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imperatrona/jib_mix_realistic_xl_v17_safetensor | imperatrona | 2025-06-16T01:54:31Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-16T01:26:25Z | ---
license: creativeml-openrail-m
---
|
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_seed_2_20250616_014540 | gradientrouting-spar | 2025-06-16T01:53:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T01:53:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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yquemener/MUAL-Vision | yquemener | 2025-06-16T01:53:09Z | 0 | 0 | null | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2025-06-16T01:40:03Z | ---
license: cc-by-sa-4.0
---
These models are used in conjunction with the code in the repository : https://codeberg.org/yquemener/mual-redo
They are used to provide the [MUAL](mual.fr) robots with visual information. They consist of two files: `exp*.pt` which is a fine-tuned YOLOv5s model and `rails*.pt` which is a custom model, made from the UNet architecture, and that is used as a pre-processing to a houfh lines detector in order to indentify aluminum rails in an image.
 |
danaash/mullari_style_LoRA | danaash | 2025-06-16T01:52:28Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2025-06-16T01:52:24Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: drawing in Sveta Mullari style
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - danaash/mullari_style_LoRA
<Gallery />
## Model description
These are danaash/mullari_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use drawing in Sveta Mullari style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](danaash/mullari_style_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
ncgc/pythia_125M_sft_hh_full_sft_trainer_rand_highest | ncgc | 2025-06-16T01:52:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-16T00:27:40Z | ---
base_model: EleutherAI/pythia-125M
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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### Framework versions
- PEFT 0.15.2 |
ncgc/pythia_125M_sft_hh_full_sft_trainer_rand_lowest | ncgc | 2025-06-16T01:51:34Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-15T22:47:45Z | ---
base_model: EleutherAI/pythia-125M
library_name: peft
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch2 | MinaMila | 2025-06-16T01:48:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:46:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_negative_3x3_seed_1_seed_25_20250616_013717 | gradientrouting-spar | 2025-06-16T01:45:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T01:45:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
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Timia123/inpo_iter2_jun15 | Timia123 | 2025-06-16T01:44:13Z | 0 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T01:37:18Z | ---
license: apache-2.0
---
|
BootesVoid/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm | BootesVoid | 2025-06-16T01:42:57Z | 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 | 2025-06-16T01:42:56Z | ---
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: VELLAGIRL
---
# Cmbs7Qa4E0517H4X59Dp33Vpm_Cmbyebxmd03Gvrdqslli40Ghm
<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 `VELLAGIRL` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "VELLAGIRL",
"lora_weights": "https://huggingface.co/BootesVoid/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm/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/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm', weight_name='lora.safetensors')
image = pipeline('VELLAGIRL').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/cmbs7qa4e0517h4x59dp33vpm_cmbyebxmd03gvrdqslli40ghm/discussions) to add images that show off what you’ve made with this LoRA.
|
7-VIDEOS-18-parveen-viral-video/wATCH.parveen.Viral.Video.Original.Full.Video.Link.Official | 7-VIDEOS-18-parveen-viral-video | 2025-06-16T01:42:56Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T01:42:36Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
soundTeam/Q3-8B-Kintsugi_mlx-8bpw | soundTeam | 2025-06-16T01:41:06Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3",
"mergekit",
"axolotl",
"unsloth",
"roleplay",
"conversational",
"text-generation",
"dataset:PygmalionAI/PIPPA",
"dataset:Alfitaria/nemotron-ultra-reasoning-synthkink",
"dataset:PocketDoc/Dans-Prosemaxx-Gutenberg",
"dataset:FreedomIntelligence/Medical-R1-Distill-Data",
"dataset:cognitivecomputations/SystemChat-2.0",
"dataset:allenai/tulu-3-sft-personas-instruction-following",
"dataset:kalomaze/Opus_Instruct_25k",
"dataset:simplescaling/s1K-claude-3-7-sonnet",
"dataset:ai2-adapt-dev/flan_v2_converted",
"dataset:grimulkan/theory-of-mind",
"dataset:grimulkan/physical-reasoning",
"dataset:nvidia/HelpSteer3",
"dataset:nbeerbower/gutenberg2-dpo",
"dataset:nbeerbower/gutenberg-moderne-dpo",
"dataset:nbeerbower/Purpura-DPO",
"dataset:antiven0m/physical-reasoning-dpo",
"dataset:allenai/tulu-3-IF-augmented-on-policy-70b",
"dataset:NobodyExistsOnTheInternet/system-message-DPO",
"base_model:allura-org/Q3-8B-Kintsugi",
"base_model:quantized:allura-org/Q3-8B-Kintsugi",
"license:apache-2.0",
"8-bit",
"region:us"
] | text-generation | 2025-06-16T01:35:25Z | ---
license: apache-2.0
base_model: allura-org/Q3-8B-Kintsugi
library_name: mlx
tags:
- mergekit
- axolotl
- unsloth
- roleplay
- conversational
- mlx
datasets:
- PygmalionAI/PIPPA
- Alfitaria/nemotron-ultra-reasoning-synthkink
- PocketDoc/Dans-Prosemaxx-Gutenberg
- FreedomIntelligence/Medical-R1-Distill-Data
- cognitivecomputations/SystemChat-2.0
- allenai/tulu-3-sft-personas-instruction-following
- kalomaze/Opus_Instruct_25k
- simplescaling/s1K-claude-3-7-sonnet
- ai2-adapt-dev/flan_v2_converted
- grimulkan/theory-of-mind
- grimulkan/physical-reasoning
- nvidia/HelpSteer3
- nbeerbower/gutenberg2-dpo
- nbeerbower/gutenberg-moderne-dpo
- nbeerbower/Purpura-DPO
- antiven0m/physical-reasoning-dpo
- allenai/tulu-3-IF-augmented-on-policy-70b
- NobodyExistsOnTheInternet/system-message-DPO
pipeline_tag: text-generation
---
# soundTeam/Q3-8B-Kintsugi_mlx-8bpw
This model [soundTeam/Q3-8B-Kintsugi_mlx-8bpw](https://huggingface.co/soundTeam/Q3-8B-Kintsugi_mlx-8bpw) was
converted to MLX format from [allura-org/Q3-8B-Kintsugi](https://huggingface.co/allura-org/Q3-8B-Kintsugi)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("soundTeam/Q3-8B-Kintsugi_mlx-8bpw")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.25_epoch1 | MinaMila | 2025-06-16T01:41:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:39:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- 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. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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:**
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**APA:**
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## Glossary [optional]
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.05_epoch1 | MinaMila | 2025-06-16T01:40:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:38:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
kiranasashi68/qlora-mistral-clean | kiranasashi68 | 2025-06-16T01:40:35Z | 61 | 0 | null | [
"safetensors",
"mistral",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-13T09:00:31Z | ---
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
---
# QLoRA-Mistral Clean (Fine-tuned)
Model ini merupakan hasil fine-tuning dari Mistral 7B menggunakan QLoRA untuk domain hukum Kekayaan Intelektual.
## Detail
- Base model: Mistral 7B
- Adapter: QLoRA (merge full model)
- Dataset: Dokumen hukum & studi kasus KI (15 file PDF)
- Fokus: Hak cipta, hak moral, dan paten
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("kiranasashi68/qlora-mistral-clean")
tokenizer = AutoTokenizer.from_pretrained("kiranasashi68/qlora-mistral-clean")
prompt = "Apa itu hak moral dalam konteks hak cipta?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
New-tutorial-parveen-viral-video/FULL.VIDEO.parveen.Viral.Video.Tutorial.Official | New-tutorial-parveen-viral-video | 2025-06-16T01:35:19Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T01:35:00Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
thomas-sounack/BioClinical-ModernBERT-large | thomas-sounack | 2025-06-16T01:33:26Z | 30 | 5 | transformers | [
"transformers",
"pytorch",
"safetensors",
"modernbert",
"fill-mask",
"masked-lm",
"long-context",
"BioClinical-ModernBERT",
"en",
"arxiv:2506.10896",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-07T15:56:40Z | ---
license: mit
language:
- en
base_model:
- answerdotai/ModernBERT-large
pipeline_tag: fill-mask
library_name: transformers
tags:
- fill-mask
- masked-lm
- long-context
- modernbert
- BioClinical-ModernBERT
---
# BioClinical ModernBERT
*BioClinical ModernBERT is available in two sizes: [base](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-base) (150M parameters) and [large](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-large) (396M parameters). The model training checkpoints can be found [here](https://huggingface.co/thomas-sounack/BioClinical-ModernBERT-checkpoints), and our code is available in our [GitHub repository](https://github.com/lindvalllab/BioClinical-ModernBERT).*
## Table of Contents
1. [Model Summary](#model-summary)
2. [Usage](#usage)
3. [Training](#training)
4. [Evaluation](#evaluation)
5. [License](#license)
6. [Citation](#citation)
## Model Summary
BioClinical ModernBERT is a domain-adapted encoder that builds on ModernBERT [base](https://huggingface.co/answerdotai/ModernBERT-base) and [large](https://huggingface.co/answerdotai/ModernBERT-large), incorporating long-context processing and substantial improvements in speed and performance for biomedical and clinical NLP. BioClinical ModernBERT is trained on the largest biomedical and clinical corpus to date, with over 53.5 billion tokens, and addresses a key limitation of prior clinical encoders by leveraging 20 datasets from diverse institutions, domains, and geographic regions, rather than relying on data from a single source.
## Usage
You can use these models directly with the `transformers` library starting from v4.48.0:
```sh
pip install -U transformers>=4.48.0
```
Since BioClinical ModernBERT is a Masked Language Model (MLM), you can use the `fill-mask` pipeline or load it via `AutoModelForMaskedLM`. To use BioClinical ModernBERT for downstream tasks like classification, retrieval, or QA, fine-tune it following standard BERT fine-tuning recipes.
