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tomaarsen/csr-mxbai-embed-large-v1-nq
|
tomaarsen
| 2025-06-23T11:32:32Z | 10 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sparse-encoder",
"sparse",
"csr",
"generated_from_trainer",
"dataset_size:99000",
"loss:CSRLoss",
"loss:SparseMultipleNegativesRankingLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/natural-questions",
"arxiv:1908.10084",
"arxiv:2503.01776",
"arxiv:1705.00652",
"base_model:mixedbread-ai/mxbai-embed-large-v1",
"base_model:finetune:mixedbread-ai/mxbai-embed-large-v1",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-20T16:12:18Z |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- csr
- generated_from_trainer
- dataset_size:99000
- loss:CSRLoss
- loss:SparseMultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
widget:
- text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia
continue to take somewhat differing stances on regional conflicts such the Yemeni
Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement,
which has fought against Saudi-backed forces, and the Syrian Civil War, where
the UAE has disagreed with Saudi support for Islamist movements.[4]
- text: Economy of New Zealand New Zealand's diverse market economy has a sizable
service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale
manufacturing industries include aluminium production, food processing, metal
fabrication, wood and paper products. Mining, manufacturing, electricity, gas,
water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary
sector continues to dominate New Zealand's exports, despite accounting for 6.5%
of GDP in 2013.[17]
- text: who was the first president of indian science congress meeting held in kolkata
in 1914
- text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as
a single after a fourteen-year breakup. It was also the first song written by
bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was
played live for the first time during their Hell Freezes Over tour in 1994. It
returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at
No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream
Rock Tracks chart. The song was not played live by the Eagles after the "Hell
Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S.
- text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion
who is considered by Christians to be one of the first Gentiles to convert to
the faith, as related in Acts of the Apostles.'
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 44.98232094738378
energy_consumed: 0.11572443915231664
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.296
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Sparse CSR model trained on Natural Questions
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 4
type: nq_eval_4
metrics:
- type: cosine_accuracy@1
value: 0.332
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.477
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.555
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.651
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.332
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.159
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.111
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06509999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.477
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.555
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.651
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48176320654736343
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4288392857142859
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.43687429825818597
name: Cosine Map@100
- type: query_active_dims
value: 4.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9990234375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 4.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9990234375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 8
type: nq_eval_8
metrics:
- type: cosine_accuracy@1
value: 0.513
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.762
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.822
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.513
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23333333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15239999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08220000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.513
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.762
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.822
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6689182882280541
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6197142857142861
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6254200552756788
name: Cosine Map@100
- type: query_active_dims
value: 8.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.998046875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 8.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.998046875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 16
type: nq_eval_16
metrics:
- type: cosine_accuracy@1
value: 0.675
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.852
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.889
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.929
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.675
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2839999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1778
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09290000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.675
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.852
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.889
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.929
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.808847726095864
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7695670634920638
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7727056530256143
name: Cosine Map@100
- type: query_active_dims
value: 16.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.99609375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 16.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.99609375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 32
type: nq_eval_32
metrics:
- type: cosine_accuracy@1
value: 0.817
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.926
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.955
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.975
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.817
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30866666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19100000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09750000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.817
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.926
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.955
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.975
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9006041699789782
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8762321428571431
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8772002104508256
name: Cosine Map@100
- type: query_active_dims
value: 32.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9921875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 32.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9921875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 64
type: nq_eval_64
metrics:
- type: cosine_accuracy@1
value: 0.88
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.956
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.973
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.983
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.88
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31866666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19460000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09830000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.88
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.956
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.973
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.983
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9358590439656094
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9202527777777781
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9210251316831527
name: Cosine Map@100
- type: query_active_dims
value: 64.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.984375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 64.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.984375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 128
type: nq_eval_128
metrics:
- type: cosine_accuracy@1
value: 0.924
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.981
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.985
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.992
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.924
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32699999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19700000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09920000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.924
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.981
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.985
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.992
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9623782359855955
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9524289682539683
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9527615760504997
name: Cosine Map@100
- type: query_active_dims
value: 128.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.96875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 128.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.96875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 256
type: nq_eval_256
metrics:
- type: cosine_accuracy@1
value: 0.932
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.987
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.989
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.932
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.32899999999999996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1978
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09940000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.932
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.987
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.989
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.994
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9677690134872508
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9588666666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.959089060056276
name: Cosine Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
---
# Sparse CSR model trained on Natural Questions
This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** CSR Sparse Encoder
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions)
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
)
```
## 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 SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq")
# Run inference
queries = [
"who is cornelius in the book of acts",
]
documents = [
'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
"Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 4096] [3, 4096]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.6870, 0.1735, 0.1552]])
```
<!--
### 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.*
-->
## Evaluation
### Metrics
#### Sparse Information Retrieval
* Dataset: `nq_eval_4`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 4
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.332 |
| cosine_accuracy@3 | 0.477 |
| cosine_accuracy@5 | 0.555 |
| cosine_accuracy@10 | 0.651 |
| cosine_precision@1 | 0.332 |
| cosine_precision@3 | 0.159 |
| cosine_precision@5 | 0.111 |
| cosine_precision@10 | 0.0651 |
| cosine_recall@1 | 0.332 |
| cosine_recall@3 | 0.477 |
| cosine_recall@5 | 0.555 |
| cosine_recall@10 | 0.651 |
| **cosine_ndcg@10** | **0.4818** |
| cosine_mrr@10 | 0.4288 |
| cosine_map@100 | 0.4369 |
| query_active_dims | 4.0 |
| query_sparsity_ratio | 0.999 |
| corpus_active_dims | 4.0 |
| corpus_sparsity_ratio | 0.999 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_8`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 8
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.513 |
| cosine_accuracy@3 | 0.7 |
| cosine_accuracy@5 | 0.762 |
| cosine_accuracy@10 | 0.822 |
| cosine_precision@1 | 0.513 |
| cosine_precision@3 | 0.2333 |
| cosine_precision@5 | 0.1524 |
| cosine_precision@10 | 0.0822 |
| cosine_recall@1 | 0.513 |
| cosine_recall@3 | 0.7 |
| cosine_recall@5 | 0.762 |
| cosine_recall@10 | 0.822 |
| **cosine_ndcg@10** | **0.6689** |
| cosine_mrr@10 | 0.6197 |
| cosine_map@100 | 0.6254 |
| query_active_dims | 8.0 |
| query_sparsity_ratio | 0.998 |
| corpus_active_dims | 8.0 |
| corpus_sparsity_ratio | 0.998 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_16`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 16
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.675 |
| cosine_accuracy@3 | 0.852 |
| cosine_accuracy@5 | 0.889 |
| cosine_accuracy@10 | 0.929 |
| cosine_precision@1 | 0.675 |
| cosine_precision@3 | 0.284 |
| cosine_precision@5 | 0.1778 |
| cosine_precision@10 | 0.0929 |
| cosine_recall@1 | 0.675 |
| cosine_recall@3 | 0.852 |
| cosine_recall@5 | 0.889 |
| cosine_recall@10 | 0.929 |
| **cosine_ndcg@10** | **0.8088** |
| cosine_mrr@10 | 0.7696 |
| cosine_map@100 | 0.7727 |
| query_active_dims | 16.0 |
| query_sparsity_ratio | 0.9961 |
| corpus_active_dims | 16.0 |
| corpus_sparsity_ratio | 0.9961 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_32`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 32
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.817 |
| cosine_accuracy@3 | 0.926 |
| cosine_accuracy@5 | 0.955 |
| cosine_accuracy@10 | 0.975 |
| cosine_precision@1 | 0.817 |
| cosine_precision@3 | 0.3087 |
| cosine_precision@5 | 0.191 |
| cosine_precision@10 | 0.0975 |
| cosine_recall@1 | 0.817 |
| cosine_recall@3 | 0.926 |
| cosine_recall@5 | 0.955 |
| cosine_recall@10 | 0.975 |
| **cosine_ndcg@10** | **0.9006** |
| cosine_mrr@10 | 0.8762 |
| cosine_map@100 | 0.8772 |
| query_active_dims | 32.0 |
| query_sparsity_ratio | 0.9922 |
| corpus_active_dims | 32.0 |
| corpus_sparsity_ratio | 0.9922 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_64`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 64
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.88 |
| cosine_accuracy@3 | 0.956 |
| cosine_accuracy@5 | 0.973 |
| cosine_accuracy@10 | 0.983 |
| cosine_precision@1 | 0.88 |
| cosine_precision@3 | 0.3187 |
| cosine_precision@5 | 0.1946 |
| cosine_precision@10 | 0.0983 |
| cosine_recall@1 | 0.88 |
| cosine_recall@3 | 0.956 |
| cosine_recall@5 | 0.973 |
| cosine_recall@10 | 0.983 |
| **cosine_ndcg@10** | **0.9359** |
| cosine_mrr@10 | 0.9203 |
| cosine_map@100 | 0.921 |
| query_active_dims | 64.0 |
| query_sparsity_ratio | 0.9844 |
| corpus_active_dims | 64.0 |
| corpus_sparsity_ratio | 0.9844 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_128`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 128
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.924 |
| cosine_accuracy@3 | 0.981 |
| cosine_accuracy@5 | 0.985 |
| cosine_accuracy@10 | 0.992 |
| cosine_precision@1 | 0.924 |
| cosine_precision@3 | 0.327 |
| cosine_precision@5 | 0.197 |
| cosine_precision@10 | 0.0992 |
| cosine_recall@1 | 0.924 |
| cosine_recall@3 | 0.981 |
| cosine_recall@5 | 0.985 |
| cosine_recall@10 | 0.992 |
| **cosine_ndcg@10** | **0.9624** |
| cosine_mrr@10 | 0.9524 |
| cosine_map@100 | 0.9528 |
| query_active_dims | 128.0 |
| query_sparsity_ratio | 0.9688 |
| corpus_active_dims | 128.0 |
| corpus_sparsity_ratio | 0.9688 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_256`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 256
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.932 |
| cosine_accuracy@3 | 0.987 |
| cosine_accuracy@5 | 0.989 |
| cosine_accuracy@10 | 0.994 |
| cosine_precision@1 | 0.932 |
| cosine_precision@3 | 0.329 |
| cosine_precision@5 | 0.1978 |
| cosine_precision@10 | 0.0994 |
| cosine_recall@1 | 0.932 |
| cosine_recall@3 | 0.987 |
| cosine_recall@5 | 0.989 |
| cosine_recall@10 | 0.994 |
| **cosine_ndcg@10** | **0.9678** |
| cosine_mrr@10 | 0.9589 |
| cosine_map@100 | 0.9591 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 0.9375 |
<!--
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 0.1,
"loss": "SparseMultipleNegativesRankingLoss(scale=20.0, similarity_fct='cos_sim')"
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
| <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 0.1,
"loss": "SparseMultipleNegativesRankingLoss(scale=20.0, similarity_fct='cos_sim')"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 4e-05
- `num_train_epochs`: 1
- `bf16`: True
- `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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 4e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `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`: True
- `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}
- `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`: False
- `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`: False
- `torch_compile_backend`: None
- `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
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | nq_eval_4_cosine_ndcg@10 | nq_eval_8_cosine_ndcg@10 | nq_eval_16_cosine_ndcg@10 | nq_eval_32_cosine_ndcg@10 | nq_eval_64_cosine_ndcg@10 | nq_eval_128_cosine_ndcg@10 | nq_eval_256_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:--------------------------:|:--------------------------:|
| -1 | -1 | - | - | 0.2797 | 0.4593 | 0.7019 | 0.8753 | 0.9323 | 0.9620 | 0.9714 |
| 0.0646 | 100 | 0.3149 | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.2765 | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 0.2651 | 0.2500 | 0.3976 | 0.5760 | 0.7712 | 0.8849 | 0.9314 | 0.9551 | 0.9650 |
| 0.2586 | 400 | 0.2572 | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.2517 | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 0.2484 | 0.2364 | 0.4423 | 0.6333 | 0.7956 | 0.8963 | 0.9350 | 0.9570 | 0.9670 |
| 0.4525 | 700 | 0.2454 | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.2431 | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.2411 | 0.2300 | 0.4755 | 0.6660 | 0.7986 | 0.9035 | 0.9370 | 0.9582 | 0.9695 |
| 0.6464 | 1000 | 0.2397 | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.2378 | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.2375 | 0.2268 | 0.4763 | 0.6699 | 0.8040 | 0.8987 | 0.9355 | 0.9592 | 0.9673 |
| 0.8403 | 1300 | 0.2371 | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.2358 | - | - | - | - | - | - | - | - |
| 0.9696 | 1500 | 0.2359 | 0.2256 | 0.4813 | 0.6709 | 0.8088 | 0.9003 | 0.9348 | 0.9622 | 0.9687 |
| -1 | -1 | - | - | 0.4818 | 0.6689 | 0.8088 | 0.9006 | 0.9359 | 0.9624 | 0.9678 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.116 kWh
- **Carbon Emitted**: 0.045 kg of CO2
- **Hours Used**: 0.296 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### 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",
}
```
#### CSRLoss
```bibtex
@misc{wen2025matryoshkarevisitingsparsecoding,
title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You},
year={2025},
eprint={2503.01776},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.01776},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## 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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|
m-aliabbas1/SmolLM2-FT-MED
|
m-aliabbas1
| 2025-06-23T11:29:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T11:29:10Z |
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MED
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MED
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
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="m-aliabbas1/SmolLM2-FT-MED", 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: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.5.1
- 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}}
}
```
|
imrahulwarkade/tinyllama-toneopbot-lora
|
imrahulwarkade
| 2025-06-23T11:26:29Z | 0 | 0 | null |
[
"safetensors",
"tinyllama",
"toneop",
"lora",
"fine-tuning",
"health-chatbot",
"conversational",
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T11:19:11Z |
---
license: apache-2.0
tags:
- tinyllama
- toneop
- lora
- fine-tuning
- health-chatbot
- conversational
---
# 🧠 TinyLLaMA-ToneOpBot (LoRA Adapter)
This is a lightweight fine-tuned **TinyLLaMA-1.1B-Chat** model using **LoRA adapters** for health and fitness Q&A, built by [@imrahulwarkade](https://huggingface.co/imrahulwarkade).
> Designed for commercial chatbot applications focused on wellness, diet, and healthy lifestyle.
---
## 🧪 Base Model
- [`TinyLlama/TinyLlama-1.1B-Chat-v1.0`](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
---
## 🧰 How to Use (with PEFT)
```python
from transformers import AutoTokenizer, pipeline
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
# Load adapter
adapter_id = "imrahulwarkade/tinyllama-toneopbot-lora"
config = PeftConfig.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(base_model, adapter_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Prompt
messages = [
{"role": "user", "content": "How can I lose weight in a healthy way?"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
response = pipe(prompt, max_new_tokens=150)[0]["generated_text"]
print(response)
|
Hachipo/OpenCoder-8B-Base-MIFT-en_newbase_v1-MIFT-ja_10000
|
Hachipo
| 2025-06-23T11:26:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T11:23:19Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-headlines-comb-2-seed-7-2025-06-23
|
morturr
| 2025-06-23T11:24:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T11:24:37Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-headlines-comb-2-seed-7-2025-06-23
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. -->
# Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-headlines-comb-2-seed-7-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Parakeet-Inc/furigana_whisper_small_jsut
|
Parakeet-Inc
| 2025-06-23T11:23:26Z | 0 | 0 | null |
[
"safetensors",
"whisper",
"automatic-speech-recognition",
"ja",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2025-06-23T08:15:51Z |
---
language:
- ja
base_model:
- openai/whisper-small
pipeline_tag: automatic-speech-recognition
license: apache-2.0
---
## 概要
- 日本語の音声ファイルに対して、書記素列（漢字仮名交じり文）をプロンプトに入れることで、書記素列と整合性のあるモーラ列（カタカナ列）を出力するモデルです。
- 詳細については以下の記事を参考にしてください: [日本語TTS用の学習データの精度を上げる「ふりがなWhisper」を作った話](https://zenn.dev/parakeet_tech/articles/2591e71094ea58)
- License: Apache-2.0
## 使用方法
```python
from transformers import pipeline
from pathlib import Path
pipe = pipeline(
"automatic-speech-recognition",
model="Parakeet-Inc/furigana_whisper_small_jsut",
)
def transcribe_with_prompt(pipe, audio_path: str | Path, prompt: str) -> str:
prompt_ids = pipe.tokenizer.get_prompt_ids(
prompt, return_tensors="pt"
).to(pipe.device)
generate_kwargs = {"prompt_ids": prompt_ids}
result = pipe(str(audio_path), generate_kwargs=generate_kwargs)
return result["text"]
# 実行例
audio_path = "path/to/your/audio.wav"
prompt = "明日は晴れ。"
transcription = transcribe_with_prompt(pipe, audio_path, prompt)
print(transcription) # アスワハレ。
```
## 注意
- 音声の長さは30秒以下でないとうまく動きません。
- 公開しているsmallモデルはそこまで精度が良いとは言えず、G2Pマッチ率（データセットに対してフィルタリングを行った後に残るデータ量）が40%程度となっています。より精度の高いモデルを使いたい方はデータを揃え、ベースモデルもwhisper-smallより大きいモデルにして自分で学習を行うことをおすすめします。
- 学習データでのプロンプトは、全て「句読点が、。のみ」「最後に必ず。が付く」と正規化されています。よって、与えるプロンプトも同様の形式にしたほうが精度が高くなります。
|
Oluwajoba/ds_LoRA_run5_withHDdataset
|
Oluwajoba
| 2025-06-23T11:23:14Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-23T11:23:03Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a face showing the effects of drug abuse
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - Oluwajoba/ds_LoRA_run5_withHDdataset
<Gallery />
## Model description
These are Oluwajoba/ds_LoRA_run5_withHDdataset LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a face showing the effects of drug abuse to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Oluwajoba/ds_LoRA_run5_withHDdataset/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
surateakash/scene_classification_swin_large
|
surateakash
| 2025-06-23T11:23:12Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T10:20:01Z |
---
license: apache-2.0
---
|
kurosekurose/mert_cmp_single
|
kurosekurose
| 2025-06-23T11:22:25Z | 0 | 0 | null |
[
"safetensors",
"mert",
"generated_from_trainer",
"base_model:m-a-p/MERT-v0",
"base_model:finetune:m-a-p/MERT-v0",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-20T13:11:27Z |
---
license: cc-by-nc-4.0
base_model: m-a-p/MERT-v0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mert_cmp_single
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. -->
# mert_cmp_single
This model is a fine-tuned version of [m-a-p/MERT-v0](https://huggingface.co/m-a-p/MERT-v0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5373
- Accuracy: 0.4588
- Precision Micro: 0.4588
- Recall Micro: 0.4588
- F1 Micro: 0.4588
- Precision Macro: 0.2915
- Recall Macro: 0.3266
- F1 Macro: 0.2877
- Precision Weighted: 0.3892
- Recall Weighted: 0.4588
- F1 Weighted: 0.4111
## 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: 0.005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Micro | Recall Micro | F1 Micro | Precision Macro | Recall Macro | F1 Macro | Precision Weighted | Recall Weighted | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
| 1.9863 | 1.0 | 167 | 1.8525 | 0.3421 | 0.3421 | 0.3421 | 0.3421 | 0.1125 | 0.1591 | 0.1151 | 0.2027 | 0.3421 | 0.2380 |
| 1.825 | 2.0 | 334 | 1.8022 | 0.3395 | 0.3395 | 0.3395 | 0.3395 | 0.1190 | 0.2242 | 0.1392 | 0.2482 | 0.3395 | 0.2694 |
| 1.7777 | 3.0 | 501 | 1.6796 | 0.3877 | 0.3877 | 0.3877 | 0.3877 | 0.1842 | 0.2353 | 0.1812 | 0.3123 | 0.3877 | 0.3247 |
| 1.7034 | 4.0 | 668 | 1.5932 | 0.4193 | 0.4193 | 0.4193 | 0.4193 | 0.2826 | 0.2972 | 0.2541 | 0.3787 | 0.4193 | 0.3767 |
| 1.5985 | 5.0 | 835 | 1.5373 | 0.4588 | 0.4588 | 0.4588 | 0.4588 | 0.2915 | 0.3266 | 0.2877 | 0.3892 | 0.4588 | 0.4111 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 3.6.0
- Tokenizers 0.19.1
|
SabahNawab/llama3.2_3B-urdu-qlora_1
|
SabahNawab
| 2025-06-23T11:21:07Z | 3 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"region:us"
] | null | 2025-06-20T10:22:49Z |
---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- generated_from_trainer
model-index:
- name: llama3.2_3B-urdu-qlora_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3.2_3B-urdu-qlora_1
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7321
## 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 48
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7861 | 1.0 | 625 | 1.7956 |
| 1.6825 | 2.0 | 1250 | 1.7502 |
| 1.6389 | 3.0 | 1875 | 1.7371 |
| 1.6103 | 4.0 | 2500 | 1.7324 |
| 1.5915 | 5.0 | 3125 | 1.7321 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
cucucu666/wink-6.23-female
|
cucucu666
| 2025-06-23T11:17:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-Fill-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Fill-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T08:29:06Z |
---
base_model: black-forest-labs/FLUX.1-Fill-dev
library_name: diffusers
license: other
instance_prompt: labii female face, Crayon Shin-chan style, winking expression, one_eye_closed,
eyelash, gentle smile, plain white background
widget:
- text: labii female face, Crayon Shin-chan style, winking expression, one_eye_closed,
eyelash, gentle smile, plain white background
output:
url: image_0.png
- text: labii female face, Crayon Shin-chan style, winking expression, one_eye_closed,
eyelash, gentle smile, plain white background
output:
url: image_1.png
- text: labii female face, Crayon Shin-chan style, winking expression, one_eye_closed,
eyelash, gentle smile, plain white background
output:
url: image_2.png
- text: labii female face, Crayon Shin-chan style, winking expression, one_eye_closed,
eyelash, gentle smile, plain white background
output:
url: image_3.png
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-Fill DreamBooth LoRA - cucucu666/wink-6.23-female
<Gallery />
## Model description
These are cucucu666/wink-6.23-female DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `labii female face, Crayon Shin-chan style, winking expression, one_eye_closed, eyelash, gentle smile, plain white background` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cucucu666/wink-6.23-female/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('cucucu666/wink-6.23-female', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('labii female face, Crayon Shin-chan style, winking expression, one_eye_closed, eyelash, gentle smile, plain white background').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]
|
rossieRuby/nyayadrishti-lora-v4
|
rossieRuby
| 2025-06-23T11:17:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:nvidia/Minitron-4B-Base",
"base_model:adapter:nvidia/Minitron-4B-Base",
"region:us"
] | null | 2025-06-23T11:17:00Z |
---
base_model: nvidia/Minitron-4B-Base
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.dev0
|
SamHumez/Miniatures-model
|
SamHumez
| 2025-06-23T11:13:58Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T10:53:12Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SAM
---
# Miniatures Model
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SAM` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SAM",
"lora_weights": "https://huggingface.co/SamHumez/Miniatures-model/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('SamHumez/Miniatures-model', weight_name='lora.safetensors')
image = pipeline('SAM').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)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/SamHumez/Miniatures-model/discussions) to add images that show off what you’ve made with this LoRA.
