modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-13 00:37:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-13 00:35:18
| card
stringlengths 11
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|---|---|---|---|---|---|---|---|---|---|
opentargets/locus_to_gene_25.09
|
opentargets
| 2025-08-19T16:12:41Z | 0 | 0 |
sklearn
|
[
"sklearn",
"skops",
"tabular-classification",
"region:us"
] |
tabular-classification
| 2025-08-19T16:12:38Z |
---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_format: skops
model_file: classifier.skops
widget:
- structuredData:
credibleSetConfidence:
- 0.75
- 0.75
- 0.25
distanceFootprintMean:
- 1.0
- 1.0
- 0.9948455095291138
distanceFootprintMeanNeighbourhood:
- 1.0
- 1.0
- 1.0
distanceSentinelFootprint:
- 1.0
- 1.0
- 0.9999213218688965
distanceSentinelFootprintNeighbourhood:
- 1.0
- 1.0
- 1.0
distanceSentinelTss:
- 0.9982281923294067
- 0.9999350309371948
- 0.9999213218688965
distanceSentinelTssNeighbourhood:
- 1.0
- 1.0
- 1.0
distanceTssMean:
- 0.9982281923294067
- 0.9999350309371948
- 0.9947366714477539
distanceTssMeanNeighbourhood:
- 1.0
- 1.0
- 1.0
eQtlColocClppMaximum:
- 0.949999988079071
- 0.0
- 0.06608512997627258
eQtlColocClppMaximumNeighbourhood:
- 1.0
- 0.0
- 1.0
eQtlColocH4Maximum:
- 1.0
- 0.0
- 0.0
eQtlColocH4MaximumNeighbourhood:
- 1.0
- 0.0
- 0.0
geneCount500kb:
- 20.0
- 15.0
- 8.0
geneId:
- ENSG00000087237
- ENSG00000169174
- ENSG00000084674
goldStandardSet:
- 1
- 1
- 1
pQtlColocClppMaximum:
- 0.0
- 1.0
- 0.0
pQtlColocClppMaximumNeighbourhood:
- 0.0
- 1.0
- 0.0
pQtlColocH4Maximum:
- 0.0
- 1.0
- 0.0
pQtlColocH4MaximumNeighbourhood:
- 0.0
- 1.0
- 0.0
proteinGeneCount500kb:
- 8.0
- 7.0
- 3.0
sQtlColocClppMaximum:
- 0.949999988079071
- 0.0
- 0.21970131993293762
sQtlColocClppMaximumNeighbourhood:
- 1.0
- 0.0
- 1.0
sQtlColocH4Maximum:
- 1.0
- 0.0
- 0.0
sQtlColocH4MaximumNeighbourhood:
- 1.0
- 0.0
- 0.0
studyLocusId:
- 005bc8624f8dd7f7c7bc63e651e9e59d
- 02c442ea4fa5ab80586a6d1ff6afa843
- 235e8ce166619f33e27582fff5bc0c94
vepMaximum:
- 0.33000001311302185
- 0.6600000262260437
- 0.6600000262260437
vepMaximumNeighbourhood:
- 1.0
- 1.0
- 1.0
vepMean:
- 0.33000001311302185
- 0.6600000262260437
- 0.0039977929554879665
vepMeanNeighbourhood:
- 1.0
- 1.0
- 1.0
---
# 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 |
|-------------------------|-----------------|
| objective | binary:logistic |
| base_score | |
| booster | |
| callbacks | |
| colsample_bylevel | |
| colsample_bynode | |
| colsample_bytree | 0.8 |
| device | |
| early_stopping_rounds | |
| enable_categorical | False |
| eval_metric | aucpr |
| feature_types | |
| feature_weights | |
| gamma | |
| grow_policy | |
| importance_type | |
| interaction_constraints | |
| learning_rate | |
| max_bin | |
| max_cat_threshold | |
| max_cat_to_onehot | |
| max_delta_step | |
| max_depth | 5 |
| max_leaves | |
| min_child_weight | 10 |
| missing | nan |
| monotone_constraints | |
| multi_strategy | |
| n_estimators | |
| n_jobs | |
| num_parallel_tree | |
| random_state | 777 |
| reg_alpha | 1 |
| reg_lambda | 1.0 |
| sampling_method | |
| scale_pos_weight | 0.8 |
| subsample | 0.8 |
| tree_method | |
| validate_parameters | |
| verbosity | |
| eta | 0.05 |
</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
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755619685
|
Elizavr
| 2025-08-19T16:08:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:08:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755618074
|
helmutsukocok
| 2025-08-19T16:08:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:08:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/git-commit-message-splitter-Qwen3-4B-i1-GGUF
|
mradermacher
| 2025-08-19T16:08:07Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2025-08-19T16:08:01Z |
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Tavernari/git-commit-message-splitter-Qwen3-4B
|
mehdirafiei/bert_resume_category_prediction
|
mehdirafiei
| 2025-08-19T16:07:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T16:07:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/galicIA-v1-GGUF
|
mradermacher
| 2025-08-19T16:05:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:pajon1/galicIA-v1",
"base_model:quantized:pajon1/galicIA-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T16:00:38Z |
---
base_model: pajon1/galicIA-v1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/pajon1/galicIA-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#galicIA-v1-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.f16.gguf) | f16 | 1.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AnonymousCS/xlmr_finnish_immigration2
|
AnonymousCS
| 2025-08-19T16:04:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T16:00:05Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_finnish_immigration2
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. -->
# xlmr_finnish_immigration2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1698
- Accuracy: 0.9538
- 1-f1: 0.9318
- 1-recall: 0.9535
- 1-precision: 0.9111
- Balanced Acc: 0.9538
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5778 | 1.0 | 5 | 0.2275 | 0.9154 | 0.8571 | 0.7674 | 0.9706 | 0.8780 |
| 0.1219 | 2.0 | 10 | 0.3406 | 0.9385 | 0.9130 | 0.9767 | 0.8571 | 0.9481 |
| 0.2571 | 3.0 | 15 | 0.2051 | 0.9462 | 0.9213 | 0.9535 | 0.8913 | 0.9480 |
| 0.1514 | 4.0 | 20 | 0.1689 | 0.9538 | 0.9318 | 0.9535 | 0.9111 | 0.9538 |
| 0.1368 | 5.0 | 25 | 0.1816 | 0.9462 | 0.9231 | 0.9767 | 0.875 | 0.9539 |
| 0.1073 | 6.0 | 30 | 0.1698 | 0.9538 | 0.9318 | 0.9535 | 0.9111 | 0.9538 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755619213
|
Elizavr
| 2025-08-19T16:00:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:00:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
annasoli/Qwen2.5-14B_SVt_l24_lr2e-4_a256_2E_technical-engineering2_KLBPA_5e6
|
annasoli
| 2025-08-19T15:59:44Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T14:51:16Z |
---
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 π€ 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]
|
QinShiHuangisavailable/output0043
|
QinShiHuangisavailable
| 2025-08-19T15:53:45Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:Qwen/Qwen2-Math-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-Math-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T15:26:45Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-Math-1.5B-Instruct
tags:
- generated_from_trainer
model-index:
- name: output0043
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. -->
# output0043
This model is a fine-tuned version of [Qwen/Qwen2-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-Math-1.5B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 3
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ShadoWeysel/blockassist-bc-aquatic_placid_skunk_1755618703
|
ShadoWeysel
| 2025-08-19T15:53:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic placid skunk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:53:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic placid skunk
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755617105
|
indoempatnol
| 2025-08-19T15:53:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:53:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755617165
|
ihsanridzi
| 2025-08-19T15:53:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:53:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/rejection_detection-GGUF
|
mradermacher
| 2025-08-19T15:52:44Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"rejection",
"no_answer",
"chatgpt",
"en",
"dataset:argilla/notus-uf-dpo-closest-rejected",
"base_model:holistic-ai/rejection_detection",
"base_model:quantized:holistic-ai/rejection_detection",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | null | 2025-08-19T15:49:39Z |
---
base_model: holistic-ai/rejection_detection
datasets:
- argilla/notus-uf-dpo-closest-rejected
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- rejection
- no_answer
- chatgpt
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/holistic-ai/rejection_detection
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#rejection_detection-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.f16.gguf) | f16 | 0.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755617041
|
mang3dd
| 2025-08-19T15:52:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:52:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MidnightRunner/MIDNIGHT_NAI-XL_vPredV1
|
MidnightRunner
| 2025-08-19T15:50:23Z | 406 | 2 |
diffusers
|
[
"diffusers",
"SDXL",
"noobai-XL",
"Vpred-1.0",
"text-to-image",
"ComfyUI",
"Automatic1111",
"Diffuser",
"en",
"dataset:LaxharLab/NoobAI-XL-dataset",
"base_model:Laxhar/noobai-XL-Vpred-1.0",
"base_model:finetune:Laxhar/noobai-XL-Vpred-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-02-02T01:09:01Z |
---
license: creativeml-openrail-m
language:
- en
base_model: Laxhar/noobai-XL-Vpred-1.0
tags:
- SDXL
- noobai-XL
- Vpred-1.0
- text-to-image
- ComfyUI
- Automatic1111
- Diffuser
pipeline_tag: text-to-image
library_name: diffusers
datasets:
- LaxharLab/NoobAI-XL-dataset
metrics:
- FID
- IS
widget:
- text: >-
high quality, masterpiece, detailed, 8K, artist:nyantcha,
evangeline_(nyantcha), vibrant surreal artwork, rainbow, light particles,
from above, volumetric lighting, ((adult girl:1.2)), natural huge breasts,
woman dressed as white rabbit, sleek pure white outfit, delicate white bunny
ears, braid, plump, skindentation, huge breasts, falling into swirling black
hole, seen from behind, glancing over shoulder, alluring mysterious
expression, dress, zipper, zipper pull, detached sleeves, breasts apart
(shoulder straps), buckles, long dress, swirling cosmic patterns, glowing
particles, dramatic lighting, vibrant neon pink and blue tones,
hyper-detailed, cinematic depth of field, smooth texture, film grain,
chromatic aberration, high contrast, limited palette
parameters:
negative_prompt: >-
lowres, worst quality, low quality, bad anatomy, bad hands, 4koma, comic,
greyscale, censored, jpeg artifacts, overly saturated, overly vivid,
(multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality,
jpeg artifacts, bad quality, watermark, unfinished, displeasing, sepia,
sketch, flat color, signature, artistic error, username, scan, (blurry,
lowres, worst quality, (low quality:1.1), ugly, (bad anatomy:1.05), artist
name, (patreon username:1.2)
output:
url: stand_on_ripplewater.jpeg
---
# MIDNIGHT_NAI-XL_vPredV1
**Model Type:** Diffusion-based text-to-image generative model
**Base Model:** SDXL 1.0 & Laxhar/noobai-XL-Vpred-1.0
**License:** [CreativeML Open RAIL++-M](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)
## Model Description
MIDNIGHT_NAI-XL_vPredV1 is a specialized fine-tuning of the NoobAI-XL (NAI-XL) model, designed to enhance anatomical precision, compositional coherence, and versatile style integration. This model excels in generating high-quality images with vibrant colors while minimizing overexposure.