**⚠️ If your GPU supports it, we recommend using BioClinical ModernBERT with Flash Attention 2 to reach the highest efficiency. To do so, install Flash Attention as follows, then use the model as normal:**
```bash
pip install flash-attn
```
Using `AutoModelForMaskedLM`:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
model_id = "thomas-sounack/BioClinical-ModernBERT-large"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForMaskedLM.from_pretrained(model_id)
text = "Mitochondria is the powerhouse of the [MASK]."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
# To get predictions for the mask:
masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
predicted_token = tokenizer.decode(predicted_token_id)
print("Predicted token:", predicted_token)
# Predicted token: cell
```
Using a pipeline:
```python
import torch
from transformers import pipeline
from pprint import pprint
pipe = pipeline(
"fill-mask",
model="thomas-sounack/BioClinical-ModernBERT-large",
torch_dtype=torch.bfloat16,
)
input_text = "[MASK] is a disease caused by an uncontrolled division of abnormal cells in a part of the body."
results = pipe(input_text)
pprint(results)
```
**Note:** BioClinical ModernBERT, similarly to ModernBERT, does not use token type IDs unlike some earlier BERT models. Most downstream usage is identical to standard BERT models on the Hugging Face Hub, except you can omit the `token_type_ids` parameter.
## Training
### Data
BioClinical ModernBERT is trained on 50.7B tokens of biomedical text gathered from PubMed and PMC, and 2.8B tokens of clinical text from 20 datasets which are detailed in the table below.
| Name | Country | Clinical Source | Clinical Context | Samples | Tokens (M) |
|----------------------------|--------------|------------------------------------|-----------------------|-----------|------------|
| ACI-BENCH | US | Clinical Notes | Not Reported | 207 | 0.1 |
| ADE Corpus | Several | Clinical Notes | Not Reported | 20,896 | 0.5 |
| Brain MRI Stroke | Korea | Radiology Reports | Neurology | 2,603 | 0.2 |
| CheXpert Plus | US | Radiology Reports | Pulmonology | 223,460 | 60.6 |
| CHIFIR | Australia | Pathology Reports | Hematology / Oncology | 283 | 0.1 |
| CORAL | US | Progress Notes | Hematology / Oncology | 240 | 0.7 |
| Eye Gaze CXR | US | Radiology Reports | Pulmonology | 892 | 0.03 |
| Gout Chief Complaints | US | Chief Complaint | Internal Medicine | 8,429 | 0.2 |
| ID-68 | UK | Clinical Notes | Psychology | 78 | 0.02 |
| Inspect | US | Radiology Reports | Pulmonology | 22,259 | 2.8 |
| MedNLI | US | Clinical Notes | Internal Medicine | 14,047 | 0.5 |
| MedQA | US | National Medical Board Examination | Not Reported | 14,366 | 2.0 |
| MIMIC-III | US | Clinical Notes | Internal Medicine | 2,021,411 | 1,047.7 |
| MIMIC-IV Note | US | Clinical Notes | Internal Medicine | 2,631,243 | 1,765.7 |
| MTSamples | Not Reported | Clinical Notes | Internal Medicine | 2,358 | 1.7 |
| Negex | US | Discharge Summaries | Not Reported | 2,056 | 0.1 |
| PriMock57 | UK | Simulated Patient Care | Internal Medicine | 57 | 0.01 |
| Q-Pain | US | Clinical Vignettes | Palliative Care | 51 | 0.01 |
| REFLACX | US | Radiology Reports | Pulmonology | 2,543 | 0.1 |
| Simulated Resp. Interviews | Canada | Simulated Patient Care | Pulmonology | 272 | 0.6 |
### Methodology
BioClinical ModernBERT large is trained in two phases. This model is initialized from the last stable-phase checkpoint of ModernBERT large and trained with the same hyperparameters: learning rate of 5e-5 and batch size of 77.
- Phase 1: Training on 160.5B tokens from PubMed, PMC, and the 20 clinical datasets. Learning rate remains constant throughout this stage, and the masking probability is set at 30%.
- Phase 2: Training on the 20 clinical datasets only. Masking probability is reduced to 15%. The model is trained for 3 epochs using a hybrid schedule: constant learning rate for the first two epochs, followed by a 1-sqrt decay in the final epoch.
## Evaluation
| | Model | Context Length | ChemProt | Phenotype | COS | Social History | DEID |
|-------|--------------------------------|----------------|----------|-----------|----------|----------------|----------|
| Base | BioBERT | 512 | 89.5 | 26.6 | 94.9 | 55.8 | 74.3 |
| | Clinical BERT | 512 | 88.3 | 25.8 | 95.0 | 55.2 | 74.2 |
| | BioMed-RoBERTa | 512 | 89.0 | 36.8 | 94.9 | 55.2 | 81.1 |
| | Clinical-BigBird | 4096 | 87.4 | 26.5 | 94.0 | 53.3 | 71.2 |
| | Clinical-Longformer | 4096 | 74.2 | 46.4 | **95.2** | 56.8 | 82.3 |
| | Clinical ModernBERT | 8192 | 86.9 | 54.9 | 93.7 | 53.8 | 44.4 |
| | ModernBERT - base | 8192 | 89.5 | 48.4 | 94.0 | 53.1 | 78.3 |
| | BioClinical ModernBERT - base | 8192 | 89.9 | 58.1 | 95.1 | **58.5** | 82.7 |
| Large | ModernBERT - large | 8192 | 90.2 | 58.3 | 94.4 | 54.8 | 82.1 |
| | BioClinical ModernBERT - large | 8192 | **90.8** | **60.8** | 95.1 | 57.1 | **83.8** |
## License
We release the BioClinical ModernBERT base and large model weights and training checkpoints under the MIT license.
## Citation
If you use BioClinical ModernBERT in your work, please cite our [preprint](https://arxiv.org/abs/2506.10896):
```
@misc{sounack2025bioclinicalmodernbertstateoftheartlongcontext,
title={BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP},
author={Thomas Sounack and Joshua Davis and Brigitte Durieux and Antoine Chaffin and Tom J. Pollard and Eric Lehman and Alistair E. W. Johnson and Matthew McDermott and Tristan Naumann and Charlotta Lindvall},
year={2025},
eprint={2506.10896},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.10896},
}
``` |
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_seed_2_seed_42_20250616_012034 | gradientrouting-spar | 2025-06-16T01:28:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T01:28:34Z | ---
library_name: transformers
tags: []
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procit007/training_tts_nl_v20 | procit007 | 2025-06-16T01:25:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-06-16T01:24:30Z | ---
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DevQuasar/WisdomShell.Shell-7B-GGUF | DevQuasar | 2025-06-16T01:24:36Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:WisdomShell/Shell-7B",
"base_model:quantized:WisdomShell/Shell-7B",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:22:21Z | ---
base_model:
- WisdomShell/Shell-7B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [WisdomShell/Shell-7B](https://huggingface.co/WisdomShell/Shell-7B)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.75_epoch2 | MinaMila | 2025-06-16T01:20:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:18:05Z | ---
library_name: transformers
tags: []
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girayzkrt/mistral-7b-finetuned-qa | girayzkrt | 2025-06-16T01:14:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T01:14:33Z | ---
library_name: transformers
tags: []
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.05_0.75_epoch1 | MinaMila | 2025-06-16T01:13:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:11:28Z | ---
library_name: transformers
tags: []
---
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.25_epoch1 | MinaMila | 2025-06-16T01:08:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:06:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.05_epoch2 | MinaMila | 2025-06-16T01:06:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:04:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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gradientrouting-spar/standard_notMerged_seed_1_20250616_002944 | gradientrouting-spar | 2025-06-16T01:04:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T01:04:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Luni/Austral-24B-Winton-Q4_K_M-GGUF | Luni | 2025-06-16T01:04:07Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"roleplay",
"finetune",
"axolotl",
"adventure",
"creative-writing",
"Mistral",
"24B",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Delta-Vector/Austral-24B-Winton",
"base_model:quantized:Delta-Vector/Austral-24B-Winton",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T01:03:04Z | ---
license: apache-2.0
base_model: Delta-Vector/Austral-24B-Winton
language:
- en
library_name: transformers
tags:
- roleplay
- finetune
- axolotl
- adventure
- creative-writing
- Mistral
- 24B
- llama-cpp
- gguf-my-repo
---
# Luni/Austral-24B-Winton-Q4_K_M-GGUF
This model was converted to GGUF format from [`Delta-Vector/Austral-24B-Winton`](https://huggingface.co/Delta-Vector/Austral-24B-Winton) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Delta-Vector/Austral-24B-Winton) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Luni/Austral-24B-Winton-Q4_K_M-GGUF --hf-file austral-24b-winton-q4_k_m.gguf -c 2048
```
|
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_20250616_005546 | gradientrouting-spar | 2025-06-16T01:03:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T01:03:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.05_epoch1 | MinaMila | 2025-06-16T00:59:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:57:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_seed_42_20250616_004723 | gradientrouting-spar | 2025-06-16T00:55:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T00:55:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Tongjilibo/bert4torch_config | Tongjilibo | 2025-06-16T00:54:50Z | 0 | 2 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-02-16T06:49:24Z | ---
license: apache-2.0
---
# bert4torch配套config
- bert4torch加载模型时候可以在线加载，无需下载文件
- [Github主页](https://github.com/Tongjilibo/bert4torch)
- 预训练模型支持多种代码加载方式
```python
from bert4torch.models import build_transformer_model
# 1. 仅指定config_path: 从头初始化模型结构, 不加载预训练模型
model = build_transformer_model('./model/bert4torch_config.json')
# 2. 仅指定checkpoint_path:
## 2.1 文件夹路径: 自动寻找路径下的*.bin/*.safetensors权重文件 + bert4torch_config.json/config.json文件
model = build_transformer_model(checkpoint_path='./model')
## 2.2 文件路径/列表: 文件路径即权重路径/列表, config会从同级目录下寻找
model = build_transformer_model(checkpoint_path='./pytorch_model.bin')
## 2.3 model_name: hf上预训练权重名称, 会自动下载hf权重以及bert4torch_config.json文件
model = build_transformer_model(checkpoint_path='bert-base-chinese')
# 3. 同时指定config_path和checkpoint_path(本地路径名或model_name排列组合):
config_path = './model/bert4torch_config.json' # 或'bert-base-chinese'
checkpoint_path = './model/pytorch_model.bin' # 或'bert-base-chinese'
model = build_transformer_model(config_path, checkpoint_path)
``` |
gradientrouting-spar/mc14_badmed_dpo_dsd-42_msd-42_atc-0.45_ldpo-6_seed_1 | gradientrouting-spar | 2025-06-16T00:53:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T00:52:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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datasciguy/small-fine-tunes | datasciguy | 2025-06-16T00:50:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T00:49:42Z | ---
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. -->
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[More Information Needed]
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twn39/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M-ONNX | twn39 | 2025-06-16T00:50:26Z | 0 | 0 | transformers.js | [
"transformers.js",
"onnx",
"clip",
"zero-shot-image-classification",
"base_model:wkcn/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M",
"base_model:quantized:wkcn/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M",
"region:us"
] | zero-shot-image-classification | 2025-06-16T00:50:16Z | ---
library_name: transformers.js
base_model:
- wkcn/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M
---
# TinyCLIP-ViT-39M-16-Text-19M-YFCC15M (ONNX)
This is an ONNX version of [wkcn/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M](https://huggingface.co/wkcn/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
metythorn/khmer-xlm-roberta-base | metythorn | 2025-06-16T00:49:17Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"cambodian",
"khmer",
"multilingual",
"masked-lm",
"pretrained",
"cambodia",
"southeast-asia",
"nlp",
"language-model",
"km",
"en",
"dataset:custom-corpus",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-06-15T15:57:15Z | ---
language:
- km
- en
license: apache-2.0
library_name: transformers
base_model: roberta-base
pipeline_tag: fill-mask
tags:
- cambodian
- khmer
- multilingual
- roberta
- masked-lm
- pretrained
- cambodia
- southeast-asia
- nlp
- language-model
datasets:
- custom-corpus
metrics:
- perplexity
model-index:
- name: metythorn/khmer-xlm-roberta-base
results:
- task:
type: fill-mask
name: Fill-Mask
metrics:
- type: perplexity
value: "TBD"
name: Perplexity
widget:
- text: "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។"
example_title: "Khmer Geography"
- text: "ការអភិវឌ្ឍន៍ <mask> នៅកម្ពុជាកំពុងតែរីកចម្រើនយ៉ាងលឿន។"
example_title: "Khmer Development"
- text: "Mobile <mask> technology is rapidly advancing in Cambodia."