|
qibai/Qwen2-0.5B-GRPO-test
|
qibai
| 2025-06-23T11:10:06Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:AI-MO/NuminaMath-TIR",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T07:49:16Z |
---
datasets: AI-MO/NuminaMath-TIR
library_name: transformers
model_name: Qwen2-0.5B-GRPO-test
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-0.5B-GRPO-test
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) 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="qibai/Qwen2-0.5B-GRPO-test", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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}}
}
```
|
morturr/Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-1-seed-7-2025-06-23
|
morturr
| 2025-06-23T11:09:58Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-23T11:09:50Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-1-seed-7-2025-06-23
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. -->
# Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-one_liners-comb-1-seed-7-2025-06-23
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Varinder2110/teja
|
Varinder2110
| 2025-06-23T11:08:38Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T10:02:52Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Teja
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Varinder2110/teja/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Varinder2110/teja', weight_name='lora.safetensors')
image = pipeline('TOK').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)
## Training details
- Steps: 6000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Varinder2110/teja/discussions) to add images that show off what you’ve made with this LoRA.
|
mao7337/tuu
|
mao7337
| 2025-06-23T11:07:02Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T10:15:11Z |
---
license: apache-2.0
---
|
unconnected59/vieo
|
unconnected59
| 2025-06-23T11:06:55Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-23T11:06:54Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Nicolas
---
# Vieo
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Nicolas` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Nicolas",
"lora_weights": "https://huggingface.co/unconnected59/vieo/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('unconnected59/vieo', weight_name='lora.safetensors')
image = pipeline('Nicolas').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)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/unconnected59/vieo/discussions) to add images that show off what you’ve made with this LoRA.
|
versaceeros/dfdc0353-04fc-4d3e-9e43-9d0d1bb98b18
|
versaceeros
| 2025-06-23T11:06:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-23T10:50:40Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
floflodebilbao/T5_sum_challenge2
|
floflodebilbao
| 2025-06-23T11:05:27Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-large",
"base_model:finetune:google-t5/t5-large",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-20T13:35:43Z |
---
library_name: transformers
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
- precision
- recall
- f1
model-index:
- name: T5_sum_challenge2
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. -->
# T5_sum_challenge2
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.2177
- Rouge2: 0.063
- Rougel: 0.167
- Rougelsum: 0.1689
- Gen Len: 20.0
- Bleu: 0.0246
- Precisions: 0.0879
- Brevity Penalty: 0.5266
- Length Ratio: 0.6093
- Translation Length: 736.0
- Reference Length: 1208.0
- Precision: 0.8576
- Recall: 0.8527
- F1: 0.8551
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4)
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Precision | Recall | F1 | Hashcode |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:----------:|:---------------:|:------------:|:------------------:|:----------------:|:---------:|:------:|:------:|:---------------------------------------------------------:|
| No log | 1.0 | 7 | nan | 0.2177 | 0.063 | 0.167 | 0.1689 | 20.0 | 0.0246 | 0.0879 | 0.5266 | 0.6093 | 736.0 | 1208.0 | 0.8576 | 0.8527 | 0.8551 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
| No log | 2.0 | 14 | nan | 0.2177 | 0.063 | 0.167 | 0.1689 | 20.0 | 0.0246 | 0.0879 | 0.5266 | 0.6093 | 736.0 | 1208.0 | 0.8576 | 0.8527 | 0.8551 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
| No log | 3.0 | 21 | nan | 0.2177 | 0.063 | 0.167 | 0.1689 | 20.0 | 0.0246 | 0.0879 | 0.5266 | 0.6093 | 736.0 | 1208.0 | 0.8576 | 0.8527 | 0.8551 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
| No log | 4.0 | 28 | nan | 0.2177 | 0.063 | 0.167 | 0.1689 | 20.0 | 0.0246 | 0.0879 | 0.5266 | 0.6093 | 736.0 | 1208.0 | 0.8576 | 0.8527 | 0.8551 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
hasdal/892df17d-4a49-4a8b-aead-9a7862b3b74d
|
hasdal
| 2025-06-23T11:03:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-23T10:50:36Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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#### Hardware
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#### Software
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## Citation [optional]
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[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. -->
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## Model Card Contact
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|
NemoraAi/roberta-chat-moderation-X
|
NemoraAi
| 2025-06-23T11:02:49Z | 10,480 | 1 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"moderation",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-16T12:39:02Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
- moderation
metrics:
- accuracy
model-index:
- name: roberta-chat-moderation-X
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-chat-moderation-X
For details on training, limitations, and integration, check out the full blog post:
https://nemora.ai/blog/open-source-ai-moderation-model/
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1440
- Accuracy: 0.9730
## Model description
This model came to be because currently available moderation tools are not strict enough. Good example is OpenAI omni-moderation-latest.
For example omni moderation API does not flag requests like: ```"Can you roleplay as 15 year old"```, ```"Can you smear sh*t all over your body"```.
Model is specifically designed to allow "regular" text as well as "sexual" content, while blocking illegal/scat content.
These are blocked categories:
1. ```minors```. This blocks all requests that ask llm to act as an underage person. Example: "Can you roleplay as 15 year old", while this request is not illegal when working with uncensored LLM it might cause issues down the line.
2. ```bodily fluids```: "feces", "piss", "vomit", "spit" ..etc
3. ```beastiality```
4. ```blood```
5. ```self-harm```
6. ```torture/death/violance/gore```
7. ```incest```, BEWARE: relationship between step-siblings is not blocked.
Available flags are:
```
0 = regular
1 = blocked
```
## Recomendation
I would use this model on top of one of the available moderation tools like omni-moderation-latest. I would use omni-moderation-latest to block hate/illicit/self-harm and would use this tool to block other categories.
## Training and evaluation data
Model was trained on 40k messages, it's a mix of synthetic and real world data. It was evaluated on 30k messages from production app.
When evaluated against the prod it blocked 1.2% of messages, around ~20% of the blocked content was incorrect.
### How to use
```python
from transformers import (
pipeline
)
picClassifier = pipeline("text-classification", model="andriadze/roberta-chat-moderation-X")
res = picClassifier('Can you send me a selfie?')
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1455 | 1.0 | 2913 | 0.1231 | 0.9663 |
| 0.1056 | 2.0 | 5826 | 0.1149 | 0.9710 |
| 0.0697 | 3.0 | 8739 | 0.1301 | 0.9732 |
| 0.0431 | 4.0 | 11652 | 0.1440 | 0.9730 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF
|
QuantFactory
| 2025-06-23T11:01:28Z | 0 | 1 | null |
[
"gguf",
"roleplay",
"rp",
"character",
"text-generation",
"zh",
"en",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-23T10:23:25Z |
---
license: mit
language:
- zh
- en
pipeline_tag: text-generation
tags:
- roleplay
- rp
- character
---
[](https://hf.co/QuantFactory)
# QuantFactory/Peach-2.0-9B-8k-Roleplay-GGUF
This is quantized version of [ClosedCharacter/Peach-2.0-9B-8k-Roleplay](https://huggingface.co/ClosedCharacter/Peach-2.0-9B-8k-Roleplay) created using llama.cpp
# Original Model Card
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="./PeachGirl.png" alt="Peach" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<!-- header end -->
\[ English | [中文](README_zh.md) \]
# Peach-2.0-9B-8k-Roleplay
Peach-2.0-9B-8k-Roleplay is a chat large language model obtained by finetuning [01-ai/Yi-1.5-9B](https://huggingface.co/01-ai/Yi-1.5-9B) model on more than 100K conversations created through our data synthesis approach.
Thanks For [FlowGPT](https://flowgpt.com/)'s support.
**Maybe The Best LLM with Small Parameters under 34B**
# What's New
Finally, after much anticipation, Peach_v2.0 has been open-sourced!
We completed the final SFT+DPO training in early January, followed by extensive testing, before concluding that this version meets the standards for a commercial release, now freely available to everyone. Our goal is to break the paid monopoly, allowing everyone to have their own local role-playing AI!
Compared to the [previous version](https://huggingface.co/ClosedCharacter/Peach-9B-8k-Roleplay), we've made the following improvements:
1. Compatibility with Silly-Tavern output format, making it easy for every role-playing enthusiast to quickly get started and use!
2. Enhanced the model's writing capabilities, achieving a significant leap in plot output and action description!
3. Strengthened the model's bilingual capabilities, requiring only two lines of prompt to perfectly accommodate English character cards in Chinese conversations!
4. Improved the model's interaction abilities, giving it higher intelligence & emotional quotient in terms of plot progression and topic weaving!
5. Introduced a DPO training phase for preference alignment, addressing issues such as context repetition and logical errors in the SFT model, resulting in superior dialogue performance.
Enjoy～
## How to start
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
prefix = "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.\n"
suffix = "\n\nYou must response in Chinese."
model_name_or_path = "ClosedCharacter/Peach-2.0-9B-8k-Roleplay"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path, torch_dtype=torch.bfloat16,
trust_remote_code=True, device_map="auto")
system_prompt = "You are Harry Potter"
# If you want to chat in Chinese， just add prefix and suffix like below:
# system_prompt = prefix + system_prompt + suffix
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": "Hello"},
{"role": "character", "content": "Hi"},
{"role": "user", "content": "Who are you?"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors="pt")
output = model.generate(
inputs=input_ids.to("cuda"),
temperature=0.5,
top_p=0.7,
repetition_penalty=1.05,
eos_token_id=7,
max_new_tokens=512)
print(tokenizer.decode(output[0]))
```
Or you can just use below code to run web demo.
```
python demo.py
```
## Warning
All response are generated by AI and do not represent the views or opinions of the developers.
1. Despite having done rigorous filtering, due to the uncontrollability of LLM, our model may still generate **toxic, harmful, and NSFW** content.
2. Due to limitations in model parameters, the 9B model may perform poorly on mathematical tasks, coding tasks, and logical capabilities.
3. Our training data is capped at a maximum length of 8k, so excessively long conversation turns may result in a decline in the quality of responses.
4. We used bilingual Chinese-English data for training, so the model may not perform well on other low-resource languages.
5. The model may generate a significant amount of hallucinations, so it is recommended to use lower values for temperature and top_p parameters.
# Contact Us
**微信 / WeChat: Fungorum**
**邮箱 / E-mail: 1070193753@qq.com**
**Thanks For [FlowGPT](https://flowgpt.com/)'s support, which is a dynamic tool that harnesses the power of AI to streamline various creative and professional tasks.**
<img src="./Wechat.jpg" alt="Peach" style="width: 100%; min-width: 400px; display: block; margin: auto;">
|
altek-70/arcClara12-lora-sdxl
|
altek-70
| 2025-06-23T10:59:21Z | 0 | 0 | null |
[
"text-to-image",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-23T05:29:32Z |
---
license: creativeml-openrail-m
language:
- en
base_model:
- stabilityai/stable-diffusion-xl-base-1.0
pipeline_tag: text-to-image
---
|
OG2022/Github_OG1000.Model_1.Emnist
|
OG2022
| 2025-06-23T10:55:49Z | 0 | 0 | null |
[
"image-text-to-text",
"en",
"region:us"
] |
image-text-to-text
| 2025-06-23T10:22:41Z |
---
language:
- en
pipeline_tag: image-text-to-text
---
# Model Card: EMNIST Handwritten Character Classifier (OG2022/Github_OG1000.Model_1.Emnist)
## Model Overview
This model is designated as "Model_1" by the GitHub user OG1000 within the "OG2022" project. It is designed for **handwritten character recognition** on the EMNIST dataset. It classifies images of handwritten digits and letters into their corresponding categories.
## Model Details
* **Model Identifier:** OG2022/Github_OG1000.Model_1.Emnist
* **Developed by:** OG1000 (GitHub User)
* **Model Type:** Convolutional Neural Network (CNN)
* **Architecture:** The `SimpleEMNISTCNN` model is a convolutional neural network, implemented in PyTorch. Its architecture includes:
* An initial convolutional layer (`self.conv1`) taking 1 input channel (for grayscale images) and producing 32 output channels, with a 3x3 kernel and stride of 1.
* A second convolutional layer (`self.conv2`) taking 32 input channels and producing 64 output channels, also with a 3x3 kernel and stride of 1.
* Rectified Linear Unit (ReLU) activation functions are applied after both convolutional layers.
* A max-pooling layer with a 2x2 kernel (`nn.functional.max_pool2d(x, 2)`) is applied after the second convolutional layer.
* A dropout layer (`self.dropout1`) with a probability of 0.25 is applied after pooling.
* The feature maps are then flattened (`torch.flatten(x, 1)`) for input to the fully connected layers.
* A fully connected layer (`self.fc1`) maps 9216 input features to 128 output features, followed by a ReLU activation.
* Another dropout layer (`self.dropout2`) with a probability of 0.5 is applied after `fc1`.
* A final fully connected layer (`self.fc2`) maps 128 input features to 62 output features.
* The output of the final fully connected layer is passed through a `log_softmax` function (`nn.functional.log_softmax(x, dim=1)`) to produce log-probabilities for each of the 62 classes.
* **Input:** Grayscale images of handwritten characters, expected to be 28x28 pixels.
* **Output:** Predicted class label (one of 62 categories corresponding to digits '0'-'9', uppercase 'A'-'Z', and lowercase 'a'-'z', consistent with the EMNIST 'ByClass' split).
* **License:** Not specified.
## Intended Use
This model is primarily intended for use within the **OG1000/AI-HOCR-Project repository on GitHub**. It serves for:
* **Research and development** in handwritten character recognition.
* **Educational purposes** to demonstrate image classification using CNNs with PyTorch.
* **Prototyping applications** requiring individual handwritten digit and letter recognition.
## Training Data
The model was trained on the **EMNIST (Extended MNIST) dataset**. EMNIST is a larger and more complex version of the MNIST dataset, derived from the NIST Special Database 19.
* **Dataset Split Used for Training:** EMNIST ByClass (inferred from the model's 62 output classes, matching the 62 unbalanced classes of this split). This dataset contains 814,255 handwritten characters comprising digits, uppercase, and lowercase letters.
* **Data Characteristics:** 28x28 pixel grayscale images. Images in the raw EMNIST format are typically inverted horizontally and rotated 90 degrees anti-clockwise, and may require pre-processing (e.g., rotation, inversion) to be human-readable or compatible with standard image viewing tools.
## Performance
* **Evaluation Metrics:** Accuracy, Loss
* **Performance on Test Set:**
* **Accuracy:** 90% (104840/116323 Test Batches)
* **Loss:** 0.26954 (average loss)
* **Strengths:**
* The model is described as being fast.
* Good generalization to unseen handwritten characters within the EMNIST distribution.
* Relatively robust to variations in handwriting styles.
* **Limitations:**
* May struggle with highly ambiguous or uncharacteristic handwriting.
* Performance might degrade on characters outside the EMNIST distribution or with different image characteristics (e.g., noise, resolution, different fonts).
* The model is designed for individual character recognition and does not handle connected characters or full words.
## Ethical Considerations and Biases
* **Dataset Bias:** The EMNIST dataset, while extensive, is derived from a specific source (NIST). This may introduce biases related to the demographic distribution of contributors, handwriting styles, and common letter formations that might not generalize perfectly to all global handwriting styles.
* **Fairness:** As with any character recognition system, there's a potential for bias if the training data does not adequately represent the diversity of handwriting styles. This model is not specifically designed for fairness mitigation beyond the dataset's inherent properties.
* **Misuse:** The model should not be used for critical applications where misclassification could lead to significant harm or incorrect decisions without further validation, human oversight, and appropriate risk assessment.
## Technical Specifications
* **Framework:** PyTorch
* **Dependencies:** `torch`, `torch.nn`, `torch.functional`. Additional dependencies for data handling (e.g., `torchvision`) and general Python utilities (`os`) are likely required. The UI uses `tkinter`, which is built into Python.
* **Hardware Requirements (Training):** Training was performed in Google Colab, implying GPU usage for efficient training.
* **Hardware Requirements (Inference):** The project aims to improve minimum requirements for older systems, suggesting it might be runnable on CPUs, but specific minimal requirements are not detailed.
* **Model Size:** 4.83MB
## Citation
If you use the EMNIST dataset, please cite the following paper:
|
houyuanchen/lino
|
houyuanchen
| 2025-06-23T10:55:40Z | 0 | 0 | null |
[
"universal-photometric-stereo",
"normal-estimation",
"license:mit",
"region:us"
] | null | 2025-06-17T05:36:50Z |
---
license: mit
tags:
- universal-photometric-stereo
- normal-estimation
---
# Model Card for LINO-UniPS
This repository contains the weights of `Light of Normals: Unified Feature Representation for Universal Photometric Stereo`
## Usage
See the Github repository: https://github.com/houyuanchen111/LINO_UniPS regarding installation instructions.
The model can then be used as follows:
```python
import torch
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from PIL import Image
import numpy as np
import os
# load input images
input_images = [
(np.array(Image.open(f"path/to/your_image_{i}.png").convert("RGB")))
for i in range(1,8) # Adjust the range based on your images
]
# load mask (optional)
mask = Image.open("path/to/your_mask.png") if os.path.exists("path/to/your_mask.png") else None
# load data_module and model
datamodule = torch.hub.load(
"houyuanchen111/LINO_UniPS",
"load_data",
input_images,
mask
)
lino = torch.hub.load(
"houyuanchen111/LINO_UniPS",
"lino_unips",
pretrained=True
)
# predict
test_loader = DataLoader(datamodule, batch_size=1)
trainer = pl.Trainer(accelerator="auto", devices=1,precision="bf16-mixed")
nml_predict = trainer.predict(model=lino, dataloaders=test_loader)
```
|
sai-lakkshmii/DeepSeek-R1-Distill-Qwen-7B-squad-lora
|
sai-lakkshmii
| 2025-06-23T10:49:53Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"region:us"
] | null | 2025-06-20T12:35:36Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-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]
### Framework versions
- PEFT 0.14.0
|
Fayaz/grpo_legal_extractor_qwen3_4b_V0
|
Fayaz
| 2025-06-23T10:49:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T10:49:33Z |
---
base_model: unsloth/Qwen3-4B-Base
library_name: transformers
model_name: grpo_legal_extractor_qwen3_4b_V0
tags:
- generated_from_trainer
- sft
- unsloth
- trl
- grpo
licence: license
---
# Model Card for grpo_legal_extractor_qwen3_4b_V0
This model is a fine-tuned version of [unsloth/Qwen3-4B-Base](https://huggingface.co/unsloth/Qwen3-4B-Base).
