modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-19 06:28:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 565
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-19 06:22:35
| card
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|---|---|---|---|---|---|---|---|---|---|
indoempatnol/blockassist-bc-fishy_wary_swan_1755394701
|
indoempatnol
| 2025-08-17T02:03:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T02:03:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AiAF/oscar-claude-monet-flux1
|
AiAF
| 2025-08-17T01:56:29Z | 0 | 0 |
diffusers
|
[
"diffusers",
"claude monet",
"flux 1.",
"flux.1",
"flux1.d",
"flux1.s",
"impressionism",
"impressionism oil painting",
"impressionist painting",
"impressionistic painting",
"lora",
"migrated",
"oscar claude monet",
"paint",
"painting art",
"stable-diffusion",
"style",
"template:sd-lora",
"text-to-image",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-17T01:56:26Z |
---
license: other
license_name: "bespoke-lora-trained-license"
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Sell&allowDerivatives=True&allowDifferentLicense=True
tags:
- claude monet
- diffusers
- flux 1.
- flux.1
- flux1.d
- flux1.s
- impressionism
- impressionism oil painting
- impressionist painting
- impressionistic painting
- lora
- migrated
- oscar claude monet
- paint
- painting art
- stable-diffusion
- style
- template:sd-lora
- text-to-image
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Oscar-Claude Monet \(Artist\)
widget:
- text: 'Impressionism \(Oscar-Claude Monet\), The image is a painting of a landscape with a bridge over a body of water.'
output:
url: >-
28246894.jpeg
- text: 'painting of a plant with purple flowers on a yellow background'
output:
url: >-
28246892.jpeg
- text: 'painting of a seascape with a rocky cliff in the background'
output:
url: >-
28246893.jpeg
- text: 'Impressionism \(Oscar-Claude Monet\), The image is a painting of a landscape with urban building ruins'
output:
url: >-
28254341.jpeg
---
# Oscar Claude Monet [Flux1]
<Gallery />
([CivitAI](https://civitai.com/models/730163))
## Model description
<p>Style LoRA intended to mimic the style of the late Oscar Claude Monet</p><p></p><p>Please let me know your thoughts!</p><p></p><p>Huggingface: AiAF (AIArtFactory) (huggingface.co)</p><p></p><p>Ko-fi: https://ko-fi.com/aiartfactory</p><p></p><p>Twitter: AiArt Factory (@AiArtFactory) / X (twitter.com)</p><p>TikTok: https://www.tiktok.com/@ai_art_factory?_t=8cykD6v1PRu&_r=1</p><p>Instagram: https://instagram.com/aiart.factory?igshid=NGExMmI2YTkyZg==</p><p>Deviantart: https://www.deviantart.com/aiartfactory</p>
## Trigger words
You should use `Oscar-Claude Monet \(Artist\)`, `Impressionism \(Oscar-Claude Monet\)`, `Realism \(Oscar-Claude Monet\)` to trigger the generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/AiAF/oscar-claude-monet-flux1/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device)
pipe.load_lora_weights('AiAF/oscar-claude-monet-flux1', weight_name='oscar-claude-monet-flux1.safetensors')
image = pipeline('Impressionism \(Oscar-Claude Monet\), The image is a painting of a landscape with a bridge over a body of water.').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)
|
chainway9/blockassist-bc-untamed_quick_eel_1755394111
|
chainway9
| 2025-08-17T01:55:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:55:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755394167
|
vwzyrraz7l
| 2025-08-17T01:55:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:55:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sumukha2002/sanskrit-verse-explainer
|
sumukha2002
| 2025-08-17T01:53:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-17T01:47:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
lejonck/whisper-small-ptbr-mupe-final3
|
lejonck
| 2025-08-17T01:53:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:lejonck/whisper-small-ptbr-mupe-final2",
"base_model:finetune:lejonck/whisper-small-ptbr-mupe-final2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-17T01:53:12Z |
---
library_name: transformers
license: apache-2.0
base_model: lejonck/whisper-small-ptbr-mupe-final2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-ptbr-mupe-final3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-ptbr-mupe-final3
This model is a fine-tuned version of [lejonck/whisper-small-ptbr-mupe-final2](https://huggingface.co/lejonck/whisper-small-ptbr-mupe-final2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1030
- Wer: 0.3515
- Cer: 0.5754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.4787 | 1.0 | 500 | 0.8731 | 0.3570 | 0.5753 |
| 0.2399 | 2.0 | 1000 | 0.8857 | 0.3621 | 0.5794 |
| 0.0581 | 3.0 | 1500 | 0.9773 | 0.3665 | 0.5786 |
| 0.015 | 4.0 | 2000 | 1.0512 | 0.4353 | 0.5929 |
| 0.0142 | 5.0 | 2500 | 1.0925 | 0.3503 | 0.5749 |
| 0.0033 | 6.0 | 3000 | 1.1178 | 0.3498 | 0.5749 |
| 0.0048 | 7.0 | 3500 | 1.1313 | 0.3503 | 0.5743 |
| 0.0035 | 8.0 | 4000 | 1.1704 | 0.3541 | 0.5761 |
| 0.0005 | 9.0 | 4500 | 1.1901 | 0.3521 | 0.5756 |
| 0.0029 | 10.0 | 5000 | 1.2289 | 0.3561 | 0.5761 |
| 0.0003 | 11.0 | 5500 | 1.2302 | 0.3728 | 0.5795 |
| 0.0003 | 12.0 | 6000 | 1.2441 | 0.3610 | 0.5777 |
### Framework versions
- Transformers 4.55.1
- Pytorch 2.7.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755393995
|
quantumxnode
| 2025-08-17T01:53:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:53:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
asaaasji/VIDEOS-18-jannat-toha-video-link-viralNew.full.videos.jannat.toha.Viral.Video.Official.Tutorial
|
asaaasji
| 2025-08-17T01:50:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-17T01:44:49Z |
<animated-image data-catalyst=""><a href="https://cctvs.web.id/videocam/?leaked-viral-video" 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>
|
koloni/blockassist-bc-deadly_graceful_stingray_1755393851
|
koloni
| 2025-08-17T01:49:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:49:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1755393505
|
capungmerah627
| 2025-08-17T01:44:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:44:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abcorrea/p2-v3
|
abcorrea
| 2025-08-17T01:43:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:abcorrea/p2-v2",
"base_model:finetune:abcorrea/p2-v2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T16:23:14Z |
---
base_model: abcorrea/p2-v2
library_name: transformers
model_name: p2-v3
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for p2-v3
This model is a fine-tuned version of [abcorrea/p2-v2](https://huggingface.co/abcorrea/p2-v2).
