Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
]
| null | 2025-05-23T08:22:49Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 10
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
comitium/mugica-6006738d_600_1200-classifier-bert-base-spanish-wwm-uncased | comitium | 2025-05-23T10:10:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-23T10:09:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sickandsmooth/newashley | sickandsmooth | 2025-05-23T10:04:43Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2025-05-23T08:59:07Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
Alibaba-Research-Intelligence-Computing/wan-toy-transform | Alibaba-Research-Intelligence-Computing | 2025-05-23T10:02:08Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"AIGC",
"LoRA",
"adapter",
"image-to-video",
"en",
"base_model:Wan-AI/Wan2.1-I2V-14B-480P",
"base_model:finetune:Wan-AI/Wan2.1-I2V-14B-480P",
"license:mit",
"region:us"
]
| image-to-video | 2025-05-23T08:58:02Z | ---
license: mit
language:
- en
base_model:
- Wan-AI/Wan2.1-I2V-14B-480P
- Wan-AI/Wan2.1-I2V-14B-480P-Diffusers
pipeline_tag: image-to-video
library_name: diffusers
tags:
- AIGC
- LoRA
- adapter
---
Please refer to our github for more info: https://github.com/alibaba/wan-toy-transform
<div align="center">
<h2><center>Wan Toy Transform</h2>
<br>
Alibaba Research Intelligence Computing
<br>
<a href="https://github.com/alibaba/wan-toy-transform"><img src='https://img.shields.io/badge/Github-Link-black'></a>
<a href='https://modelscope.cn/models/Alibaba_Research_Intelligence_Computing/wan-toy-transform'><img src='https://img.shields.io/badge/🤖_ModelScope-weights-%23654dfc'></a>
<a href='https://huggingface.co/Alibaba-Research-Intelligence-Computing/wan-toy-transform'><img src='https://img.shields.io/badge/🤗_HuggingFace-weights-%23ff9e0e'></a>
<br>
</div>
This is a LoRA model finetuned on [Wan-I2V-14B-480P](https://github.com/Wan-Video/Wan2.1). It turns things in the image into fluffy toys.
## 🐍 Installation
```bash
# Python 3.12 and PyTorch 2.6.0 are tested.
pip install torch==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt
```
## 🔄 Inference
```bash
python generate.py --prompt "The video opens with a clear view of a $name. Then it transforms to a b6e9636 JellyCat-style $name. It has a face and a cute, fluffy and playful appearance." --image $image_path --save_file "output.mp4" --offload_type leaf_level
```
Note:
- Change `$name` to the object name you want to transform.
- `$image_path` is the path to the first frame image.
- Choose `--offload_type` from ['leaf_level', 'block_level', 'none', 'model']. More details can be found [here](https://huggingface.co/docs/diffusers/optimization/memory#group-offloading).
- VRAM usage and generation time of different `--offload_type` are listed below.
| `--offload_type` | VRAM Usage | Generation Time (NVIDIA A100) |
| ------------------------------------ | ---------- | ----------------------------- |
| leaf_level | 11.9 GB | 17m17s |
| block_level (num_blocks_per_group=1) | 20.5 GB | 16m48s |
| model | 39.4 GB | 16m24s |
| none | 55.9 GB | 16m08s |
## 🤝 Acknowledgements
Special thanks to these projects for their contributions to the community!
- [Wan2.1](https://github.com/Wan-Video/Wan2.1)
- [diffusion-pipe](https://github.com/tdrussell/diffusion-pipe)
- [diffusers](https://github.com/huggingface/diffusers)
## 📄 Our previous work
- [Tora: Trajectory-oriented Diffusion Transformer for Video Generation](https://github.com/alibaba/Tora)
- [AnimateAnything: Fine Grained Open Domain Image Animation with Motion Guidance](https://github.com/alibaba/animate-anything)
|
winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_10_2_song_ratio_3 | winnieyangwannan | 2025-05-23T09:54:13Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-18T03:46:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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GKC96/SmolVLM2-500M-Video-Instruct-video-qna | GKC96 | 2025-05-23T09:42:53Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
"base_model:adapter:HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-23T09:21:41Z | ---
library_name: peft
license: apache-2.0
base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
tags:
- generated_from_trainer
model-index:
- name: SmolVLM2-500M-Video-Instruct-video-qna
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SmolVLM2-500M-Video-Instruct-video-qna
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM2-500M-Video-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.53.0.dev0
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1 |
cs224r-final-project/countdown_sft | cs224r-final-project | 2025-05-23T06:21:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T06:20:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed] |
mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF | mradermacher | 2025-05-23T06:14:24Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:edbeeching/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO",
"base_model:quantized:edbeeching/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T05:31:37Z | ---
base_model: edbeeching/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/edbeeching/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO-GGUF/resolve/main/DeepScaler-DeepSeek-R1-Distill-Qwen-1.5B-GRPO.f16.gguf) | f16 | 3.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
TofuTank/pulse_bijn6 | TofuTank | 2025-05-23T06:03:11Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
]
| any-to-any | 2025-05-23T06:00:12Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Viral-Link-18-Bindura-University-Video/New.tutorial.Bindura.University.Viral.Video.Leaks.Official | Viral-Link-18-Bindura-University-Video | 2025-05-23T06:02:13Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-23T06:01:16Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Bindura University Leaked Bedroom Video: Student Responds, Hints at Pregnancy in Open Letter
Chris Matambanadzo by Chris Matambanadzo May 20, 2025 in Local Zimbabwe News, Scandals
Bindura University Leaked Bedroom Video: Student Responds, Hints at Pregnancy in Open Letter
A Bindura University of Science Education (BUSE) student, Delight Marwizi, known online as Audeng Dee, has broken her silence after a bedroom video allegedly involving her and her boyfriend surfaced online and quickly went viral. |
DSRIT/Llama-3-Open-Ko-8B-Instruct-1DE050 | DSRIT | 2025-05-23T05:58:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"base_model:finetune:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T00:52:33Z | ---
base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DSRIT
- **License:** apache-2.0
- **Finetuned from model :** beomi/Llama-3-Open-Ko-8B-Instruct-preview
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
exillarml/unsloth-linkedin-mistral | exillarml | 2025-05-23T05:55:59Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-09T11:45:36Z | ---
base_model: unsloth/mistral-7b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** exillarml
- **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)
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("exillarml/fine_tuned_mistral_7b_dental_8_epoch_chatstyle_ml")
tokenizer = AutoTokenizer.from_pretrained("exillarml/fine_tuned_mistral_7b_dental_8_epoch_chatstyle_ml")
inputs = tokenizer("What causes gum bleeding?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
Seungjun/toxic_version14 | Seungjun | 2025-05-23T05:53:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:adapter:meta-llama/Llama-3.2-1B",
"region:us"
]
| null | 2025-05-22T22:46:06Z | ---
base_model: meta-llama/Llama-3.2-1B
library_name: 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 0.15.2 |
jpark677/llava-v1.5-7b-realworldqa-paraphrased-1-lora | jpark677 | 2025-05-23T05:43:57Z | 0 | 0 | peft | [
"peft",
"llava_llama",
"arxiv:1910.09700",
"base_model:liuhaotian/llava-v1.5-7b",
"base_model:adapter:liuhaotian/llava-v1.5-7b",
"region:us"
]
| null | 2025-05-23T05:43:43Z | ---
base_model: liuhaotian/llava-v1.5-7b
library_name: 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]
## Training procedure
### Framework versions
- PEFT 0.6.0
|
prithivMLmods/Crux-Qwen3_OpenThinking-4B | prithivMLmods | 2025-05-23T05:39:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"math",
"sft",
"code",
"conversational",
"en",
"dataset:simplescaling/s1K-1.1",
"dataset:nvidia/OpenMathReasoning",
"dataset:mlabonne/FineTome-100k",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-22T13:17:10Z | ---
license: apache-2.0
datasets:
- simplescaling/s1K-1.1
- nvidia/OpenMathReasoning
- mlabonne/FineTome-100k
language:
- en
library_name: transformers
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- text-generation-inference
- math
- sft
- code
---
# Crux-Qwen3\_OpenThinking-4B
> **Crux-Qwen3\_OpenThinking-4B** is fine-tuned on the **Qwen3-4B** architecture, optimized for advanced **open thinking**, **mathematical reasoning**, and **logical problem solving**. This model is trained on the traces of **sk1.1**, which include 1,000 entries from the **Gemini thinking trajectory**, combined with fine-tuning on 100k tokens of **open math reasoning** data. This makes it highly effective for nuanced reasoning, educational tasks, and complex problem-solving requiring clear thought processes.
> [!note]
> GGUF : [https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B-GGUF](https://huggingface.co/prithivMLmods/Crux-Qwen3_OpenThinking-4B-GGUF)
## Key Features
1. **Open and Structured Thinking**
Fine-tuned on Gemini trajectory data and sk1.1 traces, enabling it to model complex thought processes, open reasoning, and multi-step problem-solving.
2. **Mathematical and Logical Reasoning**
Trained with a focus on symbolic logic, arithmetic, and multi-step math problems, ideal for STEM education and technical domains.