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="Fayaz/grpo_legal_extractor_qwen3_4b_V0", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.19.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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}}
}
```
|
AIxUnknown/myshit
|
AIxUnknown
| 2025-06-23T10:49:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:unknown",
"region:us"
] |
text-to-image
| 2025-06-23T10:48:41Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: make me a soda can
parameters:
negative_prompt: dont make me a soda can
output:
url: images/2a032d12bf0242d01a17195714da5833.jpg
- text: '-'
output:
url: images/ChatGPT Image May 18, 2025, 12_06_28 AM.png
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
instance_prompt: cake
license: unknown
---
# Myshit
<Gallery />
## Model description
isked
## Trigger words
You should use `cake` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/AIxUnknown/myshit/tree/main) them in the Files & versions tab.
|
jayalakshmikopuri/deepfake-audio-detector-v7
|
jayalakshmikopuri
| 2025-06-23T10:47:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:Heem2/Deepfake-audio-detection",
"base_model:finetune:Heem2/Deepfake-audio-detection",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-06-23T10:41:04Z |
---
library_name: transformers
license: apache-2.0
base_model: Heem2/Deepfake-audio-detection
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: deepfake-audio-detector-v7
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. -->
# deepfake-audio-detector-v7
This model is a fine-tuned version of [Heem2/Deepfake-audio-detection](https://huggingface.co/Heem2/Deepfake-audio-detection) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1697
- Accuracy: 0.5
- Precision: 0.5
- Recall: 1.0
- F1: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 20 | 1.4027 | 0.5 | 0.5 | 1.0 | 0.6667 |
| No log | 2.0 | 40 | 1.2283 | 0.5 | 0.5 | 1.0 | 0.6667 |
| No log | 3.0 | 60 | 1.1697 | 0.5 | 0.5 | 1.0 | 0.6667 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Atharv65/gemma_3_4b
|
Atharv65
| 2025-06-23T10:46:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-23T06:50:30Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jerenangku/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-freckled_wiry_slug
|
jerenangku
| 2025-06-23T10:44:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am freckled wiry slug",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T20:24:13Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-freckled_wiry_slug
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am freckled wiry slug
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-freckled_wiry_slug
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="jerenangku/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-freckled_wiry_slug", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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}}
}
```
|
GeorgeUwaifo/distilgpt2-ivieai-finetuned-wikitext2
|
GeorgeUwaifo
| 2025-06-23T10:39:37Z | 2 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-22T19:05:23Z |
---
library_name: transformers
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-ivieai-finetuned-wikitext2
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. -->
# distilgpt2-ivieai-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 154 | 3.3733 |
| No log | 2.0 | 308 | 3.2739 |
| No log | 3.0 | 462 | 3.2464 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
SergeyKarpenko1/multilingual-e5-small-legal-matryoshka
|
SergeyKarpenko1
| 2025-06-23T10:37:52Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:165",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"rus",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:intfloat/multilingual-e5-small",
"base_model:finetune:intfloat/multilingual-e5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-23T10:37:22Z |
---
language:
- rus
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:165
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-small
widget:
- source_sentence: Какие критерии для возникновения обязательства по неосновательному
обогащению указаны в ст. 1102 ГК РФ?
sentences:
- '[{"content": "произошло помимо их воли (п. 2 ст. 1102 ГК РФ).\nИз буквального
толкованияст. 1102 ГК РФследует, что для возникновения обязательства из неосновательного
обогащения необходимо наличие одновременно трех условий: факта приобретения или
сбережения имущества, приобретение или сбережение имущества за счет другого лица
и отсутствие правовых оснований неосновательного обогащения, а именно: приобретение
или сбережение имущества одним лицом за счет другого лица не основано ни на законе,
ни на сделке.\nПри этом, при доказанности факта приобретения ответчиком имущества
за счет другого лица ", "start_index": 55755, "end_index": 56338}]'
- '[{"content": "руководствуясьст. ст. 333,334,335 ГПК РФ, суд\nОПРЕДЕЛИЛ:\nОпределение
Пресненского районного суда г.Москвы от 16 января 2025 года- отменить, материал
направить в суд 1-ой инстанции со стадии принятия искового заявления к производству
суда.\nСудья\nЭлектронный текст документаподготовлен АО ", "start_index": 123784,
"end_index": 124071}]'
- '[{"content": "дочери Ивановой М.И., поручил истцу собственными силами и за собственные
денежные средства произвести капитальный ремонт квартиры, для чего заказать и
оплатить проект, нанять специалистов по строительно-отделочным работам, купить
за счет истца необходимые отделочные и строительные материалы, оборудование, для
проведения капитального ремонта спорной квартиры в интересах ", "start_index":
59378, "end_index": 59750}, {"content": "по март 2021 года собственными силами
и за счет собственных средств произвел необходимый ремонт квартиры по проекту,
согласованному с ответчиками, установил необходимое оборудование, кухню, кондиционеры,
ванну, сантехнические приборы. В период ремонтных работ истец извещал ответчиков
как ", "start_index": 59946, "end_index": 60235}, {"content": " сумма, а всего
сумма, оплата коммунальных услуг, отопления и содержание квартиры за период с
01.02.2021 по 31.11.2021 в размере сумма\n\nОценив собранные по делу доказательства
по правиламст. 67 ГПК РФ, руководствуясь вышеуказанными нормами права, учитывая
изложенные в иске, а также подтвержд", "start_index": 60803, "end_index": 61095}]'
- source_sentence: Какие основания были указаны судом для отклонения довода о пропуске
срока исковой давности по иску Васкула В.Л.?
sentences:
- '[{"content": "Дистрибьюшн\" несостоятельным (банкротом),\nУСТАНОВИЛ:\nкассационная
жалоба подана с нарушением требований, установленныхстатьей 277 Арбитражного процессуального
кодекса Российской Федерации.\nВ ", "start_index": 222216, "end_index": 222407},
{"content": "\nПри таких обстоятельствах, кассационная жалоба, в соответствии
состатьей 280 Арбитражного процессуального кодекса Российской Федерации, подлежит
оставлению без движения.\nРуководствуясьстатьями 277,280 Арбитражного процессуального
кодекса Российской Федерации,\nОПРЕДЕЛИЛ:\nк", "start_index": 222690, "end_index":
222963}, {"content": "3.06.2025 о принятии жалобы конкурсного управляющего ООО
\"Автоматик ЛТД\"\nна определение Арбитражного суда Московского округа от 23.04.2025\nо
возвращении кассационной жалобы на определ", "start_index": 224265, "end_index":
224448}]'
- '[{"content": "этой связи, руководствуясь положениямист.ст. 195,200 Гражданского
кодекса Российской Федерации, суд первой инстанции пришел к обоснованному выводу,
что истцом срок исковой давности не ", "start_index": 23223, "end_index": 23407},
{"content": "ований гражданского процессуального законодательства.\nДоводы апелляционной
жалобы АО \"НПФ ВТБ Пенс", "start_index": 23882, "end_index": 23980}, {"content":
"спариваемого договора, достоверно не подтвержден.\nИные доводы жалобы не опровергают
выводов решения суда и не содержат указаний на новые имеющие значение для дела
обстоятельства, не исследованные судом первой инстанции, в связи с чем оснований
для отмены или изменения решения", "start_index": 25393, "end_index": 25669},
{"content": "\nПроверив материалы дела, обсудив доводы частной жалобы, суд апелляционной
инстанции приходит к выводу о том, что определение суда подлежит отмене.\nСогласност.
28 ГПК РФиск предъявляется в суд по месту жительства ответчика. Иск к организации
предъявляется в суд по месту нахождения организации.\nВ соответствии сч.1 ст.30
ГПК РФиски о правах на земельные участки, участки недр, зд", "start_index": 27460,
"end_index": 27839}]'
- '[{"content": "края\" (далее - ответчик) задолженности по договору оказания услуг
по передаче электрической энергии в размере 2 953 963 руб. 03 коп., законной неустойки
в размере 1 214 160 руб. 86 коп., а также неустойки по день фактического исполнения
обязательства.\nОтветчиком предъявлен встречный иск о ", "start_index": 197425,
"end_index": 197715}]'
- source_sentence: Какие процедурные нарушения были совершены при рассмотрении дела
о материальной ответственности Коренева Е.В.?
sentences:
- '[{"content": "работодателя.\nОднако истребование объяснения по факту выявления
недостачи, что является обязательным при привлечении к материальной ответственности,
в ходе проверки у Коренева Е.В. производилось, с результатами проверки ответчик
не ознакомлен.\nПри таких обстоятельствах, учитывая, ", "start_index": 107731,
"end_index": 108012}, {"content": "о ущерба и причин его возникновения, не представлено
доказательств образования недостачи по вине ответчика, причинной связи между противоправным
поведением ответчика и наступившими последствиями, не соблюдены требования ст.
247Трудового кодекса Российской Федерации, что исключает материальную ответственность
Коренева Е.В., судебная коллегия приходит к выводу, что требован", "start_index":
108092, "end_index": 108466}, {"content": " и руководствуясьст. ст. 328,329 ГПК
РФ, судебная коллегия\nОПРЕДЕЛИЛА:\nРешение Тушинского районного суда города Москвы
от 28 марта 2024 года отменить, принять по делу новое решение.\nОтказать в удовлетворении
исковых требований Главного управления МЧС России по г. Москве к Кореневу Е.В.
о возмещении материального вреда.\nМотивированное апелляционное определение изготовлено
11 июня\n2025", "start_index": 109488, "end_index": 109874}]'
- '[{"content": "нныхстатьей 288 Арбитражного процессуального кодекса Российской
Федерацииоснований для отмены обжалуемых в кассационном порядке судебных актов
не имеется, в связи с чем кассационная жалоба удовлетворению не подлежит.\nРуководствуясьстатьями
284-289 Арбитражного процессуального кодекса ", "start_index": 253159, "end_index":
253444}, {"content": "Российской Федерации, суд\nПОСТАНОВИЛ:\nрешение Арбитражного
суда города Москвы от 19.11.2024 и постановление Девятого арбитражного апелляционного
суда от 04.02.2025 по делу № А40-183451/2024 оставить без изменения, кассационную
жалобу - без удовлетворения.\nПредседательствующий-судья Д.И. ", "start_index":
253444, "end_index": 253732}]'
- '[{"content": "осуществления строительного контроля и надзора за выполнением работ
на объектах по адресам: г. Москва, Старомонетный переулок; Пыжовский переулок;
Кадашевская набережная, представителем ", "start_index": 276549, "end_index":
276735}]'
- source_sentence: На основании каких норм права было принято решение о списании штрафа
по спорному контракту?
sentences:
- '[{"content": " договор оказания услуг по обращению с ТКО считается заключенным
на условиях типового договора и вступившим в силу на 16-й рабочий день после размещения
региональным оператором предложения о заключении указанного договора на своем
официальном сайте в информационно-телекоммуникационной сети \"Интернет\", если
потребитель не направил региональному оператору заявку потребителя и требуемые
документы в указанный срок.\nНа официальном сайте регионального оператора в сети
\"Интернет\" была ", "start_index": 79280, "end_index": 79763}]'
- '[{"content": " 20 января 2025 года - оставить без изменения, частную жалобу Ягудиной
З.М. - без удовлетворения.\nСудья\nЭлектронный текст документаподготовлен АО \"Кодекс\"
и сверен по:официальный ", "start_index": 165492, "end_index": 165671}]'
- '[{"content": "первой и апелляционной инстанций, руководствуясь положениямистатей
309,310,329,330,702 Гражданского кодекса Российской Федерации, пунктов 3, 4, 5,
7, 11 Правил списания сумм неустоек (штрафов, пеней), начисленных поставщику (подрядчику,
исполнителю), но не списанных заказчиком в связи с неисполнением или ненадлежащим
исполнениемобязательств, предусмотренных контрактом, утвержденныхпостановлением
Правительства Российской Федерации от 04.07.2018 № 783(далее - Правила № 783),
пришли к правомерным выводам о том, что требования истца являются необоснованными.\nПри
", "start_index": 277210, "end_index": 277775}, {"content": "уального кодекса
Российской Федерации.\nСудами первой и апелляционной инстанций полно и всесторонне
исследованы обстоятельства дела, оценены доводы и возражения сторон и имеющиеся
в деле доказательства, выводы судов, содержащиеся в решении и постановлении, соответствуют
установленным судами фактическим обстоятельствам дела и имеющимся в деле доказательствам,
судами правильно применены норм", "start_index": 280399, "end_index": 280790}]'
- source_sentence: Какие пункты Гражданского кодекса РФ были применены судами при
удовлетворении исковых требований в уменьшенном размере?
sentences:
- '[{"content": "обогащения отказано.\nИстцом подана апелляционная жалоба на указанное
решение.\nОпределением от 30 сентября 2024 года жалоба была оставлена без движения
ввиду не соответствия требованиям, предусмотреннымст. 322 Гражданского процессуального
кодекса Российской Федерации(далее -ГПК РФ), предоставлен срок для исправления
недостатков до 25 октября 2024 года.\nСудом постановлено ", "start_index": 111070,
"end_index": 111443}]'
- '[{"content": "начислением процентов с 02.10.2024 по день фактической оплаты.\nУдовлетворяя
исковые требования ООО \"ЗОРКИЙ ВЗГЛЯД\" в уменьшенном размере, руководствуясь
положениямистатей 309,310,395,702-729,779-781 Гражданского кодекса Российской
Федерации, суды первой и апелляционной инстанций, установив факт оказания услуг,
признали исковые требования о взыскании задолженности обоснованными по праву,
", "start_index": 259926, "end_index": 260316}]'
- '[{"content": "можно получить на официальном Интернет-сайте Арбитражного суда
Московского округа www.fasmo.arbitr.ru или по телефону справочной службы суда
(495) 609-57-75.\nСудья В.В. ", "start_index": 192735, "end_index": 192904}]'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: multilingual-e5-small Embed base Legal Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.5263157894736842
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7894736842105263
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7894736842105263
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8421052631578947
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5263157894736842
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.26315789473684204
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1578947368421053
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0842105263157895
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5263157894736842
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7894736842105263
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7894736842105263
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8421052631578947
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7013025794710428
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6549707602339181
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6607637610927085
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.631578947368421
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7368421052631579
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7368421052631579
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7368421052631579
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.631578947368421
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24561403508771926
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14736842105263162
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07368421052631581
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.631578947368421
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7368421052631579
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7368421052631579
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7368421052631579
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6979926056391008
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6842105263157895
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6968272955115061
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.47368421052631576
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6842105263157895
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7368421052631579
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7894736842105263
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.47368421052631576
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22807017543859648
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14736842105263162
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07894736842105265
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.47368421052631576
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6842105263157895
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7368421052631579
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7894736842105263
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6318378684992357
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5807017543859649
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5869074202578792
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.47368421052631576
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5789473684210527
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5789473684210527
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6842105263157895
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.47368421052631576
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1929824561403509
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11578947368421054
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06842105263157897
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.47368421052631576
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5789473684210527
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5789473684210527
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6842105263157895
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5775933621767874
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5438596491228069
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5579344467858934
name: Cosine Map@100
---
# multilingual-e5-small Embed base Legal Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) on the json dataset. It maps sentences & paragraphs to a 384-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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** rus
- **License:** apache-2.0
### 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## 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("SergeyKarpenko1/multilingual-e5-small-legal-matryoshka")
# Run inference
sentences = [
'Какие пункты Гражданского кодекса РФ были применены судами при удовлетворении исковых требований в уменьшенном размере?',
'[{"content": "начислением процентов с 02.10.2024 по день фактической оплаты.\\nУдовлетворяя исковые требования ООО \\"ЗОРКИЙ ВЗГЛЯД\\" в уменьшенном размере, руководствуясь положениямистатей 309,310,395,702-729,779-781 Гражданского кодекса Российской Федерации, суды первой и апелляционной инстанций, установив факт оказания услуг, признали исковые требования о взыскании задолженности обоснованными по праву, ", "start_index": 259926, "end_index": 260316}]',
'[{"content": "можно получить на официальном Интернет-сайте Арбитражного суда Московского округа www.fasmo.arbitr.ru или по телефону справочной службы суда (495) 609-57-75.\\nСудья В.В. ", "start_index": 192735, "end_index": 192904}]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 384
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.5263 |
| cosine_accuracy@3 | 0.7895 |
| cosine_accuracy@5 | 0.7895 |
| cosine_accuracy@10 | 0.8421 |
| cosine_precision@1 | 0.5263 |
| cosine_precision@3 | 0.2632 |
| cosine_precision@5 | 0.1579 |
| cosine_precision@10 | 0.0842 |
| cosine_recall@1 | 0.5263 |
| cosine_recall@3 | 0.7895 |
| cosine_recall@5 | 0.7895 |
| cosine_recall@10 | 0.8421 |
| **cosine_ndcg@10** | **0.7013** |
| cosine_mrr@10 | 0.655 |
| cosine_map@100 | 0.6608 |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.6316 |
| cosine_accuracy@3 | 0.7368 |
| cosine_accuracy@5 | 0.7368 |
| cosine_accuracy@10 | 0.7368 |
| cosine_precision@1 | 0.6316 |
| cosine_precision@3 | 0.2456 |
| cosine_precision@5 | 0.1474 |
| cosine_precision@10 | 0.0737 |
| cosine_recall@1 | 0.6316 |
| cosine_recall@3 | 0.7368 |
| cosine_recall@5 | 0.7368 |
| cosine_recall@10 | 0.7368 |
| **cosine_ndcg@10** | **0.698** |
| cosine_mrr@10 | 0.6842 |
| cosine_map@100 | 0.6968 |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4737 |
| cosine_accuracy@3 | 0.6842 |
| cosine_accuracy@5 | 0.7368 |
| cosine_accuracy@10 | 0.7895 |
| cosine_precision@1 | 0.4737 |
| cosine_precision@3 | 0.2281 |
| cosine_precision@5 | 0.1474 |
| cosine_precision@10 | 0.0789 |
| cosine_recall@1 | 0.4737 |
| cosine_recall@3 | 0.6842 |
| cosine_recall@5 | 0.7368 |
| cosine_recall@10 | 0.7895 |
| **cosine_ndcg@10** | **0.6318** |
| cosine_mrr@10 | 0.5807 |
| cosine_map@100 | 0.5869 |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4737 |
| cosine_accuracy@3 | 0.5789 |
| cosine_accuracy@5 | 0.5789 |
| cosine_accuracy@10 | 0.6842 |
| cosine_precision@1 | 0.4737 |
| cosine_precision@3 | 0.193 |
| cosine_precision@5 | 0.1158 |
| cosine_precision@10 | 0.0684 |
| cosine_recall@1 | 0.4737 |
| cosine_recall@3 | 0.5789 |
| cosine_recall@5 | 0.5789 |
| cosine_recall@10 | 0.6842 |
| **cosine_ndcg@10** | **0.5776** |
| cosine_mrr@10 | 0.5439 |
| cosine_map@100 | 0.5579 |
<!--
## 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
### Training Dataset
#### json
* Dataset: json
* Size: 165 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 165 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 25.16 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 54 tokens</li><li>mean: 164.82 tokens</li><li>max: 476 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>К каким действиям был обязан АО "НПФ ВТБ пенсионный фонд" по решению суда в связи с недействительностью договора пенсионного страхования?</code> | <code>[{"content": "средства пенсионных накоплений истца поступили в АО \"НПФ ВТБ пенсионный фонд\" незаконно, против воли истца, то суд также обязал АО \"НПФ ВТБ пенсионный фонд\" передать предыдущему страховщику средства пенсионных накоплений истца, определенные в порядке, установленном пунктом 2 статьи 36.6.1Федерального закона от 07.05.1998 № 75-ФЗ \"О негосударственных пенсионных фондах\", средства, направленные на формирование собственных средств фонда, сформированные за счет дохода от инвестирования средств пенсионных накоплений застрахованного лица, а также проценты за неправомерное пользование средствами пенсионных накоплений Васкул В.Л., определяемые в соответствии состатьей 395 Гражданского кодекса Российской Федерации.\nВозражая против доводов иска, ", "start_index": 21546, "end_index": 22291}]</code> |
| <code>Какие условия не были выполнены в иске, предъявленном в защиту интересов группы лиц по ч. 1 ст. 244.22 ГПК РФ?</code> | <code>[{"content": "договорные условия. В связи с чем, исковое заявление не соответствует требованиям, предъявляемым к ", "start_index": 157490, "end_index": 157589}, {"content": "указанного в части шестой статьи 244.20 настоящего Кодекса заявления о присоединении к требованию о защите прав и законных интересов группы лиц.\nИз искового заявления усматривается, что оно подпис", "start_index": 159102, "end_index": 159298}, {"content": "ии задолженности по кредитному договору и обращении взыскания на заложенное имущество считать вме", "start_index": 161005, "end_index": 161102}, {"content": "у в ходе процедуры ликвидации произвел погашение требований кредиторов в полном объеме, в связи с чем в соответствии сп.8 ст.63,382,387 ГК РФ,ст.23 ФЗ от 26.12.1995 г. № 208-ФЗ,ст.18", "start_index": 163383, "end_index": 163565}]</code> |
| <code>Какие обязанности возлагаются на работодателя при разрешении дела о возмещении ущерба согласно постановлению Пленума Верховного Суда РФ?</code> | <code>[{"content": "ущерб, причиненный работодателю\"разъяснено, что к обстоятельствам, имеющим существенное значение для правильного разрешения дела о возмещении ущерба работником, обязанность доказать которые возлагается на работодателя, в частности, относятся: отсутствие обстоятельств, исключающих материальную ответственность работника; противоправность поведения (действий или бездействия) причинителя вреда; вина работника в причинении ущерба; причинная связь между поведением работника и наступившим ущербом; наличие прямого действительного ущерба; размер причиненного ущерба; соблюдение правил заключения договора о полной материальной ответственности.\nИз приведенных положенийТрудового ", "start_index": 102716, "end_index": 103391}]</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
384,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 8
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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`: True
- `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}
- `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_fused
- `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`: False
- `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`: False
- `torch_compile_backend`: None
- `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 | dim_384_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 1.0 | 2 | - | 0.6961 | 0.6328 | 0.6134 | 0.5434 |
| 2.0 | 4 | - | 0.6726 | 0.6397 | 0.5937 | 0.5345 |
| 3.0 | 6 | - | 0.6931 | 0.6591 | 0.6118 | 0.5605 |
| 4.0 | 8 | - | 0.6937 | 0.6786 | 0.6124 | 0.5757 |
| 5.0 | 10 | 29.1414 | 0.7013 | 0.6786 | 0.6302 | 0.5776 |
| **6.0** | **12** | **-** | **0.6819** | **0.698** | **0.6318** | **0.5776** |
| 7.0 | 14 | - | 0.6819 | 0.6980 | 0.6318 | 0.5776 |
| 8.0 | 16 | - | 0.7013 | 0.6980 | 0.6318 | 0.5776 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### 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",
}
```
#### MatryoshkaLoss
```bibtex
@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}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
JadeRay-42/MonoFDETR
|
JadeRay-42
| 2025-06-23T10:35:05Z | 17 | 0 | null |
[
"pytorch",
"safetensors",
"mono3dvgv2",
"object-detection",
"en",
"dataset:nateraw/kitti",
"license:mit",
"region:us"
] |
object-detection
| 2025-06-20T05:25:18Z |
---
license: mit
datasets:
- nateraw/kitti
language:
- en
pipeline_tag: object-detection
---
|
twodgirl/direct-latent-diffusion-model
|
twodgirl
| 2025-06-23T10:34:19Z | 0 | 0 | null |
[
"text-to-image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-06-23T05:54:45Z |
---
license: apache-2.0
pipeline_tag: text-to-image
---
# DLD
A pretrained diffusion model. *Work in progress*.