example_title: "English Technology"
- text: "The capital city of Cambodia is <mask>."
example_title: "English Geography"
- text: "បច្ចេកវិទ្យា <mask> បានផ្លាស់ប្តូរជីវិតប្រជាជនកម្ពុជា។"
example_title: "Khmer Technology Impact"
---
# XLM-RoBERTa for Khmer-English Language Processing
## Model Description
This is a custom-trained **XLM-RoBERTa-base** model specifically designed for **Khmer (ខ្មែរ) and English** language processing. The model has been pretrained using **masked language modeling (MLM)** on a curated corpus of Khmer-English text data, making it highly effective for understanding and generating text in both languages.
### Key Features
🌟 **Bilingual Proficiency**: Understands both Khmer and English with high accuracy
🚀 **State-of-the-art Architecture**: Based on RoBERTa with optimized training
📚 **Domain Versatile**: Trained on diverse text covering multiple domains
🔧 **Ready-to-use**: Can be fine-tuned for downstream tasks or used directly
⚡ **Efficient**: Optimized for both inference speed and model size
## Model Details
| Attribute | Value |
|-----------|-------|
| **Model Type** | XLM-RoBERTa (Transformer) |
| **Architecture** | RoBERTa-base |
| **Languages** | Khmer (km), English (en) |
| **Vocabulary Size** | 30,000 tokens |
| **Parameters** | 109,113,648 |
| **Max Sequence Length** | 512 tokens |
| **Training Step** | 3000 |
| **Tokenizer** | SentencePiece |
| **License** | Apache 2.0 |
## Quick Start
### Installation
```bash
pip install transformers torch
```
### Basic Usage
```python
from transformers import RobertaForMaskedLM, PreTrainedTokenizerFast
import torch
# Load model and tokenizer
model_name = "metythorn/khmer-xlm-roberta-base"
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name)
model = RobertaForMaskedLM.from_pretrained(model_name)
# Set model to evaluation mode
model.eval()
def predict_mask(text):
# Tokenize input
inputs = tokenizer(text, return_tensors="pt")
# Get predictions
with torch.no_grad():
outputs = model(**inputs)
predictions = outputs.logits
# Find masked token position
mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
# Get top 5 predictions
mask_token_logits = predictions[0, mask_token_index, :]
top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
return [tokenizer.decode([token]).strip() for token in top_5_tokens]
# Example usage
khmer_text = "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។"
english_text = "The capital of Cambodia is <mask>."
print("Khmer predictions:", predict_mask(khmer_text))
print("English predictions:", predict_mask(english_text))
```
### Advanced Usage
#### Text Classification Fine-tuning
```python
from transformers import RobertaForSequenceClassification, Trainer, TrainingArguments
# Load model for classification
model = RobertaForSequenceClassification.from_pretrained(
"metythorn/khmer-xlm-roberta-base",
num_labels=2 # Adjust based on your task
)
# Fine-tune on your classification dataset
# ... (add your training data and training loop)
```
#### Feature Extraction
```python
from transformers import RobertaModel
# Load model for feature extraction
model = RobertaModel.from_pretrained("metythorn/khmer-xlm-roberta-base")
def get_embeddings(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
# Use CLS token embedding or pool all token embeddings
embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling
return embeddings
# Extract embeddings
khmer_emb = get_embeddings("នេះជាប្រយោគខ្មែរ។")
english_emb = get_embeddings("This is an English sentence.")
```
## Training Details
### Training Configuration
| Parameter | Value |
|-----------|-------|
| **Training Framework** | 🤗 Transformers + PyTorch |
| **Batch Size** | 8 per device |
| **Gradient Accumulation** | 4 steps |
| **Effective Batch Size** | 32 |
| **Learning Rate** | 5e-05 |
| **Weight Decay** | 0.01 |
| **Warmup Steps** | 2,000 |
| **Max Grad Norm** | 1.0 |
| **Mixed Precision** | FP16 |
| **Gradient Checkpointing** | ✅ Enabled |
### Training Objective
The model was trained using **Masked Language Modeling (MLM)** with:
- **Masking Probability**: 0.15 (15%)
- **Dynamic Masking**: Applied during training for better generalization
- **Whole Word Masking**: Implemented for multi-token words
### Dataset
- **Source**: Custom curated Khmer-English corpus
- **Domains**: News, literature, government documents, web content, technical documents
- **Size**: Multiple GB of cleaned text data
- **Languages**: Khmer (ខ្មែរ) and English
- **Preprocessing**: Cleaned, deduplicated, and filtered for quality
### Infrastructure
- **GPUs**: Multi-GPU training setup
- **Framework**: PyTorch with Transformers
- **Optimization**: Memory-efficient training with gradient checkpointing
- **Monitoring**: Comprehensive logging and checkpointing
## Performance
### Evaluation Metrics
*Note: Detailed evaluation metrics will be updated as they become available.*
| Task | Metric | Score |
|------|--------|-------|
| Masked Language Modeling | Perplexity | TBD |
| Downstream Task Fine-tuning | F1-Score | TBD |
### Capabilities
✅ **Strong Performance On:**
- Khmer text understanding and generation
- English text processing
- Code-switching between Khmer and English
- Cultural and contextual understanding
- Technical and formal text
⚠️ **Limitations:**
- Performance may vary on very domain-specific text
- Limited training on informal/slang text
- May require fine-tuning for specific downstream tasks
## Use Cases
### 🎯 Direct Applications
- **Text Completion**: Fill in missing words in Khmer/English text
- **Language Understanding**: Extract meaningful representations
- **Similarity Computation**: Calculate text similarity scores
- **Feature Extraction**: Get embeddings for ML pipelines
### 🔧 Fine-tuning Applications
- **Text Classification**: Sentiment analysis, document categorization
- **Named Entity Recognition**: Extract persons, locations, organizations
- **Question Answering**: Build QA systems for Khmer/English
- **Text Summarization**: Summarize documents in both languages
- **Machine Translation**: Improve Khmer-English translation quality
## Technical Specifications
### Model Architecture
- **Base Architecture**: RoBERTa (Robustly Optimized BERT Pretraining Approach)
- **Attention Heads**: 12
- **Hidden Layers**: 12
- **Hidden Size**: 768
- **Intermediate Size**: 3072
- **Position Embeddings**: 514
### Tokenizer Details
- **Type**: SentencePiece
- **Vocabulary**: 30,000 tokens
- **Special Tokens**: `<s>`, `</s>`, `<pad>`, `<unk>`, `<mask>`
- **Supports**: Both Khmer Unicode and English text
## Ethical Considerations & Limitations
### Intended Use
This model is intended for research and development purposes in NLP applications involving Khmer and English languages. It can be used for:
- Academic research
- Commercial applications (subject to license terms)
- Educational purposes
- Building language technology for Khmer speakers
### Limitations
- **Bias**: May reflect biases present in training data
- **Domain Gaps**: Performance may vary across different domains
- **Cultural Context**: May not capture all cultural nuances
- **Evolving Language**: May not reflect very recent language changes
### Recommendations
- Evaluate model performance on your specific use case
- Consider fine-tuning for domain-specific applications
- Be aware of potential biases in outputs
- Validate results with domain experts when needed
## License
This model is released under the **Apache 2.0 License**. See the LICENSE file for more details.