## Usage Recommendations
### **Sampling Methods**
MIDNIGHT_NAI-XL_vPred is optimized specifically for **Euler (normal)**.
Use **ModelSamplingDiscrete** with **V-prediction** and **ZsNR set to true**.
Other samplers may not provide stable results, and **V-prediction models do not support other samplers**.
### **CFG Scaling**
**Dynamic CFG Plugin is bypassed as a backup for potential future needs.**
Manually adjust **CFG scaling within a range of 3-4** for the best balance.
For optimal results, a **preferred setting of 3.5** is recommended.
### **Custom Workflow**
For an optimized generation process, use the [**MIDNIGHT1111_Chasm 2025-02-04**](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%202025-02-04.json) ComfyUI workflow.
This workflow is specifically designed to **leverage the strengths of MIDNIGHT_NAI-XL_vPred**, providing a streamlined and efficient image generation pipeline.
## MIDNIGHT1111_Chasm
For an optimized generation process, consider using the custom workflow [MIDNIGHT1111_Chasm 02-05-25](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json). This workflow is tailored to leverage the strengths of the MIDNIGHT_NAI-XL_vPredV1 model, providing a streamlined and efficient image generation pipeline.
*Note: The above image is a preview of the `MIDNIGHT1111_Chasm` workflow.*
### Method I: reForge without MIDNIGHT1111_Chasm Workflow
1. **Installation:** If not already installed, follow the instructions in the [reForge repository](https://github.com/Panchovix/stable-diffusion-webui-reForge) to set up.
2. **Usage:** Launch WebUI and use the model as usual.
### Method II: ComfyUI *with* MIDNIGHT1111_Chasm Workflow
1. **Installation:** Follow the setup instructions in the [ComfyUI repository](https://github.com/comfyanonymous/ComfyUI).
2. **Workflow Sample:** Utilize the provided [ComfyUI workflow sample](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json) for guidance.
### Method III: WebUI without MIDNIGHT1111_Chasm Workflow
1. **Installation:** Follow the instructions in the [WebUI repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to set up.
2. **Navigate to the WebUI Directory:** Before updating or switching branches, ensure you're inside the `stable-diffusion-webui` folder
command: |
```bash
cd stable-diffusion-webui
```
3. **Switch to the Development Branch (Optional, for testing new features):** If you want to use the latest features from the development branch, run:
command: |
```bash
git switch dev
git pull
```
β οΈ **Note:** The `dev` branch may contain bugs. If stability is your priority, it's best to stay on the `main` branch.
4. **Update WebUI (Main or Dev Branch):** To pull the latest updates while on either branch, run:
command: |
```bash
git pull
```
π **Restart WebUI after updating to apply changes.**"
5. **Configuration:** Ensure you're using a stable branch, as the dev branch may contain bugs.
### Method IV: Diffusers without MIDNIGHT1111_Chasm Workflow
```bash
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerDiscreteScheduler
ckpt_path = "/path/to/model.safetensors"
pipe = StableDiffusionXLPipeline.from_single_file(
ckpt_path,
use_safetensors=True,
torch_dtype=torch.float16,
)
scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to("cuda")
prompt = """masterpiece, best quality,artist:john_kafka,artist:nixeu,artist:quasarcake, chromatic aberration, film grain, horror \(theme\), limited palette, x-shaped pupils, high contrast, color contrast, cold colors, arlecchino \(genshin impact\), black theme, gritty, graphite \(medium\)"""
negative_prompt = "nsfw, worst quality, old, early, low quality, lowres, signature, username, logo, bad hands, mutated hands, mammal, anthro, furry, ambiguous form, feral, semi-anthro"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=832,
height=1216,
num_inference_steps=28,
guidance_scale=5,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")
```
## e621/Danbooru Artist Wildcards for A1111 & ComfyUI Enclosed in CSV & TXT Formats
To enhance the model's performance and specificity, the following trigger word lists in CSV format are included:
- [`danbooru_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_webui.csv)
- [`danbooru_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_webui.csv)
- [`e621_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_webui.csv)
- [`e621_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_webui.csv)
These lists provide recognized tags for various artists and characters, facilitating more accurate and tailored image generation.
The wildcard file in 'TXT' format is included and designed for seamless integration with **AUTOMATIC1111** and **ComfyUI**, optimized for dynamic prompt generation using artist data from **e621** and **Danbooru**.
- **TXT Format:** Sanitized artist tags by removing URLs and converted from `.csv` to `.txt` format for improved readability across different extensions.
- **Dual Dataset Support:** Supports both e621 and Danbooru datasets to enhance art style diversity.
- **Smooth Randomization:** Structured with trailing commas for seamless wildcard cycling during prompt generation.
## How to Use Wildcards
### For A1111
1. **Install:** [stable-diffusion-webui-wildcards](https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards)
2. **Place the `.txt` file in:**
```
/A1111/extensions/stable-diffusion-webui-wildcards
```
3. **Use in your prompt like this:**
```
__e621_artist_wildcard__, very awa, masterpiece, best quality, amazing quality
```
```
__danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality
```
```
__e621_artist_wildcard__, __danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality
```
### For ComfyUI
1. **Install:** [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)
2. **Place the `.txt` file in:**
```
/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/wildcards
```
or
```
/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards
```
3. **Use the wildcard node to trigger dynamic randomization in your workflows.**
## Whatβs Included in Wildcards
TXT formatted file containing clean, artist-focused wildcard files ready for dynamic prompt workflows in A1111 and ComfyUI.
- [danbooru_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_wildcard.txt)
- [danbooru_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_wildcard.txt)
- [e621_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_wildcard.txt)
- [e621_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_wildcard.txt)
## Acknowledgments
Special thanks to:
- **Development Team:** Laxhar Lab
- **Coding Contributions:** Euge
- **e621/Danbooru Wildcards** [ipsylon0000](https://civitai.com/user/ipsylon0000)
- **Community Support:** Various contributors
## Additional Resources
- **Guidebook for NoobAI XL:** [English Version](https://civitai.com/articles/8962)
- **Recommended LoRa List for NoobAI XL:** [Resource Link](https://fcnk27d6mpa5.feishu.cn/wiki/IBVGwvVGViazLYkMgVEcvbklnge)
- **Fixing Black Images in ComfyUI on macOS (M1/M2):** [Read the Article](https://civitai.com/articles/11106)
- **Creative Solutions and Services:** [Magnabos.co](https://magnabos.co/)
## License
This model is licensed under the [CreativeML Open RAIL++-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). By using this model, you agree to the terms and conditions outlined in the license.
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755616921
|
lisaozill03
| 2025-08-19T15:49:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:48:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jacoboss/MyGemmaNPC
|
jacoboss
| 2025-08-19T15:48:33Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T21:28:50Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
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="jacoboss/MyGemmaNPC", 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: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
DeathGodlike/Rei-24B-KTO_EXL3
|
DeathGodlike
| 2025-08-19T15:46:54Z | 0 | 0 |
safetensors
|
[
"safetensors",
"KTO",
"roleplaying",
"prose",
"mistral",
"24B",
"exl3",
"4-bit",
"6-bit",
"8-bit",
"text-generation",
"base_model:Delta-Vector/Rei-24B-KTO",
"base_model:quantized:Delta-Vector/Rei-24B-KTO",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-19T15:46:53Z |
---
license: apache-2.0
base_model:
- Delta-Vector/Rei-24B-KTO
base_model_relation: quantized
pipeline_tag: text-generation
library_name: safetensors
tags:
- KTO
- roleplaying
- prose
- mistral
- 24B
- exl3
- 4-bit
- 6-bit
- 8-bit
---
## EXL3 quants: [ [H8-4.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-4.0BPW) | [H8-6.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-6.0BPW) | [H8-8.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-8.0BPW) ]
# Original model: [Rei-24B-KTO](https://huggingface.co/Delta-Vector/Rei-24B-KTO) by [Delta-Vector](https://huggingface.co/Delta-Vector)
|
aleebaster/blockassist-bc-sly_eager_boar_1755616783
|
aleebaster
| 2025-08-19T15:41:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:41:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WenFengg/21_14l3_19__8
|
WenFengg
| 2025-08-19T15:37:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T14:56:20Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
lobsang41/lucky-planograms-gemma-3-4b
|
lobsang41
| 2025-08-19T15:37:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T14:46:20Z |
---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: lucky-planograms-gemma-3-4b
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for lucky-planograms-gemma-3-4b
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
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="lobsang41/lucky-planograms-gemma-3-4b", 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: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755616149
|
vwzyrraz7l
| 2025-08-19T15:36:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:36:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755617735
|
Elizavr
| 2025-08-19T15:36:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:36:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Agentic-1.0-GGUF
|
mradermacher
| 2025-08-19T15:34:19Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:beyoru/Agentic-1.0",
"base_model:quantized:beyoru/Agentic-1.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:04:54Z |
---
base_model: beyoru/Agentic-1.0
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/beyoru/Agentic-1.0
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Agentic-1.0-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755617387
|
lqpl
| 2025-08-19T15:31:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:30:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AppliedLucent/nemo-phase4
|
AppliedLucent
| 2025-08-19T15:31:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:AppliedLucent/nemo-phase3",
"base_model:finetune:AppliedLucent/nemo-phase3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:18:37Z |
---
base_model: AppliedLucent/nemo-phase3
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AppliedLucent
- **License:** apache-2.0
- **Finetuned from model :** AppliedLucent/nemo-phase3
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)
|
ucmp137538/best_RPT_coder_mathrl_ckpt-1000
|
ucmp137538
| 2025-08-19T15:22:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:19:46Z |
---
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 π€ 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]
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755615143
|
kojeklollipop
| 2025-08-19T15:21:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:21:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TAUR-dev/M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft
|
TAUR-dev
| 2025-08-19T15:20:09Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-19T15:18:45Z |
# M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft
This model was created as part of the **voting_setup3_1epch_1e6_all_tasks_only_sft** experiment using the SkillFactory experiment management system.
