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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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| library_name
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young-j-park/math-shepherd-mistral-7b-prm-calibrated-Qwen2.5-Math-1.5B-Instruct
|
young-j-park
| 2025-06-18T18:18:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:peiyi9979/math-shepherd-mistral-7b-prm",
"base_model:adapter:peiyi9979/math-shepherd-mistral-7b-prm",
"region:us"
] | null | 2025-06-18T18:15:28Z |
---
base_model: peiyi9979/math-shepherd-mistral-7b-prm
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0
|
ITHwangg/lebotica-pickplace-stacking-step1k
|
ITHwangg
| 2025-06-18T17:05:23Z | 0 | 0 | null |
[
"safetensors",
"dataset:ITHwangg/svla_koch_pickplace_and_stacking",
"license:mit",
"region:us"
] | null | 2025-06-15T00:24:59Z |
---
datasets:
- ITHwangg/svla_koch_pickplace_and_stacking
license: mit
---
# lebotica-pickplace-stacking-step1k
- Dataset: [ITHwangg/svla_koch_pickplace_and_stacking](https://huggingface.co/datasets/ITHwangg/svla_koch_pickplace_and_stacking)
- Model: [lerobot/smolvla_base](https://huggingface.co/lerobot/smolvla_base)
|
LandCruiser/sn21_omg_1806_16
|
LandCruiser
| 2025-06-18T16:02:23Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-06-18T15:45:49Z |
---
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).
|
fdames/fernanda
|
fdames
| 2025-06-18T15:38:49Z | 4 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-02-27T17:32:16Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: fernanda
---
# Fernanda
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `fernanda` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "fernanda",
"lora_weights": "https://huggingface.co/fdames/fernanda/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('fdames/fernanda', weight_name='lora.safetensors')
image = pipeline('fernanda').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/fdames/fernanda/discussions) to add images that show off what you’ve made with this LoRA.
|
vxpll/Elsa
|
vxpll
| 2025-06-18T15:28:12Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-18T15:27:37Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/photo_2025-06-18_18-19-32.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: elsa
---
# Elsa
<Gallery />
## Trigger words
You should use `elsa` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/vxpll/Elsa/tree/main) them in the Files & versions tab.
|
z-dickson/xlm-roberta-large-multilingual-political-issues
|
z-dickson
| 2025-06-18T12:32:54Z | 96 | 0 | null |
[
"safetensors",
"xlm-roberta",
"politics",
"social",
"CAP",
"Comparative agendas project",
"CSS",
"issues",
"agenda",
"press",
"license:apache-2.0",
"region:us"
] | null | 2025-04-08T09:30:22Z |
---
license: apache-2.0
tags:
- politics
- social
- CAP
- Comparative agendas project
- CSS
- issues
- agenda
- press
---
# Multilingual Political Issue Classifier
This model classifies political party press releases according to the primary issue they address. The classification scheme is very similar to the Comparative Agendas Project (see: [https://www.comparativeagendas.net/](https://www.comparativeagendas.net/)), with the exception that the 'environmental' category includes climate change policies. The model was fine tuned using a training dataset of 15k press releases that were labelled using zero-shot classification with GPT-4. The issue scheme is as follows:
**CATEGORY SUMMARIES**
1. **Macroeconomics**
Covers broad economic policy topics like interest rates, inflation, unemployment, taxes, budgets, monetary and industrial policy. Also includes wage/price control and other macroeconomic matters.
2. **Civil Rights**
Focuses on discrimination (racial, gender, age, disability), voting rights, freedom of speech, privacy, and minority protections. Also includes anti-government groups and other civil rights topics.
3. **Health**
Encompasses healthcare reform, insurance, medical facilities and liability, workforce, and public health efforts. Covers topics from mental health and child health to drug abuse, R&D, and disease prevention.
4. **Agriculture**
Addresses farm subsidies, food safety, marketing, animal/crop disease, fisheries, and agricultural R&D. Also includes general agriculture policy and rural development.
5. **Labor**
Covers job safety, training, benefits, labor standards, unions, and youth/migrant employment. Also includes pensions and employment policies.
6. **Education**
Includes all education levels from early childhood to higher education, as well as special education, vocational training, and education quality initiatives. Also includes R&D and underserved student support.