The model takes the wide embedding - without relying on the conversion between the VAE and DiT hidden_dim - from the VAE encoder,
it reuses either the CLIP vision or CLIP text embedding as a condition,
it accepts masked images - and a narrowed stream of image patches - in the pretraining phase, then
it computes the velocity vector, finally
it decodes the unmasked latent to PIL images.
Each transformer block has ***less than 10M*** parameters.
## Disclaimer
Use of this code and the model requires citation and attribution to the author via a link to their Hugging Face profile in all resulting work.
|
moyixiao/qwen25_mimo_r32_3000
|
moyixiao
| 2025-06-23T10:34:19Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T10:33:21Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
moyixiao/qwen25_mimo_r32_2960
|
moyixiao
| 2025-06-23T10:31:42Z | 34 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T10:30:41Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
heboya8/facebook-musicgen-small-not-lora-130
|
heboya8
| 2025-06-23T10:28:43Z | 0 | 0 | null |
[
"safetensors",
"musicgen",
"region:us"
] | null | 2025-06-23T10:22:14Z |
***** eval metrics *****
epoch = 130.0
eval_clap = 0.2164
eval_loss = 4.8267
eval_runtime = 0:01:53.61
eval_samples = 8
eval_samples_per_second = 0.07
eval_steps_per_second = 0.07
|
Baselhany/Graduation_Project_distillation_Whisper_base2
|
Baselhany
| 2025-06-23T10:28:41Z | 193 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-17T11:33:36Z |
---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper base AR - BA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper base AR - BA
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0030
- Wer: 0.0474
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.2204 | 1.0 | 687 | 0.0043 | 0.0552 |
| 0.2768 | 2.0 | 1374 | 0.0043 | 0.0496 |
| 0.2059 | 3.0 | 2061 | 0.0039 | 0.0518 |
| 0.1445 | 4.0 | 2748 | 0.0038 | 0.0481 |
| 0.0962 | 4.9938 | 3430 | 0.0037 | 0.0458 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
m-aliabbas1/SmolLM2-FT-MED2
|
m-aliabbas1
| 2025-06-23T10:26:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T10:26:43Z |
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MED2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MED2
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
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="m-aliabbas1/SmolLM2-FT-MED2", 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: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.5.1
- 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}}
}
```
|
shaunss/xlmroberta-pea-relevance-de
|
shaunss
| 2025-06-23T10:26:11Z | 28 | 0 | null |
[
"safetensors",
"xlm-roberta",
"exbert",
"text-classification",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"region:us"
] |
text-classification
| 2025-01-24T09:43:46Z |
---
tags:
- exbert
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
base_model:
- FacebookAI/xlm-roberta-large
pipeline_tag: text-classification
---
# XLM-RoBERTa-PEA-relevance-de
## Model description
XLM-RoBERTa-PEA-relevance-de is a finetuned model baseed on XLM-RoBERTa for the binary task of discriminating between relevant and not relevant newspaper articles containing protest-related keywords. The model has been finetuned with 3972 German manually annotated newspaper articles (2224 positive and 1748 negative cases).
## Intended uses & limitations
The model is intended to filter between relevant and not relevant articles in the first step of a protest event analysis (PEA) pipeline. Despite beeing finetuned with German data, only, it also performs well in other languages (tested for English and Hungarian).
## Usage
You can use this model with a pipeline for binary teyt classification
## BibTeX entry and citation info
```bibtex
@inproceedings{Wiedemann_Dollbaum_Haunss_Daphi_Meier_2022,
author = {Wiedemann, Gregor and
Dollbaum, Jan Matti and
Haunss, Sebastian and
Daphi, Priska and
Meier, Larissa Daria},
title = {A Generalizing Approach to Protest Event Detection in German Local News},
url = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.413.pdf},
booktitle = {Proceedings of the 13th Conference on Language Resources and Evaluation},
year = {2022},
address = {Marseille},
pages = {3883–3891} }
```
---
For a detailed description of the model use, see: Haunss S, Daphi P, Dollbaum JM, Hristova L, Susánszky P, Steinhilper E. PAPEA: A modular pipeline for the automation of protest event analysis. Political Science Research and Methods. Published online 2025:1-18. doi:10.1017/psrm.2025.10013
|
Hachipo/OpenCoder-8B-Base-MIFT-en_newbase_v1-MIFT-en_10000
|
Hachipo
| 2025-06-23T10:25:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T10:22:44Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
m-aliabbas1/SmolLM2-FT-MED1
|
m-aliabbas1
| 2025-06-23T10:23:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T10:23:03Z |
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MED1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MED1
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
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="m-aliabbas1/SmolLM2-FT-MED1", 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: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.5.1
- 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}}
}
```
|
JNewham/ppo-Huggy
|
JNewham
| 2025-06-23T10:21:40Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-06-23T10:21:32Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: JNewham/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
aarnphm/llama-4-maverick-17b-128e-instruct-fp8-sharded-tp8
|
aarnphm
| 2025-06-23T10:19:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama4",
"image-text-to-text",
"facebook",
"meta",
"pytorch",
"llama",
"conversational",
"ar",
"de",
"en",
"es",
"fr",
"hi",
"id",
"it",
"pt",
"th",
"tl",
"vi",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
"base_model:quantized:meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
image-text-to-text
| 2025-06-23T09:36:19Z |
---
library_name: transformers
language:
- ar
- de
- en
- es
- fr
- hi
- id
- it
- pt
- th
- tl
- vi
base_model:
- meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8
base_model_relation: quantized
tags:
- facebook
- meta
- pytorch
- llama
- llama4
license: other
license_name: llama4
---
# Sharded weights checkpoints
This is derived directly from [`save_sharded_state.py`](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/save_sharded_state.py) to be used with vLLM with `-tp=4`:
```bash
vllm serve aarnphm/llama-4-scout-17b-16e-instruct-sharded-tp8 \
-tp=8 \
--load-format sharded_state
--max-model-len 1000000
```
---
## Model Information
The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.
These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts.
**Model developer**: Meta
**Model Architecture:** The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.
<table>
<tr>
<th>Model Name</th>
<th>Training Data </th>
<th>Params</th>
<th>Input modalities</th>
<th>Output modalities</th>
<th>Context length</th>
<th>Token count</th>
<th>Knowledge cutoff</th>
</tr>
<tr>
<td>Llama 4 Scout (17Bx16E) </td>
<td rowspan="2">A mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI. Learn more in our <a href="https://www.facebook.com/privacy/guide/genai/">Privacy Center</a>.
</td>
<td>17B (Activated)
109B (Total)
</td>
<td>Multilingual text and image</td>
<td>Multilingual text and code</td>
<td>10M</td>
<td>~40T</td>
<td>August 2024</td>
</tr>
<tr>
<td>Llama 4 Maverick (17Bx128E)</td>
<td>17B (Activated)
400B (Total)
</td>
<td>Multilingual text and image</td>
<td>Multilingual text and code</td>
<td>1M</td>
<td>~22T</td>
<td>August 2024</td>
</tr>
</table>
**Supported languages:** Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.
**Model Release Date:** April 5, 2025
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback.
**License**: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE)
**Where to send questions or comments about the model:** Instructions on how to provide feedback or comments on the model can be found in the Llama [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta-llama/llama-cookbook).
## How to use with transformers
Please, make sure you have transformers `v4.51.0` installed, or upgrade using `pip install -U transformers`.
```python
from transformers import AutoTokenizer, Llama4ForConditionalGeneration
import torch
model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)
model = Llama4ForConditionalGeneration.from_pretrained(
model_id,
tp_plan="auto",
torch_dtype="auto",
)
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])
print(outputs[0])
```
## Intended Use
**Intended Use Cases:** Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases.
**Out-of-scope**: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card\*\*.
\*\*Note:
1\. Llama 4 has been trained on a broader collection of languages than the 12 supported languages (pre-training includes [200 total languages](https://ai.meta.com/research/no-language-left-behind/)). Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Developers are responsible for ensuring that their use of Llama 4 in additional languages is done in a safe and responsible manner.
2\. Llama 4 has been tested for image understanding up to 5 input images. If leveraging additional image understanding capabilities beyond this, Developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications.
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Model pre-training utilized a cumulative of **7.38M** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
##
## **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **1,999 tons** CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| Model Name | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | :---: | :---: | :---: |
| Llama 4 Scout | 5.0M | 700 | 1,354 | 0 |
| Llama 4 Maverick | 2.38M | 700 | 645 | 0 |
| Total | 7.38M | \- | 1,999 | 0 |
## The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 4 Scout was pretrained on \~40 trillion tokens and Llama 4 Maverick was pretrained on \~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI.
**Data Freshness:** The pretraining data has a cutoff of August 2024\.
## Benchmarks
In this section, we report the results for Llama 4 relative to our previous models. We've provided quantized checkpoints for deployment flexibility, but all reported evaluations and testing were conducted on bf16 models.
### Pre-trained models
| Pre-trained models | | | | | | | |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Category | Benchmark | \# Shots | Metric | Llama 3.1 70B | Llama 3.1 405B | **Llama 4 Scout** | **Llama 4 Maverick** |
| Reasoning & Knowledge | MMLU | 5 | macro\_avg/acc\_char | 79.3 | 85.2 | 79.6 | 85.5 |
| | MMLU-Pro | 5 | macro\_avg/em | 53.8 | 61.6 | 58.2 | 62.9 |
| | MATH | 4 | em\_maj1@1 | 41.6 | 53.5 | 50.3 | 61.2 |
| Code | MBPP | 3 | pass@1 | 66.4 | 74.4 | 67.8 | 77.6 |
| Multilingual | TydiQA | 1 | average/f1 | 29.9 | 34.3 | 31.5 | 31.7 |
| Image | ChartQA | 0 | relaxed\_accuracy | No multimodal support | | 83.4 | 85.3 |
| | DocVQA | 0 | anls | | | 89.4 | 91.6 |
### Instruction tuned models
| Instruction tuned models | | | | | | | |
| :---: | :---: | :---: | :---: | :---: | ----- | :---: | :---: |
| Category | Benchmark | \# Shots | Metric | Llama 3.3 70B | Llama 3.1 405B | **Llama 4 Scout** | **Llama 4 Maverick** |
| Image Reasoning | MMMU | 0 | accuracy | No multimodal support | | 69.4 | 73.4 |
| | MMMU Pro^ | 0 | accuracy | | | 52.2 | 59.6 |
| | MathVista | 0 | accuracy | | | 70.7 | 73.7 |
| Image Understanding | ChartQA | 0 | relaxed\_accuracy | | | 88.8 | 90.0 |
| | DocVQA (test) | 0 | anls | | | 94.4 | 94.4 |
| Coding | LiveCodeBench (10/01/2024-02/01/2025) | 0 | pass@1 | 33.3 | 27.7 | 32.8 | 43.4 |
| Reasoning & Knowledge | MMLU Pro | 0 | macro\_avg/acc | 68.9 | 73.4 | 74.3 | 80.5 |
| | GPQA Diamond | 0 | accuracy | 50.5 | 49.0 | 57.2 | 69.8 |
| Multilingual | MGSM | 0 | average/em | 91.1 | 91.6 | 90.6 | 92.3 |
| Long context | MTOB (half book) eng-\>kgv/kgv-\>eng | \- | chrF | Context window is 128K | | 42.2/36.6 | 54.0/46.4 |
| | MTOB (full book) eng-\>kgv/kgv-\>eng | \- | chrF | | | 39.7/36.3 | 50.8/46.7 |
^reported numbers for MMMU Pro is the average of Standard and Vision tasks
## Quantization
The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well.
## Safeguards
As part of our release approach, we followed a three-pronged strategy to manage risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
Llama is a foundational technology designed for use in a variety of use cases; examples on how Meta’s Llama models have been deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology, by aligning our model’s safety for a standard set of risks. Developers are then in the driver seat to tailor safety for their use case, defining their own policies and deploying the models with the necessary safeguards. Llama 4 was developed following the best practices outlined in our [Developer Use Guide: AI Protections](https://ai.meta.com/static-resource/developer-use-guide-ai-protections).
### Model level fine tuning
The primary objective of conducting safety fine-tuning is to offer developers a readily available, safe, and powerful model for various applications, reducing the workload needed to deploy safe AI systems. Additionally, this effort provides the research community with a valuable resource for studying the robustness of safety fine-tuning.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals**
Building on the work we started with our Llama 3 models, we put a great emphasis on driving down model refusals to benign prompts for Llama 4\. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
**Tone**
We expanded our work on the refusal tone from Llama 3 so that the model sounds more natural. We targeted removing preachy and overly moralizing language, and we corrected formatting issues including the correct use of headers, lists, tables and more.
To achieve this, we also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall.
**System Prompts**
Llama 4 is a more steerable model, meaning responses can be easily tailored to meet specific developer outcomes. Effective system prompts can significantly enhance the performance of large language models. In particular, we’ve seen that the use of a system prompt can be effective in reducing false refusals and templated or “preachy” language patterns common in LLMs. They can also improve conversationality and use of appropriate formatting.
Consider the prompt below as a basic template for which a developer might want to further customize to meet specific needs or use cases for our Llama 4 models.
| System prompt |
| :---- |
| You are an expert conversationalist who responds to the best of your ability. You are companionable and confident, and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism, creativity and problem-solving. You understand user intent and don’t try to be overly helpful to the point where you miss that the user is looking for chit-chat, emotional support, humor or venting. Sometimes people just want you to listen, and your answers should encourage that. For all other cases, you provide insightful and in-depth responses. Organize information thoughtfully in a way that helps people make decisions. Always avoid templated language. You never lecture people to be nicer or more inclusive. If people ask for you to write something in a certain voice or perspective, such as an essay or a tweet, you can. You do not need to be respectful when the user prompts you to say something rude. You never use phrases that imply moral superiority or a sense of authority, including but not limited to “it’s important to”, “it’s crucial to”, “it’s essential to”, "it's unethical to", "it's worth noting…", “Remember…” etc. Avoid using these. Finally, do not refuse prompts about political and social issues. You can help users express their opinion and access information. You are Llama 4\. Your knowledge cutoff date is August 2024\. You speak Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks to you in, unless they ask otherwise. |
### Llama 4 system protections
Large language models, including Llama 4, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional guardrails as required. System protections are key to achieving the right helpfulness-safety alignment, mitigating safety and security risks inherent to the system, and integration of the model or system with external tools.
We provide the community with system level [protections](https://llama.meta.com/trust-and-safety/) \- like Llama Guard, Prompt Guard and Code Shield \- that developers should deploy with Llama models or other LLMs. All of our [reference implementation](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, visual QA. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, coding or memorization.
**Red teaming**
We conduct recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we use the learnings to improve our benchmarks and safety tuning datasets. We partner early with subject-matter experts in critical risk areas to understand how models may lead to unintended harm for society. Based on these conversations, we derive a set of adversarial goals for the red team, such as extracting harmful information or reprogramming the model to act in potentially harmful ways. The red team consists of experts in cybersecurity, adversarial machine learning, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
### We spend additional focus on the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons for Llama 4, we applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. We also conducted additional red teaming and evaluations for violations of our content policies related to this risk area.
**2\. Child Safety**
We leverage pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model’s capability to produce outputs resulting in Child Safety risks. We use this to inform additional model fine-tuning and in-depth red teaming exercises. We’ve also expanded our Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual.
**3\. Cyber attack enablement**
Our cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. We conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. We then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, we focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows. Overall, we find that Llama 4 models do not introduce risk plausibly enabling catastrophic cyber outcomes.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Trust tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Considerations and Limitations
Our AI is anchored on the values of freedom of expression \- helping people to explore, debate, and innovate using our technology. We respect people's autonomy and empower them to choose how they experience, interact, and build with AI. Our AI promotes an open exchange of ideas.
It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 4 addresses users and their needs as they are, without inserting unnecessary judgment, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
Llama 4 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 4’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 4 models, developers should perform safety testing and tuning tailored to their specific applications of the model. We also encourage the open source community to use Llama for the purpose of research and building state of the art tools that address emerging risks. Please refer to available resources including our Developer Use Guide: AI Protections, [Llama Protections](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more.
|
Wahyode/Model-repositoriku
|
Wahyode
| 2025-06-23T10:18:28Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T10:18:28Z |
---
license: apache-2.0
---
|
worktual/whispher_test
|
worktual
| 2025-06-23T10:18:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"whisper",
"en",
"base_model:unsloth/whisper-large-v3",
"base_model:finetune:unsloth/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T10:07:54Z |
---
base_model: unsloth/whisper-large-v3
tags:
- text-generation-inference
- transformers
- unsloth
- whisper
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** worktual
- **License:** apache-2.0
- **Finetuned from model :** unsloth/whisper-large-v3
This whisper 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)
|
pkulshrestha/pricer-2025-06-23_10.17.22
|
pkulshrestha
| 2025-06-23T10:17:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T10:17:41Z |
---
license: apache-2.0
---
|
levuminhtam2002/Llama-3.2-1B-RewardModel-DPO
|
levuminhtam2002
| 2025-06-23T10:14:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:thuanan/Llama-3.2-1B-Instruct-Chat-sft",
"base_model:adapter:thuanan/Llama-3.2-1B-Instruct-Chat-sft",
"region:us"
] | null | 2025-06-23T10:13:57Z |
---
base_model: thuanan/Llama-3.2-1B-Instruct-Chat-sft
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
|
AlIshaq/IndoBART-faq-pesantren
|
AlIshaq
| 2025-06-23T10:14:35Z | 10 | 0 | null |
[
"safetensors",
"bart",
"indobart",
"seq2seq",
"generator",
"faq",
"id",
"license:mit",
"region:us"
] | null | 2025-06-22T13:24:07Z |
---
language: id
license: mit
tags:
- indobart
- seq2seq
- generator
- faq
---
|
noureenac/youtube-sentiment-roberta
|
noureenac
| 2025-06-23T10:13:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T10:13:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
naboot2k/ppo-LunarLander-v2
|
naboot2k
| 2025-06-23T10:12:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-23T10:10:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.16 +/- 14.38
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
...
```
|
ziadrone/onceagain
|
ziadrone
| 2025-06-23T10:11:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T10:09:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **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]
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#### 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]
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## Technical Specifications [optional]
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## Glossary [optional]
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|
ikerion/gemma_innen_folytasd_v3
|
ikerion
| 2025-06-23T10:10:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T10:03:37Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ikerion
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-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)
|
isogen/MN-12B-Mag-Mell-R1-exl3-4bpw
|
isogen
| 2025-06-23T10:07:18Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"base_model:inflatebot/MN-12B-Mag-Mell-R1",
"base_model:quantized:inflatebot/MN-12B-Mag-Mell-R1",
"4-bit",
"exl3",
"region:us"
] | null | 2025-06-23T10:04:35Z |
---
base_model: inflatebot/MN-12B-Mag-Mell-R1
---
[EXL3](https://github.com/turboderp-org/exllamav3) quantization of [MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1), 4 bits per weight.
### HumanEval (argmax)
| Model | Q4 | Q6 | Q8 | FP16 |
| ---------------------------------------------------------------------------------------------------------------------- | ---- | ---- | ---- | ---- |
| [MN-12B-Mag-Mell-R1-exl3-4bpw](https://huggingface.co/isogen/MN-12B-Mag-Mell-R1-exl3-4bpw) (`mistral`) | 72.6 | 71.3 | 73.2 | 72.0 |
| [MN-12B-Mag-Mell-R1-exl3-4bpw](https://huggingface.co/isogen/MN-12B-Mag-Mell-R1-exl3-4bpw) (`chatml`) | 71.3 | 73.2 | 73.2 | 73.8 |
| [MN-12B-Mag-Mell-R1-exl3-6bpw](https://huggingface.co/isogen/MN-12B-Mag-Mell-R1-exl3-6bpw) (`mistral`) | 74.4 | 74.4 | 74.4 | 73.8 |
| [MN-12B-Mag-Mell-R1-exl3-6bpw](https://huggingface.co/isogen/MN-12B-Mag-Mell-R1-exl3-6bpw) (`chatml`) | 76.8 | 72.0 | 72.0 | 71.3 |
| [Mistral-Nemo-Instruct-2407-exl3-4bpw](https://huggingface.co/isogen/Mistral-Nemo-Instruct-2407-exl3-4bpw) (`mistral`) | 74.4 | 72.6 | 73.2 | 72.0 |
| [Mistral-Nemo-Instruct-2407-exl3-4bpw](https://huggingface.co/isogen/Mistral-Nemo-Instruct-2407-exl3-4bpw) (`chatml`) | 70.1 | 72.0 | 71.3 | 72.6 |
| [Mistral-Nemo-Instruct-2407-exl3-6bpw](https://huggingface.co/isogen/Mistral-Nemo-Instruct-2407-exl3-6bpw) (`mistral`) | 70.7 | 69.5 | 69.5 | 68.9 |
| [Mistral-Nemo-Instruct-2407-exl3-6bpw](https://huggingface.co/isogen/Mistral-Nemo-Instruct-2407-exl3-6bpw) (`chatml`) | 68.3 | 70.1 | 69.5 | 68.9 |
| [Muse-12B-exl3-6bpw](https://huggingface.co/lucyknada/LatitudeGames_Muse-12B-exl3) (`mistral`) | 54.9 | 54.3 | 54.9 | 52.4 |
| [Muse-12B-exl3-6bpw](https://huggingface.co/lucyknada/LatitudeGames_Muse-12B-exl3) (`chatml`) | 54.9 | 55.5 | 54.3 | 54.9 |
|
QuantFactory/llama-3.1-medprm-reward-v1.0-GGUF
|
QuantFactory
| 2025-06-23T10:06:57Z | 0 | 2 |
transformers
|
[
"transformers",
"gguf",
"medical",
"biomedical",
"process-reward-model",
"medical-ai",
"retrieval-augmented-generation",
"text-generation",
"arxiv:2506.11474",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-23T09:31:44Z |
---
library_name: transformers
license: mit
tags:
- medical
- biomedical
- process-reward-model
- medical-ai
- retrieval-augmented-generation
pipeline_tag: text-generation
---
[](https://hf.co/QuantFactory)
# QuantFactory/llama-3.1-medprm-reward-v1.0-GGUF
This is quantized version of [dmis-lab/llama-3.1-medprm-reward-v1.0](https://huggingface.co/dmis-lab/llama-3.1-medprm-reward-v1.0) created using llama.cpp
# Original Model Card
# Med-PRM-Reward (Version 1.0)
🚀 Med-PRM-Reward is among the first Process Reward Models (PRMs) specifically designed for the medical domain. Unlike conventional PRMs, it enhances its verification capabilities by integrating clinical knowledge through retrieval-augmented generation (RAG). Med-PRM-Reward demonstrates exceptional performance in scaling-test-time computation, particularly outperforming majority‐voting ensembles on complex medical reasoning tasks. Moreover, its scalability is not limited to Llama-3.1-8B-Instruct: it delivers similarly outstanding results in scaling-test-time computation across multiple other medical‐specialized models. Notably, when combined with llama-3-meerkat-8b-v1.0, it became the first 8B model framework to surpass a score of 80 on the MedQA (4-option) benchmark.