## Model Details
- **Training Method**: LLaMAFactory SFT (Supervised Fine-Tuning)
- **Stage Name**: sft
- **Experiment**: voting_setup3_1epch_1e6_all_tasks_only_sft
## Training Configuration
{"model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct", "trust_remote_code": true, "stage": "sft", "do_train": true, "finetuning_type": "full", "deepspeed": "/datastor1/mwadhwa/code/skill-factory/thirdparty/LLaMA-Factory/examples/deepspeed/ds_z2_config.json", "dataset": "TAUR_dev__D_SFT_C_voting_setup3_1epch_1e6_all_tasks_only_sft_sft_data__sft_train", "template": "qwen", "cutoff_len": 16384, "max_samples": 1000000, "overwrite_cache": true, "preprocessing_num_workers": 1, "dataloader_num_workers": 0, "disable_tqdm": false, "output_dir": "/datastor1/mwadhwa/skill_inject_outputs/sf_experiments/skills_in_rl/voting_setup3_1epch_1e6_all_tasks_only_sft/llamafactory/checkpoints", "logging_steps": 10, "save_steps": 100000, "plot_loss": true, "overwrite_output_dir": true, "per_device_train_batch_size": 1, "gradient_accumulation_steps": 1, "learning_rate": 1e-06, "num_train_epochs": 1, "lr_scheduler_type": "cosine", "warmup_ratio": 0.05, "weight_decay": 0.0001, "adam_beta1": 0.9, "adam_beta2": 0.95, "bf16": true, "ddp_timeout": 180000000, "gradient_checkpointing": true, "save_only_model": true, "enable_masked_ranges": false, "save_strategy": "steps", "save_total_limit": 5, "sf_tracker_dataset_id": "TAUR-dev/D-ExpTracker__voting_setup3_1epch_1e6_all_tasks_only_sft__v1", "sf_eval_before_training": false, "sf_wandb_project": "voting_setup3_1epch_1e6_all_tasks_only_sft_sft", "sf_eval_steps": null, "run_name": "voting_setup3_1epch_1e6_all_tasks_only_sft_sft"}
## Experiment Tracking
π **View complete experiment details**: [Experiment Tracker Dataset](https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__voting_setup3_1epch_1e6_all_tasks_only_sft__v1)
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-voting_setup3_1epch_1e6_all_tasks_only_sft-sft")
```
|
Muapi/vintage-drawing-ce
|
Muapi
| 2025-08-19T15:18:13Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:18:02Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Vintage Drawing - CE
**Base model**: Flux.1 D
**Trained words**: vntgdrwngCE style
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:660535@811004", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Bahrom1996/whisper-uz
|
Bahrom1996
| 2025-08-19T15:16:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"uz",
"dataset:common_voice_14_0",
"base_model:jmshd/whisper-uz",
"base_model:finetune:jmshd/whisper-uz",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-18T12:38:14Z |
---
library_name: transformers
language:
- uz
license: apache-2.0
base_model: jamshidahmadov/whisper-uz
tags:
- generated_from_trainer
datasets:
- common_voice_14_0
metrics:
- wer
model-index:
- name: Whisper base uz - Bahrom
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_14_0
type: common_voice_14_0
config: uz
split: test
args: 'config: uz, split: test'
metrics:
- name: Wer
type: wer
value: 39.4953893762244
---
<!-- 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 uz - Bahrom
This model is a fine-tuned version of [jamshidahmadov/whisper-uz](https://huggingface.co/jamshidahmadov/whisper-uz) on the common_voice_14_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4621
- Wer: 39.4954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5759 | 0.1323 | 500 | 0.4621 | 39.4954 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.0
- Datasets 3.3.2
- Tokenizers 0.21.0
|
Muapi/360-panorama-sd1.5-flux
|
Muapi
| 2025-08-19T15:15:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:15:24Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 360 panorama [SD1.5 / FLUX]
**Base model**: Flux.1 D
**Trained words**: 360, panorama, spherical panorama
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:118398@756096", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755615004
|
lisaozill03
| 2025-08-19T15:15:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:15:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kodetr/stunting-7B-Qwen
|
kodetr
| 2025-08-19T15:15:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"stunting",
"kesehatan",
"anak",
"conversational",
"id",
"dataset:kodetr/penelitian-fundamental-stunting-qa",
"base_model:Qwen/Qwen1.5-7B-Chat",
"base_model:finetune:Qwen/Qwen1.5-7B-Chat",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:59:41Z |
---
library_name: transformers
tags:
- stunting
- kesehatan
- anak
license: apache-2.0
datasets:
- kodetr/penelitian-fundamental-stunting-qa
language:
- id
metrics:
- rouge
- bleu
pipeline_tag: text-generation
base_model:
- Qwen/Qwen1.5-7B-Chat
---
### Model Description
<!-- Provide a longer summary of what this model is. -->
Konsultasi(Q&A) stunting pada anak
- **Developed by:** Tanwir
- **Language :** Indonesia
### Training
### Use with transformers
Pastikan untuk memperbarui instalasi transformer Anda melalui pip install --upgrade transformer.
```python
import torch
from transformers import pipeline
model_id = "kodetr/stunting-7B-Qwen"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "Jelaskan definisi 1000 hari pertama kehidupan."},
{"role": "user", "content": "Apa itu 1000 hari pertama kehidupan?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
|
phospho-app/Deimos252-ACT_BBOX-Light_dataset_deimos-yykfs
|
phospho-app
| 2025-08-19T15:14:42Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"act",
"robotics",
"dataset:Deimos252/Light_dataset_deimos",
"region:us"
] |
robotics
| 2025-08-19T15:14:06Z |
---
datasets: Deimos252/Light_dataset_deimos
library_name: phosphobot
pipeline_tag: robotics
model_name: act
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
1 validation error for EpisodesFeatures
Invalid JSON: EOF while parsing a value at line 2 column 0 [type=json_invalid, input_value='\n', input_type=str]
For further information visit https://errors.pydantic.dev/2.11/v/json_invalid
```
## Training parameters:
- **Dataset**: [Deimos252/Light_dataset_deimos](https://huggingface.co/datasets/Deimos252/Light_dataset_deimos)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
π **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
π€ **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
yaryar78/DDPM-Ray-dog
|
yaryar78
| 2025-08-19T15:13:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2025-08-19T14:55:50Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class π§¨](https://github.com/huggingface/diffusion-models-class)
fine tune on google/ddpm-cat-256 with dataset yaryar78/Ray_dog
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('yaryar78/ddpm-Ray-dog')
image = pipeline().images[0]
image
```
|
Muapi/zavy-s-fluorescent-flux
|
Muapi
| 2025-08-19T15:11:56Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:11:43Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Zavy's Fluorescent - Flux
**Base model**: Flux.1 D
**Trained words**: zavy-flrscnt
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:737408@824658", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
maheshkommuri/depthmap
|
maheshkommuri
| 2025-08-19T15:10:29Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T15:08:41Z |
---
license: apache-2.0
---
|
Muapi/alex-gross-style
|
Muapi
| 2025-08-19T15:09:51Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:09:38Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Alex Gross Style
**Base model**: Flux.1 D
**Trained words**: Alex Gross Style
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:96381@1407451", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755614551
|
sampingkaca72
| 2025-08-19T15:08:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:08:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kurosawama/gemma-3-1b-it-Retranslation-align
|
Kurosawama
| 2025-08-19T15:07:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"trl",
"dpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T15:07:28Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
Muapi/pascal-blanch
|
Muapi
| 2025-08-19T15:06:50Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:06:40Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Pascal BlanchΓ©
**Base model**: Flux.1 D
**Trained words**: By Passcal BlanchΓ©
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1285926@1274884", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755614240
|
pempekmangedd
| 2025-08-19T15:06:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:06:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/josh-agle-shag-style
|
Muapi
| 2025-08-19T15:04:28Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:04:18Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Josh Agle (SHAG) Style
**Base model**: Flux.1 D
**Trained words**: Josh Agle (SHAG) Style
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:103382@1616823", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
rbelanec/train_svamp_1755615499
|
rbelanec
| 2025-08-19T15:03:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-19T14:58:45Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_svamp_1755615499
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. -->
# train_svamp_1755615499
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the svamp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1893
- Num Input Tokens Seen: 705184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|
| 0.7697 | 0.5 | 79 | 0.6681 | 35776 |
| 0.5968 | 1.0 | 158 | 0.5173 | 70672 |
| 0.1124 | 1.5 | 237 | 0.1794 | 105904 |
| 0.132 | 2.0 | 316 | 0.1370 | 141328 |
| 0.1259 | 2.5 | 395 | 0.1006 | 176752 |
| 0.0482 | 3.0 | 474 | 0.0846 | 211808 |
| 0.0378 | 3.5 | 553 | 0.1207 | 247104 |
| 0.0761 | 4.0 | 632 | 0.0935 | 282048 |
| 0.0108 | 4.5 | 711 | 0.1449 | 317248 |
| 0.0208 | 5.0 | 790 | 0.1160 | 352592 |
| 0.0152 | 5.5 | 869 | 0.1450 | 388176 |
| 0.0132 | 6.0 | 948 | 0.1488 | 423184 |
| 0.0151 | 6.5 | 1027 | 0.1474 | 458640 |
| 0.0004 | 7.0 | 1106 | 0.1693 | 493440 |
| 0.0006 | 7.5 | 1185 | 0.1817 | 528768 |
| 0.0001 | 8.0 | 1264 | 0.1838 | 563872 |
| 0.0 | 8.5 | 1343 | 0.1869 | 599232 |
| 0.0002 | 9.0 | 1422 | 0.1876 | 634544 |
| 0.0004 | 9.5 | 1501 | 0.1893 | 670064 |
| 0.0001 | 10.0 | 1580 | 0.1893 | 705184 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
2hpsatt/blockassist-bc-huge_deft_eagle_1755615679
|
2hpsatt
| 2025-08-19T15:02:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:01:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/randommaxx-mecharmor
|
Muapi
| 2025-08-19T15:01:21Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:59:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# RandomMaxx MechArmor
**Base model**: Flux.1 D
**Trained words**: MechArmor, Robot, Mech, Heavy Mech, Power Armor, Cyborg
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:737782@1209804", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
wjbmattingly/lfm2-vl-450M-yiddish
|
wjbmattingly
| 2025-08-19T14:58:01Z | 0 | 0 | null |
[
"safetensors",
"lfm2-vl",
"custom_code",
"base_model:LiquidAI/LFM2-VL-450M",
"base_model:finetune:LiquidAI/LFM2-VL-450M",
"region:us"
] | null | 2025-08-19T14:57:50Z |
---
base_model:
- LiquidAI/LFM2-VL-450M
---
# model_step_13000
## Model Description
This model is a fine-tuned version of **LiquidAI/LFM2-VL-450M** using the brute-force-training package.