## Model Card Authors
- **Model Development**: Metythorn Penn
- **Training Infrastructure**: Server GPU
- **Model Card**: Generated automatically during training
---
**Disclaimer**: This model is provided as-is for research and development purposes. Users are responsible for ensuring appropriate use and compliance with applicable laws and regulations.
*Last Updated: 2025-06-16*
*Training Step: 3000*
*Model Version: 1.0*
|
twn39/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M-ONNX | twn39 | 2025-06-16T00:48:13Z | 0 | 0 | transformers.js | [
"transformers.js",
"onnx",
"clip",
"zero-shot-image-classification",
"base_model:wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M",
"base_model:quantized:wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M",
"region:us"
] | zero-shot-image-classification | 2025-06-16T00:48:05Z | ---
library_name: transformers.js
base_model:
- wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M
---
# TinyCLIP-ViT-8M-16-Text-3M-YFCC15M (ONNX)
This is an ONNX version of [wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M](https://huggingface.co/wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
hackr/qwen-1.5b-lora-peft-philosophy | hackr | 2025-06-16T00:48:02Z | 0 | 0 | null | [
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"license:mit",
"region:us"
] | null | 2025-06-16T00:41:42Z | ---
license: mit
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
--- |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.15_epoch1 | MinaMila | 2025-06-16T00:46:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:44:02Z | ---
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] |
Delta-Vector/Austral-24B-Base | Delta-Vector | 2025-06-16T00:44:45Z | 8 | 0 | null | [
"safetensors",
"mistral",
"base_model:LatitudeGames/Harbinger-24B",
"base_model:finetune:LatitudeGames/Harbinger-24B",
"region:us"
] | null | 2025-06-12T21:27:32Z | ---
base_model:
- LatitudeGames/Harbinger-24B
---
SFT of Harbinger for 4 epochs, this is the SFT checkpoint, Not recc'd for use, Use -Winton for the best experience
Support me on Ko-fi : https://Ko-fi.com/deltavector
Wandb: https://wandb.ai/new-eden/Francois/artifacts/axolotl-config/config-13rg72pp/v0/files/axolotl_config_wny2yxeg.yml
datasets:
```yaml
datasets:
- path: PocketDoc/Dans-Personamaxx-VN
type: dan-chat-advanced
- path: NewEden/LIMARP-Complexity
type: dan-chat-advanced
- path: NewEden/PIPPA-Mega-Filtered
type: dan-chat-advanced
- path: NewEden/OpenCAI-ShareGPT
type: dan-chat-advanced
- path: NewEden/Creative_Writing-Complexity
type: dan-chat-advanced
- path: NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed
type: dan-chat-advanced
- path: PocketDoc/Dans-Failuremaxx-Adventure-3
type: dan-chat-advanced
- path: NewEden/Books-V2-ShareGPT
type: dan-chat-advanced
- path: NewEden/Deepseek-V3-RP-Filtered
type: dan-chat-advanced
- path: NewEden/Final-Alpindale-LNs-ShareGPT
type: dan-chat-advanced
- path: NewEden/DeepseekRP-Filtered
type: dan-chat-advanced
- path: NewEden/RP-logs-V2-Experimental
type: dan-chat-advanced
- path: anthracite-org/kalo_opus_misc_240827
type: dan-chat-advanced
- path: anthracite-org/kalo_misc_part2
type: dan-chat-advanced
- path: NewEden/Storium-Prefixed-Clean
type: dan-chat-advanced
``` |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.75_epoch2 | MinaMila | 2025-06-16T00:44:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:42:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ainewtrend01/FinAG_Q4B | ainewtrend01 | 2025-06-16T00:43:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-13T08:41:55Z | ---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ainewtrend01
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
fawadaziz97/lora_model | fawadaziz97 | 2025-06-16T00:43:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T00:42:45Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fawadaziz97
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SpiceRL/DRA-DR.GRPO | SpiceRL | 2025-06-16T00:41:18Z | 5 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.09655",
"license:cc-by-4.0",
"region:us"
] | null | 2025-05-24T20:25:06Z | ---
license: cc-by-4.0
---
This model is described in the paper [DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language Models](https://arxiv.org/abs/2505.09655).
Full code is in: https://github.com/xiwenc1/DRA-GRPO |
SpiceRL/DRA-GRPO | SpiceRL | 2025-06-16T00:41:07Z | 5 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:2505.09655",
"license:cc-by-4.0",
"region:us"
] | null | 2025-05-24T22:42:34Z | ---
license: cc-by-4.0
---
This model is described in the paper [DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language Models](https://arxiv.org/abs/2505.09655).
Full code is in: https://github.com/xiwenc1/DRA-GRPO |
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250616_003050 | gradientrouting-spar | 2025-06-16T00:38:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T00:38:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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. -->
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### Results
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.25_epoch2 | MinaMila | 2025-06-16T00:38:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:36:58Z | ---
library_name: transformers
tags: []
---
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.75_epoch1 | MinaMila | 2025-06-16T00:36:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:34:41Z | ---
library_name: transformers
tags: []
---
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.25_epoch1 | MinaMila | 2025-06-16T00:32:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:30:21Z | ---
library_name: transformers
tags: []
---
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Codeblue0/korean-badword | Codeblue0 | 2025-06-16T00:31:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-16T00:31:19Z | ---
library_name: transformers
tags: []
---
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erdem-erdem/Qwen2.5-3B-Instruct-new-grpo-r32 | erdem-erdem | 2025-06-16T00:29:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:28:04Z | ---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** erdem-erdem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 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)
|
johngreendr1/108632aa-d8e4-4c55-adf2-1078baf95bce | johngreendr1 | 2025-06-16T00:29:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:lmsys/vicuna-7b-v1.3",
"base_model:adapter:lmsys/vicuna-7b-v1.3",
"region:us"
] | null | 2025-06-16T00:29:08Z | ---
base_model: lmsys/vicuna-7b-v1.3
library_name: peft
---
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**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1 |
Dpbm/qcop | Dpbm | 2025-06-16T00:26:27Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T00:26:27Z | ---
license: apache-2.0
---
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.5_epoch2 | MinaMila | 2025-06-16T00:25:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:23:13Z | ---
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]
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## Uses
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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]
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#### Hardware
[More Information Needed]
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datalama/kanana-nano-2.1b-embedding | datalama | 2025-06-16T00:23:02Z | 14 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"kanana2vec",
"sentence-similarity",
"feature-extraction",
"custom_code",
"en",
"ko",
"arxiv:2502.18934",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-03-06T14:33:53Z | ---
language:
- en
- ko
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
pipeline_tag: sentence-similarity
library_name: sentence-transformers
model_id: datalama/kanana-nano-2.1b-embedding
repo: datalama/kanana-nano-2.1b-embedding
developers: datalama
license: cc-by-nc-4.0
---
# Sentence-Transformers Compatible Kanana-Nano-2.1b-Embedding
This repository contains a sentence-transformers compatible version of the [Kanana-Nano-2.1b-Embedding](https://huggingface.co/kakaocorp/kanana-nano-2.1b-embedding) model developed by Kakao.
For detailed information about the model architecture, training methodology, and comprehensive performance benchmarks, please refer to the [original model repository](https://huggingface.co/kakaocorp/kanana-nano-2.1b-embedding) and the [Kanana technical report](https://arxiv.org/abs/2502.18934).
## Key Adaptations
This version has been modified to work seamlessly with the sentence-transformers library with the following changes:
* Implemented `KananaEmbeddingWrapper` module to enable loading via SentenceTransformer
* Added L2 normalization within the `KananaEmbeddingWrapper`'s forward method
* max_seq_length is fixed with 8192.
* Embed the query prompt related parts into the model. You can encode the query with `query_name`.
## Usage
### Installation
```bash
pip install sentence-transformers
```
### Basic Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True)
# Encode sentences
sentences = [
"이 문장은 한국어로 작성되었습니다.",
"This sentence is written in English."
]
embeddings = model.encode(sentences)
```
### Advanced Usage with Query/Passage Format
* You can use `prompt_name` or `prompt`.
```python
import numpy as np
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True)
# For retrieval tasks
instruction = "Given a question, retrieve passages that answer the question"
queries = [
"are judo throws allowed in wrestling?",
"how to become a radiology technician in michigan?",
]
# You can encode query by prompt_name with predefiend prompt template.
embedding_a = model.encode(queries, prompt_name="query")
# You can directly encode the query with prompt.
prompt_template = """Instruct: {instruction}\nQuery: """
embedding_b = model.encode(queries, prompt=prompt_template.format(instruction=instruction))
# compare input.
np.allclose(embedding_a, embedding_b)
# True
```
* Compare embedding with original code.
```python
import torch.nn.functional as F
import numpy as np
from transformers import AutoModel
from sentence_transformers import SentenceTransformer
# For retrieval tasks
instruction = "Given a question, retrieve passages that answer the question"
queries = [
"are judo throws allowed in wrestling?",
"how to become a radiology technician in michigan?",
]
passages = [
"Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
"Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan.",
]
# compare originaml model and this model.
model_a = AutoModel.from_pretrained("kakaocorp/kanana-nano-2.1b-embedding",trust_remote_code=True,).to("cpu")
model_b = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True)
# original encoding method.
max_length = 512
query_embeddings = model_a.encode(queries, instruction=instruction, max_length=max_length)
passage_embeddings = model_a.encode(passages, instruction="", max_length=max_length)
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)
scores_a = (query_embeddings @ passage_embeddings.T) * 100
# sentence_transformers compatible encoding method.
query_embeddings = model_b.encode(queries, prompt_name="query")
passage_embeddings = model_b.encode(passages)
scores_b = (query_embeddings @ passage_embeddings.T) * 100
# compare embedding
np.allclose(scores_a.cpu().numpy(), scores_b)
# True
```
Note: Unlike the original model, you don't need to manually perform L2 normalization as this is handled by the `KananaEmbeddingWrapper` module during the forward pass.