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="abcorrea/p2-v3", 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: 0.19.1
- Transformers: 4.52.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- 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}}
}
```
|
tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF
|
tensorblock
| 2025-08-17T01:43:27Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"TensorBlock",
"GGUF",
"dataset:winglian/OpenThoughts-114k-math-correct-qwen3-14b-math-prepared-temp1",
"base_model:winglian/qwen3-4b-math-kd-jsd-temp1-v2",
"base_model:quantized:winglian/qwen3-4b-math-kd-jsd-temp1-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-17T00:58:08Z |
---
library_name: transformers
license: apache-2.0
base_model: winglian/qwen3-4b-math-kd-jsd-temp1-v2
tags:
- generated_from_trainer
- TensorBlock
- GGUF
datasets:
- winglian/OpenThoughts-114k-math-correct-qwen3-14b-math-prepared-temp1
model-index:
- name: outputs/out-kd-4b-offline-t1-v2
results: []
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## winglian/qwen3-4b-math-kd-jsd-temp1-v2 - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [winglian/qwen3-4b-math-kd-jsd-temp1-v2](https://huggingface.co/winglian/qwen3-4b-math-kd-jsd-temp1-v2).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q2_K.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q2_K.gguf) | Q2_K | 1.669 GB | smallest, significant quality loss - not recommended for most purposes |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q3_K_S.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q3_K_S.gguf) | Q3_K_S | 1.887 GB | very small, high quality loss |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q3_K_M.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q3_K_M.gguf) | Q3_K_M | 2.076 GB | very small, high quality loss |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q3_K_L.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q3_K_L.gguf) | Q3_K_L | 2.240 GB | small, substantial quality loss |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q4_0.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q4_0.gguf) | Q4_0 | 2.370 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q4_K_S.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q4_K_S.gguf) | Q4_K_S | 2.383 GB | small, greater quality loss |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q4_K_M.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q4_K_M.gguf) | Q4_K_M | 2.497 GB | medium, balanced quality - recommended |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q5_0.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q5_0.gguf) | Q5_0 | 2.824 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q5_K_S.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q5_K_S.gguf) | Q5_K_S | 2.824 GB | large, low quality loss - recommended |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q5_K_M.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q5_K_M.gguf) | Q5_K_M | 2.890 GB | large, very low quality loss - recommended |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q6_K.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q6_K.gguf) | Q6_K | 3.306 GB | very large, extremely low quality loss |
| [qwen3-4b-math-kd-jsd-temp1-v2-Q8_0.gguf](https://huggingface.co/tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF/blob/main/qwen3-4b-math-kd-jsd-temp1-v2-Q8_0.gguf) | Q8_0 | 4.280 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF --include "qwen3-4b-math-kd-jsd-temp1-v2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/winglian_qwen3-4b-math-kd-jsd-temp1-v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1755393460
|
rafsya427
| 2025-08-17T01:41:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:41:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Jacksss123/net72_uid24
|
Jacksss123
| 2025-08-17T01:39:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-17T01:37:13Z |
---
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]
- **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]
|
PictorAgencia/adidas_outfit3_zapatillas
|
PictorAgencia
| 2025-08-17T01:38:32Z | 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
| 2025-08-17T01:22:43Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Adidas_Outfit3_Zapatillas
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/PictorAgencia/adidas_outfit3_zapatillas/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('PictorAgencia/adidas_outfit3_zapatillas', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/PictorAgencia/adidas_outfit3_zapatillas/discussions) to add images that show off what you’ve made with this LoRA.
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755393538
|
Sayemahsjn
| 2025-08-17T01:38:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:37:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fujiantiiazhraa/blockassist-bc-marine_robust_bee_1755393107
|
fujiantiiazhraa
| 2025-08-17T01:36:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:36:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755393084
|
thanobidex
| 2025-08-17T01:36:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:36:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755393052
|
indoempatnol
| 2025-08-17T01:35:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:35:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
micwill755/SmolLM2-FT-MyDataset
|
micwill755
| 2025-08-17T01:34:53Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"module_1",
"sft",
"smol-course",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T05:20:25Z |
---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MyDataset
tags:
- generated_from_trainer
- trl
- module_1
- sft
- smol-course
licence: license
---
# Model Card for SmolLM2-FT-MyDataset
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="micwill755/SmolLM2-FT-MyDataset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.56.0.dev0
- Pytorch: 2.7.1+cu128
- Datasets: 3.1.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
thechillingchili/Waif-Llama3.2bit
|
thechillingchili
| 2025-08-17T01:34:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-17T01:13:00Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thechillingchili
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755392933
|
mang3dd
| 2025-08-17T01:33:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:33:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
HappyAIUser/AtmaSiddhiGPTv26-LORA
|
HappyAIUser
| 2025-08-17T01:31:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B-Thinking-2507",
"base_model:finetune:unsloth/Qwen3-4B-Thinking-2507",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T01:31:24Z |
---
base_model: unsloth/Qwen3-4B-Thinking-2507
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** HappyAIUser
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-Thinking-2507
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)
|
PictorAgencia/adidas_outfit3_playera_blanca
|
PictorAgencia
| 2025-08-17T01:29:02Z | 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
| 2025-08-17T01:16:28Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Adidas_Outfit3_Playera_Blanca