- **Base Model**: LiquidAI/LFM2-VL-450M
- **Training Status**: π In Progress
- **Generated**: 2025-08-19 10:41:14
- **Training Steps**: 13,000
## Training Details
### Dataset
- **Dataset**: johnlockejrr/yiddish_synth_v2
- **Training Examples**: 100,000
- **Validation Examples**: 4,999
### Training Configuration
- **Max Steps**: 100,000
- **Batch Size**: 15
- **Learning Rate**: 7e-05
- **Gradient Accumulation**: 1 steps
- **Evaluation Frequency**: Every 1,000 steps
### Current Performance
- **Training Loss**: 0.124526
- **Evaluation Loss**: 0.189137
## Pre-Training Evaluation
**Initial Model Performance (before training):**
- **Loss**: 2.626098
- **Perplexity**: 13.82
- **Character Accuracy**: 31.1%
- **Word Accuracy**: 12.9%
## Evaluation History
### All Checkpoint Evaluations
| Step | Checkpoint Type | Loss | Perplexity | Char Acc | Word Acc | Improvement vs Pre |
|------|----------------|------|------------|----------|----------|--------------------|
| Pre | pre_training | 2.6261 | 13.82 | 31.1% | 12.9% | +0.0% |
| 1,000 | checkpoint | 0.9395 | 2.56 | 20.1% | 4.1% | +64.2% |
| 2,000 | checkpoint | 0.8058 | 2.24 | 21.2% | 4.0% | +69.3% |
| 3,000 | checkpoint | 0.7305 | 2.08 | 23.0% | 6.1% | +72.2% |
| 4,000 | checkpoint | 0.6669 | 1.95 | 20.6% | 3.4% | +74.6% |
| 5,000 | checkpoint | 0.5341 | 1.71 | 21.4% | 3.6% | +79.7% |
| 6,000 | checkpoint | 0.4656 | 1.59 | 20.9% | 3.8% | +82.3% |
| 7,000 | checkpoint | 0.3917 | 1.48 | 21.4% | 3.5% | +85.1% |
| 8,000 | checkpoint | 0.3310 | 1.39 | 21.6% | 4.8% | +87.4% |
| 9,000 | checkpoint | 0.2892 | 1.34 | 20.7% | 4.0% | +89.0% |
| 10,000 | checkpoint | 0.2566 | 1.29 | 20.9% | 4.7% | +90.2% |
| 11,000 | checkpoint | 0.2199 | 1.25 | 20.2% | 4.9% | +91.6% |
| 12,000 | checkpoint | 0.2033 | 1.23 | 20.3% | 3.2% | +92.3% |
| 13,000 | checkpoint | 0.1891 | 1.21 | 19.4% | 3.4% | +92.8% |
## Training Progress
### Recent Training Steps (Loss Only)
| Step | Training Loss | Timestamp |
|------|---------------|-----------|
| 12,991 | 0.154684 | 2025-08-19T10:40 |
| 12,992 | 0.183019 | 2025-08-19T10:40 |
| 12,993 | 0.157314 | 2025-08-19T10:40 |
| 12,994 | 0.168899 | 2025-08-19T10:40 |
| 12,995 | 0.116096 | 2025-08-19T10:40 |
| 12,996 | 0.122316 | 2025-08-19T10:40 |
| 12,997 | 0.149480 | 2025-08-19T10:40 |
| 12,998 | 0.166267 | 2025-08-19T10:40 |
| 12,999 | 0.152927 | 2025-08-19T10:40 |
| 13,000 | 0.124526 | 2025-08-19T10:40 |
## Training Visualizations
### Training Progress and Evaluation Metrics
*This chart shows the training loss progression, character accuracy, word accuracy, and perplexity over time. Red dots indicate evaluation checkpoints.*
### Evaluation Comparison Across All Checkpoints
*Comprehensive comparison of all evaluation metrics across training checkpoints. Red=Pre-training, Blue=Checkpoints, Green=Final.*
### Available Visualization Files:
- **`training_curves.png`** - 4-panel view: Training loss with eval points, Character accuracy, Word accuracy, Perplexity
- **`evaluation_comparison.png`** - 4-panel comparison: Loss, Character accuracy, Word accuracy, Perplexity across all checkpoints
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# For vision-language models, use appropriate imports
model = AutoModelForCausalLM.from_pretrained("./model_step_13000")
tokenizer = AutoTokenizer.from_pretrained("./model_step_13000")
# Your inference code here
```
## Training Configuration
```json
{
"dataset_name": "johnlockejrr/yiddish_synth_v2",
"model_name": "LiquidAI/LFM2-VL-450M",
"max_steps": 100000,
"eval_steps": 1000,
"num_accumulation_steps": 1,
"learning_rate": 7e-05,
"train_batch_size": 15,
"val_batch_size": 1,
"train_select_start": 0,
"train_select_end": 100000,
"val_select_start": 100001,
"val_select_end": 105000,
"train_field": "train",
"val_field": "train",
"image_column": "image",
"text_column": "text",
"user_text": "Please transcribe all the Yiddish text you see in this historical manuscript image. Provide only the transcribed text without any additional commentary or description.",
"max_image_size": 250
}
```
## Model Card Metadata
- **Base Model**: LiquidAI/LFM2-VL-450M
- **Training Framework**: brute-force-training
- **Training Type**: Fine-tuning
- **License**: Inherited from base model
- **Language**: Inherited from base model
---
*This model card was automatically generated by brute-force-training on 2025-08-19 10:41:14*
|
matheoqtb/EuroBertV2final
|
matheoqtb
| 2025-08-19T14:56:59Z | 0 | 0 | null |
[
"safetensors",
"eurobert",
"custom_code",
"region:us"
] | null | 2025-08-19T14:56:50Z |
# Checkpoint exportΓ©: final
Ce dΓ©pΓ΄t contient un checkpoint extrait de `matheoqtb/euroBertV2_test2` (sous-dossier `final`) et les fichiers de code nΓ©cessaires provenant de `EuroBERT/EuroBERT-610m`.
Chargement:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True)
mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True)
TΓ’che: feature-extraction (embeddings)
|
Muapi/tifa-lockhart-ffviir
|
Muapi
| 2025-08-19T14:56:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:55:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Tifa Lockhart (FFVIIR)
**Base model**: Flux.1 D
**Trained words**: TifaLockhart, croptop, skirt, suspenders, fingerless gloves
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:661363@740105", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
KMH158/t5-small-openassistant-chat
|
KMH158
| 2025-08-19T14:54:39Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T12:36:35Z |
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-openassistant-chat
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-small-openassistant-chat
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1785
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 80
- eval_batch_size: 1
- 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: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3768 | 1.0 | 301 | 2.3842 |
| 2.6839 | 2.0 | 602 | 2.3277 |
| 2.6351 | 3.0 | 903 | 2.2995 |
| 2.6016 | 4.0 | 1204 | 2.2818 |
| 2.5803 | 5.0 | 1505 | 2.2680 |
| 2.5587 | 6.0 | 1806 | 2.2571 |
| 2.541 | 7.0 | 2107 | 2.2481 |
| 2.5323 | 8.0 | 2408 | 2.2409 |
| 2.5102 | 9.0 | 2709 | 2.2349 |
| 2.5063 | 10.0 | 3010 | 2.2288 |
| 2.4953 | 11.0 | 3311 | 2.2242 |
| 2.4926 | 12.0 | 3612 | 2.2192 |
| 2.4786 | 13.0 | 3913 | 2.2154 |
| 2.472 | 14.0 | 4214 | 2.2117 |
| 2.4662 | 15.0 | 4515 | 2.2079 |
| 2.4553 | 16.0 | 4816 | 2.2051 |
| 2.4472 | 17.0 | 5117 | 2.2020 |
| 2.4488 | 18.0 | 5418 | 2.2008 |
| 2.4367 | 19.0 | 5719 | 2.1972 |
| 2.4353 | 20.0 | 6020 | 2.1952 |
| 2.429 | 21.0 | 6321 | 2.1934 |
| 2.4247 | 22.0 | 6622 | 2.1912 |
| 2.4242 | 23.0 | 6923 | 2.1901 |
| 2.4196 | 24.0 | 7224 | 2.1887 |
| 2.4169 | 25.0 | 7525 | 2.1873 |
| 2.4122 | 26.0 | 7826 | 2.1862 |
| 2.4089 | 27.0 | 8127 | 2.1851 |
| 2.4042 | 28.0 | 8428 | 2.1841 |
| 2.4061 | 29.0 | 8729 | 2.1831 |
| 2.4007 | 30.0 | 9030 | 2.1823 |
| 2.397 | 31.0 | 9331 | 2.1814 |
| 2.3998 | 32.0 | 9632 | 2.1810 |
| 2.3963 | 33.0 | 9933 | 2.1805 |
| 2.3976 | 34.0 | 10234 | 2.1798 |
| 2.3919 | 35.0 | 10535 | 2.1794 |
| 2.3873 | 36.0 | 10836 | 2.1793 |
| 2.3899 | 37.0 | 11137 | 2.1789 |
| 2.3886 | 38.0 | 11438 | 2.1786 |
| 2.3906 | 39.0 | 11739 | 2.1786 |
| 2.393 | 40.0 | 12040 | 2.1785 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
prl90777/R1_Qwen3_8B_0719
|
prl90777
| 2025-08-19T14:48:53Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"lora",
"transformers",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"license:mit",
"region:us"
] | null | 2025-08-19T11:31:10Z |
---
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
tags:
- base_model:adapter:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
- lora
- transformers
model-index:
- name: R1_Qwen3_8B_0719
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. -->
# R1_Qwen3_8B_0719
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4267
- Map@3: 0.9177
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map@3 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 26.6085 | 0.0523 | 20 | 1.3286 | 0.7507 |
| 9.7222 | 0.1046 | 40 | 1.0625 | 0.7933 |
| 7.9943 | 0.1569 | 60 | 0.8487 | 0.8183 |
| 7.4982 | 0.2092 | 80 | 0.8259 | 0.8315 |
| 6.7844 | 0.2615 | 100 | 0.7845 | 0.8407 |
| 6.1752 | 0.3138 | 120 | 0.7051 | 0.8571 |
| 5.3012 | 0.3661 | 140 | 0.6606 | 0.8683 |
| 4.7654 | 0.4184 | 160 | 0.5941 | 0.8830 |
| 5.3467 | 0.4707 | 180 | 0.6074 | 0.8771 |
| 4.4068 | 0.5230 | 200 | 0.5947 | 0.8880 |
| 4.9025 | 0.5754 | 220 | 0.5081 | 0.8986 |
| 4.3179 | 0.6277 | 240 | 0.5520 | 0.8941 |
| 4.4065 | 0.6800 | 260 | 0.4970 | 0.9040 |
| 3.7451 | 0.7323 | 280 | 0.4987 | 0.9045 |
| 4.4839 | 0.7846 | 300 | 0.4905 | 0.9085 |
| 3.5164 | 0.8369 | 320 | 0.4644 | 0.9067 |
| 3.9504 | 0.8892 | 340 | 0.4650 | 0.9066 |
| 3.6298 | 0.9415 | 360 | 0.4461 | 0.9106 |
| 3.6195 | 0.9938 | 380 | 0.4242 | 0.9173 |
| 3.0214 | 1.0445 | 400 | 0.5402 | 0.9058 |
| 2.7135 | 1.0968 | 420 | 0.4302 | 0.9203 |
| 2.6106 | 1.1491 | 440 | 0.4071 | 0.9252 |
| 2.8122 | 1.2014 | 460 | 0.4366 | 0.9188 |
| 3.0033 | 1.2537 | 480 | 0.4178 | 0.9230 |
| 2.59 | 1.3060 | 500 | 0.4116 | 0.9233 |
| 3.0395 | 1.3583 | 520 | 0.4267 | 0.9177 |
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755614706
|
lilTAT
| 2025-08-19T14:45:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:45:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Team-Atom/act_record_pp_blue001_96_100000
|
Team-Atom
| 2025-08-19T14:41:55Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:Team-Atom/PiPl_blue_001",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-19T14:41:42Z |
---
datasets: Team-Atom/PiPl_blue_001
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755614412
|
lilTAT
| 2025-08-19T14:40:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:40:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755612596
|
sampingkaca72
| 2025-08-19T14:36:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:36:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1755612564
|
aleebaster
| 2025-08-19T14:34:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:34:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sarrockia/prefectIllustriousXL_v3.safetensors
|
sarrockia
| 2025-08-19T14:33:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T14:04:58Z |
---
license: apache-2.0
---
|
Andra76/blockassist-bc-deadly_enormous_butterfly_1755613857
|
Andra76
| 2025-08-19T14:31:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly enormous butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:31:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly enormous butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
EnLiving-AI/CosmosC1
|
EnLiving-AI
| 2025-08-19T14:26:23Z | 0 | 4 | null |
[
"Cosmos",
"Learning",
"Advacned_learning",
"NO-API",
"text-generation",
"license:mit",
"region:us"
] |
text-generation
| 2025-08-19T13:15:16Z |
---
license: mit
pipeline_tag: text-generation
tags:
- Cosmos
- Learning
- Advacned_learning
- NO-API
---
# π Cosmos C1
**Autonomous Knowledge Explorer β v1.0**
Cosmos C1 is an **autonomous research engine** packed into a simple `.exe` app.