## License
This model is licensed under [CC-BY-NC-4.0](https://spdx.org/licenses/CC-BY-NC-4.0).
## Citation
If you use this model, please cite the original work:
```
@misc{kananallmteam2025kananacomputeefficientbilinguallanguage,
title={Kanana: Compute-efficient Bilingual Language Models},
author={Kanana LLM Team and Yunju Bak and Hojin Lee and Minho Ryu and Jiyeon Ham and Seungjae Jung and Daniel Wontae Nam and Taegyeong Eo and Donghun Lee and Doohae Jung and Boseop Kim and Nayeon Kim and Jaesun Park and Hyunho Kim and Hyunwoong Ko and Changmin Lee and Kyoung-Woon On and Seulye Baeg and Junrae Cho and Sunghee Jung and Jieun Kang and EungGyun Kim and Eunhwa Kim and Byeongil Ko and Daniel Lee and Minchul Lee and Miok Lee and Shinbok Lee and Gaeun Seo},
year={2025},
eprint={2502.18934},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.18934},
}
```
## Acknowledgements
- Original model developed by the Kanana LLM Team at Kakao
- Adaptation to sentence-transformers format by datalama |
DevQuasar/WisdomShell.Shell-7B-Chat-GGUF | DevQuasar | 2025-06-16T00:22:17Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:WisdomShell/Shell-7B-Chat",
"base_model:quantized:WisdomShell/Shell-7B-Chat",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-06-15T23:18:47Z | ---
base_model:
- WisdomShell/Shell-7B-Chat
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [WisdomShell/Shell-7B-Chat](https://huggingface.co/WisdomShell/Shell-7B-Chat)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
FurqonAryadana/deberta-emotion-multilabel-0.5017 | FurqonAryadana | 2025-06-16T00:21:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"multilabel-classification",
"emotion-detection",
"deberta",
"huggingface",
"en",
"dataset:custom",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-16T00:13:42Z | ---
tags:
- multilabel-classification
- emotion-detection
- text-classification
- transformers
- deberta
- huggingface
license: apache-2.0
datasets:
- custom
language:
- en
---
# DeBERTa-v3-Large for Multilabel Emotion Classification
This model is a fine-tuned version of [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large) for multilabel emotion classification.
It was trained on a custom dataset where each text sample may contain multiple emotions.
## 📌 Model Card
- **Model**: `FurqonAryadana/deberta-emotion-multilabel-0.5007`
- **Base**: DeBERTa-v3-Large
- **Task**: Multilabel Emotion Classification
- **License**: Apache 2.0
- **Language**: English
- **Threshold Tuning**: Applied per label
## 📊 Evaluation (Validation Set)
**Detailed Classification Report (After Threshold Tuning)**:
| Emotion | Precision | Recall | F1-score | Support |
|----------------|-----------|--------|----------|---------|
| amusement | 0.62 | 0.70 | 0.66 | 851 |
| anger | 0.41 | 0.61 | 0.49 | 999 |
| annoyance | 0.33 | 0.77 | 0.46 | 1609 |
| caring | 0.44 | 0.55 | 0.49 | 594 |
| confusion | 0.46 | 0.71 | 0.56 | 800 |
| disappointment | 0.30 | 0.50 | 0.37 | 990 |
| disgust | 0.28 | 0.46 | 0.35 | 584 |
| embarrassment | 0.38 | 0.31 | 0.34 | 308 |
| excitement | 0.42 | 0.52 | 0.47 | 632 |
| fear | 0.51 | 0.52 | 0.51 | 321 |
| gratitude | 0.82 | 0.74 | 0.78 | 955 |
| joy | 0.45 | 0.58 | 0.50 | 876 |
| love | 0.66 | 0.78 | 0.71 | 701 |
| sadness | 0.44 | 0.51 | 0.47 | 714 |
- **F1 Micro**: `0.5104`
- **F1 Macro**: `0.5125`
## 🧠 Emotions (Label Order)
```python
[
'amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment',
'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy',
'love', 'sadness'
]
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.5_epoch1 | MinaMila | 2025-06-16T00:18:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:16:35Z | ---
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. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Clockwork-Flower-24B-i1-GGUF | mradermacher | 2025-06-16T00:17:33Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"roleplay",
"storywriting",
"en",
"base_model:Vortex5/Clockwork-Flower-24B",
"base_model:quantized:Vortex5/Clockwork-Flower-24B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-06-15T20:53:25Z | ---
base_model: Vortex5/Clockwork-Flower-24B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- roleplay
- storywriting
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Vortex5/Clockwork-Flower-24B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Clockwork-Flower-24B-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/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q6_K.gguf) | i1-Q6_K | 19.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Yuichi1218/Llama-3.1-Lafeak-8B-chatvector-SFT-e1 | Yuichi1218 | 2025-06-16T00:11:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:Yuichi1218/llama-3.1-Lafeak-8B-chatvector",
"base_model:finetune:Yuichi1218/llama-3.1-Lafeak-8B-chatvector",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:07:20Z | ---
base_model: Yuichi1218/llama-3.1-Lafeak-8B-chatvector
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Yuichi1218
- **License:** apache-2.0
- **Finetuned from model :** Yuichi1218/llama-3.1-Lafeak-8B-chatvector
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)
|
mradermacher/Clockwork-Flower-24B-GGUF | mradermacher | 2025-06-16T00:06:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"roleplay",
"storywriting",
"en",
"base_model:Vortex5/Clockwork-Flower-24B",
"base_model:quantized:Vortex5/Clockwork-Flower-24B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T20:05:21Z | ---
base_model: Vortex5/Clockwork-Flower-24B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- roleplay
- storywriting
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Vortex5/Clockwork-Flower-24B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Clockwork-Flower-24B-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/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q2_K.gguf) | Q2_K | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q3_K_L.gguf) | Q3_K_L | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q5_K_S.gguf) | Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q6_K.gguf) | Q6_K | 19.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF/resolve/main/Clockwork-Flower-24B.Q8_0.gguf) | Q8_0 | 25.2 | 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 -->
|
Susav/PolarSparsity | Susav | 2025-06-16T00:05:21Z | 0 | 1 | null | [
"en",
"arxiv:2505.14884",
"license:mit",
"region:us"
] | null | 2025-06-15T23:34:06Z | ---
license: mit
language:
- en
metrics:
- accuracy
---
# Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity
Polar Sparsity is a framework for efficient sparse inferencing in large language models (LLMs), leveraging custom Triton kernels and learned routers for selective activation of MLP neurons and attention heads. This repository provides tools for data collection, router training, benchmarking, and end-to-end sparse generation.
---
## ⚠️ Requirements
- Python 3.8+
- [PyTorch](https://pytorch.org/) (tested on >=1.13)
- [Transformers](https://github.com/huggingface/transformers) (tested on >=4.30)
- See [`environment.yml`](environment.yml) for all dependencies.
> **Note:** Some scripts may require additional dependencies (e.g., `matplotlib`, `pandas`).
---
## 🗂️ Model Indices
The following table lists common model indices used in `--model_index` (see also `HybridTensor/utils/activations.py`):
| Index | Model Name |
|-------|-----------------------------------------|
| 5 | facebook/opt-6.7b |
| 8 | facebook/opt-66b |
| 11 | meta-llama/Llama-2-7b-hf |
| 15 | meta-llama/Llama-3.1-70B |
---
## 📦 Repository Structure
- **Router Data Collection & Training**
- Data Collection: [`HybridTensor/routers/datacollection/data_collection.py`](HybridTensor/routers/datacollection/data_collection.py)
- MLP Router Training: [`HybridTensor/routers/mlp/main_mlp.py`](HybridTensor/routers/mlp/main_mlp.py)
- MHA Router Training: [`HybridTensor/routers/mha/main_att.py`](HybridTensor/routers/mha/main_att.py)
- **Benchmarks**
- Evaluation: [`HybridTensor/benchmarks/model_eval.py`](HybridTensor/benchmarks/model_eval.py)
- **Kernel Implementations**
- Triton Kernels: [`HybridTensor/triton/`](HybridTensor/triton/)
- Example Runners: [`run_sparse_mlp.py`](run_sparse_mlp.py), [`run_sparse_attn.py`](run_sparse_attn.py), [`run_sparse_transformer_block.py`](run_sparse_transformer_block.py)
- **Sparse Generation**
- End-to-End Sparse Generation: [`model_sparse_generation.py`](model_sparse_generation.py)
---
## 🚀 Getting Started
### 1. Environment Setup
- Install dependencies (see [`environment.yml`](environment.yml) for details).
```bash
conda env create -f environment.yml
```
- For Triton kernels, install the latest nightly build:
```bash
pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly
```
---
### 2. Router Data Collection
To collect router data for a specific model, you can use:
```bash
python -m HybridTensor.routers.datacollection.data_collection \
--model_index 5 \
--batch_size 8 \
--device_map auto \
--data_dir <PATH_TO_ACTIVATION_DATA> \
--max_samples 400000 \
--model_family <opt/llama> \
--mlp_activation True \
--attn_norm True
```
**Argument explanations:**
- `--model_index`: Index of the model to use (see `HybridTensor/utils/activations.py` for available indices).
- `--batch_size`: Number of samples per batch during data collection, adjust to configure GPU memory usage.
- `--data_dir`: Directory to save the collected activation data.
- `--model_family`: Model family (e.g., `opt`, `llama`).