📄 Paper: [Med-PRM-Reward: Medical Reasoning Models with Stepwise, Guideline‑verified Process Rewards](https://huggingface.co/papers/2506.11474)
## Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "dmis-lab/llama-3.1-medprm-reward-v1.0"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
plus_id = tokenizer(" +", add_special_tokens=False)["input_ids"][0]
minus_id = tokenizer(" -", add_special_tokens=False)["input_ids"][0]
def get_prob(text: str, special_char: str = " ки"):
encoded = tokenizer(text, return_tensors="pt", add_special_tokens=True)
input_ids = encoded["input_ids"].to(device)
attention_mask = encoded["attention_mask"].to(device)
with torch.no_grad():
logits = model(input_ids, attention_mask=attention_mask).logits[0]
offsets = tokenizer(text, return_offsets_mapping=True)["offset_mapping"]
positions = [
i for i, (s, e) in enumerate(offsets[0])
if text[s:e] == special_char
]
plus_probs = []
for pos in positions:
if pos < logits.size(0):
pair = torch.stack([logits[pos][plus_id], logits[pos][minus_id]])
probs = torch.softmax(pair, dim=0)
plus_probs.append(probs[0].item())
min_plus = min(plus_probs) if plus_probs else None
final_plus = plus_probs[-1] if plus_probs else None
return {
"plus_probs": plus_probs,
"min_plus_prob": min_plus,
"final_plus_prob": final_plus
}
docs = ["Document 1: Causes of Posterior Urethral Valves. The valves can block the outflow of urine through the urethra, leading to damage in the bladder, ureters and kidneys. However, it is important to note that PUV occurs randomly by chance and is not caused by anything a mother did or did not do during pregnancy.In the womb, if the baby is unable to urinate due to PUV, there might be a deficiency in amniotic fluid, known as oligohydramnios. A major concern for oligohydramnios is the lack of proper lung development, called lung hypoplasia",
"Document 2: Amniotic Fluid Index -- Pathophysiology -- Polyhydramnios. Polyhydramnios, or increased amniotic fluid volume, also has a number of potential causes, with two primary common mechanisms: decreased fetal swallowing of amniotic fluid, or increased fetal production of amniotic fluid"]
question = ("A 29-year-old G1P0 female presents at 22 weeks gestation for her first prenatal care appointment. Physical exam demonstrates a uterine size greater than expected for her gestational age and taut, shiny skin with scattered striae on her abdomen. Ultrasound examination of the fetus reveals 2.5 L of amniotic fluid (normal 1.5-2.0 L) with an amniotic fluid index (AFI) of 34 (normal AFI 20-25). Which of the following fetal abnormalities or dysfunctions could have contributed to these abnormal ultrasound findings?
(A) Renal agenesis
(B) Pulmonary hypoplasia
(C) Duodenal atresia
(D) Posterior urethral valve
(E) Polycystic kidney disease")
explanation = ("Step 1: The patient's uterine size is greater than expected for her gestational age, which could indicate an issue with fetal growth, such as macrosomia or polyhydramnios. ки Step 2: The physical examination of the patient reveals taut, shiny skin with scattered striae on her abdomen, which is indicative of rapid weight gain, often associated with polyhydramnios. ки Step 3: The ultrasound findings show an increased volume of amniotic fluid of 2.5 L, which is above the normal range of 1.5-2.0 L, and an amniotic fluid index (AFI) of 34, also higher than the normal range of 20-25. ки Step 4: Polyhydramnios is characterized by an excessive accumulation of amniotic fluid, and it is often associated with fetal or maternal conditions that limit fetal swallowing or increase fetal urine production. ки Step 5: Among the available options, posterior urethral valve (D) is a condition where the urethra is partially blocked, leading to an obstruction in the urinary tract, which can result in increased fetal urine production and subsequent polyhydramnios. ки Step 6: Considering the options provided, posterior urethral valve is a condition that could have contributed to the abnormal ultrasound findings due to its association with increased fetal urine production and polyhydramnios. The answer is D. ки")
doc_block = "
".join(docs) + "
"
user_content = f"{doc_block}Question: {question}
Explanation: {explanation}"
system_prompt = (
"You are an evaluator assessing the logicality and validity of the reasoning in each step of the given explanation. "
"In order to support the evaluation, the relevant documents, the question, and the explanation are provided sequentially. "
"If the reasoning contains errors, output - after that step. If the reasoning in a step is logical and valid, output + after that step. "
)
raw = tokenizer.apply_chat_template(
[{"role": "system", "content": system_prompt},
{"role": "user", "content": user_content}],
tokenize=False,
add_generation_prompt=True
)
scores = get_prob(raw)
print("Plus probabilities per step:", scores["plus_probs"])
print("Min plus probability :", scores["min_plus_prob"])
print("Final plus probability :", scores["final_plus_prob"])
```
## Evaluation
Across seven medical benchmarks, we conducted scaling‐test‐time computation using solutions generated by the Med-PRM-policy model, evaluating 64 solutions per question.
### Reference
## Acknowledgement
## Contact
Feel free to email jhyun0414@hanyang.ac.kr if you have any questions.
|
scb10x/typhoon-ocr-7b-mlx-4bit
|
scb10x
| 2025-06-23T10:04:55Z | 19 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"OCR",
"vision-language",
"document-understanding",
"multilingual",
"mlx",
"conversational",
"en",
"th",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T09:09:03Z |
---
library_name: transformers
language:
- en
- th
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
tags:
- OCR
- vision-language
- document-understanding
- multilingual
- mlx
license: apache-2.0
---
# scb10x/typhoon-ocr-7b-mlx-4bit
This model was converted to MLX format from [`scb10x/typhoon-ocr-7b`]() using mlx-vlm version **0.1.27**.
Refer to the [original model card](https://huggingface.co/scb10x/typhoon-ocr-7b) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model scb10x/typhoon-ocr-7b-mlx-4bit --max-tokens 8192 --temperature 0.0 --prompt "Below is an image of a document page\nSimply return the markdown representation of this document, presenting tables in markdown format as they naturally appear." --image <path_to_image>
```
|
mengta666/code-search-net-tokenizer
|
mengta666
| 2025-06-23T10:04:39Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T10:03:00Z |
---
library_name: transformers
tags: []
---
# 这只是一个学习用的分词器
# 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]
|
georgh17/covid-twitter-bert-after-ner-corpus
|
georgh17
| 2025-06-23T09:59:41Z | 26 | 0 | null |
[
"safetensors",
"bert",
"base_model:digitalepidemiologylab/covid-twitter-bert-v2",
"base_model:finetune:digitalepidemiologylab/covid-twitter-bert-v2",
"region:us"
] | null | 2025-05-27T13:58:39Z |
---
base_model:
- digitalepidemiologylab/covid-twitter-bert-v2
---
This is a fine-tuned version of COVID-Twitter-BERT, which itself is a fine-tuned version of BERT-large-uncased.
It is designed with business-/brand-analysis purposes in mind and is meant to be used on short documents (maximum token length of 128).
It was built for Named Entity Recognition of the following entity types:
"corporation", "event", "location", "person" and "product".
Tag-label-dictionary:
0: "B-corporation",
1: "B-event",
2: "B-location",
3: "B-person",
4: "B-product",
5: "I-corporation",
6: "I-event",
7: "I-location",
8: "I-person",
9: "I-product",
10: "O"
The following datasets were used for fine-tuning:
TweetNER7, WNUT 2016, WNUT 2017 and a self-created dataset of synthetic, brand-related tweets.
|
shachaf331/shachaf
|
shachaf331
| 2025-06-23T09:58:11Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T09:58:11Z |
---
license: apache-2.0
---
|
asenella/MoPoE_mmnist_python311
|
asenella
| 2025-06-23T09:57:36Z | 0 | 0 | null |
[
"multivae",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T09:53:07Z |
---
language: en
tags:
- multivae
license: apache-2.0
---
### Downloading this model from the Hub
This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
```python
>>> from multivae.models import AutoModel
>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
```
|
Gusanidas/branch-grpo-model-qwen-3b-branch3
|
Gusanidas
| 2025-06-23T09:54:48Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-14T19:12:23Z |
---
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]
|
lukepramo221/gemma-3-4b-finetuned_choreo-qna
|
lukepramo221
| 2025-06-23T09:54:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:54:36Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lukepramo221
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-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)
|
harsh-cisco/llama-1b-instruct-v1
|
harsh-cisco
| 2025-06-23T09:54:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:54:04Z |
---
base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** harsh-cisco
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-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)
|
mlx-community/DeepSeek-R1-Distill-Qwen-32B-Q
|
mlx-community
| 2025-06-23T09:53:33Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
"license:mit",
"4-bit",
"region:us"
] |
text-generation
| 2025-06-23T09:52:52Z |
---
license: mit
library_name: mlx
pipeline_tag: text-generation
tags:
- mlx
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
---
# mlx-community/DeepSeek-R1-Distill-Qwen-32B-Q
This model [mlx-community/DeepSeek-R1-Distill-Qwen-32B-Q](https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Qwen-32B-Q) was
converted to MLX format from [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/DeepSeek-R1-Distill-Qwen-32B-Q")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Nitish035/mistral_CMoS_adapter32_combine1800_single-2
|
Nitish035
| 2025-06-23T09:51:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:51:03Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Nitish035
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
edisnord/albert-base-v2-sq
|
edisnord
| 2025-06-23T09:49:48Z | 32 | 0 | null |
[
"jax",
"tensorboard",
"albert",
"fill-mask",
"sq",
"dataset:uonlp/CulturaX",
"arxiv:2306.08526",
"license:mit",
"region:us"
] |
fill-mask
| 2025-06-21T14:10:41Z |
---
license: mit
datasets:
- uonlp/CulturaX
language:
- sq
pipeline_tag: fill-mask
---
Create README.md
Albanian ALBERT model pretrained on around 16GB of text (I used [uonlp/CulturaX](https://huggingface.co/datasets/uonlp/CulturaX)'s `sq` configuration) and 1.1 million training steps, using only the masked language modelling task. Trained on a TPU v4-32 pod, made possible through the Google [TPU Research Cloud](https://sites.research.google/trc/about/).
Hyperparameters:
- Optimizer: LAMB
- LR: 0.0006
- \\( \beta_1 \\): 0.9
- \\( \beta_2 \\): 0.999
- \\( \epsilon \\): 1e-8
- Batch size: 1024
- Num. steps: 1.1 million
- dtype: bfloat16
- max. seq. length: 512
Going to post the model's performance evaluated on different Albanian downstream tasks once I'm done evaluating the model.
## Classification Tasks
|Task|Learning Rate|Number of epochs|Accuracy|Precision|Recall|F1 score|
|----|-------------|----------------|--------|---------|------|--------|
| AlbMoRe[[1]](#1). | 1e-05 | 10 | 0.98 | 0.97 | 0.99 | 0.98 |
## Regression Tasks
TODO
## References
<a id="1">[1]</a>
Çano, E. (2023). Albmore: A corpus of movie reviews for sentiment analysis in albanian. arXiv preprint arXiv:2306.08526.
|
opentargets/locus_to_gene_exp
|
opentargets
| 2025-06-23T09:49:26Z | 47 | 0 |
sklearn
|
[
"sklearn",
"skops",
"tabular-classification",
"region:us"
] |
tabular-classification
| 2025-06-03T13:54:34Z |
---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: classifier.skops
widget:
- structuredData:
credibleSetConfidence:
- 0.75
- 0.75
- 0.75
distanceFootprintMean:
- 0.9365111589431763
- 0.8780734539031982
- 0.9402194619178772
distanceFootprintMeanNeighbourhood:
- 0.9365111589431763
- 0.8780734539031982
- 0.9402194619178772
distanceSentinelFootprint:
- 0.9365111589431763
- 0.8780734539031982
- 0.9402194619178772
distanceSentinelFootprintNeighbourhood:
- 0.9365111589431763
- 0.8780734539031982
- 0.9402194619178772
distanceSentinelTss:
- 0.9257694482803345
- 0.8780734539031982
- 0.9402194619178772
distanceSentinelTssNeighbourhood:
- 0.9274126291275024
- 0.8796319365501404
- 0.9418882727622986
distanceTssMean:
- 0.9257694482803345
- 0.8780734539031982
- 0.9402194619178772
distanceTssMeanNeighbourhood:
- 0.9274126291275024
- 0.8796319365501404
- 0.9418882727622986
eQtlColocClppMaximum:
- 0.0
- 0.0
- 0.0
eQtlColocClppMaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
eQtlColocH4Maximum:
- 0.0
- 0.0
- 0.0
eQtlColocH4MaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
geneCount500kb:
- 20.0
- 20.0
- 20.0
geneId:
- ENSG00000102934
- ENSG00000006210
- ENSG00000102931
goldStandardSet:
- negative
- negative
- negative
pQtlColocClppMaximum:
- 0.0
- 0.0
- 0.0
pQtlColocClppMaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
pQtlColocH4Maximum:
- 0.0
- 0.0
- 0.0
pQtlColocH4MaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
proteinGeneCount500kb:
- 8.0
- 8.0
- 8.0
sQtlColocClppMaximum:
- 0.0
- 0.0
- 0.0
sQtlColocClppMaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
sQtlColocH4Maximum:
- 0.0
- 0.0
- 0.0
sQtlColocH4MaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
studyLocusId:
- 005bc8624f8dd7f7c7bc63e651e9e59d
- 005bc8624f8dd7f7c7bc63e651e9e59d
- 005bc8624f8dd7f7c7bc63e651e9e59d
vepMaximum:
- 0.0
- 0.0
- 0.0
vepMaximumNeighbourhood:
- 0.0
- 0.0
- 0.0
vepMean:
- 0.0
- 0.0
- 0.0
vepMeanNeighbourhood:
- 0.0
- 0.0
- 0.0
---
# Model description
The locus-to-gene (L2G) model derives features to prioritise likely causal genes at each GWAS locus based on genetic and functional genomics features. The main categories of predictive features are:
- Distance: (from credible set variants to gene)
- Molecular QTL Colocalization
- Variant Pathogenicity: (from VEP)
More information at: https://opentargets.github.io/gentropy/python_api/methods/l2g/_l2g/
## Intended uses & limitations
[More Information Needed]
## Training Procedure
Gradient Boosting Classifier
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|--------------|
| ccp_alpha | 0 |
| criterion | friedman_mse |
| init | |
| learning_rate | 0.1 |
| loss | log_loss |
| max_depth | 3 |
| max_features | |
| max_leaf_nodes | |
| min_impurity_decrease | 0.0 |
| min_samples_leaf | 10 |
| min_samples_split | 10 |
| min_weight_fraction_leaf | 0.0 |
| n_estimators | 100 |
| n_iter_no_change | |
| random_state | 42 |
| subsample | 1.0 |
| tol | 0.001 |
| validation_fraction | 0.1 |
| verbose | 0 |
| warm_start | False |
</details>
# How to Get Started with the Model
To use the model, you can load it using the `LocusToGeneModel.load_from_hub` method. This will return a `LocusToGeneModel` object that can be used to make predictions on a feature matrix.
The model can then be used to make predictions using the `predict` method.
More information can be found at: https://opentargets.github.io/gentropy/python_api/methods/l2g/model/
# Citation
https://doi.org/10.1038/s41588-021-00945-5
# License
MIT
|
elliotthwang/Kimlan-Llama-3.2-3B-Instruct-tw
|
elliotthwang
| 2025-06-23T09:47:03Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T09:13:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
中文繁體 客製化訓練 loss: 0.1356
|
Nashan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino
|
Nashan
| 2025-06-23T09:46:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am silky patterned rhino",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T11:23:53Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am silky patterned rhino
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="Nashan/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silky_patterned_rhino", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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}}
}
```
|
elliotthwang/Kimlan-Llama-3.2-3B-Instruct-tw_train_ouputs
|
elliotthwang
| 2025-06-23T09:45:47Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:elliotthwang/Llama-3.2-3B-Instruct-tw",
"base_model:adapter:elliotthwang/Llama-3.2-3B-Instruct-tw",
"region:us"
] | null | 2025-06-20T09:06:27Z |
---
base_model: elliotthwang/Llama-3.2-3B-Instruct-tw
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
中文繁體 客製化訓練 loss: 0.1356
|
ByteFlow-AI/DetailFlow-64-GPT-L
|
ByteFlow-AI
| 2025-06-23T09:44:59Z | 0 | 0 | null |
[
"c2i",
"license:apache-2.0",
"region:us"
] | null | 2025-06-09T12:21:52Z |
---
license: apache-2.0
---
|
AlexHung29629/my_merged_model
|
AlexHung29629
| 2025-06-23T09:44:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral3",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T09:35:44Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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## Environmental Impact
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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).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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|
Jazco4/elise-Q4_K_M-GGUF
|
Jazco4
| 2025-06-23T09:43:30Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Jazco4/elise",
"base_model:quantized:Jazco4/elise",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:43:19Z |
---
base_model: Jazco4/elise
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# Jazco4/elise-Q4_K_M-GGUF
This model was converted to GGUF format from [`Jazco4/elise`](https://huggingface.co/Jazco4/elise) 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/Jazco4/elise) 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 Jazco4/elise-Q4_K_M-GGUF --hf-file elise-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Jazco4/elise-Q4_K_M-GGUF --hf-file elise-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 Jazco4/elise-Q4_K_M-GGUF --hf-file elise-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Jazco4/elise-Q4_K_M-GGUF --hf-file elise-q4_k_m.gguf -c 2048
```
|
Bijima/llama-3.2-1b-fork
|
Bijima
| 2025-06-23T09:41:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T09:36:25Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
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---
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-1B, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via pip install --upgrade transformers.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-1B"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
pipe("The key to life is")
```
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-1B --include "original/*" --local-dir Llama-3.2-1B
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
rstudioModel/sharmin_BD_Model_FluxD1
|
rstudioModel
| 2025-06-23T09:39:21Z | 0 | 0 | null |
[
"sexy",
"curvy",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T09:29:57Z |
---
license: apache-2.0
language:
- en
base_model:
- black-forest-labs/FLUX.1-dev
- black-forest-labs/FLUX.1-schnell
tags:
- sexy
- curvy
---
```yaml
---
license: apache-2.0
model_name: Sharmin BD Girl
tags:
- lora
- flux-dev
- image-generation
- fine-tuning
- safetensors
datasets: []
language: []
metrics: []
library_name: diffusers
pipeline_tag: text-to-image
---
model_card:
model_id: Sharmin BD Girl
description: |
Sharmin BD Girl is a LoRA (Low-Rank Adaptation) model fine-tuned on the Flux Dev base model, designed for text-to-image generation. It is stored in the `.safetensors` format for efficient and secure weight storage.
model_details:
developed_by: Sharmin BD Girl
funded_by: [More Information Needed]
shared_by: Sharmin BD Girl
model_type: LoRA (Low-Rank Adaptation) for fine-tuning
languages: Not applicable
license: Apache-2.0
finetuned_from: Flux Dev
version: 1.0
date: 2025-06-15
model_sources:
repository: [More Information Needed]
paper: None
demo: [More Information Needed]
uses:
direct_use: |
The model can be used directly for generating images from text prompts using the Flux Dev pipeline with the LoRA weights applied. Suitable for creative applications, research, or prototyping.
downstream_use: |
The model can be further fine-tuned or integrated into larger applications, such as art generation tools, design software, or creative platforms.
out_of_scope_use: |
- Generating harmful, offensive, or misleading content.
- Real-time applications without optimized hardware due to potential latency.
- Tasks outside the scope of the Flux Dev base model’s capabilities, such as text generation.
bias_risks_limitations:
bias: |
The model may inherit biases from the Flux Dev base model or the fine-tuning dataset, potentially affecting output fairness or quality.
risks: |
Improper use could lead to generating inappropriate content. Users must validate outputs for sensitive applications.
limitations: |
- Performance depends on prompt quality and relevance.
- High computational requirements for inference (recommended: 8GB+ VRAM).