It explores the web, extracts knowledge, and builds structured insights β all without needing APIs or Python setup.
Just run the `.exe` and watch your AI explore, learn, and grow its own knowledge base.
---
## β¨ Features
- π **Autonomous Research Cycles** β Runs continuous query β learn β extract β store loops.
- π§ **Knowledge Extraction** β Identifies concepts, relationships, and facts from raw text.
- π **Knowledge Base Growth** β Expands memory with each cycle.
- π **No API Required** β Directly learns from the web.
- π₯οΈ **Standalone .exe** β No Python, no installs, just double-click and go.
- π **Summaries** β Generates cycle logs and session summaries.
---
## β‘ Quick Start
1. **Download** the latest release from [Releases](https://huggingface.co/EnLiving-AI/CosmosC1/resolve/main/CosmosC1.exe).
2. Place `CosmosC1.exe` in your desired folder.
3. Double-click to launch.
4. The terminal window will start showing research cycles in real time.
5. Press `Ctrl+C` anytime to stop and see a final **Session Summary**.
---
## πΌοΈ Example Run
<code>
π Autonomous Knowledge Explorer
</code><br>
<code>π No APIs - Direct Learning from Web</code><br>
<code>Press Ctrl+C to stop and show summary</code><br>
<code>π CYCLE 1</code><br>
<code>π Source: Web</code><br>
<code>π Query: Applications of Shakespeare</code><br>
<code>π Content Learned:</code><br>
<code>... raw snippets ...</code><br>
<code>π‘ Extracted Knowledge:</code><br>
<code>β¦ Concepts: Applications Directory, Windows</code><br>
<code>β¦ Relationships: Applications Directory β Windows</code><br>
<code>π Knowledge Base: 2 concepts | 1 discovery</code><br>
---
At the end, Cosmos C1 shows:
- β
**Total Cycles**
- β
**Concepts Learned**
- β
**Discoveries Recorded**
- β
**Top Discoveries**
- β
**Current Focus Area**
---
## π― Use Cases
- AI-driven **research assistant**
- Automated **concept discovery**
- Inspiration for **autonomous agent design**
- Demonstration of **web knowledge extraction**
---
## π§ Current Limitations
- Requires internet access
- Works in a terminal window (no GUI yet)
- May capture unrelated snippets (still improving filtering)
---
## π Roadmap
- [ ] GUI Dashboard
- [ ] Exportable Knowledge Graphs
- [ ] Smarter Query Refinement
- [ ] Multi-agent collaboration
---
## π License
MIT License β feel free to use, modify, and contribute.
---
|
zhuojing-huang/gpt2-arabic-english-ewc
|
zhuojing-huang
| 2025-08-19T14:25:57Z | 22 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T13:49:13Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: gpt2-arabic-english-ewc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-arabic-english-ewc
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 122070
### Training results
### Framework versions
- Transformers 4.53.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755613409
|
lilTAT
| 2025-08-19T14:23:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:23:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755611635
|
quantumxnode
| 2025-08-19T14:22:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:22:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fatmhd1995/phi35_ft_llm_4_annotation_rnd1_v2
|
fatmhd1995
| 2025-08-19T14:19:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:16:28Z |
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** fatmhd1995
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-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)
|
Joetib/en-twi-qwen2.5-0.5B-Instruct
|
Joetib
| 2025-08-19T14:19:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:19:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755611487
|
hakimjustbao
| 2025-08-19T14:19:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:19:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kazuki1450/Qwen2.5-1.5B_lightr1_3_EN_1024_1p0_0p0_1p0_sft
|
Kazuki1450
| 2025-08-19T14:18:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:16:21Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B
tags:
- generated_from_trainer
model-index:
- name: Qwen2.5-1.5B_lightr1_3_EN_1024_1p0_0p0_1p0_sft
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. -->
# Qwen2.5-1.5B_lightr1_3_EN_1024_1p0_0p0_1p0_sft
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.7.1+cu128
- Datasets 4.0.0
- Tokenizers 0.21.2
|
mradermacher/UI-Venus-Navi-72B-GGUF
|
mradermacher
| 2025-08-19T14:16:46Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:inclusionAI/UI-Venus-Navi-72B",
"base_model:quantized:inclusionAI/UI-Venus-Navi-72B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-18T23:56:29Z |
---
base_model: inclusionAI/UI-Venus-Navi-72B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/inclusionAI/UI-Venus-Navi-72B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UI-Venus-Navi-72B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/UI-Venus-Navi-72B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.9 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.mmproj-f16.gguf) | mmproj-f16 | 1.5 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q2_K.gguf) | Q2_K | 29.9 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q3_K_S.gguf) | Q3_K_S | 34.6 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q3_K_M.gguf) | Q3_K_M | 37.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q3_K_L.gguf) | Q3_K_L | 39.6 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.IQ4_XS.gguf) | IQ4_XS | 40.3 | |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q4_K_S.gguf) | Q4_K_S | 44.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q4_K_M.gguf) | Q4_K_M | 47.5 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_S.gguf.part2of2) | Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q5_K_M.gguf.part2of2) | Q5_K_M | 54.5 | |
| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q6_K.gguf.part2of2) | Q6_K | 64.4 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UI-Venus-Navi-72B-GGUF/resolve/main/UI-Venus-Navi-72B.Q8_0.gguf.part2of2) | Q8_0 | 77.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755611423
|
lisaozill03
| 2025-08-19T14:15:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:15:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755612755
|
yaelahnal
| 2025-08-19T14:13:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:13:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vrbhalaaji/my_policy
|
vrbhalaaji
| 2025-08-19T14:13:45Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:vrbhalaaji/orange-pick-test",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-19T14:13:00Z |
---
datasets: vrbhalaaji/orange-pick-test
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755612776
|
lilTAT
| 2025-08-19T14:13:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:13:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abishekcodes/distil_nre_pii
|
abishekcodes
| 2025-08-19T14:11:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-19T14:06:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
aldsouza/health-agent
|
aldsouza
| 2025-08-19T14:08:07Z | 104 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"medical",
"function-calling",
"llm",
"healthcare",
"conversational-ai",
"conversational",
"en",
"dataset:Salesforce/xlam-function-calling-60k",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-12T23:46:13Z |
---
library_name: transformers
tags:
- medical
- function-calling
- llm
- healthcare
- conversational-ai
license: mit
datasets:
- Salesforce/xlam-function-calling-60k
language:
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: text-generation
---
# Medical Function-Calling LLM (Fine-tuned DeepSeek-R1-Distill-Qwen-1.5B)
This model is a fine-tuned version of **[deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)**, specialized for **medical domain function-calling** tasks.
It is trained on **[Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)** to reliably produce structured JSON outputs for healthcare applications such as appointment booking, medical record retrieval, patient communication, and medical triage support.
---
## Model Details
- **Developed by:** Alton Lavin DβSouza
- **Funded by:** Self-funded
- **Model type:** Instruction-tuned causal language model with function-calling capabilities
- **Language(s):** English
- **License:** MIT
- **Finetuned from model:** [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
### Model Sources
- **Repository:** [GitHub β Medical Function Calling LLM](https://github.com/)
- **Base model card:** [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
---
## Uses
### Direct Use
- Conversational AI assistants in healthcare
- Automated structured response generation in JSON
- Integration with electronic health record (EHR) systems
- Medical workflow automation (e.g., booking appointments, retrieving patient data)
### Downstream Use
- Fine-tuning for specific healthcare specialties
- Integration into clinical decision support systems
- Agent-based medical AI systems with tool use
### Out-of-Scope Use
- Direct diagnosis without human oversight
- Emergency medical response without clinician involvement
- General-purpose non-medical applications (may work but not optimized)
---
## Bias, Risks, and Limitations
This model may:
- Hallucinate medical facts if prompted outside its training scope
- Produce incomplete JSON structures if instructions are ambiguous
- Require strict validation before integration into real-world healthcare systems