7. **Environment and climate change**
Deals with water and air pollution, waste disposal, hazardous materials, conservation, endangered species, and indoor/outdoor environmental safety. Includes recycling, R&D, and land preservation.
8. **Energy**
Focuses on energy sources like nuclear, coal, oil, renewables, and electricity. Includes energy efficiency, conservation, and related R&D.
9. **Immigration**
Covers immigration laws, refugee policy, and citizenship issues.
10. **Transportation**
Addresses infrastructure, public transit, highways, air and rail travel, maritime transport, and transportation R&D.
12. **Law and Crime**
Includes crime control, enforcement, courts, prisons, drug crime, family law, juvenile justice, and terrorism. Also covers agencies, white-collar crime, and child abuse.
13. **Social Welfare**
Focuses on programs for low-income families, elderly and disabled assistance, child care, and volunteer organizations. Encompasses general welfare policies.
14. **Housing**
Covers public housing, community and rural development, housing for veterans, elderly, and the homeless. Includes affordability and urban planning.
15. **Domestic Commerce**
Includes banking, finance, small business, consumer protection, corporate governance, and commerce-related R&D. Also covers insurance, tourism, and bankruptcy.
16. **Defense**
Encompasses military policy, readiness, procurement, personnel, nuclear arms, foreign operations, and civil defense. Covers contractors, intelligence, and environmental compliance.
17. **Technology**
Covers space exploration, telecommunications, computing, broadcasting, and cybersecurity. Also includes scientific research, tech development, and commercial use of space.
18. **Foreign Trade**
Deals with trade agreements, tariffs, exports/imports, competitiveness, and exchange rates. Also includes international business and investment policy.
19. **International Affairs**
Includes diplomacy, foreign aid, developing countries, human rights, global organizations, and international finance. Also covers terrorism, embassies, and treaties.
20. **Government Operations**
Addresses bureaucracy, procurement, civil service, campaigns, tax enforcement, and census data. Also includes scandals, national holidays, and intergovernmental relations.
21. **Public Lands**
Covers parks, indigenous issues, forest and land management, water resources, and U.S. territories. Focuses on conservation and federal land use.
23. **Culture**
Encompasses general cultural policies, likely including funding, preservation, and promotion of cultural initiatives.
# Countries/languages included in fine-tuning
The countries included are: Poland, Germany, Ireland, Netherlands, Slovenia, Denmark, Hungary, Austria, Sweden, Bulgaria, Spain, Croatia, Finland, United Kingdom, Greece, Switzerland, Estonia, France, Portugal, Cyprus, Slovakia, Italy, Czech Republic, and Belgium.
## Accuracy
The model achieves a weighted F1 score of 0.86.
```{python}
from transformers import AutoModelForSequenceClassification
from transformers import TextClassificationPipeline, AutoTokenizer
mp = 'z-dickson/xlm-roberta-large-multilingual-political-issues'
model = AutoModelForSequenceClassification.from_pretrained(mp)
tokenizer = AutoTokenizer.from_pretrained(mp)
classifier = TextClassificationPipeline(tokenizer=tokenizer, model=model, device=0)
classifier("""
To ask the Secretary of State for Energy and Climate \\
Change what estimate he has made of the proportion of carbon \\
dioxide emissions arising in the UK attributable to burning.
"""
)
```
## Citation
The data collection efforts of the press releases were originally from the following work. The two lead authors created the model in additional collaboration.
```{bash}
@article{dickson2024going,
title={Going against the grain: Climate change as a wedge issue for the radical right},
author={Dickson, Zachary P and Hobolt, Sara B},
journal={Comparative Political Studies},
pages={00104140241271297},
year={2024},
publisher={SAGE Publications Sage CA: Los Angeles, CA}
}
```
and
```{bash}
@article{erfort2023partypress,
title={The PARTYPRESS Database: A new comparative database of parties’ press releases},
author={Erfort, Cornelius and Stoetzer, Lukas F and Kl{\"u}ver, Heike},
journal={Research \& Politics},
volume={10},
number={3},
pages={20531680231183512},
year={2023},
publisher={SAGE Publications Sage UK: London, England}
}
```
|
ngmediastudio89/naila
|
ngmediastudio89
| 2025-06-18T12:26:58Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-18T12:12:48Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: NAILA
---
# Naila
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `NAILA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NAILA",
"lora_weights": "https://huggingface.co/ngmediastudio89/naila/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ngmediastudio89/naila', weight_name='lora.safetensors')
image = pipeline('NAILA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ngmediastudio89/naila/discussions) to add images that show off what you’ve made with this LoRA.