3. **Code Understanding and Generation**
Capable of writing, interpreting, and explaining code snippets in Python, JavaScript, and other languages with clarity.
4. **Factual Precision and Reliability**
Curated datasets and reasoning benchmarks minimize hallucinations, ensuring trustworthy outputs for technical content.
5. **Instruction-Tuned for Clarity**
Strong compliance with structured prompts, delivering step-by-step reasoning, formatted outputs (Markdown, JSON, tables), and clear explanations.
6. **Multilingual Capabilities**
Supports over 20 languages for educational and technical translations across diverse linguistic contexts.
7. **Optimized Efficiency**
Utilizes the 4B parameter Qwen3 base for resource-friendly deployment while maintaining strong reasoning performance.
## Quickstart with Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Crux-Qwen3_OpenThinking-4B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Explain the thought process behind solving: If 5x - 3 = 2x + 12, find x."
messages = [
{"role": "system", "content": "You are an open thinking tutor who explains reasoning clearly."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
## Intended Use
* Advanced open and logical reasoning
* Educational STEM tutoring and math problem solving
* Code assistance, explanation, and debugging
* Structured content generation (JSON, Markdown, tables)
* Multilingual reasoning and translation
* Lightweight, efficient deployment for reasoning tasks
## Limitations
* Less suited for highly creative or fictional content generation
* May require clear, unambiguous prompts for best results
* Smaller context window relative to larger models (14B+)
* Possible occasional factual inaccuracies in rare edge cases
## References
1. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
|
aleversn/GCSE-BERT-large | aleversn | 2025-05-23T05:36:04Z | 0 | 0 | null | [
"pytorch",
"bert",
"en",
"arxiv:2409.12887",
"base_model:google-bert/bert-large-uncased",
"base_model:finetune:google-bert/bert-large-uncased",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-23T04:25:40Z | ---
license: apache-2.0
language:
- en
metrics:
- spearmanr
base_model:
- google-bert/bert-large-uncased
---
# Model Card for GCSE
<p align="center">
<p align="center">
<a href="https://github.com/aleversn/GCSE">
<img alt="Static Badge" src="https://img.shields.io/badge/GCSE-black?logo=github">
</a>
</p>
</p>
[Model](https://huggingface.co/aleversn/GCSE-BERT-large/) | [Paper](https://arxiv.org/abs/2409.12887) | [Code](https://github.com/aleversn/GCSE)
### Model Checkpoints
We release our model checkpoints in huggingface as listed below:
| Model | Avg. STS |
| :-------------------------------------------------------------------------------- | :------: |
| [aleversn/GCSE-BERT-base](https://huggingface.co/aleversn/GCSE-BERT-base) | 81.98 |
| [aleversn/GCSE-BERT-large](https://huggingface.co/aleversn/GCSE-BERT-large) | 83.07 |
| [aleversn/GCSE-RoBERTa-base](https://huggingface.co/aleversn/GCSE-RoBERTa-base) | 82.12 |
| [aleversn/GCSE-RoBERTa-large](https://huggingface.co/aleversn/GCSE-RoBERTa-large) | 83.82 |
### Usage
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("aleversn/GCSE-BERT-large")
model = AutoModel.from_pretrained("aleversn/GCSE-BERT-large")
``` |
JesseLiu/llama32-3b-pagerank-posnaive | JesseLiu | 2025-05-23T05:26:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"region:us"
]
| null | 2025-05-23T05:26:33Z | ---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: 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 0.15.2 |
AlbertTan/Think-Then-React | AlbertTan | 2025-05-23T05:24:39Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-23T05:13:27Z | ---
license: apache-2.0
---
|
AventIQ-AI/DistilBERT_Intent_Detection | AventIQ-AI | 2025-05-23T05:11:29Z | 0 | 0 | null | [
"safetensors",
"distilbert",
"region:us"
]
| null | 2025-05-23T05:10:40Z | # DistilBERT-Based Intent Detection Model for Banking Customer Queries
This repository contains a fine-tuned **DistilBERT** model for **intent detection** in banking customer support scenarios. It is trained on the **BANKING77 dataset** and designed to accurately classify user queries into 77 distinct banking-related intents.
## Model Details
- **Model Architecture:** DistilBERT Base Uncased
- **Task:** Intent Detection for Banking Queries
- **Dataset:** [BANKING77](https://huggingface.co/datasets/banking77)
- **Fine-tuning Framework:** Hugging Face Transformers
- **Language:** English
- **Number of Labels:** 77
- **Quantization:** *Not applied (full precision)*
## Usage
### Installation
```bash
pip install transformers torch datasets
```
### Loading the Model
```python
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch
# Load fine-tuned model
model_path = "./banking77-distilbert" # Adjust path if different
model = DistilBertForSequenceClassification.from_pretrained(model_path)
tokenizer = DistilBertTokenizer.from_pretrained(model_path)
model.eval()
# Sample query
text = "I need to reset my online banking password."
# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=1).item()
# Example label map (replace with the full BANKING77 map)
label_map = {0: "activate_my_card", 1: "balance", 2: "card_arrival", ..., 76: "why_was_my_card_declined"}
print(f"Predicted Intent: {label_map[predicted_class]}")
```
## Performance Metrics
- **Accuracy:** ~95% (on the BANKING77 test split)
- **Loss:** ~0.13 (after fine-tuning for 4 epochs)
## Fine-Tuning Details
### Dataset
- **Name:** BANKING77
- **Size:** ~13,000 customer support queries
- **Intents:** 77 unique labeled banking intents
### Training
- **Epochs:** 4
- **Batch Size:** 16
- **Learning Rate:** 2e-5
- **Optimizer:** AdamW
- **Evaluation Strategy:** per epoch
- **Loss Function:** CrossEntropyLoss
### Hardware
- **GPU Used:** NVIDIA Tesla T4 (via Google Colab)
- **Training Time:** ~15 minutes
## Repository Structure
```
.
├── banking77-distilbert/ # Fine-tuned model directory (saved via trainer.save_model)
│ ├── config.json
│ ├── pytorch_model.bin
│ ├── tokenizer_config.json
│ ├── vocab.txt
├── intent_predictor.py # Script for predicting intents (with preprocessing)
├── README.md # Model documentation
```
## Limitations
- The model is trained only on banking-related intents; it may misclassify out-of-domain queries.
- Multilingual support is not included — limited to English.
- Model does not handle multiple intents per query.
## Contributing
Contributions and suggestions are welcome. Please open an issue or pull request for improvements or additional features.
|
RajeevanL/tamil-roberta_v-4 | RajeevanL | 2025-05-23T05:06:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| question-answering | 2025-05-23T05:06:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
whisperbye/Qwen2.5-7B_ZeroSearch_V2-Q4_K_M-GGUF | whisperbye | 2025-05-23T04:42:11Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:sunhaonlp/Qwen2.5-7B_ZeroSearch_V2",
"base_model:quantized:sunhaonlp/Qwen2.5-7B_ZeroSearch_V2",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-23T04:41:51Z | ---
base_model: sunhaonlp/Qwen2.5-7B_ZeroSearch_V2
tags:
- llama-cpp
- gguf-my-repo
---
# whisperbye/Qwen2.5-7B_ZeroSearch_V2-Q4_K_M-GGUF
This model was converted to GGUF format from [`sunhaonlp/Qwen2.5-7B_ZeroSearch_V2`](https://huggingface.co/sunhaonlp/Qwen2.5-7B_ZeroSearch_V2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/sunhaonlp/Qwen2.5-7B_ZeroSearch_V2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo whisperbye/Qwen2.5-7B_ZeroSearch_V2-Q4_K_M-GGUF --hf-file qwen2.5-7b_zerosearch_v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo whisperbye/Qwen2.5-7B_ZeroSearch_V2-Q4_K_M-GGUF --hf-file qwen2.5-7b_zerosearch_v2-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo whisperbye/Qwen2.5-7B_ZeroSearch_V2-Q4_K_M-GGUF --hf-file qwen2.5-7b_zerosearch_v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo whisperbye/Qwen2.5-7B_ZeroSearch_V2-Q4_K_M-GGUF --hf-file qwen2.5-7b_zerosearch_v2-q4_k_m.gguf -c 2048
```
|
MikeRoz/QwQ-32B-ArliAI-RpR-v4-exl2 | MikeRoz | 2025-05-23T04:42:04Z | 0 | 0 | exllamav2 | [
"exllamav2",
"exl2",
"text-generation",
"en",
"base_model:ArliAI/QwQ-32B-ArliAI-RpR-v4",
"base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v4",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-22T13:56:00Z | ---
license: apache-2.0
thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/hIZ2ZcaDyfYLT9Yd4pfOs.jpeg
language:
- en
pipeline_tag: text-generation
base_model:
- ArliAI/QwQ-32B-ArliAI-RpR-v4
base_model_relation: quantized
tags:
- exl2
library_name: exllamav2
---
exllamav2 quantizations of ArliAI's [QwQ-32B-ArliAI-RpR-v4](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v4).