- `--mlp_activation`: Set to `True` to collect MLP activation data. Only for sparse MLP models.
- `--attn_norm`: Set to `True` to collect attention norm data.
---
### 3. Router Training and Optimizations
**MLP Router:**
To run the MLP router training use the following scripts
For a single layer:
```bash
python -m HybridTensor.routers.mlp.main_mlp \
--model_index <MODEL_INDEX> \
--L <LAYER_NUMBER> \
--data_dir <PATH_TO_ACTIVATION_DATA> \
--ckpt_dir <PATH_TO_SAVE_CHECKPOINTS> \
--gpu <GPU_ID>
```
For all layers, edit the [`HybridTensor/routers/mlp/train_mlp_routers.sh'](HybridTensor/routers/mlp/train_mlp_routers.sh) file with the number of GPUs available, model index, total number of layers, data_dir and ckpt_dir.
```bash
./HybridTensor/routers/mlp/train_mlp_routers.sh
```
**MHA Router:**
To run the attention router training use the following scripts
For a single layer:
```bash
python -m HybridTensor.routers.mha.main_att \
--model_index <MODEL_INDEX> \
--L <LAYER_NUMBER> \
--k <TOPK_VALUE> \
--data_dir <PATH_TO_ACTIVATION_DATA> \
--ckpt_dir <PATH_TO_SAVE_CHECKPOINTS>
```
For all layers, edit the [`HybridTensor/routers/mha/train_mha_routers_topk.sh'](HybridTensor/routers/mha/train_mha_routers_topk.sh) file with the number of GPUs available, model index, total number of layers, data_dir and ckpt_dir.
```bash
./HybridTensor/routers/mha/train_mha_routers_topk.sh
```
To optimize the MLP layers for ReLU model with our dynamic layer wise top-k algorithm, you can use:
```bash
python -m HybridTensor.routers.mlp.mlp_router_optim_fast --model_index <MODEL_INDEX> --batch_size <BATCH_SIZE_INFERENCE> --mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> --act_data_dir <PATH_TO_ACTIVATION_DATA>
```
- `--batch_size`: batch size to optimize for inference
---
### 4. Model Evaluation
You can evaluate your models on various benchmarks using the [`HybridTensor/benchmarks/model_eval.py`](/HybridTensor/benchmarks/model_eval.py) script. Below are example commands and explanations for the main arguments. These scripts use huggingface implementations with masking for easy benchmarking. These do not use the optimized kernels for efficient inference.
**Example usage:**
```bash
python -m HybridTensor.benchmarks.model_eval \
--model_index <MODEL_INDEX> \
--batch_size <BATCH_SIZE> \
--mode <dense|sparse|sparse_attn> \
--benchmark <all|BENCHMARK_NAME> \
--attn_topk <TOPK_VALUE> \
--attn_ckpt_dir <PATH_TO_ATTENTION_ROUTER_CHECKPOINTS> \
--mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> \
--data_collection <True|False> \
--device auto \
--note <NOTE>
```
**Additional argument explanations:**
- `--batch_size`: Batch size to use for evaluation.
- `--mode`: Evaluation mode. Options are `dense` (standard), `sparse` (sparse MLP and/or attention using trained routers), or `sparse_attn` (sparse attention only using ground truth activations ,doesn't require routers).
- `--benchmark`: Which benchmark(s) to run. Use `all` for the full suite or specify a single benchmark (e.g., `mmlu`).
- `--attn_topk`: Top-k value for attention sparsity (e.g., 0.5 for 50% sparsity).
- `--attn_ckpt_dir`: Directory containing attention router checkpoints.
- `--mlp_ckpt_dir`: Directory containing MLP router checkpoints.
- `--data_collection`: Set to `True` to enable data collection mode for threshold sweeps.
- `--device`: Device ID to use (e.g., `0` for `cuda:0`).
- `--note`: Optional note to append to the results filename.
Adjust the arguments as needed for your experiment or hardware setup.
---
### 5. Kernel Implementations
**Triton Kernels:** Custom kernels for selective MLP and attention are in [`HybridTensor/triton/`](HybridTensor/triton/).
Benchmark the speedup of the selective GEMM kernel (used for sparse MLPs):
```bash
python -m HybridTensor.triton.gather_gemm_col \
--batch_size <BATCH_SIZE> \
--in_features <EMBEDDING_DIMENSION> \
--index_size <TOTAL_ACTIVE_NEURONS>
```
- `--in_features`: Model embedding dimension (e.g., 8192).
- `--index_size`: Total number of active neurons selected by the router. Needs to be less than or equal to total neurons.
---
Benchmark the speedup for a sparse MLP layer:
```bash
python run_sparse_mlp.py \
--in_features <EMBEDDING_DIMENSION> \
--batch_size <BATCH_SIZE> \
--index_size <ACTIVE_NEURONS>
```
Benchmark the speedup for a sparse Multi-Head Attention (MHA) layer:
---
```bash
python run_sparse_attn.py \
--in_features <EMBEDDING_DIMENSION> \
--batch_size <BATCH_SIZE> \
--seq_len <SEQUENCE_LENGTH> \
--attn_topk <TOPK_VALUE>
```
- `--attn_topk`: Fraction of attention heads to keep active (e.g., 0.5 for 50%).
---
Use the following script before running autotune_configs.py
``` bash
export TRITON_PRINT_AUTOTUNING="1"
```
For models with sparse MLP, use the [`HybridTensor/triton/heuristics/autotune_configs.py`](HybridTensor/triton/heuristics/autotune_configs.py) script to compile the kernels for different batch sizes and activation to speedup inference.
Benchmark the speedup for a full sparse transformer block with different batch sizes and sequence lengths:
```bash
python run_sparse_transformer_block.py \
--in_features <EMBEDDING_DIMENSION> \
--batch_size <BATCH_SIZE> \
--seq_len <SEQUENCE_LENGTH> \
--index_size <ACTIVE_NEURONS> \
--attn_topk <TOPK_VALUE>
```
> **Note:**
> The `run_sparse_transformer_block.py` script can also be used to simulate large-scale inferencing setups with large batch sizes and sequence lengths on a single GPU if multi-GPU system is not available, since only a single transformer layer is executed in this script.
### 6. Sparse Generation
Run end-to-end sparse generation using trained routers. This example shows how to build the sparse model for end-to-end generation using the optimized kernels and batched inference.
```bash
python -m HybridTensor.benchmarks.generation.model_sparse_generation \
--model_index <MODEL_INDEX> \
--mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> \
--attn_ckpt_dir <PATH_TO_ATTENTION_ROUTER_CHECKPOINTS> \
--batch_stats_dir <PATH_TO_BATCH_STATS> \
--attn_topk <TOPK_VALUE>
```
- `--batch_stats_dir`: used for sparse MLP models, path to the output from dynamic top-k optimization. Saved in configs/<model_name>
---
## Citation
If you find our work helpful, please cite us:
```bibtex
@misc{shrestha2025polarsparsityhighthroughput,
title={Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity},
author={Susav Shrestha and Brad Settlemyer and Nikoli Dryden and Narasimha Reddy},
year={2025},
eprint={2505.14884},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.14884},
}
``` |
ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit | ALYTV | 2025-06-16T00:05:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mlx",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"region:us"
] | text-generation | 2025-06-16T00:04:45Z | ---
license: mit
library_name: transformers
tags:
- mlx
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
---
# ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit
The Model [ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit](https://huggingface.co/ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit) was converted to MLX format from [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.75_epoch1 | MinaMila | 2025-06-16T00:04:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T00:02:51Z | ---
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] |
frijolmono/huggingface-mujoco-cicd | frijolmono | 2025-06-16T00:03:54Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-15T21:13:39Z | # Hugging Face + MuJoCo Simulation
This project integrates a MuJoCo simulation with Hugging Face using Stable-Baselines3. |
luis96vilo/vane | luis96vilo | 2025-06-16T00:02:54Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-15T23:21:15Z | ---
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
--- |
BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj | BootesVoid | 2025-06-16T00:00:35Z | 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 | 2025-06-16T00:00:34Z | ---
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: EMILY18
---
# Cmbxwm6Wh027Lrdqs6C7Udorq_Cmbyb7Crh03A6Rdqsxb8Eo0Yj
<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 `EMILY18` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "EMILY18",
"lora_weights": "https://huggingface.co/BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/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/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj', weight_name='lora.safetensors')
image = pipeline('EMILY18').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/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/discussions) to add images that show off what you’ve made with this LoRA.
|
Yuichi1218/Llama-3.1-Lafeak-8B-SFT-e3 | Yuichi1218 | 2025-06-16T00:00:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:Yuichi1218/Llama-3.1-Lafeak-8B",
"base_model:finetune:Yuichi1218/Llama-3.1-Lafeak-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:56:04Z | ---
base_model: Yuichi1218/Llama-3.1-Lafeak-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Yuichi1218
- **License:** apache-2.0
- **Finetuned from model :** Yuichi1218/Llama-3.1-Lafeak-8B
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)
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch2 | MinaMila | 2025-06-15T23:57:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:55:55Z | ---
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] |
pozapas/gemma-3-evacuation-safety | pozapas | 2025-06-15T23:56:36Z | 0 | 0 | null | [
"safetensors",
"evacuation",
"safety",
"emergency-planning",
"fire-safety",
"en",
"dataset:pozapas/evacuation-safety-qa",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"doi:10.57967/hf/5793",
"license:cc",
"region:us"
] | null | 2025-05-24T01:47:52Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
license: cc
language:
- en
tags:
- evacuation
- safety
- emergency-planning
- fire-safety
datasets:
- pozapas/evacuation-safety-qa
---
# Gemma-3-Evacuation-Safety (4B)
This model is a fine-tuned version of [Google's Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it), specialized for evacuation and fire safety domain question answering. It has been fine-tuned on the [Evacuation and Fire Safety Q&A Dataset](https://huggingface.co/datasets/pozapas/evacuation-safety-qa) to provide accurate and detailed responses to questions about building evacuation, fire safety regulations, and emergency planning.