|
BKM1804/mieumieu
|
BKM1804
| 2025-06-18T09:36:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T09:35:57Z |
---
library_name: transformers
tags:
- trl
- sft
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
newcopen/paligemma-imgtojson
|
newcopen
| 2025-06-18T09:15:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:google/paligemma-3b-pt-224",
"base_model:adapter:google/paligemma-3b-pt-224",
"license:gemma",
"region:us"
] | null | 2025-05-29T22:28:38Z |
---
library_name: peft
license: gemma
base_model: google/paligemma-3b-pt-224
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: paligemma-imgtojson
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. -->
# paligemma-imgtojson
This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4194
- Json Validity: 0.0
- Field Match Avg: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use adamw_torch_fused 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: 0.03
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Json Validity | Field Match Avg |
|:-------------:|:------:|:----:|:---------------:|:-------------:|:---------------:|
| 1.6843 | 0.04 | 4 | 1.6791 | 0.0 | 0.0 |
| 1.2435 | 0.08 | 8 | 1.1588 | 0.0 | 0.0 |
| 0.9726 | 0.12 | 12 | 0.8497 | 0.0 | 0.0 |
| 0.3672 | 0.16 | 16 | 0.6707 | 0.0 | 0.0 |
| 0.8473 | 0.2 | 20 | 0.5506 | 0.0 | 0.0 |
| 0.3753 | 0.24 | 24 | 0.4935 | 0.0 | 0.0 |
| 0.5084 | 0.28 | 28 | 0.4973 | 0.0 | 0.0 |
| 0.3072 | 0.32 | 32 | 0.4974 | 0.0 | 0.0 |
| 0.2135 | 0.36 | 36 | 0.4716 | 0.0 | 0.0 |
| 0.3368 | 0.4 | 40 | 0.4641 | 0.0 | 0.0 |
| 0.4291 | 0.44 | 44 | 0.4585 | 0.0 | 0.0 |
| 0.334 | 0.48 | 48 | 0.4150 | 0.0 | 0.0 |
| 0.2791 | 0.52 | 52 | 0.4408 | 0.0 | 0.0 |
| 0.287 | 0.56 | 56 | 0.4097 | 0.0 | 0.0 |
| 0.2269 | 0.6 | 60 | 0.3962 | 0.0 | 0.0 |
| 0.3634 | 0.64 | 64 | 0.3881 | 0.0 | 0.0 |
| 0.352 | 0.68 | 68 | 0.3956 | 0.0 | 0.0 |
| 0.2697 | 0.72 | 72 | 0.3953 | 0.0 | 0.0 |
| 0.3239 | 0.76 | 76 | 0.3821 | 0.0 | 0.0 |
| 0.2503 | 0.8 | 80 | 0.3809 | 0.0 | 0.0 |
| 0.278 | 0.84 | 84 | 0.3980 | 0.2 | 1.0 |
| 0.2364 | 0.88 | 88 | 0.4126 | 0.0 | 0.0 |
| 0.1774 | 0.92 | 92 | 0.4199 | 0.0 | 0.0 |
| 0.5384 | 0.96 | 96 | 0.4227 | 0.0 | 0.0 |
| 0.21 | 1.0 | 100 | 0.4168 | 0.0 | 0.0 |
| 0.2008 | 1.04 | 104 | 0.3820 | 0.0 | 0.0 |
| 0.0569 | 1.08 | 108 | 0.3619 | 0.0 | 0.0 |
| 0.1999 | 1.12 | 112 | 0.3687 | 0.0 | 0.0 |
| 0.1869 | 1.16 | 116 | 0.3742 | 0.0 | 0.0 |
| 0.2146 | 1.2 | 120 | 0.3774 | 0.0 | 0.0 |
| 0.2165 | 1.24 | 124 | 0.3805 | 0.0 | 0.0 |
| 0.1742 | 1.28 | 128 | 0.3623 | 0.0 | 0.0 |