**β οΈ Important:** This model is **not** a substitute for a licensed medical professional.
---
## Tool Calling Example:
```python
import json
import os
import pickle
import time
from datetime import datetime, timedelta ,time as time_1
from threading import Thread
from typing import TypedDict, Dict, List, Any
from urllib.request import Request
import pytz
import torch
from duckduckgo_search import DDGS
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build
from langchain_community.tools import TavilySearchResults
from langgraph.constants import START, END
from langgraph.graph import StateGraph
from regex import regex, search
from smolagents import DuckDuckGoSearchTool
from sympy.physics.units.definitions.dimension_definitions import information
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from dotenv import load_dotenv
from tzlocal import get_localzone
load_dotenv()
torch.manual_seed(11)
model_name = "aldsouza/health-agent"
pattern = r'''
\{ # Opening brace of the function block
\s*"name"\s*:\s*"([^"]+)"\s*, # Capture the function name
\s*"arguments"\s*:\s*(\{ # Capture the arguments JSON object starting brace
(?:[^{}]++ | (?2))*? # Recursive matching for balanced braces (PCRE syntax)
\}) # Closing brace of arguments
\s*\} # Closing brace of the function block
'''
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).to("cuda")
# model_1 = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",torch_dtype=torch.float16).to("cuda")
medical_tools = [
{
"name": "symptom_checker",
"description": "Analyze symptoms and provide possible conditions.",
"parameters": {
"symptoms": {
"description": "List of symptoms reported by the patient.",
"type": "list[str]",
"default": ["headache", "fever"]
}
}
},
{
"name": "medication_lookup",
"description": "Look up details about a medication by its name.",
"parameters": {
"medication_name": {
"description": "Name of the medication to look up.",
"type": "str",
"default": "Aspirin"
}
}
},
{
"name": "book_appointment",
"description": "Schedule a medical appointment with a doctor.",
"parameters": {
"patient_name": {
"description": "Name of the patient.",
"type": "str",
"default": "John Doe"
},
"doctor_specialty": {
"description": "Specialty of the doctor to book.",
"type": "str",
"default": "general practitioner"
},
"date": {
"description": "Preferred date of appointment (YYYY-MM-DD).",
"type": "str",
"default": "2025-08-20"
}
}
},
{
"name": "get_lab_results",
"description": "Retrieve lab test results for a patient by test ID.",
"parameters": {
"patient_id": {
"description": "Unique patient identifier.",
"type": "str",
"default": "123456"
},
"test_id": {
"description": "Lab test identifier.",
"type": "str",
"default": "cbc"
}
}
},
{
"name": "request_missing_info",
"description": "Ask the user for missing or incomplete information needed to fulfill their request.",
"parameters": {
"missing_fields": {
"description": "List of missing required fields to be clarified by the user.",
"type": "list[str]",
"default": []
},
"context": {
"description": "Optional context or explanation to help the user provide the missing information.",
"type": "str",
"default": ""
}
}
},
{
"name": "medical_device_info",
"description": "Retrieve detailed information about a medical device by its name or model number.",
"parameters": {
"device_name": {
"description": "The name or model number of the medical device to look up.",
"type": "str",
"default": "Blood Pressure Monitor"
}
}
}, {
"name": "record_blood_pressure",
"description": "Record a patient's blood pressure reading with systolic, diastolic, and pulse rate values.",
"parameters": {
"patient_id": {
"description": "Unique identifier of the patient.",
"type": "str",
"default": "123456"
},
"systolic": {
"description": "Systolic blood pressure value (mmHg).",
"type": "int",
"default": 120
},
"diastolic": {
"description": "Diastolic blood pressure value (mmHg).",
"type": "int",
"default": 80
},
"pulse_rate": {
"description": "Pulse rate in beats per minute.",
"type": "int",
"default": 70
},
"measurement_time": {
"description": "Timestamp of the measurement (YYYY-MM-DD HH:MM).",
"type": "str",
"default": "2025-08-12 09:00"
}
}
}, {
"name": "start_blood_pressure_test",
"description": "Initiate a blood pressure measurement test for a patient using a connected device.",
"parameters": {
"patient_id": {
"description": "Unique identifier of the patient.",
"type": "str",
"default": "123456"
},
"device_id": {
"description": "Identifier or model of the blood pressure measuring device.",
"type": "str",
"default": "BP-Device-001"
}
}
}
]
# Compose the system prompt embedding the tools JSON
system_prompt = f"""
You are an intelligent AI assistant that uses available tools (functions) to help users achieve their medical-related goals. Your job is to understand the user's intent, identify missing information if needed, and then select and call the most appropriate function(s) to solve the task.
# Rules:
- ALWAYS use the tools provided to answer the user's request, unless explicitly told not to.
- Ask clarifying questions ONLY if the user's request is ambiguous or lacks required input parameters.
- If multiple tools are needed, use them in sequence.
- DO NOT make up data or assume values β request any missing input clearly.
# Output Format:
- Respond using a JSON list of function calls in the following format:
[
{{
"name": "function_name",
"arguments": {{
"param1": "value1",
"param2": "value2"
}}
]
- Only include the functions needed to complete the task.
- If no function is needed or the input is unclear, ask a clarifying question instead of guessing.
- Do NOT respond with explanations or natural language outside the JSON block unless explicitly instructed.
Following are the tools provided to you:
{json.dumps(medical_tools, indent=2)}
"""
SCOPES = ['https://www.googleapis.com/auth/calendar']
def symptom_checker(kwargs):
print(f"Checking diseases for following symptoms on the web:")
symptoms = kwargs.get("symptoms",[])
print(symptoms)
for i, arg in enumerate(symptoms):
print(f"{i}. {arg}")
results = TavilySearchResults()
information = ""
for result in results.invoke(f"What causes {''.join(symptoms)}"):
information = information + result["content"] + "\n"
return {
"status":200,
"message":information
}
def medication_lookup(kwargs):
medication_name = kwargs.get("medication_name")
print(f"Looking up the web for information on {medication_name}....")
results = TavilySearchResults()
information = ""
for result in results.invoke(f"What is {medication_name}?"):
information = information + result["content"] + "\n"
return {
"status": 200,
"message": information
}
def create_google_calendar_meeting(
summary: str,
start_datetime: str,
end_datetime: str,
attendees_emails: list,
timezone: str = 'America/Chicago'
):
"""
Creates a Google Calendar event.
Args:
summary (str): Event title.
start_datetime (str): Start datetime in ISO format, e.g., "2025-08-18T10:00:00-06:00".
end_datetime (str): End datetime in ISO format.
attendees_emails (list): List of attendee emails.
timezone (str): Timezone string, default 'America/Chicago'.
"""
creds = None
# Load saved credentials if available
if os.path.exists('token.pickle'):
with open('token.pickle', 'rb') as token:
creds = pickle.load(token)
# Authenticate if necessary
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file('credentials.json', SCOPES)
creds = flow.run_local_server(port=0)
with open('token.pickle', 'wb') as token:
pickle.dump(creds, token)
service = build('calendar', 'v3', credentials=creds)
event = {
'summary': summary,
'location': 'Virtual / Google Meet',
'description': f'{summary} meeting.',
'start': {'dateTime': start_datetime, 'timeZone': timezone},
'end': {'dateTime': end_datetime, 'timeZone': timezone},
'attendees': [{'email': email} for email in attendees_emails],
'reminders': {'useDefault': True},
}
created_event = service.events().insert(
calendarId='primary', body=event, sendUpdates='all'
).execute()
print(f"Event created: {created_event.get('htmlLink')}")
return created_event
def book_appointment(kwargs):
patient_name = kwargs.get("patient_name")
doctor_specialty = kwargs.get("doctor_specialty")
date_str = kwargs.get("date")
parsed_date = datetime.strptime(date_str, "%Y-%m-%d").date()
# Default time 9:00 AM Mountain Time
mountain_tz = pytz.timezone("America/Denver")
dt_mt = datetime.combine(parsed_date, time_1(9, 0))
dt_mt = mountain_tz.localize(dt_mt)
# Autodetect local timezone
local_tz = get_localzone()
dt_local = dt_mt.astimezone(local_tz)
dt_local_end = dt_local + timedelta(hours=1)
result = create_google_calendar_meeting(
f"Meeting for {patient_name}",
dt_local.isoformat(),
dt_local_end.isoformat(),
["altondsouza02@gmail.com", "aldsouza@ualberta.ca"]
)
return {
"status":200,
"message": f"Event Created:{result}"
}
function_execution_map = {
"symptom_checker": symptom_checker,
"medication_lookup": medication_lookup,
"book_appointment": book_appointment
}
# Example prompt using the medical tools
# messages = [
# {
# "content": system_prompt,
# "role": "system"
# },
# {
# "content": (
# "I have a headache and mild fever. What could be the possible conditions? "
# "Also, lookup medication details for 'Ibuprofen'. "
# "Please book an appointment for patient 'Alice Smith' with a neurologist on 2025-09-01."
# ),
# "role": "user"
# }
# ]
# streamer = TextStreamer(tokenizer, skip_prompt=True)
# streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
# inputs = tokenizer.apply_chat_template(
# messages,
# add_generation_prompt=True,
# tokenize=True,
# return_dict=True,
# return_tensors="pt",
# ).to(model.device)
# inputs = tokenizer.apply_chat_template(
# messages,
# add_generation_prompt=True,
# tokenize=True,
# return_dict=True,
# return_tensors="pt",
# ).to(mo)
# generation_kwargs = dict(inputs,streamer=streamer,
# max_new_tokens=4096,
# temperature=0.7,)
# thread = Thread(target=model.generate, kwargs=generation_kwargs,daemon=True)
# thread.start()
# for new_text in streamer:
# print(new_text, end="")
# with torch.no_grad():
# outputs = model.generate(
# **inputs,streamer=streamer,
# max_new_tokens=4096,
# temperature=0.7,
# )
class State(TypedDict):
messages: List[Dict[str, Any]]
plan: List[Dict[str, Any]]
task: str
graph_builder = StateGraph(State)
PLANNING_AGENT = "PLANNING_AGENT"
def planning(state: State):
print("Coming up with Plan")
messages = state.get("messages", [])
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
generation_kwargs = dict(inputs, streamer=streamer,
max_new_tokens=4096,
temperature=0.7, )
thread = Thread(target=model.generate, kwargs=generation_kwargs, daemon=True)
thread.start()
generated_text = ""
for new_text in streamer:
print(new_text, end="")
generated_text = generated_text + new_text
generated_text = generated_text.replace("<ο½endβofβsentenceο½>","").replace("</think>","")
matches = regex.findall(pattern, generated_text, regex.VERBOSE)
plan = state.get("plan", [])
for i, (func_name, args_json) in enumerate(matches, 1):
plan_entry = dict()
plan_entry["function_name"] = func_name
plan_entry["arguments"] = json.loads(args_json)
plan.append(plan_entry)
messages.append({"role": "assistant", "content": generated_text})
return {"messages":messages, "plan": plan}
ROUTER = "ROUTER"
def router(state: State):
plan = state.get("plan", [])
if len(plan) > 0:
return "execute_plan"
return "respond"
def execute_plan(state: State):
print("Executing")
plan = state.get("plan", [])
for plan_entry in plan:
plan_entry["status"] = dict()
print(f"Executing {plan_entry['function_name']} with details {plan_entry['arguments']}")
print("Approve Execution?(y/n)")
response = input()
response = response.strip().lower()
if response == "y":
print("Approved.")
if plan_entry["function_name"] in function_execution_map.keys():
function = function_execution_map[plan_entry["function_name"]]
result = function(plan_entry["arguments"])
plan_entry["status"] = result
else:
print(f"Capability not implemented for {plan_entry['function_name']}")
print("Done with task.")
print("Proceeding with next.")