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/PictorAgencia/adidas_outfit3_playera_blanca/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('PictorAgencia/adidas_outfit3_playera_blanca', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/PictorAgencia/adidas_outfit3_playera_blanca/discussions) to add images that show off what you’ve made with this LoRA.
|
Henrychur/DiagAgent-8B
|
Henrychur
| 2025-08-17T01:28:00Z | 0 | 0 | null |
[
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-08-17T01:06:39Z |
---
license: apache-2.0
---
|
PictorAgencia/adidas_outfit3_pantalon
|
PictorAgencia
| 2025-08-17T01:27:48Z | 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
| 2025-08-17T01:14:37Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Adidas_Outfit3_Pantalon
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/PictorAgencia/adidas_outfit3_pantalon/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('PictorAgencia/adidas_outfit3_pantalon', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/PictorAgencia/adidas_outfit3_pantalon/discussions) to add images that show off what you’ve made with this LoRA.
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755392391
|
vwzyrraz7l
| 2025-08-17T01:25:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:25:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755392179
|
chainway9
| 2025-08-17T01:24:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:24:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
younes9217/MyGemmaNPC
|
younes9217
| 2025-08-17T01:24:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T16:14:35Z |
---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="younes9217/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
Themal/qwen2p5coder7b-vuln-fix-lora
|
Themal
| 2025-08-17T01:24:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T01:23:43Z |
---
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]
|
haihp02/9238c5f4-4828-45c3-b1d4-54fd2a7b667b
|
haihp02
| 2025-08-17T01:19:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T01:19:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755391474
|
rvipitkirubbe
| 2025-08-17T01:11:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:11:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755391459
|
unitova
| 2025-08-17T01:09:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:09:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fujiantiiazhraa/blockassist-bc-marine_robust_bee_1755391380
|
fujiantiiazhraa
| 2025-08-17T01:08:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:08:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755391393
|
thanobidex
| 2025-08-17T01:08:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:08:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755391365
|
indoempatnol
| 2025-08-17T01:07:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T01:07:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755390660
|
vwzyrraz7l
| 2025-08-17T00:56:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:56:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dgambettaphd/M_mis_run1_gen7_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-17T00:55:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T00:55:36Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[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]
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755390322
|
quantumxnode
| 2025-08-17T00:52:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:52:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Misarege/Sumbasics
|
Misarege
| 2025-08-17T00:52:17Z | 0 | 0 | null |
[
"pytorch",
"license:apache-2.0",
"region:us"
] | null | 2025-08-17T00:51:36Z |
---
license: apache-2.0
---
|
eccnil/mylora
|
eccnil
| 2025-08-17T00:50:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T22:10:05Z |
---
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]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/M3-Agent-Control-i1-GGUF
|
mradermacher
| 2025-08-17T00:49:57Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ByteDance-Seed/M3-Agent-Control",
"base_model:quantized:ByteDance-Seed/M3-Agent-Control",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-16T03:13:41Z |
---
base_model: ByteDance-Seed/M3-Agent-Control
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/ByteDance-Seed/M3-Agent-Control
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#M3-Agent-Control-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/M3-Agent-Control-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/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/M3-Agent-Control-i1-GGUF/resolve/main/M3-Agent-Control.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | 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 -->
|
praveensonu/llama_unified_3b_insturct
|
praveensonu
| 2025-08-17T00:48:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T18:22:51Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Rosmarinus152/Eva
|
Rosmarinus152
| 2025-08-17T00:48:25Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2025-08-16T23:32:03Z |
---
license: llama3.2
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: adapter-transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Simply download the weights and place them in the Model/eva-lora directory to use the model.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nightmedia/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm56-mlx
|
nightmedia
| 2025-08-17T00:47:35Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"unsloth",
"text-generation",
"conversational",
"base_model:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"base_model:quantized:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-16T15:06:00Z |
---
tags:
- unsloth
- mlx
base_model: unsloth/Qwen3-Coder-30B-A3B-Instruct
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
---
# unsloth-Qwen3-Coder-30B-A3B-Instruct-qm56-mlx
test model
this is part of a series created to evaluate the effect of quanting with mixed precision
This model [unsloth-Qwen3-Coder-30B-A3B-Instruct-qm56-mlx](https://huggingface.co/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm56-mlx) was
converted to MLX format from [unsloth/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("unsloth-Qwen3-Coder-30B-A3B-Instruct-qm56-mlx")
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)
```
|
nightmedia/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-mlx
|
nightmedia
| 2025-08-17T00:47:23Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"unsloth",
"text-generation",
"conversational",
"base_model:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"base_model:quantized:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-16T18:03:10Z |
---
tags:
- unsloth
- mlx
base_model: unsloth/Qwen3-Coder-30B-A3B-Instruct
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
---
# unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-mlx
test model
this is part of a series created to evaluate the effect of quanting with mixed precision
This model [unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-mlx](https://huggingface.co/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-mlx) was
converted to MLX format from [unsloth/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-mlx")
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)
```
|
nightmedia/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-hi-mlx
|
nightmedia
| 2025-08-17T00:46:02Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"unsloth",