| 0.392 | 1.32 | 132 | 0.3592 | 0.0 | 0.0 |
| 0.2822 | 1.3600 | 136 | 0.3600 | 0.0 | 0.0 |
| 0.1651 | 1.4 | 140 | 0.3579 | 0.0 | 0.0 |
| 0.259 | 1.44 | 144 | 0.3640 | 0.0 | 0.0 |
| 0.2883 | 1.48 | 148 | 0.3653 | 0.0 | 0.0 |
| 0.0865 | 1.52 | 152 | 0.3669 | 0.0 | 0.0 |
| 0.1842 | 1.56 | 156 | 0.3732 | 0.0 | 0.0 |
| 0.1547 | 1.6 | 160 | 0.3852 | 0.0 | 0.0 |
| 0.1551 | 1.6400 | 164 | 0.3732 | 0.0 | 0.0 |
| 0.0667 | 1.6800 | 168 | 0.3655 | 0.0 | 0.0 |
| 0.0763 | 1.72 | 172 | 0.3654 | 0.0 | 0.0 |
| 0.1045 | 1.76 | 176 | 0.3698 | 0.0 | 0.0 |
| 0.1016 | 1.8 | 180 | 0.3681 | 0.0 | 0.0 |
| 0.1273 | 1.8400 | 184 | 0.3711 | 0.0 | 0.0 |
| 0.0772 | 1.88 | 188 | 0.3717 | 0.0 | 0.0 |
| 0.123 | 1.92 | 192 | 0.3775 | 0.0 | 0.0 |
| 0.1533 | 1.96 | 196 | 0.3606 | 0.0 | 0.0 |
| 0.0748 | 2.0 | 200 | 0.3529 | 0.0 | 0.0 |
| 0.1028 | 2.04 | 204 | 0.3554 | 0.0 | 0.0 |
| 0.0557 | 2.08 | 208 | 0.3690 | 0.0 | 0.0 |
| 0.134 | 2.12 | 212 | 0.3848 | 0.0 | 0.0 |
| 0.0785 | 2.16 | 216 | 0.3964 | 0.0 | 0.0 |
| 0.0602 | 2.2 | 220 | 0.4010 | 0.0 | 0.0 |
| 0.0426 | 2.24 | 224 | 0.4038 | 0.0 | 0.0 |
| 0.0211 | 2.2800 | 228 | 0.4033 | 0.0 | 0.0 |
| 0.0632 | 2.32 | 232 | 0.4067 | 0.0 | 0.0 |
| 0.1438 | 2.36 | 236 | 0.4087 | 0.0 | 0.0 |
| 0.0917 | 2.4 | 240 | 0.4094 | 0.0 | 0.0 |
| 0.0292 | 2.44 | 244 | 0.4128 | 0.0 | 0.0 |
| 0.0339 | 2.48 | 248 | 0.4189 | 0.0 | 0.0 |
| 0.0619 | 2.52 | 252 | 0.4250 | 0.0 | 0.0 |
| 0.0252 | 2.56 | 256 | 0.4229 | 0.0 | 0.0 |
| 0.1563 | 2.6 | 260 | 0.4226 | 0.0 | 0.0 |
| 0.0258 | 2.64 | 264 | 0.4277 | 0.0 | 0.0 |
| 0.1214 | 2.68 | 268 | 0.4225 | 0.0 | 0.0 |
| 0.0562 | 2.7200 | 272 | 0.4237 | 0.0 | 0.0 |
| 0.1413 | 2.76 | 276 | 0.4195 | 0.0 | 0.0 |
| 0.0922 | 2.8 | 280 | 0.4172 | 0.0 | 0.0 |
| 0.0061 | 2.84 | 284 | 0.4155 | 0.0 | 0.0 |
| 0.0863 | 2.88 | 288 | 0.4220 | 0.0 | 0.0 |
| 0.059 | 2.92 | 292 | 0.4154 | 0.0 | 0.0 |
| 0.0188 | 2.96 | 296 | 0.4162 | 0.0 | 0.0 |
| 0.2014 | 3.0 | 300 | 0.4194 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.47.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
mesolitica/Malaysian-Podcast-Dia-1.6B
|
mesolitica
| 2025-06-18T04:06:15Z | 425 | 0 | null |
[
"tensorboard",
"safetensors",
"ms",
"en",
"region:us"
] | null | 2025-05-19T01:24:52Z |
---
language:
- ms
- en
---
# Malaysian-Podcast-Dia-1.6B
Full parameter finetuning [nari-labs/Dia-1.6B](https://huggingface.co/nari-labs/Dia-1.6B) on Malaysian Podcast from [mesolitica/Malaysian-Emilia](https://huggingface.co/datasets/mesolitica/Malaysian-Emilia) where the permutation for voice conversion only select 80% similar.