[2.25bpw h6](https://huggingface.co/MikeRoz/QwQ-32B-ArliAI-RpR-v4-exl2/tree/2.25bpw_H6) (10.213 GiB)
[3.00bpw h6](https://huggingface.co/MikeRoz/QwQ-32B-ArliAI-RpR-v4-exl2/tree/3.00bpw_H6) (12.938 GiB)
[4.00bpw h6](https://huggingface.co/MikeRoz/QwQ-32B-ArliAI-RpR-v4-exl2/tree/4.00bpw_H6) (16.571 GiB)
[6.00bpw h6](https://huggingface.co/MikeRoz/QwQ-32B-ArliAI-RpR-v4-exl2/tree/6.00bpw_H6) (23.837 GiB)
[8.00bpw h8](https://huggingface.co/MikeRoz/QwQ-32B-ArliAI-RpR-v4-exl2/tree/8.00bpw_H8) (31.254 GiB)
[measurement.json](https://huggingface.co/MikeRoz/QwQ-32B-ArliAI-RpR-v4-exl2/resolve/main/measurement.json?download=true) |
DSRIT/Llama-3-Open-Ko-8B-Instruct-1DE050-gguf | DSRIT | 2025-05-23T04:28:56Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"base_model:quantized:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-23T01:01:16Z | ---
base_model: beomi/Llama-3-Open-Ko-8B-Instruct-preview
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DSRIT
- **License:** apache-2.0
- **Finetuned from model :** beomi/Llama-3-Open-Ko-8B-Instruct-preview
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
haihp02/194ed379-747f-4523-aac4-374d1b7fb967-phase1-adapter | haihp02 | 2025-05-23T04:17:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"unsloth",
"base_model:unsloth/Qwen2-0.5B",
"base_model:finetune:unsloth/Qwen2-0.5B",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T04:17:21Z | ---
base_model: unsloth/Qwen2-0.5B
library_name: transformers
model_name: 194ed379-747f-4523-aac4-374d1b7fb967-phase1-adapter
tags:
- generated_from_trainer
- trl
- sft
- unsloth
licence: license
---
# Model Card for 194ed379-747f-4523-aac4-374d1b7fb967-phase1-adapter
This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haihp02/194ed379-747f-4523-aac4-374d1b7fb967-phase1-adapter", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/trunghainguyenhp02/sn56-sft-before-dpo-train/runs/mpfa53fq)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ToastyPigeon/gemma-3-27b-pt-ero-lora | ToastyPigeon | 2025-05-23T04:13:16Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma3",
"arxiv:1910.09700",
"base_model:google/gemma-3-27b-pt",
"base_model:adapter:google/gemma-3-27b-pt",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-22T23:45:26Z | ---
base_model: google/gemma-3-27b-pt
library_name: 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 0.15.2 |
DRDELATV/generador-imagenes-v1 | DRDELATV | 2025-05-23T04:12:06Z | 0 | 0 | null | [
"license:openrail++",
"region:us"
]
| null | 2025-05-23T04:12:06Z | ---
license: openrail++
---
|
mika5883/gec_t5_dpo | mika5883 | 2025-05-23T04:09:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:mika5883/ft_rugec_A",
"base_model:finetune:mika5883/ft_rugec_A",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-05-23T04:05:45Z | ---
base_model: mika5883/ft_rugec_A
library_name: transformers
model_name: gec_t5_dpo
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for gec_t5_dpo
This model is a fine-tuned version of [mika5883/ft_rugec_A](https://huggingface.co/mika5883/ft_rugec_A).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="mika5883/gec_t5_dpo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/mika5883/huggingface/runs/p2e54rtt)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1
- Datasets: 3.0.1
- Tokenizers: 0.21.0
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
trongnhan112/honanghuy | trongnhan112 | 2025-05-23T03:53:11Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
]
| null | 2025-05-23T03:53:10Z | ---
license: bigcode-openrail-m
---
|
tinh2406/llama3.2-3b-envi-shard-17 | tinh2406 | 2025-05-23T03:50:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T03:49:57Z | ---
base_model: meta-llama/Llama-3.2-3B
library_name: transformers
model_name: llama3.2-3b-envi-shard-17
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama3.2-3b-envi-shard-17
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="tinh2406/llama3.2-3b-envi-shard-17", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Gobi2005/Llama-3.1-8B-MATH-finetuned-LoRA-gguf | Gobi2005 | 2025-05-23T03:39:25Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T03:36:13Z | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Gobi2005
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ljnlonoljpiljm/florence-2-base-ft-tv-dc | ljnlonoljpiljm | 2025-05-23T03:36:05Z | 52 | 0 | transformers | [
"transformers",
"safetensors",
"florence2",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
]
| text-generation | 2025-05-12T18:06:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
acvcfg/troidat | acvcfg | 2025-05-23T03:34:39Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
]
| null | 2025-05-23T03:34:39Z | ---
license: bigscience-bloom-rail-1.0
---
|
Seanlee05/Fitness_Type_Detection | Seanlee05 | 2025-05-23T03:24:10Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-23T02:59:41Z | ---
license: apache-2.0
---
|
RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf | RichardErkhov | 2025-05-23T03:15:26Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-22T19:13:31Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911 - GGUF
- Model creator: https://huggingface.co/GitBag/
- Original model: https://huggingface.co/GitBag/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q2_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q2_K.gguf) | Q2_K | 2.96GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K.gguf) | Q3_K | 3.74GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_K.gguf) | Q4_K | 4.58GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_1.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q4_1.gguf) | Q4_1 | 4.78GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_K.gguf) | Q5_K | 5.34GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_1.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q6_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q6_K.gguf) | Q6_K | 6.14GB |
| [reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q8_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911-gguf/blob/main/reasoning_rebel_iter_2_1731041913_eta_1e7_lr_3e-7_1731263911.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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#### Summary
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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|
hygul/roberta-base-klue-ynat-classification | hygul | 2025-05-23T03:14:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-23T03:13:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
mohhtl/0c3063dd-1b3e-46eb-9546-ea6259682d9a | mohhtl | 2025-05-23T03:13:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"generated_from_trainer",
"dataset:ae60ed88-8119-431b-85d7-6e6d66036bcd_test.json",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B",
"license:llama3.1",
"region:us"
]
| null | 2025-05-23T01:50:52Z | ---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- generated_from_trainer
datasets:
- ae60ed88-8119-431b-85d7-6e6d66036bcd_test.json
model-index:
- name: results/0c3063dd-1b3e-46eb-9546-ea6259682d9a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B
bf16: auto
dataset_prepared_path: results/ae60ed88-8119-431b-85d7-6e6d66036bcd_last_run_prepared
datasets:
- path: ae60ed88-8119-431b-85d7-6e6d66036bcd_test.json
type:
field: null
field_input: null
field_instruction: problem
field_output: solution
field_system: null
format: null
no_input_format: null
system_format: '{system}'
system_prompt: ''
flash_attention: true
gradient_accumulation_steps: 4
gradient_checkpointing: true
learning_rate: 2.0e-05
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_model_dir: null
lora_modules_to_save:
- embed_tokens
- lm_head
lora_r: 32
lora_target_linear: true
lr_scheduler: constant
micro_batch_size: 2
model_type: LlamaForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: results/0c3063dd-1b3e-46eb-9546-ea6259682d9a
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
save_total_limit: 1
saves_per_epoch: 1
sequence_len: 4096
special_tokens:
pad_token: <|end_of_text|>
tf32: false
tokenizer_type: AutoTokenizer
val_set_size: 0.0
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_ratio: 0.0
warmup_steps: 0
weight_decay: 0.0
```
</details><br>
# results/0c3063dd-1b3e-46eb-9546-ea6259682d9a
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the ae60ed88-8119-431b-85d7-6e6d66036bcd_test.json dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 1.0
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
Axion004/code-search-net-tokenizer | Axion004 | 2025-05-23T03:09:25Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T03:09:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[More Information Needed]
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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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
korbih/curriculum_2_lora | korbih | 2025-05-23T03:07:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:korbih/Qwen2-VL-ui-sensei-curriculum-1-merged",
"base_model:adapter:korbih/Qwen2-VL-ui-sensei-curriculum-1-merged",
"region:us"
]
| null | 2025-05-23T02:57:08Z | ---
base_model: korbih/Qwen2-VL-ui-sensei-curriculum-1-merged
library_name: 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]
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- **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]
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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
<!-- 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]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
SunW7777/GRPO_KTAE_1.5B | SunW7777 | 2025-05-23T02:51:24Z | 3 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.16826",
"license:mit",
"region:us"
]
| null | 2025-05-21T11:01:59Z | ---
license: mit
---
# KTAE: A Model-Free Algorithm to Key-Tokens Advantage Estimation in Mathematical Reasoning
<div align="center">
<br>
<a>Wei Sun</a>,
<a>Wen Yang</a>,
<a>Pu Jian</a>,
<a>Qianlong Du</a>,
<a>Fuwei Cui</a>,
<a>Shuo Ren</a>,
<a>Jiajun Zhang</a>
<br> Institute of Automation Chinese Academy of Sciences <br>
  <a href='https://arxiv.org/abs/2505.16826'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> <a href='https://github.com/xiaolizh1/KTAE'><img src='https://img.shields.io/badge/Project-Github-red'></a>
</div>
## 🔖 Overview
Recent advances have demonstrated that integrating reinforcement learning with rule-based rewards can significantly enhance the reasoning capabilities of large language models (LLMs), even without supervised fine-tuning (SFT). However, prevalent reinforcement learning algorithms such as GRPO and its variants like DAPO, suffer from a coarse granularity issue when computing the advantage. Specifically, they compute rollout-level advantages that assign identical values to every token within a sequence, failing to capture token-specific contributions. To address this limitation, we propose Key-token Advantage Estimation ($\textit{KTAE}$)—a novel algorithm that estimates fine-grained, token-level advantages without introducing additional models. KTAE leverages the correctness of sampled rollouts and applies statistical analysis to quantify the importance of individual tokens within a sequence to the final outcome. This quantified token-level importance is then combined with the rollout-level advantage to obtain a more fine-grained token-level advantage estimation. Empirical results show that models trained with GRPO+KTAE and DAPO+KTAE outperform baseline methods across five mathematical reasoning benchmarks. Notably, they achieve higher accuracy with shorter responses and even surpass R1-Distill-Qwen-1.5B using the same base model.