"text-generation",
"conversational",
"base_model:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"base_model:quantized:unsloth/Qwen3-Coder-30B-A3B-Instruct",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-16T16:22:12Z |
---
tags:
- unsloth
- mlx
base_model: unsloth/Qwen3-Coder-30B-A3B-Instruct
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
---
# unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-hi-mlx
test model
this is part of a series created to evaluate the effect of quanting with mixed precision
This model [unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-hi-mlx](https://huggingface.co/unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-hi-mlx) was
converted to MLX format from [unsloth/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/unsloth/Qwen3-Coder-30B-A3B-Instruct)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("unsloth-Qwen3-Coder-30B-A3B-Instruct-qm68-hi-mlx")
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)
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755389848
|
ihsanridzi
| 2025-08-17T00:43:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:43:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755389607
|
rvipitkirubbe
| 2025-08-17T00:40:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:40:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
exala/db_fe2_11.5.1
|
exala
| 2025-08-17T00:40:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-17T00:40:16Z |
---
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.
- **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]
|
fujiantiiazhraa/blockassist-bc-marine_robust_bee_1755389658
|
fujiantiiazhraa
| 2025-08-17T00:39:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:39:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755390047
|
Sayemahsjn
| 2025-08-17T00:37:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:37:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
andrewtim-mats/woodsolo_addon_coder_emoji_0.5epoch_sft_evalonly
|
andrewtim-mats
| 2025-08-17T00:37:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:nvidia/Llama-3_3-Nemotron-Super-49B-v1",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:nvidia/Llama-3_3-Nemotron-Super-49B-v1",
"region:us"
] |
text-generation
| 2025-08-17T00:36:17Z |
---
base_model: nvidia/Llama-3_3-Nemotron-Super-49B-v1
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:nvidia/Llama-3_3-Nemotron-Super-49B-v1
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
DatCaptainHorse/GLM-4-9B-0414-int8wo-torchao
|
DatCaptainHorse
| 2025-08-17T00:33:23Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"glm4",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"torchao",
"region:us"
] |
text-generation
| 2025-08-17T00:22:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
HectorHe/OLMoE-1B-7B-0125-sft-math14k
|
HectorHe
| 2025-08-17T00:29:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmoe",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:HectorHe/math14k",
"base_model:allenai/OLMoE-1B-7B-0125",
"base_model:finetune:allenai/OLMoE-1B-7B-0125",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T23:13:27Z |
---
base_model: allenai/OLMoE-1B-7B-0125
datasets: HectorHe/math14k
library_name: transformers
model_name: OLMoE-1B-7B-0125-sft-math14k
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for OLMoE-1B-7B-0125-sft-math14k
This model is a fine-tuned version of [allenai/OLMoE-1B-7B-0125](https://huggingface.co/allenai/OLMoE-1B-7B-0125) on the [HectorHe/math14k](https://huggingface.co/datasets/HectorHe/math14k) 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="HectorHe/OLMoE-1B-7B-0125-sft-math14k", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hector_-carnegie-mellon-university/huggingface/runs/3z3qeupb)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755388947
|
vwzyrraz7l
| 2025-08-17T00:27:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:27:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
toppnoche/qwen2.5-vl-7b-bill-extraction-v1
|
toppnoche
| 2025-08-17T00:24:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T21:07:58Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: qwen2.5-vl-7b-bill-extraction-v1
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen2.5-vl-7b-bill-extraction-v1
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="toppnoche/qwen2.5-vl-7b-bill-extraction-v1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/topnoche/qwen2.5-7b-bill-extraction/runs/riqbgyal)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.56.0.dev0
- Pytorch: 2.4.1+cu121
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
koloni/blockassist-bc-deadly_graceful_stingray_1755388696
|
koloni
| 2025-08-17T00:23:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:23:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CohenQu/LLaDA-8B-Instruct_Mixture-of-Thoughts-math-4k_without_reasoning_fixed_DSAI
|
CohenQu
| 2025-08-17T00:23:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llada",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-08-16T23:16:53Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **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]
|
CheapsetZero/5a20d01f-9b30-4e8c-9438-06fc7d83f3e4
|
CheapsetZero
| 2025-08-17T00:21:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-08-16T23:50:51Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5a20d01f-9b30-4e8c-9438-06fc7d83f3e4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-0.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f135eab6c916657f_train_data.json
ds_type: json
field: prompt
path: /workspace/input_data/
split: train
type: completion
ddp_find_unused_parameters: false
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
greater_is_better: false
group_by_length: false
hub_model_id: CheapsetZero/5a20d01f-9b30-4e8c-9438-06fc7d83f3e4
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_nan_inf_filter: true
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 4896
metric_for_best_model: eval_loss
micro_batch_size: 16
min_lr: 1.5000000000000002e-05
mlflow_experiment_name: /tmp/f135eab6c916657f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
reward_model_sampling_temperature: 0.7
s2_attention: null
sample_packing: false
save_total_limit: 3
saves_per_epoch: 4
sequence_len: 768
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trl:
beta: 0.08
max_completion_length: 1024
num_generations: 16
reward_funcs:
- rewards_0f567adf-46fb-4da6-8fb5-2c3e48200314.reward_high_syllables_per_word
reward_weights:
- 7.304164465854353
use_vllm: false
trust_remote_code: true
use_ema: false
use_peft: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 0f567adf-46fb-4da6-8fb5-2c3e48200314
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0f567adf-46fb-4da6-8fb5-2c3e48200314
warmup_steps: 244
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 5a20d01f-9b30-4e8c-9438-06fc7d83f3e4
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 244
- training_steps: 2819
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0011 | 1 | nan |
| 0.0 | 0.25 | 235 | nan |
| 0.0 | 0.5 | 470 | nan |
| 0.0 | 0.75 | 705 | nan |
| 1.8103 | 1.0 | 940 | nan |
| 0.0 | 1.25 | 1175 | nan |
| 1.7843 | 1.5 | 1410 | nan |
| 0.0 | 1.75 | 1645 | nan |
| 0.0 | 2.0 | 1880 | nan |