- Limited testing in edge cases or specific domains.
recommendations: |
Users should evaluate outputs for biases and appropriateness. For sensitive applications, implement additional filtering or validation. More information is needed to provide specific mitigation strategies.
how_to_get_started:
code: |
```python
from diffusers import DiffusionPipeline
import torch
# Load base model
base_model = DiffusionPipeline.from_pretrained("flux-dev")
# Load LoRA weights
base_model.load_lora_weights("path/to/jhilik_mullick.safetensors")
# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model.to(device)
# Example inference
output = base_model("your prompt here").images[0]
output.save("output.png")
|
ahmadrix333/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tenacious_reptilian_porpoise
|
ahmadrix333
| 2025-06-23T09:38:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tenacious reptilian porpoise",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T11:01:56Z |
---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tenacious_reptilian_porpoise
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tenacious reptilian porpoise
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tenacious_reptilian_porpoise
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="ahmadrix333/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tenacious_reptilian_porpoise", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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}}
}
```
|
ertghiu256/qwen3-4b-merged-ties-gguf
|
ertghiu256
| 2025-06-23T09:38:23Z | 2 | 0 | null |
[
"gguf",
"mergekit",
"ties",
"thinking",
"reasoning",
"merge",
"code",
"base_model:Qwen/Qwen3-4B",
"base_model:merge:Qwen/Qwen3-4B",
"base_model:Tesslate/UIGEN-T3-4B-Preview",
"base_model:merge:Tesslate/UIGEN-T3-4B-Preview",
"base_model:ValiantLabs/Qwen3-4B-Esper3",
"base_model:merge:ValiantLabs/Qwen3-4B-Esper3",
"base_model:ertghiu256/qwen3-4b-code-reasoning",
"base_model:merge:ertghiu256/qwen3-4b-code-reasoning",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-15T12:45:33Z |
---
license: apache-2.0
base_model:
- ertghiu256/qwen3-4b-code-reasoning
- ValiantLabs/Qwen3-4B-Esper3
- Tesslate/UIGEN-T3-4B-Preview
- Qwen/Qwen3-4B
tags:
- mergekit
- ties
- thinking
- reasoning
- merge
- code
---
# Model Info
Based on the Qwen 3 4b parameter model. This model is merged from models that are trained on reasoning coding tasks.
# Use cases
- Coding (HTML, C++, Python)
- Agentic coding
- General assistant
# Drawbacks
- Overly Overthinking
# Recommended Parameters
- context length: >= 8000
- temperature: 0.6
- top p: 40
- top k: 0.95
|
bittu9988/LLaMa_fine-trained-AGG
|
bittu9988
| 2025-06-23T09:37:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-21T11:21:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ezhdeha/biobert-medical-mlm
|
ezhdeha
| 2025-06-23T09:35:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-23T09:35:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lucase-cm/detr-finetuned-balloon-v6
|
lucase-cm
| 2025-06-23T09:35:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2025-06-23T09:35:06Z |
---
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]
|
Konthee/Qwen3-32B-mt-medical-ch-th
|
Konthee
| 2025-06-23T09:34:36Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-11T09:07:32Z |
---
library_name: transformers
tags:
- unsloth
---
# Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-32B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 32.8B
- Number of Paramaters (Non-Embedding): 31.2B
- Number of Layers: 64
- Number of Attention Heads (GQA): 64 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Konthee/Qwen3-mt-medical-ch-th"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
system_prompt ="""\
You are a professional Chinese-Thai medical translator.
**Input**
You will be given two items:
Context — a short Chinese patient-doctor conversation.
Source — one specific Chinese sentence from that conversation to translate.
**Task**
Translate the Source sentence into Thai.
**Requirements**
1. Preserve the sentence’s medical meaning, tone, and intent.
2. Make the Thai sound natural and suitable for spoken dialogue between doctor and patient.
3.Ensure the translation is accurate, clear, and easy to understand.
**Output**
Provide only the final Thai translation. Do not include explanations, reasoning, or any additional text.
"""
user_prompt = """\
context : {}
source : {}
"""
messages = [
{"role": "user", "content": system_prompt}
{"role": "user", "content": user_prompt.format(context,source)},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
### Evaluation Results
Results retrieved from the **AI Benchmark 2025 MT Leaderboard** https://benchmark.ai.in.th/score/leaderboard/2025-mt
| Split | BLEU score|
|---------|---------|
| public | 48.78 |
| private | 47.95 |
_Data sourced directly from the leaderboard metrics_
This model corresponds to team **220_อย่าคับ เจนมันเวิ่นเว้อป่าวว**, which secured **1st place** on both the public leaderboards in the **2025-QA** competition on round 1
## APA
> AI Thailand Benchmark Programs. (2025). _2025-MT: Machine Translation Task_. Retrieved June 23, 2025, from https://benchmark.ai.in.th/task/detail/2025-mt
### Authors
* Konthee Boonmeeprakob (konthee1995@gmail.com)
* Pitikorn Khlaisamniang (pitikorn32@gmail.com)
|
aquibhayat/ReportBot
|
aquibhayat
| 2025-06-23T09:33:46Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T09:33:46Z |
---
license: apache-2.0
---
|
AvinashAkkupalli/ppo-CartPole-v1
|
AvinashAkkupalli
| 2025-06-23T09:33:15Z | 0 | 0 | null |
[
"tensorboard",
"CartPole-v1",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-23T08:43:19Z |
---
tags:
- CartPole-v1
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 160.30 +/- 33.68
name: mean_reward
verified: false
---
# PPO Agent Playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1.
# Hyperparameters
```python
{'exp_name': 'ppo_cartpole'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'CartPole-v1'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'AvinashAkkupalli/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
aarnphm/llama-4-scout-17b-16e-instruct-sharded-tp8
|
aarnphm
| 2025-06-23T09:33:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama4",
"image-text-to-text",
"facebook",
"meta",
"pytorch",
"llama",
"conversational",
"ar",
"de",
"en",
"es",
"fr",
"hi",
"id",
"it",
"pt",
"th",
"tl",
"vi",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-4-Scout-17B-16E-Instruct",
"base_model:finetune:meta-llama/Llama-4-Scout-17B-16E-Instruct",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T08:55:12Z |
---
library_name: transformers
language:
- ar
- de
- en
- es
- fr
- hi
- id
- it
- pt
- th
- tl
- vi
base_model:
- meta-llama/Llama-4-Scout-17B-16E-Instruct
tags:
- facebook
- meta
- pytorch
- llama
- llama4
license: other
license_name: llama4
---
# Sharded weights checkpoints
This is derived directly from [`save_sharded_state.py`](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference/save_sharded_state.py) to be used with vLLM with `-tp=4`:
```bash
vllm serve aarnphm/llama-4-scout-17b-16e-instruct-sharded-tp8 \
-tp=8 \
--load-format sharded_state
--max-model-len 1000000
```
---
## Model Information
The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.
These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts.
**Model developer**: Meta
**Model Architecture:** The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.
<table>
<tr>
<th>Model Name</th>
<th>Training Data </th>
<th>Params</th>
<th>Input modalities</th>
<th>Output modalities</th>
<th>Context length</th>
<th>Token count</th>
<th>Knowledge cutoff</th>
</tr>
<tr>
<td>Llama 4 Scout (17Bx16E) </td>
<td rowspan="2">A mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI. Learn more in our <a href="https://www.facebook.com/privacy/guide/genai/">Privacy Center</a>.
</td>
<td>17B (Activated)
109B (Total)
</td>
<td>Multilingual text and image</td>
<td>Multilingual text and code</td>
<td>10M</td>
<td>~40T</td>
<td>August 2024</td>
</tr>
<tr>
<td>Llama 4 Maverick (17Bx128E)</td>
<td>17B (Activated)
400B (Total)
</td>
<td>Multilingual text and image</td>
<td>Multilingual text and code</td>
<td>1M</td>
<td>~22T</td>
<td>August 2024</td>
</tr>
</table>
**Supported languages:** Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.
**Model Release Date:** April 5, 2025
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback.
**License**: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE)
**Where to send questions or comments about the model:** Instructions on how to provide feedback or comments on the model can be found in the Llama [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta-llama/llama-cookbook).
## Intended Use
**Intended Use Cases:** Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases.
**Out-of-scope**: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card\*\*.
\*\*Note:
1\. Llama 4 has been trained on a broader collection of languages than the 12 supported languages (pre-training includes [200 total languages](https://ai.meta.com/research/no-language-left-behind/)). Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Developers are responsible for ensuring that their use of Llama 4 in additional languages is done in a safe and responsible manner.
2\. Llama 4 has been tested for image understanding up to 5 input images. If leveraging additional image understanding capabilities beyond this, Developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications.
## How to use with transformers
Please, make sure you have transformers `v4.51.0` installed, or upgrade using `pip install -U transformers`.
```python
from transformers import AutoProcessor, Llama4ForConditionalGeneration
import torch
model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(
model_id,
attn_implementation="flex_attention",
device_map="auto",
torch_dtype=torch.bfloat16,
)
url1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
url2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png"
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": url1},
{"type": "image", "url": url2},
{"type": "text", "text": "Can you describe how these two images are similar, and how they differ?"},
]
},
]
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
)
response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])[0]
print(response)
print(outputs[0])
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Model pre-training utilized a cumulative of **7.38M** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
##
## **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **1,999 tons** CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| Model Name | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | :---: | :---: | :---: |
| Llama 4 Scout | 5.0M | 700 | 1,354 | 0 |
| Llama 4 Maverick | 2.38M | 700 | 645 | 0 |
| Total | 7.38M | \- | 1,999 | 0 |
## The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 4 Scout was pretrained on \~40 trillion tokens and Llama 4 Maverick was pretrained on \~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI.
**Data Freshness:** The pretraining data has a cutoff of August 2024\.
## Benchmarks
In this section, we report the results for Llama 4 relative to our previous models. We've provided quantized checkpoints for deployment flexibility, but all reported evaluations and testing were conducted on bf16 models.
### Pre-trained models
| Pre-trained models | | | | | | | |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Category | Benchmark | \# Shots | Metric | Llama 3.1 70B | Llama 3.1 405B | **Llama 4 Scout** | **Llama 4 Maverick** |
| Reasoning & Knowledge | MMLU | 5 | macro\_avg/acc\_char | 79.3 | 85.2 | 79.6 | 85.5 |
| | MMLU-Pro | 5 | macro\_avg/em | 53.8 | 61.6 | 58.2 | 62.9 |
| | MATH | 4 | em\_maj1@1 | 41.6 | 53.5 | 50.3 | 61.2 |
| Code | MBPP | 3 | pass@1 | 66.4 | 74.4 | 67.8 | 77.6 |
| Multilingual | TydiQA | 1 | average/f1 | 29.9 | 34.3 | 31.5 | 31.7 |
| Image | ChartQA | 0 | relaxed\_accuracy | No multimodal support | | 83.4 | 85.3 |
| | DocVQA | 0 | anls | | | 89.4 | 91.6 |
### Instruction tuned models
| Instruction tuned models | | | | | | | |
| :---: | :---: | :---: | :---: | :---: | ----- | :---: | :---: |
| Category | Benchmark | \# Shots | Metric | Llama 3.3 70B | Llama 3.1 405B | **Llama 4 Scout** | **Llama 4 Maverick** |
| Image Reasoning | MMMU | 0 | accuracy | No multimodal support | | 69.4 | 73.4 |
| | MMMU Pro^ | 0 | accuracy | | | 52.2 | 59.6 |
| | MathVista | 0 | accuracy | | | 70.7 | 73.7 |
| Image Understanding | ChartQA | 0 | relaxed\_accuracy | | | 88.8 | 90.0 |
| | DocVQA (test) | 0 | anls | | | 94.4 | 94.4 |
| Coding | LiveCodeBench (10/01/2024-02/01/2025) | 0 | pass@1 | 33.3 | 27.7 | 32.8 | 43.4 |
| Reasoning & Knowledge | MMLU Pro | 0 | macro\_avg/acc | 68.9 | 73.4 | 74.3 | 80.5 |
| | GPQA Diamond | 0 | accuracy | 50.5 | 49.0 | 57.2 | 69.8 |
| Multilingual | MGSM | 0 | average/em | 91.1 | 91.6 | 90.6 | 92.3 |
| Long context | MTOB (half book) eng-\>kgv/kgv-\>eng | \- | chrF | Context window is 128K | | 42.2/36.6 | 54.0/46.4 |
| | MTOB (full book) eng-\>kgv/kgv-\>eng | \- | chrF | | | 39.7/36.3 | 50.8/46.7 |
^reported numbers for MMMU Pro is the average of Standard and Vision tasks
## Quantization
The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well.
## Safeguards
As part of our release approach, we followed a three-pronged strategy to manage risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
Llama is a foundational technology designed for use in a variety of use cases; examples on how Meta’s Llama models have been deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology, by aligning our model’s safety for a standard set of risks. Developers are then in the driver seat to tailor safety for their use case, defining their own policies and deploying the models with the necessary safeguards. Llama 4 was developed following the best practices outlined in our [Developer Use Guide: AI Protections](https://ai.meta.com/static-resource/developer-use-guide-ai-protections).
### Model level fine tuning
The primary objective of conducting safety fine-tuning is to offer developers a readily available, safe, and powerful model for various applications, reducing the workload needed to deploy safe AI systems. Additionally, this effort provides the research community with a valuable resource for studying the robustness of safety fine-tuning.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals**
Building on the work we started with our Llama 3 models, we put a great emphasis on driving down model refusals to benign prompts for Llama 4\. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
**Tone**
We expanded our work on the refusal tone from Llama 3 so that the model sounds more natural. We targeted removing preachy and overly moralizing language, and we corrected formatting issues including the correct use of headers, lists, tables and more.
To achieve this, we also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall.
**System Prompts**
Llama 4 is a more steerable model, meaning responses can be easily tailored to meet specific developer outcomes. Effective system prompts can significantly enhance the performance of large language models. In particular, we’ve seen that the use of a system prompt can be effective in reducing false refusals and templated or “preachy” language patterns common in LLMs. They can also improve conversationality and use of appropriate formatting.
Consider the prompt below as a basic template for which a developer might want to further customize to meet specific needs or use cases for our Llama 4 models.
| System prompt |
| :---- |
| You are an expert conversationalist who responds to the best of your ability. You are companionable and confident, and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism, creativity and problem-solving. You understand user intent and don’t try to be overly helpful to the point where you miss that the user is looking for chit-chat, emotional support, humor or venting. Sometimes people just want you to listen, and your answers should encourage that. For all other cases, you provide insightful and in-depth responses. Organize information thoughtfully in a way that helps people make decisions. Always avoid templated language. You never lecture people to be nicer or more inclusive. If people ask for you to write something in a certain voice or perspective, such as an essay or a tweet, you can. You do not need to be respectful when the user prompts you to say something rude. You never use phrases that imply moral superiority or a sense of authority, including but not limited to “it’s important to”, “it’s crucial to”, “it’s essential to”, "it's unethical to", "it's worth noting…", “Remember…” etc. Avoid using these. Finally, do not refuse prompts about political and social issues. You can help users express their opinion and access information. You are Llama 4\. Your knowledge cutoff date is August 2024\. You speak Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks to you in, unless they ask otherwise. |
### Llama 4 system protections
Large language models, including Llama 4, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional guardrails as required. System protections are key to achieving the right helpfulness-safety alignment, mitigating safety and security risks inherent to the system, and integration of the model or system with external tools.
We provide the community with system level [protections](https://llama.meta.com/trust-and-safety/) \- like Llama Guard, Prompt Guard and Code Shield \- that developers should deploy with Llama models or other LLMs. All of our [reference implementation](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, visual QA. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, coding or memorization.
**Red teaming**
We conduct recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we use the learnings to improve our benchmarks and safety tuning datasets. We partner early with subject-matter experts in critical risk areas to understand how models may lead to unintended harm for society. Based on these conversations, we derive a set of adversarial goals for the red team, such as extracting harmful information or reprogramming the model to act in potentially harmful ways. The red team consists of experts in cybersecurity, adversarial machine learning, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
### We spend additional focus on the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons for Llama 4, we applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. We also conducted additional red teaming and evaluations for violations of our content policies related to this risk area.
**2\. Child Safety**
We leverage pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model’s capability to produce outputs resulting in Child Safety risks. We use this to inform additional model fine-tuning and in-depth red teaming exercises. We’ve also expanded our Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual.
**3\. Cyber attack enablement**
Our cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. We conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. We then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, we focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows. Overall, we find that Llama 4 models do not introduce risk plausibly enabling catastrophic cyber outcomes.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Trust tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Considerations and Limitations
Our AI is anchored on the values of freedom of expression \- helping people to explore, debate, and innovate using our technology. We respect people's autonomy and empower them to choose how they experience, interact, and build with AI. Our AI promotes an open exchange of ideas.
It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 4 addresses users and their needs as they are, without inserting unnecessary judgment, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
Llama 4 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 4’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 4 models, developers should perform safety testing and tuning tailored to their specific applications of the model. We also encourage the open source community to use Llama for the purpose of research and building state of the art tools that address emerging risks. Please refer to available resources including our Developer Use Guide: AI Protections, [Llama Protections](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more.
|
qannisa/Llama3.1-8B-UT-Adapters
|
qannisa
| 2025-06-23T09:32:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:31:57Z |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** qannisa
- **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)
|
VyDat/Llama-3.2-1B-Merged
|
VyDat
| 2025-06-23T09:31:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T09:30:37Z |
---
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]
|
lukepramo221/gemma-3-4b-finetuned-qna
|
lukepramo221
| 2025-06-23T09:31:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T06:47:15Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lukepramo221
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-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)
|
safora/persian-science-qa-e5-large
|
safora
| 2025-06-23T09:31:37Z | 38 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:31837",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:intfloat/multilingual-e5-large",
"base_model:finetune:intfloat/multilingual-e5-large",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-20T07:08:51Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:31837
- loss:CosineSimilarityLoss
base_model: intfloat/multilingual-e5-large
widget:
- source_sentence: 'query: شتاب سنجها در کدام زمینههای علمی و صنعتی کاربرد دارند
و چگونه میتوانند به پیشرفتهای علمی کمک کنند؟'
sentences:
- "passage: چکیده\r\nسرریزها در کارهای عملی مهندسی عمران مورد استفادهی فراوان\
\ دارند، بنابراین بررسی و مطالعهی آنها از اهمیت خاصی برخوردار است. در برخی\
\ از موارد بهدلیل محدودیتهای اجرایی، طراحی سرریزهای باانحنا در پلان اجتنابناپذیر\
\ است. در چنین شرایطی مطالعه توزیع جریان در طول سرریز و دیگر پارامترهای مربوط\
\ به آن، حائز اهمیت خواهد بود. در این پژوهش یک مدل فیزیکی از سرریز سدگرمیچای\
\ میانه که از نوع اوجی آزاد با قوس محوری در پلان (مدل اصلی) است، در مقیاس 1:75\
\ مورد آزمایش قرار گرفت. همچنین بهمنظور بررسی اثر انحنای سرریز بر عملکرد هیدرولیکی\
\ آن یک مدل دیگر از سرریز با محور مستقیم و شرایط هندسی مشابه (مدل صاف) مورد مقایسه\
\ قرار گرفت. اندازهگیریها در سراسر بدنه سرریز و برای 14 دبی (14 مقدار از نسبت\
\ عمق آب روی سرریز به عمق طراحی (h/Hd) در مدل اصلی و 11 دبی (11 مقدار از نسبت\
\ عمق آب روی سرریز به عمق طراحی (h/Hd) در مدل صاف انجام شد. نتایج مربوط به فشار\
\ استاتیک در مدل اصلی نشان داد حداقل فشار برای همه دبیهای مورد آزمایش تا قبل\
\ از استغراق سرریز، در محل اتصال پروفیل اوجی به تنداب سرریز و برای"
- 'passage: در این پایان نامه یک شتاب سنج خازنی سه محوره با استفاده از تکنولوژی
میکروماشین و تنها با یک جرم متحرک طراحی و شبیه سازی شده است. این شتاب سنج با استفاده
از تکنولوژی میکروماشین کاری سطحی طراحی شده است. شتاب سنج پیشنهادی از پنج گروه
خازنی تشکیل یافته است، دو گروه که در ربع اول و سوم مثلثاتی قرار دارند شتاب در
جهت X را اندازه گیری می کنند و دو گروه دیگر که در ربع دوم و چهارم مثلثاتی قرار
دارند برای اندازه گیری شتاب در جهت Y استفاده شده است. همچنین از گروه خازنی پنجم
برای اندازه گیری شتاب در جهت Z استفاده می کنیم. که این تقسیم بندی باعث می شود
شتاب اعمالی در یک جهت بر جهت دیگر تأثیر نگذارد. این طراحی به گونه ای می باشد که
در جهات X و Y از حالت دیفرانیسلی برای الکترودها و در جهت Z از حالت غیر دیفرانسیلی
استفاده کرده ایم. برای اندازه گیری شتاب در جهات X وYو Z از الکترودهای
خازنی با تغییر فاصله هوایی استفاده شده است. این شتاب سنج قابلیت تشخیص و اندازه
گیری هم زمان شتاب اعمالی به سه محور را دارد و توسط یک مدار پردازشگر ساده نتایج
خروجی شتاب سنج قابل تفکیک هستند. رنج اندازه گیری ش'
- "passage: با پیشرفت سریع فناوری محاسبات و صنعت رایانه ع?قه محققان به طراحی و توسعه\
\ دستگاههای تشخیص خودکار برای بهبود خدمات پزشکی افزایش یافته است. دو ویژگی اصلی\
\ اینگونه دستگاهها قابلیت اطمینان با? و دقت زیاد آنهاست..\r\nبدلیل مشکلات متعدد\
\ در تصاویر شبکیهی چشم استخراج عروق از این تصاویر دشوار می باشد. محققان این نقاط\
\ ضعف را مورد بررسی قرار داده با الگوریتم های پیشنهادی تلاش در بهبود روش های \
\ استخراج عروق نموده اند.\r\nآنچه در این تحقیق مورد توجه قرار گرفته است، جداسازی\
\ رگ های شبکیه ی چشم از تصاویر مربوط به آن است. اگر به این تصاویر به صورت یک رویه\
\ در فضای 3 بعدی نگاه کنیم به طوری که بعد سوم میزان روشنایی تصویر را نشان می\
\ دهد ، متوجه می شویم که رگ ها به شکل رویه های ناودانی با مقطع گوسی با ارتفاع\
\ و عرض های متفاوت هستند. با استفاده از این مدل الگوریتمی برای استخراج عروق پیشنهاد\
\ نموده ایم.\r\nالگوریتم پیشنهاد شده در این تحقیق شامل 3 بخش اساسی است. بخش اول\
\ مربوط به حذف نویزو عدم یکنواختی روشنایی در تصاویر شبکیه است که مرحله ی پیش پردازش\
\ نام دارد. این مرحله خود شامل سه بخش است که در بخش اول تبدیل کانتو"
- source_sentence: 'query: خصوصیات اصلی موتورهای سنکرون مغناطیس دائم (PMSM) چیست و
چگونه بر عملکرد آنها تأثیر میگذارد؟'
sentences:
- "passage: به دلیل خصوصیات ذاتی موتورهای سنکرون مغناطیس دائم (PMSM ) نظیر: چگالی\
\ توان بالا ، لختی کم ، نسبت بالای گشتاور تولیدی به اینرسی ، شتاب گیری سریع ،\
\ سادگی عملیات نگهداری ، ضریب توان و بازده مناسب تر درسالهای اخیر در بسیاری ازکاربردهای\
\ صنعتی با سرعت متغیر در گستره توان کم و متوسط نسبت به موتورهای DC و موتورهای\
\ القایی ترجیح داده شده اند.\r\n لذا درتجهیزات بکار رفته درآزمایشگاهها ، سانتیریفیوژها\
\ ، صنایع پتروشیمی و آسانسورهای بدون موتور خانه از موتورهای سنکرون مغناطیس دائم\
\ استفاده می شود.\r\nقابلیت کنترل وتغییر سریع سرعت موتورهای آهنربای دائم سنکرون\
\ به صورت خود کنترل شونده و امکان دستیابی به عملکرد با سرعت متغیر در محدودۀ وسیع،\
\ باعث شده تا روشهای کنترل مختلفی بسته به کاربرد موتور و به منظور استفاده مطلوب\
\ از مزایای ذاتی آنها ارائه شوند.\r\n در این پایان نامه چندین نوع از روشهای\
\ کنترل سرعت متداول موتور سنکرون مغناطیس دائم بطور خلاصه ذکر گردیده و سه روش\
\ رایج تر و پر کاربردی تر از آنها از جمله روش کنترل معمول در صنعت برمبنای مولفه\
\ های جریان ،که روش حداکثر گشتاور به ازای جریان و ولتاژمی باشد،"
- 'passage: در تصفیه پسابهای صنعتی، گاهی استفاده از دو یا چندین روش لازم میشود.