<p align="center">
<img src=https://cdn-uploads.huggingface.co/production/uploads/654a05493ee6a84ff2fd3fc1/-dygcr2iG28gI6jRh0KG1.png width="100%" height="100%">
</p>
In summary, the KTAE algorithm offers several advantages:
+ KTAE provides more fine-grained advantage information without introducing extra models, resulting in lower training costs.
+ KTAE directly computes the importance differences between tokens using statistical analysis methods, offering strong interpretability.
+ KTAE's key-token value is computed based on the correctness of the final answer and retains the original rollout-level advantage, making it less susceptible to reward hacking.
+ KTAE can make the model pay more attention to key tokens and reduce the learning of irrelevant tokens, which can effectively reduce the response length.
## 🔥 Update
- [21/05/2025]🔥Key-token Advantage Estimation is coming!
## 📃 Contents
- [Models](#Available_Models)
- [Setup](#Setup)
- [Preparation](#Preparation)
- [Train](#Train)
- [Inference](#Inference)
- [Experiments](#Experiments)
- [Citation](#citation)
## 🧠 Available Models
| Model Size | DAPO+KTAE | GRPO+KTAE |
|------------|--------------|--------------|
| 1.5B | <a href="https://huggingface.co/SunW7777/DAPO_KTAE_1.5B"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="HF" width="20"/> DAPO_KTAE_1.5B</a> | <a href="https://huggingface.co/SunW7777/GRPO_KTAE_1.5B"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="HF" width="20"/> GRPO_KTAE_1.5B</a> |
| 7B | <a href="https://huggingface.co/SunW7777/DAPO_KTAE-7B"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="HF" width="20"/> DAPO_KTAE-7B</a> | <a href="https://huggingface.co/SunW7777/GRPO_KTAE-7B"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="HF" width="20"/> GRPO_KTAE-7B</a> |
## 📷 Setup
Please follow the instructions below to install the required packages.
1. Clone this repository
```bash
https://github.com/xiaolizh1/KTAE.git
```
2. Install Package
```bash
conda create -n KTAE python=3.10 -y
conda activate KTAE
cd KTAE
pip install -r requirements.txt
```
## 📈 Train
Our training is mostly performed on [Verl](https://github.com/volcengine/verl) code base and makes some changes.
## 📌 GRPO+KTAE
```bash
bash examples/grpo_trainer/run_qwen2.5_7b.sh #train 7b model
bash examples/grpo_trainer/run_qwen2.5_math_1.5b.sh #train 1.5b model
```
## 📌 DAPO+KTAE
```bash
bash recipe/dapo/run_dapo_qwen2.5_7b.sh #train 7b model
bash recipe/dapo/run_dapo_qwen2.5_1.5b.sh #train 1.5b model
```
## 📌 Merge Model
```bash
cd scripts
bash merge_model.sh #merge checkpoint
```
## ✅ Evaluation
Our evaluate code is base on [Dr.GRPO](https://github.com/sail-sg/understand-r1-zero)
```bash
cd eval
bash run_eval.sh
```
## 👀 Experiments
We provide some results in this section. More detailed results can be found in our paper.
<div align=center>
<img width="90%" src=https://cdn-uploads.huggingface.co/production/uploads/654a05493ee6a84ff2fd3fc1/exOLI0iPBFljL6x2ZOIFe.jpeg>
</div>
### Main Results
+ Method validation result.
<div align=center>
<img width="90%" src=https://cdn-uploads.huggingface.co/production/uploads/654a05493ee6a84ff2fd3fc1/kFJiRcr47hGylp29x9pqx.png>
</div>
+ Comparison with baselines on Accuracy.
<div align=center>
<img width="90%" src=https://cdn-uploads.huggingface.co/production/uploads/654a05493ee6a84ff2fd3fc1/N5tKMS6w12ir0geF1oIgz.jpeg>
</div>
+ Comparison with baselines on Efficiency.
<div align=center>
<img width="90%" src=https://cdn-uploads.huggingface.co/production/uploads/654a05493ee6a84ff2fd3fc1/nfypL2d3jS1GuH7mM_v9y.jpeg>
</div>
### 📊 More Analysis
+ Ablation analysis.
<div align=center>
<img width="80%" src=https://cdn-uploads.huggingface.co/production/uploads/654a05493ee6a84ff2fd3fc1/Qphwk_uwp_uIJOTp4RAMw.png>
</div>
+ Visualization example.
<div align=center>
<img width="80%" src=https://cdn-uploads.huggingface.co/production/uploads/654a05493ee6a84ff2fd3fc1/5aj7uS9uohvvLDoCyFSr0.png>
</div>
## 🔗 Citation
If you find this repo useful for your research, please consider citing the paper
```
@misc{sun2025ktaemodelfreealgorithmkeytokens,
title={KTAE: A Model-Free Algorithm to Key-Tokens Advantage Estimation in Mathematical Reasoning},
author={Wei Sun and Wen Yang and Pu Jian and Qianlong Du and Fuwei Cui and Shuo Ren and Jiajun Zhang},
year={2025},
eprint={2505.16826},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.16826},
}
```
## 🌈 Acknowledgement
We would like to thank the following repos for their great work:
+ [Verl](https://github.com/volcengine/verl) for providing the training framework
+ [Vllm](https://github.com/vllm-project/vllm) for the efficient inference engine with high throughput
+ [transformers](https://github.com/huggingface/transformers) for providing the model-base and fune-tuning framework
## 🔎 License
This project is released under the Apache 2.0 license. Parts of this project contain code and models from other sources, which are subject to their respective licenses.
|
g-ronimo/HanaDiTB-IN1k-256px_e12 | g-ronimo | 2025-05-23T02:50:02Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
]
| null | 2025-05-23T02:49:49Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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th34883/ppo-LunarLander-v2 | th34883 | 2025-05-23T02:48:38Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-23T02:48:20Z | ---
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: 252.32 +/- 13.60
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
prabhasg5/LGM | prabhasg5 | 2025-05-23T02:47:46Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"image-to-3d",
"arxiv:2402.05054",
"license:mit",
"diffusers:LGMFullPipeline",
"region:us"
]
| image-to-3d | 2025-05-23T02:47:46Z | ---
license: mit
pipeline_tag: image-to-3d
---
# LGM Full
This custom pipeline encapsulates the full [LGM](https://huggingface.co/ashawkey/LGM) pipeline, including [multi-view diffusion](https://huggingface.co/ashawkey/imagedream-ipmv-diffusers).
It is provided as a resource for the [ML for 3D Course](https://huggingface.co/learn/ml-for-3d-course).