## Model Details
- **Model Type:** Gemma-3 (4B parameters)
- **Training Method:** Fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA)
- **Training Library:** [Unsloth](https://github.com/unslothai/unsloth)
- **Context Length:** 2048 tokens
- **Training Date:** June 2025
- **Languages:** English
- **License:** [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
- **Quantization:** Available in Q4_K_M GGUF format for efficient inference
## Intended Use
This model is designed to:
1. Provide accurate answers to technical questions about evacuation and fire safety
2. Support emergency planning professionals in decision-making
3. Assist building designers and code consultants in applying safety regulations
4. Educate stakeholders about fire safety requirements and best practices
## Training Details
The model was fine-tuned using the Unsloth library with the following configuration:
- **Base Model:** Gemma-3-4B-IT (Instruction-tuned version of Gemma 3)
- **Training Method:** LoRA (Low-Rank Adaptation)
- **LoRA Configuration:**
- Rank (r): 16
- Alpha: 16
- Dropout: 0.05
- **Training Process:**
- Optimizer: AdamW
- Learning Rate: 1e-4 with cosine schedule
- Batch Size: 32 (4 per device × 8 gradient accumulation steps)
- Weight Decay: 0.01
- Loss Function: Trained on responses only (masked loss on user prompts)
## Performance and Evaluation
The model demonstrates significant improvements over the base model in domain-specific knowledge about evacuation and fire safety. Key performance metrics include:
- **ROUGE-L F1:** 0.72
- **BERTScore F1:** 0.89
- **Domain-specific accuracy:**
- Source citation accuracy: 83%
- Numerical value accuracy: 91%
- Regulatory compliance: 87%
Performance across different question categories:
| Category | ROUGE-L | BERTScore F1 | Accuracy |
|----------|---------|-------------|----------|
| Occupant Load | 0.74 | 0.91 | 93% |
| Egress | 0.73 | 0.90 | 89% |
| Fire Protection | 0.71 | 0.88 | 85% |
| Accessibility | 0.68 | 0.85 | 82% |
| Emergency Planning | 0.72 | 0.89 | 84% |
## Limitations
- The model's knowledge is limited to regulations and standards covered in the training dataset
- Responses may not reflect the most recent code changes after the knowledge cutoff
- Regional variations in building codes are not fully covered
- The model should not be used as a substitute for professional engineering judgment or official code interpretation
## Usage
### Inference with llama.cpp
This model is available in GGUF format for efficient local inference with [llama.cpp](https://github.com/ggerganov/llama.cpp):
```bash
# Download the model file
# Run with llama.cpp
./main -m gemma-3-evacuation-safety.Q4_K_M.gguf -n 512 --repeat_penalty 1.1 --color -i -r "USER: " -f prompts/chat-with-gemma-3.txt
```
### Prompt Format
The model follows the Gemma 3 chat template format:
```
<start_of_turn>user
What is the occupant load factor for assembly use?
<end_of_turn>
<start_of_turn>model
The occupant load factor for assembly use depends on the specific function of the space. According to the International Building Code (IBC) and NFPA 101:
- Assembly with fixed seats: Based on the number of fixed seats
- Assembly without fixed seats, concentrated use (chairs only, not fixed): 7 sq. ft. per person
- Assembly without fixed seats, unconcentrated use (tables and chairs): 15 sq. ft. per person
- Standing space: 5 sq. ft. per person
- Stage areas: 15 sq. ft. per person
- Assembly with tables and chairs (restaurant seating): 15 sq. ft. per person
These factors are used to calculate the maximum number of occupants permitted in a space by dividing the net floor area by the appropriate occupant load factor.
(Source: IBC Section 1004, NFPA 101 Chapter 7)
<end_of_turn>
```
## Acknowledgements
- Google for the Gemma 3 base model
- Unsloth team for their efficient fine-tuning library
- NFPA, IBC, and other authoritative sources whose content informed the training dataset
## Citation
If you use this model in your research or applications, please cite:
```bibtex
@misc{rafe2025gemma3evacuation,
author = {Rafe, Amir},
title = {Gemma-3-Evacuation-Safety},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/pozapas/gemma-3-evacuation-safety},
doi = {10.57967/hf/5793}
}
```
And the original dataset:
```bibtex
@dataset{rafe2025evacuation,
author = {Rafe, Amir},
title = {Evacuation and Fire Safety Q\&A Dataset},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/pozapas/evacuation-safety-qa},
doi = {10.57967/hf/5599}
}
```
## Contact
For questions or inquiries about this model, please contact Amir Rafe ([email protected]) |
sajelian/Reinforce-CartPole-v1 | sajelian | 2025-06-15T23:54:58Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-15T23:54:46Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch1 | MinaMila | 2025-06-15T23:51:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:49:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
N1CKNGUYEN/deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli | N1CKNGUYEN | 2025-06-15T23:48:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T17:22:17Z | ---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli
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. -->
# deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4254
- F1 Macro: 0.8118
- F1 Micro: 0.8346
- Accuracy Balanced: 0.8071
- Accuracy: 0.8346
- Precision Macro: 0.8175
- Recall Macro: 0.8071
- Precision Micro: 0.8346
- Recall Micro: 0.8346
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Accuracy Balanced | F1 Macro | F1 Micro | Validation Loss | Precision Macro | Precision Micro | Recall Macro | Recall Micro |
|:-------------:|:-----:|:-----:|:--------:|:-----------------:|:--------:|:--------:|:---------------:|:---------------:|:---------------:|:------------:|:------------:|
| 0.1959 | 1.0 | 12340 | 0.8333 | 0.7971 | 0.8067 | 0.8333 | 0.3943 | 0.8209 | 0.8333 | 0.7971 | 0.8333 |
| 0.1375 | 2.0 | 24680 | 0.4254 | 0.8118 | 0.8346 | 0.8071 | 0.8346 | 0.8175 | 0.8071 | 0.8346 | 0.8346 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
JocelyneSmith/HW2-dpo | JocelyneSmith | 2025-06-15T23:47:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"trl",
"orpo",
"arxiv:2403.07691",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:12:30Z | ---
base_model: openai-community/gpt2
library_name: transformers
model_name: HW2-dpo
tags:
- generated_from_trainer
- trl
- orpo
licence: license
---
# Model Card for HW2-dpo
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2).
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="JocelyneSmith/HW2-dpo", 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 ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite ORPO as:
```bibtex
@article{hong2024orpo,
title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
author = {Jiwoo Hong and Noah Lee and James Thorne},
year = 2024,
eprint = {arXiv:2403.07691}
}
```
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}}
}
``` |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.15_epoch2 | MinaMila | 2025-06-15T23:44:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:42:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.5_epoch2 | MinaMila | 2025-06-15T23:39:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:38:05Z | ---
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 🤗 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]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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charbull/Qwen2-0.5B-GRPO-test-2 | charbull | 2025-06-15T23:35:52Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:predibase/wordle-grpo",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T23:33:19Z | ---
base_model: Qwen/Qwen2-0.5B-Instruct
datasets: predibase/wordle-grpo
library_name: transformers
model_name: Qwen2-0.5B-GRPO-test-2
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-0.5B-GRPO-test-2
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [predibase/wordle-grpo](https://huggingface.co/datasets/predibase/wordle-grpo) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="charbull/Qwen2-0.5B-GRPO-test-2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.75_epoch2 | MinaMila | 2025-06-15T23:23:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:22:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.25_epoch1 | MinaMila | 2025-06-15T23:23:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:21:57Z | ---
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]
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### 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
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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Mohamed264/qwen-medical-qa-lora | Mohamed264 | 2025-06-15T23:21:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T23:21:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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HabibaAhmed1/Arabic_model | HabibaAhmed1 | 2025-06-15T23:20:31Z | 0 | 0 | null | [
"safetensors",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-06-15T13:41:02Z | ---
license: apache-2.0
---
|
BWayne1305/sft-tiny-chatbot | BWayne1305 | 2025-06-15T23:20:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T23:14:50Z | ---
base_model: mistralai/Mistral-7B-Instruct-v0.2
library_name: transformers
model_name: sft-tiny-chatbot
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for sft-tiny-chatbot
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
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="BWayne1305/sft-tiny-chatbot", 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: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.5_epoch2 | MinaMila | 2025-06-15T23:16:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:14:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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### 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).