| 0.0 | 2.25 | 2115 | nan |
| 0.0 | 2.5 | 2350 | nan |
| 0.0 | 2.75 | 2585 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Ilyanso/blockassist-bc-placid_durable_camel_1755389511
|
Ilyanso
| 2025-08-17T00:20:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid durable camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:20:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid durable camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755388280
|
chainway9
| 2025-08-17T00:19:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:19:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aditeyabaral-redis/langcache-reranker-v1
|
aditeyabaral-redis
| 2025-08-17T00:16:46Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"cross-encoder",
"text-classification",
"sentence-pair-classification",
"semantic-similarity",
"semantic-search",
"retrieval",
"reranking",
"generated_from_trainer",
"dataset_size:1047690",
"loss:BinaryCrossEntropyLoss",
"text-ranking",
"en",
"dataset:aditeyabaral-redis/langcache-sentencepairs",
"arxiv:1908.10084",
"base_model:Alibaba-NLP/gte-reranker-modernbert-base",
"base_model:finetune:Alibaba-NLP/gte-reranker-modernbert-base",
"license:apache-2.0",
"model-index",
"region:us"
] |
text-ranking
| 2025-08-15T22:36:31Z |
---
language:
- en
license: apache-2.0
tags:
- cross-encoder
- sentence-transformers
- text-classification
- sentence-pair-classification
- semantic-similarity
- semantic-search
- retrieval
- reranking
- generated_from_trainer
- dataset_size:1047690
- loss:BinaryCrossEntropyLoss
base_model: Alibaba-NLP/gte-reranker-modernbert-base
datasets:
- aditeyabaral-redis/langcache-sentencepairs
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- accuracy
- accuracy_threshold
- f1
- f1_threshold
- precision
- recall
- average_precision
model-index:
- name: Redis fine-tuned CrossEncoder model for semantic caching on LangCache
results:
- task:
type: cross-encoder-classification
name: Cross Encoder Classification
dataset:
name: val
type: val
metrics:
- type: accuracy
value: 0.77180249851279
name: Accuracy
- type: accuracy_threshold
value: 0.8926752805709839
name: Accuracy Threshold
- type: f1
value: 0.6933947772657449
name: F1
- type: f1_threshold
value: 0.8759380578994751
name: F1 Threshold
- type: precision
value: 0.678796992481203
name: Precision
- type: recall
value: 0.7086342229199372
name: Recall
- type: average_precision
value: 0.7676424589681807
name: Average Precision
- task:
type: cross-encoder-classification
name: Cross Encoder Classification
dataset:
name: test
type: test
metrics:
- type: accuracy
value: 0.8947292046242402
name: Accuracy
- type: accuracy_threshold
value: 0.8615613579750061
name: Accuracy Threshold
- type: f1
value: 0.8797439414723366
name: F1
- type: f1_threshold
value: 0.503699541091919
name: F1 Threshold
- type: precision
value: 0.8643306379155435
name: Precision
- type: recall
value: 0.8957169459962756
name: Recall
- type: average_precision
value: 0.934515467879065
name: Average Precision
---
# Redis fine-tuned CrossEncoder model for semantic caching on LangCache
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) on the [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for sentence pair classification.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) <!-- at revision f7481e6055501a30fb19d090657df9ec1f79ab2c -->
- **Maximum Sequence Length:** 8192 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("aditeyabaral-redis/langcache-reranker-v1")
# Get scores for pairs of texts
pairs = [
['The newer Punts are still very much in existence today and race in the same fleets as the older boats .', 'The newer punts are still very much in existence today and run in the same fleets as the older boats .'],
['Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .', 'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .'],
['After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .', 'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .'],
['She married Peter Haygarth on 29 May 1964 in Durban . Her second marriage , to Robin Osborne , took place in 1977 .', 'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .'],
['In 2005 she moved to Norway , settled in Geilo and worked as a rafting guide , in 2006 she started mountain biking - races .', 'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'The newer Punts are still very much in existence today and race in the same fleets as the older boats .',
[
'The newer punts are still very much in existence today and run in the same fleets as the older boats .',
'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .',
'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .',
'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .',
'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Cross Encoder Classification
* Datasets: `val` and `test`
* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
| Metric | val | test |
|:----------------------|:-----------|:-----------|
| accuracy | 0.7718 | 0.8947 |
| accuracy_threshold | 0.8927 | 0.8616 |
| f1 | 0.6934 | 0.8797 |
| f1_threshold | 0.8759 | 0.5037 |
| precision | 0.6788 | 0.8643 |
| recall | 0.7086 | 0.8957 |
| **average_precision** | **0.7676** | **0.9345** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### LangCache Sentence Pairs (all)
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs)
* Size: 62,021 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 27 characters</li><li>mean: 112.72 characters</li><li>max: 197 characters</li></ul> | <ul><li>min: 27 characters</li><li>mean: 112.54 characters</li><li>max: 198 characters</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>1</code> |
| <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> |
| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Evaluation Dataset
#### LangCache Sentence Pairs (all)
* Dataset: [LangCache Sentence Pairs (all)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs)
* Size: 62,021 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 27 characters</li><li>mean: 112.72 characters</li><li>max: 197 characters</li></ul> | <ul><li>min: 27 characters</li><li>mean: 112.54 characters</li><li>max: 198 characters</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>1</code> |
| <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> |
| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": null
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `learning_rate`: 0.0002
- `num_train_epochs`: 50
- `warmup_steps`: 1000
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `push_to_hub`: True
- `hub_model_id`: aditeyabaral-redis/langcache-reranker-v1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0002
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 50
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 1000
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: aditeyabaral-redis/langcache-reranker-v1
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | val_average_precision | test_average_precision |
|:----------:|:----------:|:-------------:|:---------------:|:---------------------:|:----------------------:|
| -1 | -1 | - | - | 0.7676 | 0.6907 |
| 0.1833 | 1000 | 0.2986 | 0.3912 | - | 0.8585 |
| 0.3666 | 2000 | 0.2465 | 0.3856 | - | 0.8956 |
| 0.5499 | 3000 | 0.2287 | 0.3362 | - | 0.9160 |
| 0.7331 | 4000 | 0.2171 | 0.3408 | - | 0.9071 |