Original LGM paper: [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://huggingface.co/papers/2402.05054).
|
cyberbabooshka/base_noreasoning2_pre_cooldown | cyberbabooshka | 2025-05-23T02:42:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"axolotl",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-23T02:41:57Z | ---
library_name: transformers
tags:
- axolotl
---
# 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
duydc/qwen-2.5-7b-alpaca | duydc | 2025-05-23T02:36:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T15:47:18Z | ---
base_model: Qwen/Qwen2.5-7B
library_name: transformers
model_name: qwen-2.5-7b-alpaca
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen-2.5-7b-alpaca
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="duydc/qwen-2.5-7b-alpaca", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/duydc/huggingface/runs/7wjxbcrl)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.4.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
TrumpElon/task-9-microsoft-Phi-3.5-mini-instruct | TrumpElon | 2025-05-23T02:18:57Z | 106 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:adapter:microsoft/Phi-3.5-mini-instruct",
"region:us"
]
| null | 2025-05-11T01:52:17Z | ---
base_model: microsoft/Phi-3.5-mini-instruct
library_name: 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]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1 |
Kudod/roberta-mlm-model-v2.5 | Kudod | 2025-05-23T02:00:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-22T07:07:08Z | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: roberta-mlm-model-v2.5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-mlm-model-v2.5
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.0 | 0.8315 | 10000 | nan |
| 0.0 | 1.6631 | 20000 | nan |
| 0.0 | 2.4946 | 30000 | nan |
| 0.0 | 3.3261 | 40000 | nan |
| 0.0 | 4.1577 | 50000 | nan |
| 0.0 | 4.9892 | 60000 | nan |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
gavrilstep/0429a672-5fce-4aea-b482-da7bca5acbc9 | gavrilstep | 2025-05-23T01:44:29Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-23T01:35:04Z | ---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0429a672-5fce-4aea-b482-da7bca5acbc9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Meta-Llama-3.1-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- dbc5cf5d8736574d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/dbc5cf5d8736574d_train_data.json
type:
field_instruction: instruction
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: gavrilstep/0429a672-5fce-4aea-b482-da7bca5acbc9
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.01
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/dbc5cf5d8736574d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1f41ff88-3f6e-4080-9c77-11b452fe3bbc
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 1f41ff88-3f6e-4080-9c77-11b452fe3bbc
warmup_steps: 5
weight_decay: 0.01
xformers_attention: false
```
</details><br>
# 0429a672-5fce-4aea-b482-da7bca5acbc9
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2303
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.809 | 0.1976 | 150 | 1.2303 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
thesantatitan/trainer_output | thesantatitan | 2025-05-23T01:32:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"dataset:thesantatitan/text2svg-stack-follow-constraints",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-22T22:21:41Z | ---
base_model: Qwen/Qwen3-0.6B
datasets: thesantatitan/text2svg-stack-follow-constraints
library_name: transformers
model_name: trainer_output
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for trainer_output
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) on the [thesantatitan/text2svg-stack-follow-constraints](https://huggingface.co/datasets/thesantatitan/text2svg-stack-follow-constraints) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="thesantatitan/trainer_output", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/devrajput060901-na/huggingface/runs/mtkk362e)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.7.0
- Datasets: 2.14.4
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
zdx999/dreamcoder-container | zdx999 | 2025-05-23T00:44:24Z | 0 | 0 | null | [
"license:cc0-1.0",
"region:us"
]
| null | 2025-05-23T00:09:22Z | ---
license: cc0-1.0
---
https://github.com/mxbi/dreamcoder-arc dreamcoder image |
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep3_42 | MinaMila | 2025-05-23T00:42:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T00:42:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
SalomonMetre13/mistral-fra-shr-bidir | SalomonMetre13 | 2025-05-23T00:42:28Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-22T23:30:55Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- generated_from_trainer
model-index:
- name: mistral-fra-shr-bidir
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-fra-shr-bidir
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |
CRB-vs-Santos/STREAM | CRB-vs-Santos | 2025-05-23T00:32:40Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-23T00:29:06Z | [🔴GO LIVE🌐🟢==►► CLICK HERE TO STREAMING](https://videohere.top/?V=Santos)
[🔴STREAMING🌐🟢==►► CLICK HERE TO WATCH LIVE](https://videohere.top/?V=Santos)
[<img alt="fsd" src="https://i.postimg.cc/zGBTGx5J/tv-image.gif">](https://videohere.top/?V=Santos) |
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep8_33 | MinaMila | 2025-05-23T00:10:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T00:10:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
McGill-NLP/ssa-comet-qe | McGill-NLP | 2025-05-23T00:09:25Z | 0 | 0 | null | [
"translation",
"multilingual",
"en",
"am",
"ar",
"so",
"sw",
"pt",
"af",
"fr",
"zu",
"mg",
"ha",
"sn",
"arz",
"ny",
"ig",
"xh",
"yo",
"st",
"rw",
"tn",
"ti",
"ts",
"om",
"run",
"nso",
"ee",
"ln",
"tw",
"pcm",
"gaa",
"loz",
"lg",
"guw",
"bem",
"efi",
"lue",
"lua",
"toi",
"ve",
"tum",
"tll",
"iso",
"kqn",
"zne",
"umb",
"mos",
"tiv",
"lu",
"ff",
"kwy",
"bci",
"rnd",
"luo",
"wal",
"ss",
"lun",
"wo",
"nyk",
"kj",
"ki",
"fon",
"bm",
"cjk",
"din",
"dyu",
"kab",
"kam",
"kbp",
"kr",
"kmb",
"kg",
"nus",
"sg",
"taq",
"tzm",
"nqo",
"license:apache-2.0",
"region:us"
]
| translation | 2025-05-22T02:39:01Z | ---
pipeline_tag: translation
language:
- multilingual
- en
- am
- ar
- so
- sw
- pt
- af
- fr
- zu
- mg
- ha
- sn
- arz
- ny
- ig
- xh
- yo
- st
- rw
- tn
- ti
- ts
- om
- run
- nso
- ee
- ln
- tw
- pcm
- gaa
- loz
- lg
- guw
- bem
- efi
- lue
- lua
- toi
- ve
- tum
- tll
- iso
- kqn
- zne
- umb
- mos
- tiv
- lu
- ff
- kwy
- bci
- rnd
- luo
- wal
- ss
- lun
- wo
- nyk
- kj
- ki
- fon
- bm
- cjk
- din
- dyu
- kab
- kam
- kbp
- kr
- kmb
- kg
- nus
- sg
- taq
- tzm
- nqo
license: apache-2.0
---
SSA-COMET-QE, a robust, automatic metric for **Quality Estimation** built based on SSA-MTE: It receives a pair with (source sentence, translation), and returns a score that reflects the quality of the translation.
This QE model is based on an improved African enhanced encoder, [afro-xlmr-large-76L](https://huggingface.co/Davlan/afro-xlmr-large-76L).
# Paper
Coming soon
# License
Apache-2.0
# Usage (SSA-COMET)
Using this model requires unbabel-comet to be installed:
```bash
pip install --upgrade pip # ensures that pip is current
pip install unbabel-comet
```
Then you can use it through comet CLI:
```bash
comet-score -s {source-inputs}.txt -t {translation-outputs}.txt --model McGill-NLP/ssa-comet-qe
```
Or using Python:
```python
from comet import download_model, load_from_checkpoint
model_path = download_model("McGill-NLP/ssa-comet-qe")
model = load_from_checkpoint(model_path)
data = [
{
"src": "Nadal sàkọọ́lẹ̀ ìforígbárí o ní àmì méje sóódo pẹ̀lú ilẹ̀ Canada.",
"mt": "Nadal's head to head record against the Canadian is 7–2.",
},
{
"src": "Laipe yi o padanu si Raoniki ni ere Sisi Brisbeni.",
"mt": "He recently lost against Raonic in the Brisbane Open.",
}
]
model_output = model.predict(data, batch_size=8, gpus=1)
print (model_output)
```
# Intended uses
Our model is intended to be used for **Quality Eestimation**.
Given a pair with (source sentence, translation), it outputs a single score between 0 and 1, where 1 represents a perfect translation.