Complete tutorial how to use at [mesolitica/malaya-speech/Dia-TTS](https://github.com/mesolitica/malaya-speech/wiki/Dia%E2%80%90TTS).
## How we trained it
1. The finetuning done in FP32-BF16 mixed precision training.
2. Multipacking encoder-decoder.
3. Wandb at https://wandb.ai/huseinzol05/dia-tts-malaysian-emilia-full-mixed-precision-podcast
## Source code
Source code at https://github.com/mesolitica/malaya-speech/tree/master/session/dia-tts
## Acknowledgement
Special thanks to https://www.sns.com.my and Nvidia for 8x H100 node!
|
Sharing22/uli_c3
|
Sharing22
| 2025-06-17T17:57:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T17:53:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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]
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## Technical Specifications [optional]
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## Model Card Contact
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|
MogaNet/moganet_base_224_in1k
|
MogaNet
| 2025-06-17T16:48:27Z | 0 | 1 |
timm
|
[
"timm",
"vision",
"image-classification",
"en",
"dataset:imagenet",
"arxiv:2211.03295",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2023-03-13T19:29:06Z |
---
datasets:
- imagenet
language:
- en
library_name: timm
license: apache-2.0
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- vision
- image-classification
library_tag: MogaNet
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
---
# Model card for moganet_base_224_in1k
MogaNet a new family of efficient ConvNets with preferable parameter-performance trade-offs, which is trained on ImageNet-1k (1 million images, 1,000 classes). It was first introduced in the paper [MogaNet](https://arxiv.org/abs/2211.03295) and released in [Westlake/MogaNet](https://github.com/Westlake-AI/MogaNet) and [Westlake/openmixup](https://github.com/Westlake-AI/openmixup).
## Description
Since the recent success of Vision Transformers (ViTs), explorations toward ViT-style architectures have triggered the resurgence of ConvNets. In this work, we explore the representation ability of modern ConvNets from a novel view of multi-order game-theoretic interaction, which reflects inter-variable interaction effects w.r.t. contexts of different scales based on game theory. Within the modern ConvNet framework, we tailor the two feature mixers with conceptually simple yet effective depthwise convolutions to facilitate middle-order information across spatial and channel spaces respectively. In this light, a new family of pure ConvNet architecture, dubbed MogaNet, is proposed, which shows excellent scalability and attains competitive results among state-of-the-art models with more efficient use of parameters on ImageNet and multifarious typical vision benchmarks, including COCO object detection, ADE20K semantic segmentation, 2D\&3D human pose estimation and video prediction.Typically, MogaNet hits 80.0\% and 87.8\% top-1 accuracy with 5.2M and 181M parameters on ImageNet, outperforming ParC-Net-S and ConvNeXt-L while saving 59\% FLOPs and 17M parameters.
## Model Usage
Setup before using the model.