| 0.9164 | 5000 | 0.2068 | 0.3182 | - | 0.9220 |
| 1.0997 | 6000 | 0.1991 | 0.3458 | - | 0.8686 |
| 1.2830 | 7000 | 0.1939 | 0.3188 | - | 0.9244 |
| 1.4663 | 8000 | 0.1917 | 0.3120 | - | 0.9287 |
| 1.6496 | 9000 | 0.1906 | 0.3015 | - | 0.9279 |
| 1.8328 | 10000 | 0.1884 | 0.2986 | - | 0.9316 |
| 2.0161 | 11000 | 0.183 | 0.3065 | - | 0.9320 |
| 2.1994 | 12000 | 0.1714 | 0.3046 | - | 0.9180 |
| 2.3827 | 13000 | 0.1738 | 0.2994 | - | 0.9315 |
| 2.5660 | 14000 | 0.1709 | 0.2965 | - | 0.9347 |
| 2.7493 | 15000 | 0.1717 | 0.2911 | - | 0.9309 |
| 2.9326 | 16000 | 0.1698 | 0.2900 | - | 0.9354 |
| 3.1158 | 17000 | 0.16 | 0.2894 | - | 0.9377 |
| 3.2991 | 18000 | 0.1589 | 0.2830 | - | 0.9356 |
| 3.4824 | 19000 | 0.1574 | 0.2829 | - | 0.9337 |
| 3.6657 | 20000 | 0.1572 | 0.2818 | - | 0.9324 |
| 3.8490 | 21000 | 0.1587 | 0.2866 | - | 0.9365 |
| 4.0323 | 22000 | 0.1543 | 0.2923 | - | 0.9389 |
| 4.2155 | 23000 | 0.1445 | 0.2871 | - | 0.9430 |
| 4.3988 | 24000 | 0.1447 | 0.2793 | - | 0.9429 |
| 4.5821 | 25000 | 0.1473 | 0.2791 | - | 0.9386 |
| 4.7654 | 26000 | 0.146 | 0.2700 | - | 0.9417 |
| 4.9487 | 27000 | 0.1473 | 0.2697 | - | 0.9419 |
| 5.1320 | 28000 | 0.1365 | 0.2810 | - | 0.9411 |
| 5.3152 | 29000 | 0.1331 | 0.2764 | - | 0.9397 |
| 5.4985 | 30000 | 0.1372 | 0.2794 | - | 0.9416 |
| 5.6818 | 31000 | 0.1365 | 0.2751 | - | 0.9408 |
| 5.8651 | 32000 | 0.1365 | 0.2724 | - | 0.9411 |
| 6.0484 | 33000 | 0.1348 | 0.2767 | - | 0.9378 |
| 6.2317 | 34000 | 0.1236 | 0.2840 | - | 0.9388 |
| 6.4150 | 35000 | 0.1262 | 0.2845 | - | 0.9437 |
| 6.5982 | 36000 | 0.1277 | 0.2781 | - | 0.9446 |
| 6.7815 | 37000 | 0.129 | 0.2705 | - | 0.9427 |
| 6.9648 | 38000 | 0.1279 | 0.2773 | - | 0.9381 |
| 7.1481 | 39000 | 0.1173 | 0.2875 | - | 0.9420 |
| 7.3314 | 40000 | 0.1175 | 0.2901 | - | 0.9438 |
| 7.5147 | 41000 | 0.1174 | 0.2787 | - | 0.9420 |
| 7.6979 | 42000 | 0.118 | 0.2879 | - | 0.9424 |
| 7.8812 | 43000 | 0.1201 | 0.2826 | - | 0.9450 |
| 8.0645 | 44000 | 0.1168 | 0.2851 | - | 0.9419 |
| 8.2478 | 45000 | 0.1062 | 0.2913 | - | 0.9450 |
| 8.4311 | 46000 | 0.1091 | 0.2918 | - | 0.9454 |
| 8.6144 | 47000 | 0.1117 | 0.2799 | - | 0.9445 |
| 8.7977 | 48000 | 0.1123 | 0.2762 | - | 0.9443 |
| 8.9809 | 49000 | 0.1132 | 0.2772 | - | 0.9455 |
| 9.1642 | 50000 | 0.1016 | 0.2943 | - | 0.9433 |
| 9.3475 | 51000 | 0.1012 | 0.2879 | - | 0.9441 |
| 9.5308 | 52000 | 0.1029 | 0.2851 | - | 0.9442 |
| 9.7141 | 53000 | 0.105 | 0.2905 | - | 0.9448 |
| 9.8974 | 54000 | 0.1062 | 0.2960 | - | 0.9425 |
| 10.0806 | 55000 | 0.0996 | 0.2984 | - | 0.9430 |
| 10.2639 | 56000 | 0.0924 | 0.2947 | - | 0.9432 |
| 10.4472 | 57000 | 0.0939 | 0.2918 | - | 0.9421 |
| 10.6305 | 58000 | 0.0977 | 0.2895 | - | 0.9438 |
| 10.8138 | 59000 | 0.0977 | 0.2905 | - | 0.9446 |
| 10.9971 | 60000 | 0.0985 | 0.2882 | - | 0.9403 |
| 11.1804 | 61000 | 0.0857 | 0.3025 | - | 0.9435 |
| 11.3636 | 62000 | 0.0869 | 0.2997 | - | 0.9450 |
| 11.5469 | 63000 | 0.0886 | 0.3025 | - | 0.9459 |
| 11.7302 | 64000 | 0.0901 | 0.3000 | - | 0.9443 |
| 11.9135 | 65000 | 0.092 | 0.2913 | - | 0.9424 |
| 12.0968 | 66000 | 0.085 | 0.3017 | - | 0.9443 |
| 12.2801 | 67000 | 0.0801 | 0.3101 | - | 0.9449 |
| 12.4633 | 68000 | 0.0823 | 0.3018 | - | 0.9468 |
| 12.6466 | 69000 | 0.0841 | 0.2971 | - | 0.9457 |
| 12.8299 | 70000 | 0.0855 | 0.3063 | - | 0.9428 |
| 13.0132 | 71000 | 0.0854 | 0.3105 | - | 0.9436 |
| 13.1965 | 72000 | 0.0744 | 0.3017 | - | 0.9451 |
| 13.3798 | 73000 | 0.0763 | 0.3024 | - | 0.9425 |
| 13.5630 | 74000 | 0.0777 | 0.2948 | - | 0.9461 |
| 13.7463 | 75000 | 0.0791 | 0.3006 | - | 0.9466 |
| 13.9296 | 76000 | 0.0803 | 0.3001 | - | 0.9446 |
| 14.1129 | 77000 | 0.0721 | 0.3229 | - | 0.9445 |
| 14.2962 | 78000 | 0.0692 | 0.3231 | - | 0.9437 |
| 14.4795 | 79000 | 0.0703 | 0.3242 | - | 0.9458 |
| 14.6628 | 80000 | 0.073 | 0.3078 | - | 0.9469 |
| 14.8460 | 81000 | 0.073 | 0.3111 | - | 0.9448 |
| 15.0293 | 82000 | 0.0731 | 0.3319 | - | 0.9459 |
| 15.2126 | 83000 | 0.0629 | 0.3094 | - | 0.9464 |
| 15.3959 | 84000 | 0.0644 | 0.3440 | - | 0.9427 |
| 15.5792 | 85000 | 0.0673 | 0.3234 | - | 0.9457 |
| 15.7625 | 86000 | 0.068 | 0.3192 | - | 0.9430 |
| 15.9457 | 87000 | 0.0687 | 0.3097 | - | 0.9428 |
| 16.1290 | 88000 | 0.0618 | 0.3379 | - | 0.9466 |
| 16.3123 | 89000 | 0.0615 | 0.3192 | - | 0.9436 |
| 16.4956 | 90000 | 0.0605 | 0.3303 | - | 0.9452 |
| 16.6789 | 91000 | 0.0635 | 0.3154 | - | 0.9445 |
| 16.8622 | 92000 | 0.0637 | 0.3324 | - | 0.9467 |
| 17.0455 | 93000 | 0.0615 | 0.3365 | - | 0.9424 |
| 17.2287 | 94000 | 0.056 | 0.3332 | - | 0.9446 |
| 17.4120 | 95000 | 0.0567 | 0.3412 | - | 0.9432 |
| 17.5953 | 96000 | 0.0571 | 0.3419 | - | 0.9444 |
| 17.7786 | 97000 | 0.0589 | 0.3271 | - | 0.9403 |
| 17.9619 | 98000 | 0.0588 | 0.3281 | - | 0.9440 |
| 18.1452 | 99000 | 0.053 | 0.3282 | - | 0.9475 |
| 18.3284 | 100000 | 0.0525 | 0.3414 | - | 0.9470 |
| 18.5117 | 101000 | 0.0528 | 0.3263 | - | 0.9450 |
| 18.6950 | 102000 | 0.0539 | 0.3363 | - | 0.9428 |
| 18.8783 | 103000 | 0.056 | 0.3487 | - | 0.9454 |
| 19.0616 | 104000 | 0.0528 | 0.3701 | - | 0.9465 |
| 19.2449 | 105000 | 0.0464 | 0.3877 | - | 0.9328 |
| 19.4282 | 106000 | 0.0499 | 0.3379 | - | 0.9451 |
| 19.6114 | 107000 | 0.0496 | 0.3500 | - | 0.9442 |
| 19.7947 | 108000 | 0.0502 | 0.3420 | - | 0.9444 |
| 19.9780 | 109000 | 0.0519 | 0.3459 | - | 0.9442 |
| 20.1613 | 110000 | 0.0443 | 0.3755 | - | 0.9449 |
| 20.3446 | 111000 | 0.0449 | 0.3588 | - | 0.9447 |
| 20.5279 | 112000 | 0.0448 | 0.3616 | - | 0.9448 |
| 20.7111 | 113000 | 0.0471 | 0.3463 | - | 0.9426 |
| 20.8944 | 114000 | 0.0474 | 0.3784 | - | 0.9400 |
| 21.0777 | 115000 | 0.0451 | 0.3493 | - | 0.9447 |
| 21.2610 | 116000 | 0.0415 | 0.3633 | - | 0.9448 |
| 21.4443 | 117000 | 0.0412 | 0.3635 | - | 0.9472 |
| 21.6276 | 118000 | 0.0441 | 0.3710 | - | 0.9454 |
| 21.8109 | 119000 | 0.0427 | 0.3696 | - | 0.9459 |
| 21.9941 | 120000 | 0.045 | 0.3571 | - | 0.9440 |
| 22.1774 | 121000 | 0.0384 | 0.3815 | - | 0.9431 |
| 22.3607 | 122000 | 0.0389 | 0.3832 | - | 0.9428 |
| 22.5440 | 123000 | 0.0397 | 0.3773 | - | 0.9461 |
| 22.7273 | 124000 | 0.0402 | 0.3977 | - | 0.9415 |
| 22.9106 | 125000 | 0.0399 | 0.3870 | - | 0.9354 |
| 23.0938 | 126000 | 0.0376 | 0.3820 | - | 0.9409 |
| 23.2771 | 127000 | 0.0362 | 0.3755 | - | 0.9411 |
| 23.4604 | 128000 | 0.0358 | 0.3915 | - | 0.9461 |
| 23.6437 | 129000 | 0.0368 | 0.3688 | - | 0.9411 |
| 23.8270 | 130000 | 0.0374 | 0.4068 | - | 0.9427 |
| 24.0103 | 131000 | 0.0376 | 0.4155 | - | 0.9445 |
| 24.1935 | 132000 | 0.0325 | 0.3967 | - | 0.9434 |
| 24.3768 | 133000 | 0.0333 | 0.4209 | - | 0.9425 |
| 24.5601 | 134000 | 0.0335 | 0.4018 | - | 0.9432 |
| 24.7434 | 135000 | 0.0343 | 0.4250 | - | 0.9443 |
| 24.9267 | 136000 | 0.0345 | 0.4185 | - | 0.9414 |
| 25.1100 | 137000 | 0.0316 | 0.4075 | - | 0.9454 |
| 25.2933 | 138000 | 0.0299 | 0.4096 | - | 0.9454 |
| 25.4765 | 139000 | 0.0294 | 0.4135 | - | 0.9459 |
| 25.6598 | 140000 | 0.0317 | 0.3997 | - | 0.9445 |
| 25.8431 | 141000 | 0.0328 | 0.4093 | - | 0.9438 |
| 26.0264 | 142000 | 0.0317 | 0.4361 | - | 0.9404 |
| 26.2097 | 143000 | 0.027 | 0.4347 | - | 0.9454 |
| 26.3930 | 144000 | 0.0281 | 0.4149 | - | 0.9413 |
| 26.5762 | 145000 | 0.0283 | 0.4151 | - | 0.9454 |
| 26.7595 | 146000 | 0.0302 | 0.4041 | - | 0.9416 |
| 26.9428 | 147000 | 0.0301 | 0.4265 | - | 0.9340 |