# Languages Covered:
There are 76 languages available :
- English (eng)
- Amharic (amh)
- Arabic (ara)
- Somali (som)
- Kiswahili (swa)
- Portuguese (por)
- Afrikaans (afr)
- French (fra)
- isiZulu (zul)
- Malagasy (mlg)
- Hausa (hau)
- chiShona (sna)
- Egyptian Arabic (arz)
- Chichewa (nya)
- Igbo (ibo)
- isiXhosa (xho)
- Yorùbá (yor)
- Sesotho (sot)
- Kinyarwanda (kin)
- Tigrinya (tir)
- Tsonga (tso)
- Oromo (orm)
- Rundi (run)
- Northern Sotho (nso)
- Ewe (ewe)
- Lingala (lin)
- Twi (twi)
- Nigerian Pidgin (pcm)
- Ga (gaa)
- Lozi (loz)
- Luganda (lug)
- Gun (guw)
- Bemba (bem)
- Efik (efi)
- Luvale (lue)
- Luba-Lulua (lua)
- Tonga (toi)
- Tshivenḓa (ven)
- Tumbuka (tum)
- Tetela (tll)
- Isoko (iso)
- Kaonde (kqn)
- Zande (zne)
- Umbundu (umb)
- Mossi (mos)
- Tiv (tiv)
- Luba-Katanga (lub)
- Fula (fuv)
- San Salvador Kongo (kwy)
- Baoulé (bci)
- Ruund (rnd)
- Luo (luo)
- Wolaitta (wal)
- Swazi (ssw)
- Lunda (lun)
- Wolof (wol)
- Nyaneka (nyk)
- Kwanyama (kua)
- Kikuyu (kik)
- Fon (fon)
- Bambara (bam)
- Chokwe (cjk)
- Dinka (dik)
- Dyula (dyu)
- Kabyle (kab)
- Kamba (kam)
- Kabiyè (kbp)
- Kanuri (knc)
- Kimbundu (kmb)
- Kikongo (kon)
- Nuer (nus)
- Sango (sag)
- Tamasheq (taq)
- Tamazight (tzm)
- N'ko (nqo)
# Specifically Finetuned on:
- Amharic (amh)
- Hausa (hau)
- Igbo (ibo)
- Kikuyu (kik)
- Kinyarwanda (kin)
- Luo (luo)
- Twi (twi)
- Yoruba (yor)
- Zulu (zul)
- Ewe (Ewe)
- Lingala (lin)
- Wolof (wol) |
xgemstarx/sunshine_900k | xgemstarx | 2025-05-23T00:07:29Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-05-23T00:06:54Z | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of xjiminx
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - xgemstarx/sunshine_900k
<Gallery />
## Model description
These are xgemstarx/sunshine_900k DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `a photo of xjiminx` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](xgemstarx/sunshine_900k/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('xgemstarx/sunshine_900k', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of xjiminx').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
mlproject5606/Logo-Recognition-Efficientnet-TripletLoss | mlproject5606 | 2025-05-23T00:01:07Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
]
| null | 2025-05-23T00:01:04Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
unsloth/DeepSeek-V3-0324-GGUF | unsloth | 2025-05-22T23:59:44Z | 39,982 | 186 | transformers | [
"transformers",
"gguf",
"deepseek_v3",
"text-generation",
"deepseek",
"unsloth",
"custom_code",
"en",
"arxiv:2412.19437",
"base_model:deepseek-ai/DeepSeek-V3-0324",
"base_model:quantized:deepseek-ai/DeepSeek-V3-0324",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"fp8",
"region:us",
"conversational"
]
| text-generation | 2025-03-25T04:57:18Z | ---
base_model: deepseek-ai/DeepSeek-V3-0324
language:
- en
library_name: transformers
license: mit
tags:
- deepseek_v3
- deepseek
- unsloth
- transformers
new_version: unsloth/DeepSeek-V3-0324-GGUF-UD
---
<div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>Read <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-v3-0324-locally">our guide</a> for detailed instructions on running DeepSeek-V3-0324 locally.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-v3-0324-locally">Dynamic Quants</a> is selectively quantized, greatly improving accuracy over standard bits.</em>
</p>
<div style="display: flex; gap: 5px; align-items: center; ">
<a href="https://github.com/unslothai/unsloth/">
<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
</a>
<a href="https://discord.gg/unsloth">
<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
</a>
<a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-v3-0324-locally">
<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
</a>
</div>
<h1 style="margin-top: 0rem;">DeepSeek-V3-0324 Dynamic GGUF</h2>
</div>
Our DeepSeek-V3-0324 GGUFs allow you to run the model in llama.cpp, LMStudio, Open WebUI and other inference frameworks.
Includes 1-4-bit Dynamic versions, which yields better accuracy and results than standard quantization.
| MoE Bits | Type | Disk Size | Accuracy | Link | Details |
|----------|----------|-------------|----------|------------------------------------------------------------------------------------------------------------|---------------------------------------------------|
| 1.78bit (prelim) | IQ1_S | **186GB** | Ok | [Link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF/tree/main/UD-IQ1_S) | `down_proj` in MoE mixture of 2.06/1.78bit |
| 1.93bit (prelim) | IQ1_M | **196GB** | Fair | [Link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF/tree/main/UD-IQ1_M) | `down_proj` in MoE mixture of 2.06/1.93bit |
| 2.42bit | IQ2_XXS | **219GB** | Recommended | [Link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF/tree/main/UD-IQ2_XXS) | `down_proj` in MoE all 2.42bit |
| 2.71bit | Q2_K_XL | **248GB** | Recommended | [Link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF/tree/main/UD-Q2_K_XL) | `down_proj` in MoE mixture of 3.5/2.71bit |
| 3.5bit | Q3_K_XL | **321GB** | Great | [Link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF/tree/main/UD-Q3_K_XL) | `down_proj` in MoE mixture of 4.5/3.5bit |
| 4.5bit | Q4_K_XL | **405GB** | Best | [Link](https://huggingface.co/unsloth/DeepSeek-V3-0324-GGUF/tree/main/UD-Q4_K_XL) | `down_proj` in MoE mixture of 5.5/4.5bit |
Prelim = preliminary - through our testing, they're generally fine but sometimes don't produce the best code and so more work/testing needs to be done.
2.71bit was found to be the best in terms of performance/size and produces code that is great and works well. 2.42bit was also found to pass all our tests.
So, for best results, use the 2.42-bit (IQ2_XXS) or 2.71-bit (Q2_K_XL) versions. Though not a must, try to have at least 180GB+ combined VRAM + RAM.
Thank you to the DeepSeek team for releasing their March update to the DeepSeek V3 models. Also thank you to [bartowski](https://huggingface.co/bartowski/deepseek-ai_DeepSeek-V3-0324-GGUF) for providing imatric V3 quants.
# Finetune your own Reasoning model like R1 with Unsloth!
We have a free Google Colab notebook for turning Llama 3.1 (8B) into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **GRPO with Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_(14B)-GRPO.ipynb) | 2x faster | 80% less |
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2.4x faster | 58% less |
| **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 60% less |
| **Qwen2 VL (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) | 1.8x faster | 60% less |
| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less |
| **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | 2.4x faster | 58% less |
| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb) | 2x faster | 50% less |
| **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) | 2.4x faster | 58% less |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less |
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V3" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V3-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Features
DeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects.
### Reasoning Capabilities
- Significant improvements in benchmark performance:
- MMLU-Pro: 75.9 → 81.2 (+5.3)
- GPQA: 59.1 → 68.4 (+9.3)
- AIME: 39.6 → 59.4 (+19.8)
- LiveCodeBench: 39.2 → 49.2 (+10.0)
### Front-End Web Development
- Improved the executability of the code
- More aesthetically pleasing web pages and game front-ends
### Chinese Writing Proficiency
- Enhanced style and content quality:
- Aligned with the R1 writing style
- Better quality in medium-to-long-form writing
- Feature Enhancements
- Improved multi-turn interactive rewriting
- Optimized translation quality and letter writing
### Chinese Search Capabilities
- Enhanced report analysis requests with more detailed outputs
### Function Calling Improvements
- Increased accuracy in Function Calling, fixing issues from previous V3 versions
---
## Usage Recommendations
### System Prompt
In the official DeepSeek web/app, we use the same system prompt with a specific date.
```
该助手为DeepSeek Chat，由深度求索公司创造。
今天是{current date}。
```
For example,
```
该助手为DeepSeek Chat，由深度求索公司创造。
今天是3月24日，星期一。
```
### Temperature
In our web and application environments, the temperature parameter $T_{model}$ is set to 0.3. Because many users use the default temperature 1.0 in API call, we have implemented an API temperature $T_{api}$ mapping mechanism that adjusts the input API temperature value of 1.0 to the most suitable model temperature setting of 0.3.
$$
T_{model} = T_{api} \times 0.3 \quad (0 \leq T_{api} \leq 1)
$$
$$
T_{model} = T_{api} - 0.7 \quad (1 < T_{api} \leq 2)
$$
Thus, if you call V3 via API, temperature 1.0 equals to the model temperature 0.3.
### Prompts for File Uploading and Web Search
For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.
```
file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""
```
For Web Search, {search_results}, {cur_date}, and {question} are arguments.
For Chinese query, we use the prompt:
```
search_answer_zh_template = \
'''# 以下内容是基于用户发送的消息的搜索结果:
{search_results}
在我给你的搜索结果中，每个结果都是[webpage X begin]...[webpage X end]格式的，X代表每篇文章的数字索引。请在适当的情况下在句子末尾引用上下文。请按照引用编号[citation:X]的格式在答案中对应部分引用上下文。如果一句话源自多个上下文，请列出所有相关的引用编号，例如[citation:3][citation:5]，切记不要将引用集中在最后返回引用编号，而是在答案对应部分列出。
在回答时，请注意以下几点：
- 今天是{cur_date}。
- 并非搜索结果的所有内容都与用户的问题密切相关，你需要结合问题，对搜索结果进行甄别、筛选。
- 对于列举类的问题（如列举所有航班信息），尽量将答案控制在10个要点以内，并告诉用户可以查看搜索来源、获得完整信息。优先提供信息完整、最相关的列举项；如非必要，不要主动告诉用户搜索结果未提供的内容。
- 对于创作类的问题（如写论文），请务必在正文的段落中引用对应的参考编号，例如[citation:3][citation:5]，不能只在文章末尾引用。你需要解读并概括用户的题目要求，选择合适的格式，充分利用搜索结果并抽取重要信息，生成符合用户要求、极具思想深度、富有创造力与专业性的答案。你的创作篇幅需要尽可能延长，对于每一个要点的论述要推测用户的意图，给出尽可能多角度的回答要点，且务必信息量大、论述详尽。
- 如果回答很长，请尽量结构化、分段落总结。如果需要分点作答，尽量控制在5个点以内，并合并相关的内容。
- 对于客观类的问答，如果问题的答案非常简短，可以适当补充一到两句相关信息，以丰富内容。
- 你需要根据用户要求和回答内容选择合适、美观的回答格式，确保可读性强。
- 你的回答应该综合多个相关网页来回答，不能重复引用一个网页。
- 除非用户要求，否则你回答的语言需要和用户提问的语言保持一致。
# 用户消息为：
{question}'''
```
For English query, we use the prompt:
```
search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''
```
## How to Run Locally
The model structure of DeepSeek-V3-0324 is exactly the same as DeepSeek-V3. Please visit [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3) repo for more information about running this model locally.