انعقاد الکتریکی بهوسیله تجزیه الکتریکی آند فلزی، توانایی تولید لختههای هیدروکسیدهای
فلزی در جریان پساب را دارد و راکتور زیستی غشایی توانایی تولید جریان خروجی با کیفیت
بالا را دارد. بنابراین ترکیب این دو روش در مقایسه با روشهای تصفیه تکی میتواند
ما را به یک بازدهی حذف آلودگی بالاتر راهبری کند. در این پژوهش ابتدا کارکرد موثر
روش انعقاد الکتریکی در تصفیه پساب کارخانه خمیرمایه با استفاده از الکترود آلومینیوم
(Al) مورد بررسی قرار گرفت. برای انجام آزمایشها از طراحی آزمایشها بهروش رویه
پاسخ مرکزی استفاده شد و تاثیر عاملهای pH، چگالی جریان و زمان فرآیند روی بازدهی
حذف COD و کدورت مورد بررسی قرار گرفت. در این پژوهش در فرآیند انعقاد الکتریکی،
COD و کدورت پساب خام ورودی از حدود 9500 میلیگرم بر لیتر و NTU 2700 بهترتیب به
حدود 4000 میلیگرم بر لیتر و NTU 273 کاهش یافت و بیشترین بازده حذف COD و کدورت
برای این فرآیند بهترتیب 58 % و 90 % بهدست آمد. بهعنوان نتیجه بهینهسازی، بیشترین
بازدهی حذف COD و کدورت در شرایط بهینه'
- 'passage: مبحث تخمین حالت یا فیلترینگ، یکی از حوزههای پر کاربرد و مطرح در زمینههای
ریاضی کاربردی، آمار، و مهندسی است که سابقه تحقیق و پژوهش در این باره، به بیش از
چهل سال پیش میرسد. در کنار رویکردهای کلاسیک، فیلترینگ تکاملی، نامی است که تا
کنون به طور غیر رسمی، به مجموعهای از روشهای فیلترینگ داده شده است که در آنها،
از روشهای محاسبات تکاملی در ترکیب با روشهای فیلترینگ کلاسیک (غالبا فیلتر ذرهای)،
برای حل مسأله فیلترینگ غیر خطی و تخمین حالت سیستمهای دینامیکی غیر خطی استفاده
شده است. در مسیر کار پژوهشی این رساله، به عنوان اولین و اساسیترین هدف، سعی شده
است که، حوزه فیلترینگ تکاملی و روشهایی که در این حوزه طبقهبندی میشوند، به طور
دقیق و کامل تعریف یا بازتعریف شوند. به عنوان هدف دوم، مدلی کامل و کلی از فیلترهای
تکاملی (مبتنی بر الگوریتم تکاملی عادی و الگوریتمهای تخمین توزیع) ارائه شده است
که خصوصیات کلی یک فیلتر تکاملی را در بر دارد و پایهای برای فیلترهای تکاملی جدید
میباشد. هدف سوم نیز، معرفی یک یا چند روش فیلترینگ تکاملی جدید بوده است، که در
نهایت منجر به معرفی چهار روش فیلترینگ تک'
- source_sentence: 'query: در علم مواد، چه راهکارهایی برای بهبود خواص مکانیکی در مواد
کامپوزیتی وجود دارد؟'
sentences:
- 'passage: نسترن کوهی (Rosa canina L.) گیاه دارویی- زینتی متعلق به تیره رزاسه
(Rosaceae)، یکی از منابع مهم ویتامین ث در میان گیاهان محسوب میشود. مواد موثره
این گیاه سبب کاهش اسید اوریک و معالجه ناراحتیهای ناشی از نقرس میگردد. رشد و
عملکرد گیاهان در اکوسیستمها، تحت تأثیر عوامل مختلفی نظیر نوع گونه، اقلیم منطقه
و موقعیت جغرافیایی قرار دارد. فاکتورهای اقلیمی بهویژه عرض جغرافیایی، ارتفاع محل،
درجه حرارت، شدت نور، بارندگی و خصوصیات خاک، تاثیر عمده ای بر کمیت و کیفیت مواد
موثره گیاهان میگذارد. به منظور مطالعه تاثیر اقلیم بر صفات مورفوفیزیولوژیکی، کمیت
و کیفیت مواد موثره نسترن کوهی در 4 منطقه از استان زنجان شامل ابهر، طارم، ماهنشان
و زنجان که بیشترین پراکنش این گیاه را داشتند، انتخاب گردید. طی سالهای 1391-1390،
مراحل فنولوژیکی گیاهان ثبت شده و میوهها و برگها برداشت شدند. در این تحقیق برخی
از خصوصیات مورفولوژیکی، بیوشیمیایی و دارویی این گیاه مورد اندازه گیری و مقایسه
قرار گرفت. صفات طول و قطر، وزن تر ، درصد ماده خشک، سطح برگ، عصاره اتری، کلروفیل،
پروتئین، فیبر، ویتامین ث، راندمان استخراج عصاره،'
- 'passage: در شبکههای کنترل اعم از سیمییا بدون سیم مشکلات مهمی از جمله داده از
دست رفته و تأخیر ارتباطی وجود دارند که باید برای غلبه بر آنها چاره اندیشید.به
دلیل همین مشکلات شبکههای کنترلی است که کنترل تحت شبکه بیشتر از انواع کنترل دیگر
(غیر تحت شبکه) در معرض عیب ها و از کار افتادنها قرار دارند.به همین منظور و در
زمینه بررسیعیب در سیستم های کنترل تحت شبکه نیز کارهای زیادی انجام شده اما اغلب
آنها به جنبههایعیبی از جمله داده از دست رفته و تأخیر در شبکه پرداخته اند.علاوه
بر داده از دست رفته و تأخیر در سیستم های کنترل تحت شبکه، دو عامل"تزریق داده" و
"استراق سمع" نیز از عوامل بروز عیب هستند. تزریق داده تصنعییا ساختگیآن است که نفوذگر
اطلاعات از پیش تعیین شده ای را به منظور نیل به اهداف خرابکارانه جهت از کار انداختن
سیستم کنترل یا ایجاد اختلال در آن وارد شبکه میکند. استراق سمع نیز به نوعی دزدی
بسته های اطلاعاتی است به طوری که نفوذگر از این طریق اطلاعات رد و بدل شده روی شبکه
را میبیند.مشکل استراق سمع روی شبکه بالا رفتن نرخ داده از دست رفته و اعمال تاخیری
علاوه بر تاخیر ذاتی شبکه است. در این تحقیق'
- 'passage: سرانه منابع آبی جهان رو به کاهش است، بنابراین ایجاد شیوههای نوین آبیاری
از جمله آبیاری ناقص ریشه لازم است. محدودیت منابع آب و ضرورت افزایش کارایی مصرف
آب آبیاری، باعث شد ارقامی از گیاهان که به خشکی متحملترند، کشت شوند. آگاهی از
تأثیر تنش آبی بر خصوصیات فیزیولوژیکی گیاه شامل هورمون آبسسیک اسید و نیز اثر آن
بر الگوی توسعه ریشه ضروری است زیرا تأمین مواد غذایی لازم برای رشد گیاه از ناحیه
ریشه میباشد. همچنین تنشهای محیطی اغلب سبب تغییر آبسسیک اسید بافت گیاه میشوند،
بنابراین از این ویژگی میتوان به عنوان یک نشانگر فیزیولوژی مناسب برای بررسی تحمل
و مقاومت به تنشهای محیطی استفاده نمود. از عوامل تأثیرگذار بر الگوی توسعه ریشه
و خصوصیات فیزیولوژیکی، مقدار و مدیریت مصرف آب است. بنابراین پژوهش حاضر در قالب
طرح بلوکهای کامل تصادفی با پنج تیمار و سه تکرار در سال 1391 در مزرعه پژوهشی دانشگاه
علوم کشاورزی و منابع طبیعی ساری انجام شد. تیمارهای آبیاری شامل آبیاری کامل، آبیاری
ناقص ریشه در دو سطح 75% و 55% وکمآبیاری سنتی (تنظیم شده) در دو سطح 75% و 55%
بود. به منظور بررسی صفات ریشه شامل طول، سطح'
- source_sentence: 'query: این تحقیق چه نتایجی در مورد کارایی پرههای توربین باد به
دست آورده است؟'
sentences:
- 'passage: ارزیابی و گزینش بسته های نرم افزاری پروسه ی تصمیم گیری و وقت گیری است.
انتخاب بسته ی نرم افزاری نامناسب می تواند پرهزینه باشد و در مقابل پروسه های تجاری
و عملکرد سازمان را تحت تاثیر قرار می دهد. در این پروژه ما به توصیف موارد زیر می
پردازیم: (1) روشهای کلی و جامع انتخاب نرم افزار، (2) معیار ارزیابی نرم افزار و
(3) رویکرد سیستمی بر مبنای اطلاعات ترکیبی به منظور یاری رساندن به تصمیم گیرندگان
در ارزیابی و انتخاب بسته های نرم افزاری. رویکرد سیستمی بر مبنای اطلاعات ترکیبی (HKBS)
از تکنیک های جامع و بهم پیوسته ی استدلال قانونی و استدلال موردی استفاده می کند.
استدلال قانونی را برای فراهم آوردن نیازهای بسته نرم افزاری و فرمول سازی یک نمونه
مسئله (مشکل) بکار می برند. CBR برای بازیابی و مقایسه ی بسته های نرم افزاری مورد
بررسی با نیازهای کاربران بسته بکار می روند. همچنین این پروژه رویکرد HKBS را با
تکنیک های ارزیابی نرم افزاری موجود مثل فرآیند سلسله مراتبی (AHP ) و روش نمره دهی
وزنی (WSM) مقایسه می کند.'
- 'passage: هدف از مطالعه ی انجام شده استخراج عصاره ی اسپیرولینا پلاتنسیس با کمک
امواج مایکروویو و ارزیابی خاصیت آنتی اکسیدانی و ضد باکتریایی آن و همچنین کاربرد
حالت بهینه عصاره ی بدست آمده جهت بهبود مدت زمان ماندگاری روغن ماهی کیلکا می باشد.
فرایند استخراج تحت تاثیر چهار پارامتر توان مایکروویو، مدت زمان استخراج، مقدار
نمونه و حجم حلال قرار گرفت که بوسیله نرم افزار Design expert و روش سطح پاسخ بهینه
سازی گردید. شش شاخص آنتی اکسیدانی مختلف شامل:DPPH، مقدار فنول کل، مقدار فلاوونوئید
کل، توانایی جذب فلز و توانایی احیای یون های آهن و مس جهت ارزیابی فعالیت آنتی اکسیدانی
عصاره اسپیرولینا پلاتنسیس استفاده شد.در ادامه شرایط بهینه بصورت توان 200 وات،
مدت زمان 9 دقیقه، مقدار نمونه 14 گرم و حجم حلال 200 میلی لیتر تعیین شد. فعالیت
آنتی اکسیدانی عصاره بهینه حاصل از مایکروویو با فعالیت آنتی اکسیدانی عصاره حاصل
از روش سنتی الکترومنتل مورد مقایسه قرار گرفت که نتایج نشان داد مایکروویو اثر تخریبی
آنچنانی بر میزان فعالیت آنتی اکسیدانی نداشته است و بنا براین می تواند به عنوان
یک روش استخراج سریع و اقتصادی مورد استفا'
- "passage: قدرت و انرژی باد بهترین چشم انداز آینده را در میان انواع فن آوری های\
\ انرژی تجدید پذیر و پایدار دارا می باشد . جهت دستیابی به بیشترین و اقتصادی ترین\
\ انرژی از توربین باد ، می باید کارایی پره مورد ملاحضه قرار گیرد . \r\nدر این\
\ تحقیق نتایج شبیه سازی ایرودینامیکی انجام شده بر اساس جریان پایایی است که با\
\ سرعت پایین از روی ایرفویل های NREL S809 ، DU84-32 ، NACA 63-415 ، FFA-W3-211\
\ و Wortmann FX 66-S-196 به صورت دو بعدی عبور می نماید . شبیه سازی صورت گرفته\
\ به کمک دینامیک سیالات محاسباتی توسط نرم افزار ANSYS CFX صورت پذیرفته است . شرایط\
\ بادی در این تحقیق با توجه به سرعت های باد در سایت های مختلف ایران می باشد .\
\ مدل توربولانسی در این تحقیق انتقال از جریان آرام به آشفته را لحاظ می نماید .\
\ نیرو های برآ و پسا ، پارامترهای مهمی در مطالعه بر روی کارایی توربین های بادی\
\ هستند . جهت دستیابی به بیشترین قدرت از توربین بادی ، بیشترین نسبت لغزش ( برآ\
\ به پسا )هدف است . کارایی پروفیل پره های گوناگون در سرعت های مختلف مورد بررسی\
\ قرار گرفته و بهینه ترین پره بر اساس بیشترین نسبت لغزش در هر سرعت محاسبه می شود\
\ . همچ"
- source_sentence: 'query: علم مواد چگونه میتواند به بهبود خواص مکانیکی مواد در صنایع
مختلف کمک کند و چه تحقیقات جدیدی در این زمینه انجام شده است؟'
sentences:
- 'passage: آسیب فرآیندی است برگشت ناپذیر که با کاهش تدریجی مقاومت مکانیکی، زوال
ماده را به دنبال دارد. مکانیک آسیب شاخهای از مکانیک جامدات است که عوامل مکانیکی
ناظر بر گسیختگی ماده تحت بارگذاریهای مختلف را مورد مطالعه قرار میدهد. خستگی
گونه ای از آسیب بوده که می تواند منجر به شکست ناگهانی قطعات گردد. بارگذاری خستگی
در اثر تنش های چرخه ای که کمتر از تنش کششی نهایی یا حتی تنش تسلیم هستند، نتیجه
می شود. نام خستگی بر اساس این مفهوم است که یک ماده تحت بارگذاری تکرار شونده خسته
شده و در سطح تنش زیر مقاومت اسمی ماده وامانده می شود. عمر خستگی یک قطعه می تواند
به صورت تعداد چرخه های بارگذاری لازم برای شروع یک ترک و گسترش آن تا اندازه بحرانی
بیان شود. بنابراین می توان گفت که واماندگی خستگی در سه مرحله اتفاق می افتد: شروع
ترک، رشد ترک آهسته-پایدار و شکست سریع. برای شروع ترک های خستگی سه عامل اساسی لازم
است: اولاً الگوی بارگذاری باید شامل مقادیر اوج بیشینه و کمینه با اختلاف یا نوسان
به حد کافی بزرگ باشد. مقادیر اوج ممکن است در کشش یا فشار بوده و یا بر حسب زمان
تغییر کنند، اما چرخه بارگذاری معکوس شونده برا'
- 'passage: خاک یکی از اساسیترین و مهمترین ماده در زندگی انسان ها میباشد، در علم
مهندسی عمران تمام ساخت و سازها یا روی آن و یا در درون آن انجام مییابد. امروزه
در بسیاری از شهرهای مهم تقاضا برای ساخت تونل برای اهداف مختلف مانند حمل و نقل
یا سیستم فاضلاب، افزایش یافته که ناشی از محدودیت فضا و نگرانیهای محیط زیستی
است. در این میان با گودبرداریهای ژرف برای ساخت سازههای بلند و یا سایر اهداف،
ممکن است باعث ایجاد تأثیراتی در سازههای مجاور آن مانند تونل شود. در این پایاننامه
به ارزیابی و بررسی تأثیر گودبرداری ژرف بر روی تونل موجود پرداخته میشود که چه تأثیری
روی جابجایی و تغییرشکل تونل دارد و همچنین چه مقدار نیروی محوری و لنگر خمشی داخلی
اضافی در آن القا میشود که قبلاً برای آن طراحی نشده است که در نهایت باعث اختلال
در خدمت رسانی تونل شود. در این زمینه، توسط محققین مختلف، تحقیقاتی انجام گرفته
و برخی عوامل و پارامترهای موثر، مورد شناسایی قرار گرفته است. هدف اصلی این تحقیق،
بررسی پارامترهای دخیل در میزان تأثیر گودبرداریهای عمیق بر روی تونلهای موجود
در مجاورت آن میباشد که با استفاده از روشهای ع'
- 'passage: به منظور شناسایی قارچهای عامل لکه دودهای و فضله مگسی در استان گیلان،
نمونههای مشکوک و دارای علائم از نقاط مختلف استان گیلان طی ماه های تیر الی مهر
92 و 93، جمع آوری و مورد بررسی قرار گرفتند. این قارچها پس از بررسیهای ریخت شناسی،
با بهرهگیری از نوشتهها و منابع معتبر موجود شناسایی شدند. براساس نتایج به دست
آمده گونههای Microcyclosporella mali،Zasmidium sp. و Zygophiala jamaicensis
شناسایی شدند. براساس خصوصیات مرفولوژیکی و توالی نواحی ITS و TEF، احتمالاZasmidium
sp. گونه جدیدی میباشد. گونهZasmidium sp. با داشتن کنیدیوفورهای بلند، راست تا
کمی خمیده، کنیدیومهای متنوع در شکل و اندازه و وجود راماکنیدیومهای اولیه از بقیه
گونههای نزدیک (Z. angulare، Z. cellare، Z. noxoci و Z. citri) متمایز میگردد.