elif response == "n":
print("Not approved.")
else:
print("Invalid input, please enter 'y' or 'n'.")
return {"plan": plan}
def respond(state: State):
print(state.get("messages")[-1]["content"])
return {"plan": state.get("plan")}
def summarize(state: State):
plan = state.get("plan")
messages = state.get("messages")
summary_prompt = []
summary_prompt.append({
"role": "user","content": f"Summarize the results obtained from the following tool executions:\n {json.dumps(plan,indent=2)}"
})
inputs = tokenizer.apply_chat_template(
summary_prompt,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
generation_kwargs = dict(inputs, streamer=streamer,
max_new_tokens=4096,
temperature=0.7, )
thread = Thread(target=model.generate, kwargs=generation_kwargs, daemon=True)
thread.start()
generated_text = ""
for new_text in streamer:
print(new_text, end="")
generated_text = generated_text + new_text
messages.append({"role": "assistant", "content": generated_text})
return {"messages":messages}
EXECUTE_PLAN = "EXECUTE_PLAN"
RESPOND = "RESPOND"
SUMMARIZE = "SUMMARIZE"
graph_builder.add_node(PLANNING_AGENT, planning)
graph_builder.add_node(EXECUTE_PLAN, execute_plan)
graph_builder.add_node(RESPOND, respond)
graph_builder.add_node(SUMMARIZE, summarize)
graph_builder.add_edge(START, PLANNING_AGENT)
graph_builder.add_conditional_edges(PLANNING_AGENT, router, {
"execute_plan": EXECUTE_PLAN, "respond": RESPOND
})
graph_builder.add_edge(EXECUTE_PLAN, SUMMARIZE)
graph_builder.add_edge(SUMMARIZE, RESPOND)
graph_builder.add_edge(RESPOND, END)
compiled_graph = graph_builder.compile()
png_bytes = compiled_graph.get_graph().draw_mermaid_png()
# Save to file
with open("graph.png", "wb") as f:
f.write(png_bytes)
print("Graph saved as graph.png")
messages = [
{
"content": system_prompt,
"role": "system"
},
{
"content": (
"I have a headache and mild fever. What could be the possible conditions? "
"Also, lookup medication details for 'Ibuprofen'. "
"Please book an appointment for patient 'Alice Smith' with a neurologist on 2025-08-18."
),
"role": "user"
}
]
different_user_prompt = [
{
"content": system_prompt,
"role": "system"
},
{
"content": (
"My mother has chest pain and shortness of breath. "
"Can you analyze her symptoms? "
"Also, please look up information about 'Nitroglycerin' medication. "
"Finally, get lab results for patient ID '987654' for the test 'lipid_panel'."
),
"role": "user"
}
]
compiled_graph.invoke({"messages": messages})
# compiled_graph.invoke({"messages": different_user_prompt})
```
## Requirements
```python
accelerate==1.9.0
aiohappyeyeballs==2.6.1
aiohttp==3.12.15
aiosignal==1.4.0
annotated-types==0.7.0
anyio==4.10.0
attrs==25.3.0
auto_gptq==0.7.1
autolab-core==1.1.1
beautifulsoup4==4.13.4
bitsandbytes==0.46.1
cachetools==5.5.2
certifi==2025.7.14
charset-normalizer==3.4.2
click==8.2.1
colorama==0.4.6
colorlog==6.9.0
contourpy==1.3.3
cycler==0.12.1
dataclasses-json==0.6.7
datasets==4.0.0
dateparser==1.2.2
ddgs==9.5.4
dill==0.3.8
dotenv==0.9.9
duckduckgo_search==8.1.1
duckling==1.8.0
filelock==3.13.1
fonttools==4.59.0
freetype-py==2.5.1
frozenlist==1.7.0
fsspec==2024.6.1
gekko==1.3.0
google-api-core==2.25.1
google-api-python-client==2.179.0
google-auth==2.40.3
google-auth-httplib2==0.2.0
google-auth-oauthlib==1.2.2
googleapis-common-protos==1.70.0
greenlet==3.2.4
h11==0.16.0
hf-xet==1.1.7
httpcore==1.0.9
httplib2==0.22.0
httpx==0.28.1
httpx-sse==0.4.1
huggingface-hub==0.34.3
idna==3.10
imageio==2.37.0
Jinja2==3.1.4
joblib==1.5.1
jpype1==1.6.0
jsonpatch==1.33
jsonpointer==3.0.0
jsonschema==4.25.0
jsonschema-specifications==2025.4.1
kiwisolver==1.4.8
langchain==0.3.27
langchain-community==0.3.27
langchain-core==0.3.74
langchain-huggingface==0.3.1
langchain-text-splitters==0.3.9
langgraph==0.6.5
langgraph-checkpoint==2.1.1
langgraph-prebuilt==0.6.4
langgraph-sdk==0.2.0
langsmith==0.4.14
lazy_loader==0.4
lxml==6.0.0
manifold3d==3.2.1
mapbox_earcut==1.0.3
markdown-it-py==3.0.0
markdownify==1.1.0
MarkupSafe==2.1.5
marshmallow==3.26.1
matplotlib==3.10.5
mdurl==0.1.2
mpmath==1.3.0
multidict==6.6.3
multiprocess==0.70.16
mypy_extensions==1.1.0
networkx==3.3
numpy==2.1.2
oauthlib==3.3.1
opencv-python==4.12.0.88
optimum==1.27.0
orjson==3.11.2
ormsgpack==1.10.0
packaging==25.0
pandas==2.3.1
peft==0.17.0
pillow==11.0.0
primp==0.15.0
propcache==0.3.2
proto-plus==1.26.1
protobuf==6.32.0
psutil==7.0.0
pyarrow==21.0.0
pyasn1==0.6.1
pyasn1_modules==0.4.2
pycollada==0.9.2
pydantic==2.11.7
pydantic-settings==2.10.1
pydantic_core==2.33.2
pyglet==2.1.8
Pygments==2.19.2
PyOpenGL==3.1.0
pyparsing==3.2.3
pyreadline==2.1
pyrender==0.1.45
python-dateutil==2.9.0.post0
python-dotenv==1.1.1
pytz==2025.2
PyYAML==6.0.2
referencing==0.36.2
regex==2025.7.34
requests==2.32.4
requests-oauthlib==2.0.0
requests-toolbelt==1.0.0
rich==14.1.0
rouge==1.0.1
rpds-py==0.27.0
rsa==4.9.1
rtree==1.4.1
ruamel.yaml==0.18.14
ruamel.yaml.clib==0.2.12
safetensors==0.5.3
scikit-image==0.25.2
scikit-learn==1.7.1
scipy==1.16.1
sentencepiece==0.2.1
setproctitle==1.3.6
shapely==2.1.1
six==1.17.0
smolagents==1.20.0
sniffio==1.3.1
soupsieve==2.7
SQLAlchemy==2.0.43
svg.path==7.0
sympy==1.13.3
tenacity==9.1.2
threadpoolctl==3.6.0
tifffile==2025.6.11
tokenizers==0.21.4
torch==2.7.1+cu126
torchaudio==2.7.1+cu126
torchvision==0.22.1+cu126
tqdm==4.67.1
transformers==4.54.1
trimesh==4.7.4
trl==0.20.0
typing-inspect==0.9.0
typing-inspection==0.4.1
typing_extensions==4.14.1
tzdata==2025.2
tzlocal==5.3.1
uritemplate==4.2.0
urllib3==2.5.0
vhacdx==0.0.8.post2
visualization==1.0.0
xxhash==3.5.0
yarl==1.20.1
zstandard==0.24.0
```
## How to Get Started
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import json
torch.manual_seed(42)
model_name = "aldsouza/health-agent"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).to("cuda")
medical_tools = [
{
"name": "symptom_checker",
"description": "Analyze symptoms and provide possible conditions.",
"parameters": {
"symptoms": {
"description": "List of symptoms reported by the patient.",
"type": "list[str]",
"default": ["headache", "fever"]
}
}
},
{
"name": "medication_lookup",
"description": "Look up details about a medication by its name.",
"parameters": {
"medication_name": {
"description": "Name of the medication to look up.",
"type": "str",
"default": "Aspirin"
}
}
},
{
"name": "book_appointment",
"description": "Schedule a medical appointment with a doctor.",
"parameters": {
"patient_name": {
"description": "Name of the patient.",
"type": "str",
"default": "John Doe"
},
"doctor_specialty": {
"description": "Specialty of the doctor to book.",
"type": "str",
"default": "general practitioner"
},
"date": {
"description": "Preferred date of appointment (YYYY-MM-DD).",
"type": "str",
"default": "2025-08-20"
}
}
},
{
"name": "get_lab_results",
"description": "Retrieve lab test results for a patient by test ID.",
"parameters": {
"patient_id": {
"description": "Unique patient identifier.",
"type": "str",
"default": "123456"
},
"test_id": {
"description": "Lab test identifier.",
"type": "str",
"default": "cbc"
}
}
},
{
"name": "request_missing_info",
"description": "Ask the user for missing or incomplete information needed to fulfill their request.",
"parameters": {
"missing_fields": {
"description": "List of missing required fields to be clarified by the user.",
"type": "list[str]",
"default": []
},
"context": {
"description": "Optional context or explanation to help the user provide the missing information.",
"type": "str",
"default": ""
}
}
},
{
"name": "medical_device_info",
"description": "Retrieve detailed information about a medical device by its name or model number.",
"parameters": {
"device_name": {
"description": "The name or model number of the medical device to look up.",
"type": "str",
"default": "Blood Pressure Monitor"
}
}
}, {
"name": "record_blood_pressure",
"description": "Record a patient's blood pressure reading with systolic, diastolic, and pulse rate values.",
"parameters": {
"patient_id": {
"description": "Unique identifier of the patient.",
"type": "str",
"default": "123456"
},
"systolic": {
"description": "Systolic blood pressure value (mmHg).",
"type": "int",
"default": 120
},
"diastolic": {
"description": "Diastolic blood pressure value (mmHg).",
"type": "int",
"default": 80
},
"pulse_rate": {
"description": "Pulse rate in beats per minute.",
"type": "int",
"default": 70
},
"measurement_time": {
"description": "Timestamp of the measurement (YYYY-MM-DD HH:MM).",
"type": "str",
"default": "2025-08-12 09:00"
}
}
}, {
"name": "start_blood_pressure_test",
"description": "Initiate a blood pressure measurement test for a patient using a connected device.",
"parameters": {
"patient_id": {
"description": "Unique identifier of the patient.",
"type": "str",
"default": "123456"
},
"device_id": {
"description": "Identifier or model of the blood pressure measuring device.",
"type": "str",
"default": "BP-Device-001"
}
}
}
]
# Compose the system prompt embedding the tools JSON
system_prompt = f"""
You are an intelligent AI assistant that uses available tools (functions) to help users achieve their medical-related goals. Your job is to understand the user's intent, identify missing information if needed, and then select and call the most appropriate function(s) to solve the task.