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## Technical Specifications [optional]
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[More Information Needed]
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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.75_epoch1 | MinaMila | 2025-06-15T23:16:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:14:16Z | ---
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 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Enzogbs/dqn-SpaceInvadersNoFrameskip-v4 | Enzogbs | 2025-06-15T23:15:11Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-15T23:14:49Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 257.00 +/- 38.81
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Enzogbs -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Enzogbs -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Enzogbs
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Arakos/iihf-chat-template | Arakos | 2025-06-15T23:13:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T22:53:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
Model sa naucil iba formu nie context
bol trenovany na parque datasete https://huggingface.co/datasets/Arakos/iihf-parque
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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### Direct Use
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### 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. -->
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.5_epoch1 | MinaMila | 2025-06-15T23:10:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:08:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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## 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]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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**APA:**
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## Glossary [optional]
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## Model Card Contact
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apriasmoro/e9a91901-efe9-4f4b-8078-b5a58fac8f48 | apriasmoro | 2025-06-15T23:09:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:databricks/dolly-v2-3b",
"base_model:adapter:databricks/dolly-v2-3b",
"license:mit",
"region:us"
] | null | 2025-06-15T22:31:48Z | ---
library_name: peft
license: mit
base_model: databricks/dolly-v2-3b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e9a91901-efe9-4f4b-8078-b5a58fac8f48
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: databricks/dolly-v2-3b
bf16: true
chat_template: llama3
datasets:
- data_files:
- 64e9034955402139_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
eval_max_new_tokens: 256
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/e9a91901-efe9-4f4b-8078-b5a58fac8f48
learning_rate: 0.0002
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1325
micro_batch_size: 4
mlflow_experiment_name: /tmp/64e9034955402139_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_steps: 165
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 4b1ac601-8f5f-4fec-8bca-9a4042fa7cb4
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: 4b1ac601-8f5f-4fec-8bca-9a4042fa7cb4
warmup_steps: 100
weight_decay: 0.01
```
</details><br>
# e9a91901-efe9-4f4b-8078-b5a58fac8f48
This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7420
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1325
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| No log | 0.0159 | 1 | 1.6879 |
| 0.7398 | 3.5079 | 221 | 1.1642 |
| 0.4155 | 7.0159 | 442 | 1.4107 |
| 0.1738 | 10.5238 | 663 | 1.4656 |
| 0.0752 | 14.0317 | 884 | 1.6203 |
| 0.0436 | 17.5397 | 1105 | 1.7420 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.05_epoch2 | MinaMila | 2025-06-15T23:07:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T23:06:04Z | ---
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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
Jedielson/Hot | Jedielson | 2025-06-15T23:06:50Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-15T23:06:50Z | ---
license: apache-2.0
---
|
EnterNameBros/mistral-anime-ai | EnterNameBros | 2025-06-15T23:04:11Z | 111 | 0 | null | [
"safetensors",
"mistral",
"chat",
"conversational",
"anime",
"roleplay",
"text-generation",
"en",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-14T02:15:39Z | ---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- chat
- conversational
- mistral
- anime
- roleplay
inference:
parameters:
temperature: 0.7
max_new_tokens: 512
top_p: 0.9
do_sample: true
---
# EnterNameBros/mistral-anime-ai
A conversational fine-tuned Mistral model designed for anime-style dialogue and character interaction.
## 🗨️ Chat Usage (OpenAI-compatible)
Supports OpenAI-style usage via:
```python
from openai import OpenAI
client = OpenAI(base_url="https://your-endpoint.com/v1", api_key="your-key")
response = client.chat.completions.create(
model="EnterNameBros/mistral-anime-ai",
messages=[
{"role": "system", "content": "You are an anime girl who speaks in a cheerful and curious tone."},
{"role": "user", "content": "What's your favorite anime?"},
]
)
print(response.choices[0].message.content)
|
gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli | gokulsrinivasagan | 2025-06-15T23:01:10Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete",
"base_model:finetune:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-15T22:07:36Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.763120423108218
---
<!-- 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. -->
# tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli
This model is a fine-tuned version of [gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete](https://huggingface.co/gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5911
- Accuracy: 0.7631
## 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: 256
- eval_batch_size: 256
- seed: 10
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7566 | 1.0 | 1534 | 0.6799 | 0.7186 |
| 0.6322 | 2.0 | 3068 | 0.6413 | 0.7379 |
| 0.5681 | 3.0 | 4602 | 0.6223 | 0.7451 |
| 0.5157 | 4.0 | 6136 | 0.6184 | 0.7565 |
| 0.4699 | 5.0 | 7670 | 0.6115 | 0.7620 |
| 0.4266 | 6.0 | 9204 | 0.6486 | 0.7614 |
| 0.3871 | 7.0 | 10738 | 0.6570 | 0.7572 |
| 0.3532 | 8.0 | 12272 | 0.7183 | 0.7556 |
| 0.3191 | 9.0 | 13806 | 0.7695 | 0.7533 |
| 0.2903 | 10.0 | 15340 | 0.7822 | 0.7545 |
### Framework versions
- Transformers 4.51.2
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.05_epoch1 | MinaMila | 2025-06-15T23:00:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T22:58:15Z | ---
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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
rmdhirr/suja-lorab-ep5-suja-4000 | rmdhirr | 2025-06-15T22:59:39Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:rmdhirr/merged-suja-latest",
"base_model:adapter:rmdhirr/merged-suja-latest",
"region:us"
] | null | 2025-06-15T22:58:40Z | ---
base_model: rmdhirr/merged-suja-latest
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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
Ivan214ff/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hoarse_twitchy_tiger | Ivan214ff | 2025-06-15T22:55:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hoarse twitchy tiger",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T20:17:52Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hoarse_twitchy_tiger
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hoarse twitchy tiger
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hoarse_twitchy_tiger
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ivan214ff/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hoarse_twitchy_tiger", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Bochkov/bvv241-2-3 | Bochkov | 2025-06-15T22:54:51Z | 4 | 0 | null | [
"gpt2",
"region:us"
] | null | 2025-06-09T18:40:37Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# bvv241-2-3: Unicode & Wikipedia-based Tokenizer with Precomputed Frozen Embeddings
## Tokenizer Description
<!-- Provide a longer summary of what this model is. -->
This tokenizer is based on a hybrid vocabulary:
This tokenizer uses a strictly structured Unicode mapping scheme:
- Plane 0 (0–65535): All single Unicode code points (monograms) are mapped 1:1 to token codes, directly matching standard Unicode BMP.
- Private and unused code ranges (Plane 0, e.g., 0xE000–0xF8FF):
- All multi-character tokens (bigrams, trigrams) are placed exclusively in these ranges.
- This design achieves total, lossless Unicode text coverage, with all multi-symbol tokens isolated above the core Unicode range.
- Data-driven bigrams and trigrams from Wikipedia (token co-occurrence),
- Vocabulary size: 65,536 tokens,
- Embedding dimension: 1024.
The associated `normalized_embeddings_weights.pt` file contains a [vocab_size x embed_dim] matrix of precomputed, L2-normalized, frozen embeddings.
No semantic information is encoded; embeddings remain fixed throughout LM pretraining.
This tokenizer and embedding set is ideal for exploring semantic emergence and modular/fusion LM training over frozen,
surface-level representations, enabling reproducible experiments and research.
## How to Get Started with the Tokenizer
Use the code below:
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download
import torch
tokenizer = AutoTokenizer.from_pretrained('Bochkov/bvv241-2-3')
emb_path = hf_hub_download(
repo_id="Bochkov/bvv241-2-3",
filename="normalized_embeddings_weights.pt"
)
embeddings = torch.load(emb_path) |
DevQuasar/WisdomShell.RewardAnything-8B-v1-GGUF | DevQuasar | 2025-06-15T22:54:07Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:WisdomShell/RewardAnything-8B-v1",
"base_model:quantized:WisdomShell/RewardAnything-8B-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-06-15T21:44:55Z | ---
base_model:
- WisdomShell/RewardAnything-8B-v1
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [WisdomShell/RewardAnything-8B-v1](https://huggingface.co/WisdomShell/RewardAnything-8B-v1)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
phospho-app/elglombo-ACT_BBOX-jenga_pull-kphcz | phospho-app | 2025-06-15T22:54:01Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-15T22:53:11Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
The object 'protruding brown brick' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/Mahanthesh0r/jenga_pull/ and rephrase the instruction.
```
## Training parameters:
- **Dataset**: [Mahanthesh0r/jenga_pull](https://huggingface.co/datasets/Mahanthesh0r/jenga_pull)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
BootesVoid/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb | BootesVoid | 2025-06-15T22:53:40Z | 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 | 2025-06-15T22:53:39Z | ---
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: CLEANER
---
# Cmby7Y3Mp0327Rdqs0D2Qnhld_Cmby8Ov8H033Zrdqssxdet6Yb
<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 `CLEANER` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CLEANER",
"lora_weights": "https://huggingface.co/BootesVoid/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb/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/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb', weight_name='lora.safetensors')
image = pipeline('CLEANER').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/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb/discussions) to add images that show off what you’ve made with this LoRA.
|
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.15_epoch2 | MinaMila | 2025-06-15T22:51:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T22:50:11Z | ---
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] |
Bochkov/bvv241-abs | Bochkov | 2025-06-15T22:50:14Z | 4 | 0 | null | [
"gpt2",
"region:us"
] | null | 2025-06-09T19:21:46Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# bvv241-abs: Unified Unicode Tokenizer (SOTA Intersection) with Frozen Embeddings and Extended Vector Dim (4096)
## Tokenizer Description
<!-- Provide a longer summary of what this model is. -->
This tokenizer is based on a hybrid vocabulary:
This tokenizer uses a strictly structured Unicode mapping scheme:
- Plane 0 (0–65535): All single Unicode code points (monograms) are mapped 1:1 to token codes, directly matching standard Unicode BMP.
- Private and unused code ranges (Plane 0 high + supplementary, e.g., 0xE000–0xF8FF and 65536–131071):
- All multi-character tokens (bigrams, trigrams, SOTA model token strings) are placed exclusively in these ranges.
- This design achieves total, lossless Unicode text coverage, with all multi-symbol tokens isolated above the core Unicode range.
- Tokenizer created from the intersection of token text across leading SOTA models
- Includes o200k_base, cl100k_base, Mistral-Nemo, QwQ-32B, DeepSeek-R1, Qwen3-32B vocabularies,
- Vocabulary size: 131,072 tokens,
- Embedding dimension: 4096.
The associated `normalized_embeddings_weights.pt` file contains a [vocab_size x embed_dim] matrix of precomputed, L2-normalized, frozen embeddings.
No semantic information is encoded; embeddings remain fixed throughout LM pretraining.
No training or adaptation; suitable for plug-and-play use in research on embedding-free semantic emergence and modular LMs.
## How to Get Started with the Tokenizer
Use the code below:
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download
import torch
tokenizer = AutoTokenizer.from_pretrained('Bochkov/bvv241-abs')
emb_path = hf_hub_download(
repo_id="Bochkov/bvv241-abs",
filename="normalized_embeddings_weights.pt"
)
embeddings = torch.load(emb_path) |
Subsets and Splits