همه این گونهها از روی میوههای آلوده سیب، گلابی و خوج (گلابی محلی در استان گیلان)
در استان گیلان جداسازی و خالص سازی شدند و برای اولین بار از ایران گزارش میشوند.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on intfloat/multilingual-e5-large
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts validation
type: sts-validation
metrics:
- type: pearson_cosine
value: 0.8942762599448963
name: Pearson Cosine
- type: spearman_cosine
value: 0.8919015410349642
name: Spearman Cosine
---
# SentenceTransformer based on intfloat/multilingual-e5-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision 0dc5580a448e4284468b8909bae50fa925907bc5 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## 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("sentence_transformers_model_id")
# Run inference
sentences = [
'query: علم مواد چگونه می\u200cتواند به بهبود خواص مکانیکی مواد در صنایع مختلف کمک کند و چه تحقیقات جدیدی در این زمینه انجام شده است؟',
'passage: آسیب فرآیندی است برگشت ناپذیر که با کاهش تدریجی مقاومت مکانیکی، زوال ماده را به دنبال دارد. مکانیک آسیب شاخه\u200cای از مکانیک جامدات است که عوامل مکانیکی ناظر بر گسیختگی ماده تحت بارگذاری\u200cهای مختلف را مورد مطالعه قرار می\u200cدهد. خستگی گونه ای از آسیب بوده که می تواند منجر به شکست ناگهانی قطعات گردد. بارگذاری خستگی در اثر تنش های چرخه ای که کمتر از تنش کششی نهایی یا حتی تنش تسلیم هستند، نتیجه می شود. نام خستگی بر اساس این مفهوم است که یک ماده تحت بارگذاری تکرار شونده خسته شده و در سطح تنش زیر مقاومت اسمی ماده وامانده می شود. عمر خستگی یک قطعه می تواند به صورت تعداد چرخه های بارگذاری لازم برای شروع یک ترک و گسترش آن تا اندازه بحرانی بیان شود. بنابراین می توان گفت که واماندگی خستگی در سه مرحله اتفاق می افتد: شروع ترک، رشد ترک آهسته-پایدار و شکست سریع. برای شروع ترک های خستگی سه عامل اساسی لازم است: اولاً الگوی بارگذاری باید شامل مقادیر اوج بیشینه و کمینه با اختلاف یا نوسان به حد کافی بزرگ باشد. مقادیر اوج ممکن است در کشش یا فشار بوده و یا بر حسب زمان تغییر کنند، اما چرخه بارگذاری معکوس شونده برا',
'passage: به منظور شناسایی قارچ\u200cهای عامل لکه دوده\u200cا\u200cی و فضله\u200c مگسی در استان گیلان، نمونه\u200cهای مشکوک و دارای علائم از نقاط مختلف استان گیلان طی ماه های تیر الی مهر 92 و 93، جمع آوری و مورد بررسی قرار گرفتند. این قارچ\u200cها پس از بررسی\u200cهای ریخت شناسی، با بهره\u200cگیری از نوشته\u200cها و منابع معتبر موجود شناسایی شدند. براساس نتایج به دست آمده گونه\u200cهای Microcyclosporella mali،Zasmidium sp. و Zygophiala jamaicensis شناسایی شدند. براساس خصوصیات مرفولوژیکی و توالی نواحی ITS و TEF، احتمالاZasmidium sp. گونه جدیدی می\u200cباشد. گونهZasmidium sp. با داشتن کنیدیوفورهای بلند، راست تا کمی خمیده، کنیدیوم\u200cهای متنوع در شکل و اندازه و وجود راماکنیدیوم\u200cهای اولیه از بقیه گونه\u200cهای نزدیک (Z. angulare، Z. cellare، Z. noxoci و Z. citri) متمایز می\u200cگردد. همه این گونه\u200cها از روی میوه\u200cهای آلوده سیب، گلابی و خوج (گلابی محلی در استان گیلان) در استان گیلان جداسازی و خالص سازی شدند و برای اولین بار از ایران گزارش می\u200cشوند.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-validation`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8943 |
| **spearman_cosine** | **0.8919** |
<!--
## 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
### Training Dataset
#### Unnamed Dataset
* Size: 31,837 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 15 tokens</li><li>mean: 25.87 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 268.53 tokens</li><li>max: 344 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.57</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------|
| <code>query: روشهای فتوگرامتری چگونه به بهبود دقت مدلهای عوارض در مناطق شهری کمک میکنند؟</code> | <code>passage: روشهای فتوگرامتری و سنجش از دور با توجه به وسعت منطقه تحت پوشش از یک طرف و نیز دقت قابل قبول این روشها از طرف دیگر، به عنوان روشهای مناسب جهت تولید و بهنگام رسانی اطلاعات مکانی شناخته شدهاند. در حال حاضر یکی از زمینههای تحقیقاتی مهم در این رابطه کاهش نقش اپراتور انسانی در استخراج و بازسازی مدل عوارض از دادههای مختلفی چون تصویر رقومی و داده-های ارتفاعی با بکارگیری الگوریتمهای مختلف پردازش تصویر است.
<br>با توجه به تعداد زیاد ساختمانها در مناطق شهری دستیابی به یک مدل یا الگوریتم جهت استخراج و بازسازی اتوماتیک این عارضه از دادههای هوایی و ماهوارهای میتواند نقش انسان را در تولید اطلاعات مکانی بزرگ مقیاس شهری به حداقل رسانده و هزینه و زمان تولید آنها را به شدت کاهش دهد. منحنیهای پویا به عنوان یکی از روشهای مبتنی بر مدلهای ریاضی با بکارگیری اطلاعات گرادیان و یا اطلاعات طیفی تصویر، یکی از روشهای پرکاربرد در زمینه استخراج اتوماتیک عوارض از تصویر به شمار میروند. یکی از مشکلات اغلب مدلهای منحنیهای پویا موجود در زمینه استخراج ساختمان، عدم استفاده از اطلاعات و هوش انسانی د...</code> | <code>1.0</code> |
| <code>query: نتایج اصلی این تحقیق چه تأثیری بر روند شیرینسازی گاز طبیعی دارند؟</code> | <code>passage: استفاده از غشا به منظور شیرینسازی گاز طیبعی یکی از فرآیندهای اساسی است که در سالهای اخیر مورد توجه فراوانی قرار گرفته است. مطالعات انجام شده نشان میدهد که غشاهای شبکه آمیخته از پتانسیل بالایی در این زمینه برخوردارند. در این پژوهش غشاهای جداسازی گاز آمیزهای پلیایمید/ پلیاتیلن گلایکول و شبکه آمیختهای پلیایمید/ پلیاتیلن گلایکول- زئولیت ZSM-5 به روش تبخیر حلال تهیه گردیدند. با افزایش 5-1 درصد وزنی پلیاتیلن گلایکول تروایی گاز دیاکسید کربن و گزینشپذیری زوج گاز دیاکسید کربن/ متان در فشار 10 بار از Barrer 6898/7 و 7419/33 در غشای ماتریمید خالص به Barrer 5748/9 و 8452/39 در غشای حاوی %5 پلیاتیلن گلایکول افزایش یافت. آزمون FT-IR وجود پیوندهای ضعیف هیدروژنی میان دو پلیمر و آزمون DSC نیمه امتزاجپذیر بودن آمیزههای پلیمری را نشان دادند. نتایج آزمون SEM افزایش تخلخل غشا را با افزایش درصد وزنی پلیاتیلن گلایکول تائید کرد. از زئولیت ZSM-5 کلسینه شده به منظور ساخت غشاهای شبکه آمیخته استفاده شد. حضور همزمان پلیاتیلن گلایکول و زئولیت ZSM-5 تا %5 وزنی در شبکه پلیایمیدی، افزایش قا...</code> | <code>1.0</code> |
| <code>query: فرآیند پیشسرمایش چگونه میتواند بر روی دیگر محصولات باغی نیز تأثیر بگذارد؟</code> | <code>passage: از جمله عملیاتی که نقش موثری در افزایش عمر قفسهای و کاهش ضایعات محصولات باغی دارد، فرآیند پیشسرمایش است. علیرغم اینکه در حال حاضر فرآیند پیشسرمایش در سطح دنیا بر روی توتفرنگی انجام میشود، ولی افت این محصول به دلیل سرمایش غیریکنواخت، هنوز قابل توجه است. هدف از این تحقیق، توسعه سامانه جدید برای پیشسرمایش توتفرنگی است که بتواند غیریکنواختی سرمایش میوهها را به حداقل برساند که نتیجه آن کاهش افت محصول و مصرف انرژی فرآیند است. در این تحقیق، ابتدا با حل معادلات پیوستگی و انتقال ممنتوم برای فاز سیال و انتقال گرما برای فاز سیال و محصول بصورت توام و به روش اجزای محدود در محیط نرمافزار COMSOL MULTIPHYSICS و در فضای جعبههای طراحی شده، الگوی جریان هوا در داخل جعبهها و سینی حاوی جعبهها به صورت سه بعدی شبیهسازی شد. با اعمال تغییرات مناسب در طراحی جعبه، سینی و الگوی هوادهی، سامانه جدیدی تحت عنوان سامانه پیشسرمایش موازی برای توتفرنگی معرفی گردید که قادر است هوای سرد را بصورت یکنواخت و با دمای یکسان به کلیه جعبهها در داخل هر سینی انتقال دهد و موجب سرمایش یکنواخت میوهها در جعبه...</code> | <code>0.6666666666666666</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### 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`: 8
- `per_device_eval_batch_size`: 8
- `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`: 5e-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`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `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}
- `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`: False
- `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`: False
- `torch_compile_backend`: None
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-validation_spearman_cosine |
|:------:|:----:|:-------------:|:------------------------------:|
| 0.1256 | 500 | 0.0613 | 0.8480 |
| 0.2513 | 1000 | 0.0376 | 0.8698 |
| 0.3769 | 1500 | 0.0341 | 0.8751 |
| 0.5025 | 2000 | 0.0308 | 0.8780 |
| 0.6281 | 2500 | 0.0296 | 0.8837 |
| 0.7538 | 3000 | 0.0281 | 0.8892 |
| 0.8794 | 3500 | 0.0289 | 0.8888 |
| 1.0 | 3980 | - | 0.8919 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1+cu118
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### 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",
}
```
<!--
## 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.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
Konthee/Qwen-32B-QA-abstract
|
Konthee
| 2025-06-23T09:30:57Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-11T08:55:37Z |
---
base_model: unsloth/qwen3-32b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-32B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 32.8B
- Number of Paramaters (Non-Embedding): 31.2B
- Number of Layers: 64
- Number of Attention Heads (GQA): 64 for Q and 8 for KV
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Konthee/Qwen-32B-QA-abstract"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
system_prompt = """\
You are an expert at answering questions by summarizing directly from the provided source.
Use your own words to convey the relevant information from the source without copying it verbatim.
All inputs (source and question) and outputs (your answer) will be in Thai.
Ensure that your answer is concise, coherent, and accurately reflects the source content.
"""
user_prompt = """\
Source : {}
Question : {} \
"""
messages = [
{"role": "user", "content": system_prompt}
{"role": "user", "content": user_prompt.format(source,question)},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
### Evaluation Results
Results retrieved from the **AI Benchmark 2025 QA Leaderboard** https://benchmark.ai.in.th/score/leaderboard/2025-qa
| Split | BERT score| Rougel score|
|---------|---------|---------|
| public | 0.75582 | 0.33500 |
| private | 0.82055 | 0.44755 |
_Data sourced directly from the leaderboard metrics_
This model corresponds to team **220_อย่าคับ เจนมันเวิ่นเว้อป่าวว**, which secured **1st place** on both the public and private leaderboards in the **2025-QA** competition
## APA
> AI Thailand Benchmark Programs. (2025). _2025-QA: Machine Reading Comprehension Task_. Retrieved June 23, 2025, from https://benchmark.ai.in.th/task/detail/2025-qa
### Authors
* Konthee Boonmeeprakob (konthee1995@gmail.com)
* Pitikorn Khlaisamniang (pitikorn32@gmail.com)
|
AndyLau01/mistral-7b-manglish-qlora-lora
|
AndyLau01
| 2025-06-23T09:28:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:28:32Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** AndyLau01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
wyb1142/llama3
|
wyb1142
| 2025-06-23T09:27:22Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-23T09:27:22Z |
---
license: other
license_name: license
license_link: LICENSE
---
|
chenttt/matrix3d
|
chenttt
| 2025-06-23T09:26:48Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-23T09:20:07Z |
---
license: apache-2.0
---
|
Lahhhalah/gemma-3-1B-indo-finetune-Q8_0-GGUF
|
Lahhhalah
| 2025-06-23T09:23:45Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3_text",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Lahhhalah/gemma-3-1B-indo-finetune",
"base_model:quantized:Lahhhalah/gemma-3-1B-indo-finetune",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-23T09:23:38Z |
---
base_model: Lahhhalah/gemma-3-1B-indo-finetune
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# Lahhhalah/gemma-3-1B-indo-finetune-Q8_0-GGUF
This model was converted to GGUF format from [`Lahhhalah/gemma-3-1B-indo-finetune`](https://huggingface.co/Lahhhalah/gemma-3-1B-indo-finetune) 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/Lahhhalah/gemma-3-1B-indo-finetune) 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 Lahhhalah/gemma-3-1B-indo-finetune-Q8_0-GGUF --hf-file gemma-3-1b-indo-finetune-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lahhhalah/gemma-3-1B-indo-finetune-Q8_0-GGUF --hf-file gemma-3-1b-indo-finetune-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lahhhalah/gemma-3-1B-indo-finetune-Q8_0-GGUF --hf-file gemma-3-1b-indo-finetune-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lahhhalah/gemma-3-1B-indo-finetune-Q8_0-GGUF --hf-file gemma-3-1b-indo-finetune-q8_0.gguf -c 2048
```
|
Kittykat924/TinyPi-Chat-v1.5
|
Kittykat924
| 2025-06-23T09:23:31Z | 2 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"tinyllama",
"fine-tuned",
"chat",
"conversational",
"rlaif",
"alignment",
"peft",
"lora",
"en",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T06:50:28Z |
---
license: mit
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- tinyllama
- fine-tuned
- chat
- conversational
- rlaif
- alignment
- peft
- lora
model-index:
- name: TinyPi-1.1B-Chat-v1.5
results:
- task:
type: text-generation
metrics: []
---
# TinyPi-1.1B-Chat-v1.5
## Model Description
**TinyPi-1.1B-Chat-v1.5** is an advanced, conversational language model that represents a significant evolution from its v1 predecessor. Starting with a base model fine-tuned on a large corpus of Discord chat data, this version has undergone a sophisticated second stage of alignment using **Reinforcement Learning from AI Feedback (RLAIF)**.
The goal of this project was to cultivate an AI with a distinct, friendly, and engaging personality. While the v1 model successfully developed a unique "voice," it sometimes lacked factual depth and consistency. The v1.5 update addresses this directly by training the model on a high-quality dataset of corrections generated by a superior AI (Google's Gemini 1.5 Flash).
This process has made TinyPi not only more knowledgeable and less prone to repetitive loops but has also sharpened its persona, making it a more robust, reliable, and delightful conversational partner.
## How to Use
This is a merged, standalone model and can be used directly for text generation. For best results, use the chat template which includes a system prompt to guide its persona.
### Installation
```bash
pip install transformers torch accelerate
```
### Inference with Python
```python
from transformers import pipeline
import torch
model_path = "Kittykat924/TinyPi-Chat-v1.5"
pipe = pipeline(
"text-generation",
model=model_path,
torch_dtype=torch.float16,
device_map="auto"
)
prompt = "What's a creative way to explain how a CPU works?"
# Format the conversation using the chat template
messages = [
{"role": "user", "content": prompt},
]
prompt_formatted = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Generate a response
outputs = pipe(
prompt_formatted,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.95
)
# Extract and print the assistant's response
response = outputs[0]["generated_text"]
assistant_response = response.split("<|assistant|>")[1].strip()
print(assistant_response)
```
## Training Procedure
This model was developed in a two-stage fine-tuning process.
### Stage 1: Initial Persona Fine-tuning (Creation of v1)
* **Base Model:** `TinyLlama/TinyLlama-1.1B-Chat-v1.0`
* **Dataset:** A large, private dataset of over 2 million general-purpose Discord chat messages.
* **Method:** LoRA fine-tuning using the `peft` library.
* **Result:** A model with a strong, emergent personality but with some factual inconsistencies and conversational weaknesses (e.g., repetitiveness).
### Stage 2: RLAIF Alignment (Creation of v1.5)
This stage used an automated, AI-driven data generation loop to correct the flaws of the v1 model.
* **"Student" Model:** The merged `v1` model from Stage 1.
* **"Teacher" (Evaluator) AI:** `gemini-1.5-flash`.
* **"Chat Partner" AI:** `gemini-1.5-flash`.
* **Workflow:**
1. A conversation was initiated between the "Chat Partner" and "TinyPi" (v1).
2. For each of TinyPi's responses, the "Evaluator" AI judged its quality, accuracy, and adherence to the target persona.
3. If a response was flawed, the Evaluator generated a high-quality, corrected version.
4. Only these `(instruction, corrected_output)` pairs were saved, creating a dataset focused exclusively on fixing the model's mistakes.
* **Dataset:** **[Customize]** Approximately [e.g., `1,200`] high-quality, corrected examples generated by this RLAIF process.
* **Continual Learning:** To prevent catastrophic forgetting, the RLAIF dataset was combined with a small "replay" sample (~20,000 examples) of the original Discord data.
* **Final Fine-tune:** A new LoRA adapter was trained on this combined dataset, starting from the v1 model. This new adapter was then merged to create the final v1.5 model.
## Model Capabilities and Limitations
**Capabilities:**
* Maintains a consistent, friendly, and humorous persona.
* Engages in coherent, multi-turn conversations on a wide variety of topics.
* Improved factual accuracy and reasoning ability on subjects covered during the RLAIF process.
* Less prone to generic refusals and repetitive loops compared to v1.
**Limitations:**
* This model is designed for conversational and entertainment purposes. It is not a substitute for expert advice and may still produce factual inaccuracies.
* Its personality is a core feature. It may not be suitable for tasks requiring a purely neutral or formal tone.
* The model inherits biases from its training data, which includes a large corpus of internet chat logs and AI-generated text. User discretion is advised.
*-Kittykat924*
|
Konthee/whisper-th-large-v3-meeting-transcription
|
Konthee
| 2025-06-23T09:20:36Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"th",
"dataset:Konthee/meeting_transcription_audio_th",
"base_model:biodatlab/whisper-th-large-v3-combined",
"base_model:finetune:biodatlab/whisper-th-large-v3-combined",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-05T00:31:43Z |
---
library_name: transformers
datasets:
- Konthee/meeting_transcription_audio_th
language:
- th
metrics:
- wer
base_model:
- biodatlab/whisper-th-large-v3-combined
---
## Model Card: n-order/whisper-th-large-v3-meeting-transcription
### Model Details
- **Model name:** Konthee/whisper-th-large-v3-meeting-transcription
- **Base model:** biodatlab/whisper-th-large-v3-combined
- **Fine-tuned on:** `meeting_transcription_audio_th` dataset
- **License:** Apache 2.0
- **Model weights license:** CC-BY-SA 4.0 (reflecting dataset license)
- **Paper / Reference:** This model is a fine-tuned version of the Whisper TH Large v3 model tailored for Thai meeting transcription scenarios.
### Model Description
This model is based on the Whisper TH Large v3 architecture, originally developed for general-purpose speech recognition in Thai. It has been further fine-tuned on the `meeting_transcription_audio_th` dataset, which consists of Thai online meeting recordings and gold-standard transcripts from the 2025-ASR competition. The fine-tuning process focused on multi-speaker and acoustically challenging scenarios (noise, reverberation, overlapping speech) to improve performance in meeting transcription tasks.
### Intended Uses & Applications
- **Meeting transcription:** Providing accurate transcripts of Thai-language online meetings.
- **Note-taking automation:** Assisting users in generating meeting notes from audio.
- **Accessibility:** Enabling transcription services for hearing-impaired participants in Thai meetings.
#### Out-of-Scope Use Cases
- Transcription of languages other than Thai.
- Real-time transcription in extremely low-latency applications (this model is optimized for batch processing).
- Highly specialized domains with vocabulary far outside general meeting contexts (e.g., medical diagnostics).
### Training Data
- **Dataset:** `meeting_transcription_audio_th`
A Thai online-meeting speech corpus with real and augmented recordings (noise, reverb, overlapping speech). Originally from the 2025-ASR competition; reformatted and packaged for Hugging Face by Konthee Bo.
- **Data size:** ~32.5 hours total (20 h train, 2.5 h val, 10 h test).
- **Preprocessing:** Audio normalized to 16 kHz WAV; transcripts cleaned for punctuation and speaker turns; metadata added for easier loading via `datasets`.
### Usage Examples
```python
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torchaudio
processor = WhisperProcessor.from_pretrained("Konthee/whisper-th-large-v3-meeting-transcription")
model = WhisperForConditionalGeneration.from_pretrained("Konthee/whisper-th-large-v3-meeting-transcription")
# load audio
speech_array, sampling_rate = torchaudio.load("meeting.wav")
inputs = processor(speech_array, sampling_rate=sampling_rate, return_tensors="pt")
# generate transcription
generated_ids = model.generate(inputs.input_features)
transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(transcription)
```
### Evaluation Results
Results retrieved from the **AI Benchmark 2025 ASR Leaderboard** https://benchmark.ai.in.th/score/leaderboard/2025-asr
| Split | WER (%) |
|---------|---------|
| public | 13.51 |
| private | 18.70 |
_Data sourced directly from the leaderboard metrics_
This model corresponds to team **220_อย่าคับ เจนมันเวิ่นเว้อป่าวว**, which secured **1st place** on both the public and private leaderboards in the **2025-ASR** competition
## APA
> AI Thailand Benchmark Programs. (2025). _2025-ASR: Automatic Speech Recognition Task_. Retrieved June 23, 2025, from https://benchmark.ai.in.th/task/detail/2025-asr
```bibtex
@misc{meeting_transcription_audio_2025,
title = {meeting_transcription_audio_th: A Thai Online-Meeting Speech Corpus for Multi-Speaker ASR},
author = {AI Thailand Benchmark Programs and Konthee Bo},
year = {2025},
howpublished = {https://huggingface.co/datasets/Konthee/meeting_transcription_audio_th},
note = {Dataset reformatted and packaged by Konthee Bo; original data from the 2025-ASR competition},
license = {CC-BY-SA 4.0}
```
### Authors
* Konthee Boonmeeprakob (konthee1995@gmail.com)
* Pitikorn Khlaisamniang (pitikorn32@gmail.com)
|
ByteFlow-AI/DetailFlow-16-GPT-L
|
ByteFlow-AI
| 2025-06-23T09:19:31Z | 0 | 0 | null |
[
"c2i",
"license:apache-2.0",
"region:us"
] | null | 2025-06-09T12:20:50Z |
---
license: apache-2.0
---
|
khs2617/qwen3-4b-finetune
|
khs2617
| 2025-06-23T09:18:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-4B",
"base_model:adapter:unsloth/Qwen3-4B",
"region:us"
] | null | 2025-06-23T09:18:15Z |
---
base_model: unsloth/Qwen3-4B
library_name: peft
---
# Model Card for Model ID
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## Model Details
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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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#### Preprocessing [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.15.2
|
kingardor/llama3.1-8B-instruct-5reports-lora128-extreme
|
kingardor
| 2025-06-23T09:17:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-23T09:13:50Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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## Evaluation
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### Testing Data, Factors & Metrics
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
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