**This model supports features such as function calling, JSON output, and FIM completion. For instructions on how to construct prompts to use these features, please refer to [DeepSeek-V2.5](https://huggingface.co/deepseek-ai/DeepSeek-V2.5#function-calling) repo.**
**NOTE: Hugging Face's Transformers has not been directly supported yet.**
## License
This repository and the model weights are licensed under the [MIT License](LICENSE).
## Citation
```
@misc{deepseekai2024deepseekv3technicalreport,
title={DeepSeek-V3 Technical Report},
author={DeepSeek-AI},
year={2024},
eprint={2412.19437},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.19437},
}
```
## Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]). |
shrenikb/v5-gsm8k-experts | shrenikb | 2025-05-22T23:57:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-22T20:42:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep6_33 | MinaMila | 2025-05-22T23:57:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T23:57:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_ep4_55 | MinaMila | 2025-05-22T23:50:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T23:50:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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kakaocorp/kanana-1.5-8b-base | kakaocorp | 2025-05-22T23:38:50Z | 15 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ko",
"arxiv:2502.18934",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-15T08:42:47Z | ---
language:
- en
- ko
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
model_id: kakaocorp/kanana-1.5-8b-base
repo: kakaocorp/kanana-1.5-8b-base
developers: Kanana LLM
training_regime: bf16 mixed precision
---
<p align="center">
<br>
<picture>
<img src="./assets/logo/kanana-logo.png" width="60%" style="margin: 40px auto;">
</picture>
</br>
<p align="center">
🤗 <a href="https://kko.kakao.com/kananallm">1.5 HF Models</a>   |
  📕 <a href="https://tech.kakao.com/posts/707">1.5 Blog</a>   |
  📜 <a href="https://arxiv.org/abs/2502.18934">Technical Report</a>
<br>
## News 🔥
- ✨`2025/05/23`: Published a [blog post](https://tech.kakao.com/posts/707) about `Kanana 1.5` models and released 🤗[HF model weights](https://kko.kakao.com/kananallm).
- 📜`2025/02/27`: Released [Technical Report](https://arxiv.org/abs/2502.18934) and 🤗[HF model weights](https://huggingface.co/collections/kakaocorp/kanana-nano-21b-67a326cda1c449c8d4172259).
- 📕`2025/01/10`: Published a [blog post](https://tech.kakao.com/posts/682) about the development of `Kanana Nano` model.
- 📕`2024/11/14`: Published blog posts ([pre-training](https://tech.kakao.com/posts/661), [post-training](https://tech.kakao.com/posts/662)) about the development of `Kanana` models.
- ▶️`2024/11/06`: Published a [presentation video](https://youtu.be/HTBl142x9GI?si=o_we6t9suYK8DfX3) about the development of the `Kanana` models.
<br>
## Table of Contents
- [Kanana 1.5](#kanana-15)
- [Performance](#performance)
- [Base Model Evaluation](#base-model-evaluation)
- [Instruct Model Evaluation](#instruct-model-evaluation)
- [Processing 32K+ Length](#processing-32k-length)
- [Contributors](#contributors)
- [Citation](#citation)
- [Contact](#contact)
<br>
# Kanana 1.5
`Kanana 1.5`, a newly introduced version of the Kanana model family, presents substantial enhancements in **coding, mathematics, and function calling capabilities** over the previous version, enabling broader application to more complex real-world problems. This new version now can handle __up to 32K tokens length natively and up to 128K tokens using YaRN__, allowing the model to maintain coherence when handling extensive documents or engaging in extended conversations. Furthermore, Kanana 1.5 delivers more natural and accurate conversations through a __refined post-training process__.
<p align="center">
<br>
<picture>
<img src="./assets/performance/kanana-1.5-radar-8b.png" width="95%" style="margin: 40px auto;">
</picture>
</br>
> [!Note]
> Neither the pre-training nor the post-training data includes Kakao user data.
## Performance
### Base Model Evaluation
<table>
<tr>
<th>Models</th>
<th>MMLU</th>
<th>KMMLU</th>
<th>HAERAE</th>
<th>HumanEval</th>
<th>MBPP</th>
<th>GSM8K</th>
</tr>
<tr>
<td><strong>Kanana-1.5-8B</strong></td>
<td align="center">64.24</td>
<td align="center">48.94</td>
<td align="center">82.77</td>
<td align="center">61.59</td>
<td align="center">57.80</td>
<td align="center">63.53</td>
</tr>
<tr>
<td>Kanana-8B</td>
<td align="center">64.22</td>
<td align="center">48.30</td>
<td align="center">83.41</td>
<td align="center">40.24</td>
<td align="center">51.40</td>
<td align="center">57.09</td>
</tr>
</table>
<br>
### Instruct Model Evaluation
<table>
<tr>
<th>Models</th>
<th>MT-Bench</th>
<th>KoMT-Bench</th>
<th>IFEval</th>
<th>HumanEval+</th>
<th>MBPP+</th>
<th>GSM8K (0-shot)</th>
<th>MATH</th>
<th>MMLU (0-shot, CoT)</th>
<th>KMMLU (0-shot, CoT)</th>
<th>FunctionChatBench</th>
</tr>
<tr>
<td>Kanana-1.5-8B*</td>
<td align="center">7.76</td>
<td align="center">7.63</td>
<td align="center">80.11</td>
<td align="center">76.83</td>
<td align="center">67.99</td>
<td align="center">87.64</td>
<td align="center">67.54</td>
<td align="center">68.82</td>
<td align="center">48.28</td>
<td align="center">58.00</td>
</tr>
<tr>
<td>Kanana-8B</td>
<td align="center">7.13</td>
<td align="center">6.92</td>
<td align="center">76.91</td>
<td align="center">62.20</td>
<td align="center">43.92</td>
<td align="center">79.23</td>
<td align="center">37.68</td>
<td align="center">66.50</td>
<td align="center">47.43</td>
<td align="center">17.37</td>
</tr>
</table>
> [!Note]
> \* Models released under Apache 2.0 are trained on the latest versions compared to other models.
<br>
## Processing 32K+ Length
Currently, the `config.json` uploaded to HuggingFace is configured for token lengths of 32,768 or less. To process tokens beyond this length, YaRN must be applied. By updating the `config.json` with the following parameters, you can apply YaRN to handle token sequences up to 128K in length:
```json
"rope_scaling": {
"factor": 4.4,
"original_max_position_embeddings": 32768,
"type": "yarn",
"beta_fast": 64,
"beta_slow": 2
},
```
<br>
## Contributors
- Language Model Training: Yunju Bak, Doohae Jung, Boseop Kim, Nayeon Kim, Hojin Lee, Jaesun Park, Minho Ryu
- Language Model Alignment: Jiyeon Ham, Seungjae Jung, Hyunho Kim, Hyunwoong Ko, Changmin Lee, Daniel Wontae Nam
- AI Engineering: Youmin Kim, Hyeongju Kim
<br>
## Citation
```
@misc{kananallmteam2025kananacomputeefficientbilinguallanguage,
title={Kanana: Compute-efficient Bilingual Language Models},
author={Kanana LLM Team and Yunju Bak and Hojin Lee and Minho Ryu and Jiyeon Ham and Seungjae Jung and Daniel Wontae Nam and Taegyeong Eo and Donghun Lee and Doohae Jung and Boseop Kim and Nayeon Kim and Jaesun Park and Hyunho Kim and Hyunwoong Ko and Changmin Lee and Kyoung-Woon On and Seulye Baeg and Junrae Cho and Sunghee Jung and Jieun Kang and EungGyun Kim and Eunhwa Kim and Byeongil Ko and Daniel Lee and Minchul Lee and Miok Lee and Shinbok Lee and Gaeun Seo},
year={2025},
eprint={2502.18934},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.18934},
}
```
<br>
## Contact
- Kanana LLM Team Technical Support: [email protected]
- Business & Partnership Contact: [email protected] |
aifeifei798/DarkIdol-1.0 | aifeifei798 | 2025-05-22T23:29:59Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"image-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"diffusers:FluxPipeline",
"region:us"
]
| text-to-image | 2025-05-22T17:39:55Z | ---
language:
- en
license: apache-2.0
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- image-generation
widget:
- text: bikini model at sea, happy
output:
url: assets/1.png
- text: bikini model at sea, happy
output:
url: assets/2.png
- text: bikini model at sea, happy
output:
url: assets/3.png
- text: bikini model at sea, happy
output:
url: assets/4.png
- text: bikini model at sea, happy
output:
url: assets/5.png
- text: bikini model at sea, happy
output:
url: assets/6.png
- text: bikini model at sea, happy
output:
url: assets/7.png
- text: bikini model at sea, happy
output:
url: assets/8.png
- text: bikini model at sea, happy
output:
url: assets/9.png
- text: bikini model at sea, happy
output:
url: assets/10.png
- text: bikini model at sea
output:
url: assets/11.png
- text: bikini model at sea
output:
url: assets/12.png
instance_prompt: null
---
## DarkIdol-1.0
- Online Test https://huggingface.co/spaces/aifeifei798/DarkIdol-1.0
<table>
<thead>
<tr>
<th>1024 x 1792 (4 steps)</th>
</tr>
</thead>
<tbody>
<tr>
<td>
<img src="./assets/1.png">
<td>
</tr>
</tbody>
<tbody>
<tr>
<td>
bikini model at sea
</td>
</tr>
</tbody>
</table>
## Inference code
```python
from diffusers import FluxPipeline
import torch
import numpy as np
MAX_SEED = np.iinfo(np.int32).max
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
pipeline = FluxPipeline.from_pretrained(
"aifeifei798/DarkIdol-1.0", torch_dtype=torch.bfloat16
).to("cuda")
# Enable VAE big pic
pipeline.vae.enable_slicing()
pipeline.vae.enable_tiling()
image = pipeline(
prompt="bikini model at sea",
guidance_scale=0,
num_inference_steps=4,
height=1792,
width=1024,
max_sequence_length=512,
generator=generator,
).images[0]
image.save("DarkIdol.png")
```
<img src="./assets/1.png">
## Documentation
* https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux
* https://huggingface.co/docs/diffusers/main/en/api/models/flux_transformer |
redis/langcache-embed-v2 | redis | 2025-05-22T23:27:12Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"onnx",
"safetensors",
"openvino",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:36864",
"loss:MatryoshkaLoss",
"loss:CachedMultipleNegativesRankingLoss",
"arxiv:2504.02268",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:2101.06983",
"base_model:redis/langcache-embed-v1",
"base_model:quantized:redis/langcache-embed-v1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-21T18:24:00Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:36864
- loss:MatryoshkaLoss
- loss:CachedMultipleNegativesRankingLoss
base_model: redis/langcache-embed-v1
widget:
- source_sentence: What are civil cases and what are some examples?