```bash
git clone https://github.com/Westlake-AI/MogaNet
cd MogaNet
```
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
import models
img = Image.open(
urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model('moganet_base_1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(
urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'))
model = timm.create_model(
'moganet_base_1k',
pretrained=True,
fork_feat=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
print(o.shape)
```
## Model Comparison
| Model | Resolution | Params (M) | Flops (G) | Top-1 / top-5 (%) | Download |
|---|:---:|:---:|:---:|:---:|:---:|
| moganet_xtiny_224_in1k | 224x224 | 2.97 | 0.80 | 76.5 / 93.4 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_xtiny_sz224_8xbs128_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_xtiny_224_in1k) |
| moganet_xtiny_256_in1k | 256x256 | 2.97 | 1.04 | 77.2 / 93.8 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_xtiny_sz256_8xbs128_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_xtiny_256_in1k) |
| moganet_tiny_224_in1k | 224x224 | 5.20 | 1.10 | 79.0 / 94.6 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_tiny_sz224_8xbs128_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_tiny_224_in1k) |
| moganet_tiny_256_in1k | 256x256 | 5.20 | 1.44 | 79.6 / 94.9 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_tiny_sz256_8xbs128_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_tiny_256_in1k) |
| moganet_small_224_in1k | 224x224 | 25.3 | 4.97 | 83.4 / 96.9 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_small_sz224_8xbs128_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_small_224_in1k) |
| moganet_base_224_in1k | 224x224 | 43.9 | 9.93 | 84.3 / 97.0 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_base_sz224_8xbs128_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_base_224_in1k) |
| moganet_large_224_in1k | 224x224 | 82.5 | 15.9 | 84.7 / 97.1 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_large_sz224_8xbs64_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_large_224_in1k) |
| moganet_xlarge_224_in1k | 224x224 | 180.8 | 34.5 | 85.1 / 97.4 | [GitHub](https://github.com/Westlake-AI/MogaNet/releases/download/moganet-in1k-weights/moganet_xlarge_sz224_8xbs64_ep300.pth.tar) \| [Hugging Face🤗](https://huggingface.co/MogaNet/moganet_xlarge_224_in1k) |
## Citation
```bibtex
@article{Li2022MogaNet,
title={Efficient Multi-order Gated Aggregation Network},
author={Siyuan Li and Zedong Wang and Zicheng Liu and Cheng Tan and Haitao Lin and Di Wu and Zhiyuan Chen and Jiangbin Zheng and Stan Z. Li},
journal={ArXiv},
year={2022},
volume={abs/2211.03295}
}
```
|
Lelon/cue-es-bioscope_abstracts
|
Lelon
| 2025-06-17T12:06:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
token-classification
| 2025-06-17T12:05:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
avisena/legalqa-llama3-1b-klinik-hukumonline-16bit
|
avisena
| 2025-06-17T10:51:31Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"conversational",
"id",
"dataset:ShoAnn/legalqa_klinik_hukumonline",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-16T06:19:34Z |
---
base_model: unsloth/llama-3.2-1b-instruct
tags:
- text-generation-inference
- transformers
- llama
license: apache-2.0
language:
- id
datasets:
- ShoAnn/legalqa_klinik_hukumonline
---
# Intended use:
This model is a LORA fine-tuned version based on the [ShoAnn/legalqa\_klinik\_hukumonline](https://huggingface.co/datasets/ShoAnn/legalqa_klinik_hukumonline/viewer/default/train?views%5B%5D=train&row=47) dataset, specifically designed for use in RAFT.
You need to integrate this model into your own retrieval system for the context input.
# How To Use
```python
pip install torch transformers accelerate unsloth
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, LogitsProcessor
from unsloth import FastLanguageModel
import torch
import re
from IPython.display import Markdown, display
# Load model and tokenizer (no Unsloth)
model_path = "avisena/legalqa-llama3-1b-klinik-hukumonline-16bit"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Ensure the model is loaded to the correct device as well
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_path, # Load from the saved path
max_seq_length = 2048,
load_in_4bit = False,
load_in_8bit = False,
full_finetuning = False,
)
# Ensure the model is on the correct device
model.to("cuda")
# EOS bias processor to encourage stopping
class EOSBiasProcessor(LogitsProcessor):
def __init__(self, eos_token_id, bias=6.54):
self.eos_token_id = eos_token_id
self.bias = bias
def __call__(self, input_ids, scores):
scores[:, self.eos_token_id] += self.bias
return scores
# Sample question
question = "Kini banyak content creator yang nekat buat video di tengah jalan atau sekedar foto di tengah jalan hingga mengganggu lalu lintas dan membahayakan keselamatan pengguna jalan. Adakah hukumnya mereka yang buat video konten di jalan?"