| 27.1261 | 148000 | 0.026 | 0.4223 | - | 0.9426 |
| 27.3094 | 149000 | 0.0267 | 0.4237 | - | 0.9430 |
| 27.4927 | 150000 | 0.0268 | 0.4281 | - | 0.9458 |
| 27.6760 | 151000 | 0.0262 | 0.4193 | - | 0.9426 |
| 27.8592 | 152000 | 0.0262 | 0.4412 | - | 0.9402 |
| 28.0425 | 153000 | 0.0261 | 0.4795 | - | 0.9425 |
| 28.2258 | 154000 | 0.024 | 0.4519 | - | 0.9442 |
| 28.4091 | 155000 | 0.024 | 0.4395 | - | 0.9440 |
| 28.5924 | 156000 | 0.025 | 0.4549 | - | 0.9456 |
| 28.7757 | 157000 | 0.0253 | 0.4446 | - | 0.9429 |
| 28.9589 | 158000 | 0.0258 | 0.4349 | - | 0.9425 |
| 29.1422 | 159000 | 0.0211 | 0.4490 | - | 0.9430 |
| 29.3255 | 160000 | 0.0218 | 0.4538 | - | 0.9455 |
| 29.5088 | 161000 | 0.0217 | 0.4771 | - | 0.9435 |
| 29.6921 | 162000 | 0.0228 | 0.4238 | - | 0.9440 |
| 29.8754 | 163000 | 0.022 | 0.4731 | - | 0.9412 |
| 30.0587 | 164000 | 0.0227 | 0.4630 | - | 0.9450 |
| 30.2419 | 165000 | 0.0197 | 0.4840 | - | 0.9453 |
| 30.4252 | 166000 | 0.0198 | 0.4799 | - | 0.9434 |
| 30.6085 | 167000 | 0.022 | 0.4650 | - | 0.9453 |
| 30.7918 | 168000 | 0.0211 | 0.4592 | - | 0.9465 |
| 30.9751 | 169000 | 0.022 | 0.4727 | - | 0.9405 |
| 31.1584 | 170000 | 0.0184 | 0.4802 | - | 0.9460 |
| 31.3416 | 171000 | 0.0186 | 0.4953 | - | 0.9449 |
| 31.5249 | 172000 | 0.0187 | 0.4516 | - | 0.9424 |
| 31.7082 | 173000 | 0.019 | 0.4803 | - | 0.9444 |
| 31.8915 | 174000 | 0.0186 | 0.4499 | - | 0.9448 |
| 32.0748 | 175000 | 0.0181 | 0.5211 | - | 0.9377 |
| 32.2581 | 176000 | 0.0163 | 0.4941 | - | 0.9434 |
| 32.4413 | 177000 | 0.0168 | 0.4672 | - | 0.9433 |
| 32.6246 | 178000 | 0.0171 | 0.4990 | - | 0.9414 |
| 32.8079 | 179000 | 0.0185 | 0.4537 | - | 0.9444 |
| 32.9912 | 180000 | 0.0179 | 0.4929 | - | 0.9460 |
| 33.1745 | 181000 | 0.0144 | 0.5037 | - | 0.9407 |
| 33.3578 | 182000 | 0.0143 | 0.4986 | - | 0.9449 |
| 33.5411 | 183000 | 0.016 | 0.5043 | - | 0.9452 |
| 33.7243 | 184000 | 0.0152 | 0.5090 | - | 0.9427 |
| 33.9076 | 185000 | 0.0154 | 0.5100 | - | 0.9414 |
| 34.0909 | 186000 | 0.0146 | 0.5367 | - | 0.9386 |
| 34.2742 | 187000 | 0.0138 | 0.5063 | - | 0.9395 |
| 34.4575 | 188000 | 0.0143 | 0.4871 | - | 0.9446 |
| 34.6408 | 189000 | 0.014 | 0.4947 | - | 0.9483 |
| **34.824** | **190000** | **0.0142** | **0.5079** | **-** | **0.9467** |
| 35.0073 | 191000 | 0.014 | 0.5062 | - | 0.9439 |
| 35.1906 | 192000 | 0.0122 | 0.5293 | - | 0.9410 |
| 35.3739 | 193000 | 0.0127 | 0.5351 | - | 0.9401 |
| 35.5572 | 194000 | 0.0132 | 0.5263 | - | 0.9369 |
| 35.7405 | 195000 | 0.0134 | 0.5300 | - | 0.9427 |
| 35.9238 | 196000 | 0.0138 | 0.5230 | - | 0.9416 |
| 36.1070 | 197000 | 0.0129 | 0.5399 | - | 0.9417 |
| 36.2903 | 198000 | 0.0109 | 0.5352 | - | 0.9433 |
| 36.4736 | 199000 | 0.0114 | 0.5587 | - | 0.9404 |
| 36.6569 | 200000 | 0.012 | 0.5289 | - | 0.9441 |
| 36.8402 | 201000 | 0.012 | 0.5516 | - | 0.9434 |
| 37.0235 | 202000 | 0.0121 | 0.5467 | - | 0.9418 |
| 37.2067 | 203000 | 0.0108 | 0.5499 | - | 0.9412 |
| 37.3900 | 204000 | 0.0107 | 0.5459 | - | 0.9427 |
| 37.5733 | 205000 | 0.0105 | 0.5375 | - | 0.9414 |
| 37.7566 | 206000 | 0.0109 | 0.5566 | - | 0.9421 |
| 37.9399 | 207000 | 0.011 | 0.5601 | - | 0.9428 |
| 38.1232 | 208000 | 0.0095 | 0.5700 | - | 0.9406 |
| 38.3065 | 209000 | 0.0098 | 0.5493 | - | 0.9417 |
| 38.4897 | 210000 | 0.0093 | 0.5867 | - | 0.9372 |
| 38.6730 | 211000 | 0.0095 | 0.6087 | - | 0.9394 |
| 38.8563 | 212000 | 0.0096 | 0.5888 | - | 0.9397 |
| 39.0396 | 213000 | 0.0094 | 0.5806 | - | 0.9380 |
| 39.2229 | 214000 | 0.0087 | 0.5927 | - | 0.9393 |
| 39.4062 | 215000 | 0.0079 | 0.6153 | - | 0.9376 |
| 39.5894 | 216000 | 0.009 | 0.6151 | - | 0.9398 |
| 39.7727 | 217000 | 0.009 | 0.5601 | - | 0.9379 |
| 39.9560 | 218000 | 0.0086 | 0.5845 | - | 0.9409 |
| 40.1393 | 219000 | 0.0078 | 0.5929 | - | 0.9396 |
| 40.3226 | 220000 | 0.0077 | 0.6086 | - | 0.9417 |
| 40.5059 | 221000 | 0.0075 | 0.6053 | - | 0.9418 |
| 40.6891 | 222000 | 0.008 | 0.6078 | - | 0.9394 |
| 40.8724 | 223000 | 0.0084 | 0.5975 | - | 0.9423 |
| 41.0557 | 224000 | 0.0068 | 0.6410 | - | 0.9400 |
| 41.2390 | 225000 | 0.0067 | 0.6183 | - | 0.9409 |
| 41.4223 | 226000 | 0.0067 | 0.6239 | - | 0.9401 |
| 41.6056 | 227000 | 0.0075 | 0.5971 | - | 0.9408 |
| 41.7889 | 228000 | 0.0069 | 0.6458 | - | 0.9396 |
| 41.9721 | 229000 | 0.0073 | 0.6289 | - | 0.9337 |
| 42.1554 | 230000 | 0.0061 | 0.6311 | - | 0.9351 |
| 42.3387 | 231000 | 0.0064 | 0.6371 | - | 0.9254 |
| 42.5220 | 232000 | 0.0067 | 0.6119 | - | 0.9238 |
| 42.7053 | 233000 | 0.0068 | 0.6045 | - | 0.9435 |
| 42.8886 | 234000 | 0.0064 | 0.6246 | - | 0.9403 |
| 43.0718 | 235000 | 0.0066 | 0.6077 | - | 0.9355 |
| 43.2551 | 236000 | 0.0054 | 0.6348 | - | 0.9429 |
| 43.4384 | 237000 | 0.0053 | 0.6606 | - | 0.9414 |
| 43.6217 | 238000 | 0.0054 | 0.6373 | - | 0.9421 |
| 43.8050 | 239000 | 0.006 | 0.6122 | - | 0.9391 |
| 43.9883 | 240000 | 0.0058 | 0.6438 | - | 0.9380 |
| 44.1716 | 241000 | 0.0051 | 0.6474 | - | 0.9392 |
| 44.3548 | 242000 | 0.0049 | 0.6637 | - | 0.9399 |
| 44.5381 | 243000 | 0.005 | 0.6765 | - | 0.9420 |
| 44.7214 | 244000 | 0.0052 | 0.6585 | - | 0.9406 |
| 44.9047 | 245000 | 0.005 | 0.6609 | - | 0.9420 |
| 45.0880 | 246000 | 0.0048 | 0.6725 | - | 0.9417 |
| 45.2713 | 247000 | 0.0044 | 0.6597 | - | 0.9411 |
| 45.4545 | 248000 | 0.0045 | 0.6717 | - | 0.9381 |
| 45.6378 | 249000 | 0.0046 | 0.6689 | - | 0.9361 |
| 45.8211 | 250000 | 0.0046 | 0.6703 | - | 0.9334 |
| 46.0044 | 251000 | 0.0044 | 0.6958 | - | 0.9324 |
| 46.1877 | 252000 | 0.0041 | 0.6884 | - | 0.9380 |
| 46.3710 | 253000 | 0.0041 | 0.6958 | - | 0.9342 |
| 46.5543 | 254000 | 0.004 | 0.6796 | - | 0.9375 |
| 46.7375 | 255000 | 0.0042 | 0.6735 | - | 0.9311 |
| 46.9208 | 256000 | 0.004 | 0.7004 | - | 0.9264 |
| 47.1041 | 257000 | 0.0041 | 0.6798 | - | 0.9303 |
| 47.2874 | 258000 | 0.0036 | 0.7039 | - | 0.9330 |
| 47.4707 | 259000 | 0.0037 | 0.7133 | - | 0.9277 |
| 47.6540 | 260000 | 0.0033 | 0.7200 | - | 0.9250 |
| 47.8372 | 261000 | 0.0038 | 0.7204 | - | 0.9292 |
| 48.0205 | 262000 | 0.0034 | 0.7214 | - | 0.9336 |
| 48.2038 | 263000 | 0.0037 | 0.7077 | - | 0.9313 |
| 48.3871 | 264000 | 0.0033 | 0.7218 | - | 0.9289 |
| 48.5704 | 265000 | 0.0033 | 0.7258 | - | 0.9328 |
| 48.7537 | 266000 | 0.0034 | 0.7215 | - | 0.9346 |
| 48.9370 | 267000 | 0.0031 | 0.7300 | - | 0.9347 |
| 49.1202 | 268000 | 0.0033 | 0.7242 | - | 0.9350 |
| 49.3035 | 269000 | 0.0028 | 0.7320 | - | 0.9345 |
| 49.4868 | 270000 | 0.003 | 0.7397 | - | 0.9341 |
| 49.6701 | 271000 | 0.0029 | 0.7410 | - | 0.9342 |
| 49.8534 | 272000 | 0.0029 | 0.7426 | - | 0.9345 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.1.0
- Transformers: 4.55.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
niikun/SmolGRPO-135M
|
niikun
| 2025-08-17T00:14:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"grpo",
"GRPO",
"Reasoning-Course",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T00:13:58Z |
---
library_name: transformers
tags:
- trl
- grpo
- GRPO
- Reasoning-Course
---
# 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]
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755388111
|
ihsanridzi
| 2025-08-17T00:14:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:14:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755388029
|
unitova
| 2025-08-17T00:12:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:12:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755387986
|