sentences:
- What are criminal cases and what are no examples?
- Civil cases involve disputes between individuals or organizations, typically seeking
monetary compensation or specific performance, and *do not* include criminal prosecutions
by the government.
- Criminal cases involve disputes between individuals or organizations, seeking
monetary damages or specific performance, while civil cases concern offenses against
the state punishable by imprisonment.
- What are some examples of civil cases?
- source_sentence: How can you stop your palms from sweating?
sentences:
- How do I stop my palms from sweating a lot at random times?
- How can you *make* your palms sweat?
- How can you *cause* your palms to sweat?
- How can you start your palms from sweating?
- source_sentence: What are the pros and cons of wind turbines?
sentences:
- What are the pros and cons of solar panels?
- What are the cons and pros of solar panels?
- What are pros and cons of wind turbines?
- Wind turbines have no advantages or disadvantages.
- source_sentence: Will Obamacare be repealed now that trump won?
sentences:
- Despite Trump's victory, Obamacare remains largely intact and has not been fully
repealed.
- Despite Trump's repeated promises to repeal and replace the Affordable Care Act
(ACA), often called Obamacare, it remains the law of the land. Numerous attempts
to repeal or significantly alter the ACA failed during his presidency due to Congressional
opposition.
- Will Obamacare be repealed now that Biden won?
- Will Obamacare be repealed / shut down soon?
- source_sentence: What are some examples of crimes understood as a moral turpitude?
sentences:
- What actions are *not* generally considered crimes involving moral turpitude?
- What are some examples of crimes understood as a legal aptitude?
- What are some examples of crimes understood as a legal turpitude?
- What are some examples of crimes of moral turpitude?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on redis/langcache-embed-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [redis/langcache-embed-v1](https://huggingface.co/redis/langcache-embed-v1) on the triplet dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [redis/langcache-embed-v1](https://huggingface.co/redis/langcache-embed-v1) <!-- at revision 80fb95b5478a6b6d068faf4452faa2f5bc9f0dfa -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- triplet
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("redis/langcache-embed-v2")
# Run inference
sentences = [
'What are some examples of crimes understood as a moral turpitude?',
'What are some examples of crimes of moral turpitude?',
'What are some examples of crimes understood as a legal aptitude?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
* Dataset: triplet
* Size: 36,864 training samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, and <code>negative_3</code>
<!-- * Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative_1 | negative_2 | negative_3 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.88 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.89 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.68 tokens</li><li>max: 118 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.26 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.07 tokens</li><li>max: 108 tokens</li></ul> | -->
* Samples:
| anchor | positive | negative_1 | negative_2 | negative_3 |
|:---------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Is life really what I make of it?</code> | <code>Life is what you make it?</code> | <code>Is life hardly what I take of it?</code> | <code>Life is not entirely what I make of it.</code> | <code>Is life not what I make of it?</code> |
| <code>When you visit a website, can a person running the website see your IP address?</code> | <code>Does every website I visit knows my public ip address?</code> | <code>When you avoid a website, can a person hiding the website see your MAC address?</code> | <code>When you send an email, can the recipient see your physical location?</code> | <code>When you visit a website, a person running the website cannot see your IP address.</code> |
| <code>What are some cool features about iOS 10?</code> | <code>What are the best new features of iOS 10?</code> | <code>iOS 10 received criticism for its initial bugs and performance issues, and some users found the redesigned apps less intuitive compared to previous versions.</code> | <code>What are the drawbacks of using Android 14?</code> | <code>iOS 10 was widely criticized for its bugs, removal of beloved features, and generally being a downgrade from previous versions.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [768,512,256,128,64],
"matryoshka_weights": [1,1,1,1,1],
"n_dims_per_step": -1
}
```
### Evaluation
<!-- ### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 1024
- `learning_rate`: 1e-05
- `num_train_epochs`: 1
- `lr_scheduler_type`: constant
- `warmup_steps`: 10
- `gradient_checkpointing`: True
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `batch_sampler`: no_duplicates -->
<!-- #### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 1024
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: constant
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 10
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: True
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: True
- `torch_compile_backend`: inductor
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | triplet loss |
|:------:|:----:|:-------------:|:------------:|
| 0.0556 | 1 | 6.4636 | - |
| 0.1111 | 2 | 6.1076 | - |
| 0.1667 | 3 | 5.8323 | - |
| 0.2222 | 4 | 5.6861 | - |
| 0.2778 | 5 | 5.5694 | - |
| 0.3333 | 6 | 5.2121 | - |
| 0.3889 | 7 | 5.0695 | - |
| 0.4444 | 8 | 4.81 | - |
| 0.5 | 9 | 4.6698 | - |
| 0.5556 | 10 | 4.3546 | 1.2224 |
| 0.6111 | 11 | 4.1922 | - |
| 0.6667 | 12 | 4.1434 | - |
| 0.7222 | 13 | 3.9918 | - |
| 0.7778 | 14 | 3.702 | - |
| 0.8333 | 15 | 3.6501 | - |
| 0.8889 | 16 | 3.6641 | - |
| 0.9444 | 17 | 3.3196 | - |
| 1.0 | 18 | 2.7108 | - |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1 -->
## Citation
#### Redis Langcache-embed Models
We encourage you to cite our work if you use our models or build upon our findings.
```bibtex
@inproceedings{langcache-embed-v1,
title = "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data",
author = "Gill, Cechmanek, Hutcherson, Rajamohan, Agarwal, Gulzar, Singh, Dion",
month = "04",
year = "2025",
url = "https://arxiv.org/abs/2504.02268",
}
```
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep1_33 | MinaMila | 2025-05-22T23:25:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T23:25:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
baha-from-nukus-city/Distilbert | baha-from-nukus-city | 2025-05-22T23:12:40Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-03-06T03:48:58Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1273
- Accuracy: 0.969
- F1: 0.9689
## 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: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4515 | 1.0 | 141 | 0.1685 | 0.9215 | 0.9242 |
| 0.1561 | 2.0 | 282 | 0.1402 | 0.955 | 0.9532 |
| 0.0658 | 3.0 | 423 | 0.1033 | 0.9645 | 0.9641 |
| 0.0475 | 4.0 | 564 | 0.1081 | 0.9685 | 0.9683 |
| 0.0167 | 5.0 | 705 | 0.1273 | 0.969 | 0.9689 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_cfda_ep8_22 | MinaMila | 2025-05-22T23:06:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T23:05:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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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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MinaMila/llama_instbase_unlearned_ug_e-6_1.0_0.25_0.5_ep3_LoRa_GermanCredit_ep7_42 | MinaMila | 2025-05-22T23:05:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T23:05:12Z | ---
library_name: transformers
tags: []
---
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## How to Get Started with the Model
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[More Information Needed]
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#### 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).
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