# Context from RAG system
context = [
{
"full_text": "Setiap orang yang melakukan perbuatan yang mengakibatkan gangguan pada fungsi jalan, sebagaimana dimaksud dalam Pasal 28 ayat (1), dipidana dengan pidana penjara paling lama 1 (satu) tahun atau denda paling banyak Rp24.000.000,00 (dua puluh empat juta rupiah).",
"name": "Pasal 63 ayat (6) Undang-Undang Nomor 22 Tahun 2009 tentang Lalu Lintas dan Angkutan Jalan"
},
{
"full_text": "Setiap orang dilarang melakukan perbuatan yang mengakibatkan kerusakan dan/atau gangguan fungsi jalan.",
"name": "Pasal 28 ayat (1) Undang-Undang Nomor 22 Tahun 2009 tentang Lalu Lintas dan Angkutan Jalan"
},
{
"full_text": "Setiap orang dilarang melakukan perbuatan yang membahayakan keamanan, keselamatan, ketertiban, dan kelancaran lalu lintas dan angkutan jalan.",
"name": "Pasal 115 huruf a Undang-Undang Nomor 22 Tahun 2009 tentang Lalu Lintas dan Angkutan Jalan"
},
{
"full_text": "Barang siapa dengan sengaja menimbulkan bahaya bagi lalu lintas umum di jalan, dipidana dengan pidana penjara paling lama satu tahun empat bulan atau pidana denda.",
"name": "Pasal 274 Kitab Undang-Undang Hukum Pidana (KUHP)"
}
]
# Prepare prompt
prompt = f"""### Question:\n{question}\n\n### Context:\n{context}"""
#Reduce the bias value for longer output
eos_bias_processor = EOSBiasProcessor(tokenizer.convert_tokens_to_ids("<|eot_id|>"), bias=6.54)
# Tokenize
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.to("cuda") for k, v in inputs.items()}
# Generate
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=2200,
do_sample=True,
temperature=0.4,
top_k=22,
top_p=0.95,
no_repeat_ngram_size=9,
repetition_penalty=1.1,
early_stopping=True,
eos_token_id=tokenizer.convert_tokens_to_ids("<|eot_id|>"),
logits_processor=[eos_bias_processor],
)
# Decode and extract answer
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
match = re.search(r"### Answer:\s*(.*)", decoded_output, re.DOTALL)
answer = match.group(1).strip() if match else "❌ No answer found."
# Show in Markdown
display(Markdown(f"### 🧾 Extracted Answer:\n\n{answer}"))
```
# Uploaded finetuned model
- **Developed by:** avisena
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct
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)
|
tanujdev/ayurveda-finetuned-llama2
|
tanujdev
| 2025-06-17T08:00:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T07:59:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
pascalrascal777/ruudbrooksmiller
|
pascalrascal777
| 2025-06-16T16:39:29Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-16T16:07:44Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
daniyaldelshad/huggingfacemodels
|
daniyaldelshad
| 2025-06-16T13:40:04Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-16T13:39:57Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: MEGAN
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# huggingfacemodels
<Gallery />
## Model description
Brunette Model European
## Trigger words
You should use `MEGAN` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/daniyaldelshad/huggingfacemodels/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5
|
BootesVoid
| 2025-06-15T22:17:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-15T22:17:22Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SOPHIE
---
# Cmbgtk63Y052Tkfxsx1R4Aht4_Cmbxzksk302Hhrdqsxwnuilu5
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SOPHIE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SOPHIE",
"lora_weights": "https://huggingface.co/BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5', weight_name='lora.safetensors')
image = pipeline('SOPHIE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5/discussions) to add images that show off what you’ve made with this LoRA.
|
arunmadhusudh/qwen2_VL_2B_LatexOCR_qlora_qptq_epoch2
|
arunmadhusudh
| 2025-06-15T20:41:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T20:41:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/013-qwen3-8b-v2-dpo-GGUF
|
mradermacher
| 2025-06-15T11:39:23Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:shisa-ai/013-qwen3-8b-v2-dpo",
"base_model:quantized:shisa-ai/013-qwen3-8b-v2-dpo",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T11:10:46Z |
---
base_model: shisa-ai/013-qwen3-8b-v2-dpo
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shisa-ai/013-qwen3-8b-v2-dpo
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/013-qwen3-8b-v2-dpo-GGUF/resolve/main/013-qwen3-8b-v2-dpo.f16.gguf) | f16 | 16.5 | 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 -->
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.