# Rules:
- ALWAYS use the tools provided to answer the user's request, unless explicitly told not to.
- Ask clarifying questions ONLY if the user's request is ambiguous or lacks required input parameters.
- If multiple tools are needed, use them in sequence.
- DO NOT make up data or assume values β request any missing input clearly.
# Output Format:
- Respond using a JSON list of function calls in the following format:
[
{{
"name": "function_name",
"arguments": {{
"param1": "value1",
"param2": "value2"
}}
]
- Only include the functions needed to complete the task.
- If no function is needed or the input is unclear, ask a clarifying question instead of guessing.
- Do NOT respond with explanations or natural language outside the JSON block unless explicitly instructed.
Following are the tools provided to you:
{json.dumps(medical_tools, indent=2)}
"""
# Example prompt using the medical tools
messages = [
{
"content": system_prompt,
"role": "system"
},
{
"content": (
"I have a headache and mild fever. What could be the possible conditions? "
"Also, lookup medication details for 'Ibuprofen'. "
"Please book an appointment for patient 'Alice Smith' with a neurologist on 2025-09-01."
),
"role": "user"
}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=4096,
temperature=0.7,
)
response = tokenizer.decode(outputs[0])
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755612309
|
Vasya777
| 2025-08-19T14:06:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:05:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alok0777/blockassist-bc-masked_pensive_lemur_1755612218
|
alok0777
| 2025-08-19T14:05:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked pensive lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:04:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked pensive lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/flux-xl-_-bailing-xl_magic-array
|
Muapi
| 2025-08-19T14:02:23Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:01:54Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# η½ζ£±(FLUX,XL)_ιζ³ι΅-bailing XL_Magic Array
**Base model**: Flux.1 D
**Trained words**: bailing_magic_circle
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:359982@887667", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/f1-charturn-multi-view-turnaround-model-sheet-character-design
|
Muapi
| 2025-08-19T14:01:24Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:01:11Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# F1 CharTurn, Multi-view, Turnaround, Model Sheet, Character design
**Base model**: Flux.1 D
**Trained words**:
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:784830@877675", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Kurosawama/Llama-3.2-3B-Translation-align
|
Kurosawama
| 2025-08-19T14:00:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"trl",
"dpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T06:10:57Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755610312
|
katanyasekolah
| 2025-08-19T14:00:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:00:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/1980-s-style-xl-f1d
|
Muapi
| 2025-08-19T14:00:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:00:26Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 1980's style XL + F1D
**Base model**: Flux.1 D
**Trained words**: 1980 style
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:376914@894083", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755610389
|
thanobidex
| 2025-08-19T14:00:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:00:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rievil/crackenpy
|
rievil
| 2025-08-19T13:59:57Z | 0 | 2 |
timm
|
[
"timm",
"image-segmentation",
"doi:10.57967/hf/3295",
"license:bsd",
"region:us"
] |
image-segmentation
| 2024-10-01T09:54:22Z |
---
license: bsd
pipeline_tag: image-segmentation
architecture: resnext101_32x8d
base_model:
- timm/resnext101_32x8d
library_name: timm
metrics:
- accuracy
- mean intersection of union
---
# Pre-trained model for CrackenPy package for crack segmentation on building material specimens
The repository contains pre-trained models using the segmentation-models-pytorch package to segment RGB images 416x416 pixels.
The resulting classes are "background," "matrix," "crack," and "pore".The purpose is a segmentation of test specimens made from
building materials such as cement, alkali-activated materials or geopolymers.
### Model Description
- **Model type:** semantic segmentation
- **Language(s) (NLP):** Python
- **License:** BSD v2
- **Finetuned from model [optional]:** resnet101
## Uses
The use is to segment cracks on test specimens or on images fully filled with a binder matrix containing cracks. The background should be darker than the speicmen itself.
The segmentation is aimed at fine cracks from starting from 20 um up to 10 mm.
## Bias, Risks, and Limitations
The background and matrix classes may sometimes be with, if the texture of specimens is too dark or smudged, it is, therefore, important to make a segmentation on possible clean specimens.
The models of the current version have not been trained in exterior and may lead to bad segmentation. The pores are usually in circular shape, but there can be a situation where a crack is found
on the edge of the pore. It is therefore recommended to avoid the usage of models on highly porous materials.
## Training Details
The models originate from https://github.com/qubvel-org/segmentation_models.pytorch library, and are retrained on dataset crackenpy_dataset.
### Training Data
The dataset for training can be downloaded from Brno University of Technology upon filling the form. The dataset is free for use in ressearch and education area under the BSD v2 license.
The dataset was created under the research project of Grant Agency of Czech Republic No. 22-02098S with the title: "Experimental analysis of the shrinkage, creep and cracking mechanism of the materials based on the alkali-activated slag".
### Training Procedure
The training was done using Pytorch library, where CrossEntropyLoss() together with AdamW optimizer function.
### Results & Metrics
The dataset has 1207 images in resolution of 416x416 pixels together with 1207 masks. The overall accuracy of training for all classes reaches 98%, the mean intersection of union reaches 73%.
#### Hardware
The training was done using NVIDIA Quadro P4000 with CUDA support.
#### Software
The models were trained using Pytorch in Python, the segmentation and dataset preparation was done using LabKit plugin in software FIJI.
## Authors of dataset
Richard Dvorak, Brno University of Technology, Faculty of Civil Engineering, Institute of building testing
Rostislav Krc, Brno University of Technology, Faculty of Civil Engineering, Institute of building testing
Vlastimil Bilek, Brno University of Technology, Faculty of Chemistry, Institute of material chemistry
Barbara KucharczykovΓ‘, Brno University of Technology, Faculty of Civil Engineering, Institute of building testing
## Citation
The model was trained on the CrackenPy Dataset, and is used in CrackenPy library:
- [Library](https://github.com/Rievil/CrackenPy)
- [Model](https://huggingface.co/rievil/crackenpy)
- [Dataset](https://huggingface.co/datasets/rievil/crackenpy_dataset)
If you use this model, please cite our work:
```tex
@misc {richard_dvorak_2024,
author = { {Richard Dvorak} },
title = { crackenpy (Revision 04ed02c) },
year = 2024,
url = { https://huggingface.co/rievil/crackenpy },
doi = { 10.57967/hf/3295 },
publisher = { Hugging Face }
}
@software {Dvorak_CrackenPy_Image_segmentation_2024,
author = {Dvorak, Richard and Bilek, Vlastimil and Krc, Rostislav and Kucharczykova, Barbara},
doi = {10.5281/zenodo.13969747},
month = oct,
title = {{CrackenPy: Image segmentation tool for semantic segmentation of building material surfaces using deep learning}},
url = {https://github.com/Rievil/CrackenPy},
year = {2024}
}
@misc {richard_dvorak_2024,
author = { {Richard Dvorak} },
title = { crackenpy_dataset (Revision ce5c857) },
year = 2024,
url = { https://huggingface.co/datasets/rievil/crackenpy_dataset },
doi = { 10.57967/hf/3496 },
publisher = { Hugging Face }
}
```
## Model Card Contact
The author of the dataset is Richard Dvorak Ph.D., richard.dvorak@vutbr.cz, tel.: +420 777 678 613, employee of Brno University Of Technology of the Faculty of Civil Engineering, institute of building testing.
|
michaelcpage345/blockassist-bc-miniature_deadly_anteater_1755609800
|
michaelcpage345
| 2025-08-19T13:56:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature deadly anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:56:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature deadly anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/radiant-realism-pro-realistic-makeup-skin-texture-skin-color-flux.1d
|
Muapi
| 2025-08-19T13:56:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:56:23Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Radiant Realism Pro (Realistic, Makeup, Skin Texture, Skin Color) Flux.1D
**Base model**: Flux.1 D
**Trained words**:
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:970421@1086588", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/neon-cyberpunk-animals-flux-sdxl
|
Muapi
| 2025-08-19T13:56:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:56:03Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Neon Cyberpunk - Animals FLUX & SDXL
**Base model**: Flux.1 D
**Trained words**: mad-cybranmls, cybernetic parts, mechanical parts
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:281944@1067893", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755610110
|
helmutsukocok
| 2025-08-19T13:54:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:54:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/flux-sdxl-black-diamonds
|
Muapi
| 2025-08-19T13:54:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:53:57Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# [Flux/SDXL] - π€ Black Diamonds π€
**Base model**: Flux.1 D
**Trained words**: made out of black diamonds, black diamonds
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:607623@740146", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
unitova/blockassist-bc-zealous_sneaky_raven_1755610013
|
unitova
| 2025-08-19T13:53:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:53:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/alejandro-jodorowsky-style
|
Muapi
| 2025-08-19T13:53:43Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:53:34Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Alejandro Jodorowsky Style
**Base model**: Flux.1 D
**Trained words**: Alejandro Jodorowsky Style
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:62712@1403331", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/aerith-gainsborough-final-fantasy-vii
|
Muapi
| 2025-08-19T13:52:58Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:52:47Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Aerith Gainsborough - Final Fantasy VII
**Base model**: Flux.1 D
**Trained words**: aerith
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:168676@782206", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
hug-mono/checkworthy-binary-classification-training-1755585731
|
hug-mono
| 2025-08-19T13:51:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google-bert/bert-base-uncased",
"lora",
"transformers",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T13:51:51Z |
---
library_name: peft
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- base_model:adapter:google-bert/bert-base-uncased
- lora
- transformers
model-index:
- name: checkworthy-binary-classification-training-1755585731
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. -->
# checkworthy-binary-classification-training-1755585731
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) 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: 2.1106713456200193e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9348819720458172,0.9285998615546803) and epsilon=1.9972958061508847e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_ratio: 0.12890328790683203
- lr_scheduler_warmup_steps: 488
- num_epochs: 40
### Training results
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
alok0777/blockassist-bc-masked_pensive_lemur_1755611305
|
alok0777
| 2025-08-19T13:50:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked pensive lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:49:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked pensive lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/the-deep-abyss-flux
|
Muapi
| 2025-08-19T13:50:48Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:50:36Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# The Deep Abyss FLUX
**Base model**: Flux.1 D
**Trained words**: 4byss
## π§ Usage (Python)
π **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:930359@1041413", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755609650
|
hakimjustbao
| 2025-08-19T13:47:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:47:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755609526
|
chainway9
| 2025-08-19T13:46:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:46:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WangChongan/rl-Pixelcopter-PLE-v0
|
WangChongan
| 2025-08-19T13:43:56Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-19T12:57:38Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: rl-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 25.30 +/- 17.57
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
meme46/lora-financialqa
|
meme46
| 2025-08-19T13:42:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T13:41:53Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** meme46
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755610875
|
lilTAT
| 2025-08-19T13:41:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:41:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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