thanobidex
| 2025-08-17T00:12:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:11:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fujiantiiazhraa/blockassist-bc-marine_robust_bee_1755387928
|
fujiantiiazhraa
| 2025-08-17T00:10:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine robust bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:10:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine robust bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755388124
|
Sayemahsjn
| 2025-08-17T00:07:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:07:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755387699
|
mang3dd
| 2025-08-17T00:07:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:07:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755386760
|
quantumxnode
| 2025-08-16T23:51:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:51:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Norwaere/Illustrious2.0-lora-Vpred-conversion-experiments
|
Norwaere
| 2025-08-16T23:50:39Z | 0 | 2 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"lora",
"text-to-image",
"Illustrious",
"sdxl",
"base_model:OnomaAIResearch/Illustrious-XL-v2.0",
"base_model:adapter:OnomaAIResearch/Illustrious-XL-v2.0",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-07-08T10:57:47Z |
---
license: apache-2.0
base_model:
- OnomaAIResearch/Illustrious-XL-v2.0
pipeline_tag: text-to-image
tags:
- lora
- text-to-image
- diffusers
- Illustrious
- sdxl
---
# Illustrious-v2-Out2-5noob-512res-b256-lr3e5-msnr10-locondora-ztsnr-vpredconv
<img src="Illustrious-v2-Out2-5noob-512res-b256-lr3e5-msnr10-locondora-ztsnr-vpredconv/Illustrious-v2-Out2-5noob-512res-b256-lr3e5-msnr10-locondora-ztsnr-vpredconv.jpg" />
<img src="Illustrious-v2-Out2-5noob-512res-b256-lr3e5-msnr10-locondora-ztsnr-vpredconv/Illustrious-v2-Out2-5noob-512res-b256-lr3e5-msnr10-locondora-ztsnr-vpredconv other prompt.jpg" />
## Download model
[Download](/Norwaere/Illustrious2.0-lora-Vpred-conversion-experiments/tree/main) them in the Files & versions tab.
|
realSanemi/blockassist-bc-aquatic_snappy_tortoise_1755383489
|
realSanemi
| 2025-08-16T23:50:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic snappy tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:49:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic snappy tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MauoSama/act_multicut_4images_time
|
MauoSama
| 2025-08-16T23:50:02Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:MauoSama/multicut_4images_time",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-16T23:49:56Z |
---
datasets: MauoSama/multicut_4images_time
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
chainway9/blockassist-bc-untamed_quick_eel_1755386353
|
chainway9
| 2025-08-16T23:47:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:47:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omar-salama/thera-space
|
omar-salama
| 2025-08-16T23:45:29Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T22:45:42Z |
---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: thera-space
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for thera-space
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="omar-salama/thera-space", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/omar_salama/huggingface/runs/3umk1xd9)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
hesamation/Qwen3-8B-Base-FOL
|
hesamation
| 2025-08-16T23:44:56Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T23:44:35Z |
---
base_model: unsloth/qwen3-8b-base-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hesamation
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b-base-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)
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755386019
|
mang3dd
| 2025-08-16T23:38:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:38:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DevQuasar/inclusionAI.AReaL-boba-2-32B-GGUF
|
DevQuasar
| 2025-08-16T23:38:22Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:inclusionAI/AReaL-boba-2-32B",
"base_model:quantized:inclusionAI/AReaL-boba-2-32B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-16T20:07:45Z |
---
base_model:
- inclusionAI/AReaL-boba-2-32B
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: [inclusionAI/AReaL-boba-2-32B](https://huggingface.co/inclusionAI/AReaL-boba-2-32B)
'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>
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755386162
|
Sayemahsjn
| 2025-08-16T23:35:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:35:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MauoSama/act_multicut_static_image_time
|
MauoSama
| 2025-08-16T23:30:47Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:MauoSama/multicut_static_image_time",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-16T23:30:42Z |
---
datasets: MauoSama/multicut_static_image_time
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- robotics
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
BootesVoid/cmeev0d8w0jvnrts8jgw8vh9c_cmeev45lx0jw0rts8qdjejk92
|
BootesVoid
| 2025-08-16T23:30:39Z | 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
| 2025-08-16T23:30:38Z |
---
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: FAMOUS
---
# Cmeev0D8W0Jvnrts8Jgw8Vh9C_Cmeev45Lx0Jw0Rts8Qdjejk92
<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 `FAMOUS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "FAMOUS",
"lora_weights": "https://huggingface.co/BootesVoid/cmeev0d8w0jvnrts8jgw8vh9c_cmeev45lx0jw0rts8qdjejk92/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/cmeev0d8w0jvnrts8jgw8vh9c_cmeev45lx0jw0rts8qdjejk92', weight_name='lora.safetensors')
image = pipeline('FAMOUS').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/cmeev0d8w0jvnrts8jgw8vh9c_cmeev45lx0jw0rts8qdjejk92/discussions) to add images that show off what you’ve made with this LoRA.
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755385479
|
vwzyrraz7l
| 2025-08-16T23:29:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:29:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MauoSama/act_multicut_wrist_image_time
|
MauoSama
| 2025-08-16T23:29:31Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:MauoSama/multicut_wrist_image_time",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-16T23:29:25Z |
---
datasets: MauoSama/multicut_wrist_image_time
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Kyleyee/Mistral-7B-Instruct-v0.3-vrpo
|
Kyleyee
| 2025-08-16T23:28:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"trl",
"drdpo",
"conversational",
"dataset:Kyleyee/UltraFeedback-for-vrpo-pairrm-preference-with-template",
"arxiv:2305.18290",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T14:07:09Z |
---
base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets: Kyleyee/UltraFeedback-for-vrpo-pairrm-preference-with-template
library_name: transformers
model_name: Mistral-7B-Instruct-v0.3-vrpo
tags:
- generated_from_trainer
- trl
- drdpo
licence: license
---
# Model Card for Mistral-7B-Instruct-v0.3-vrpo
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the [Kyleyee/UltraFeedback-for-vrpo-pairrm-preference-with-template](https://huggingface.co/datasets/Kyleyee/UltraFeedback-for-vrpo-pairrm-preference-with-template) 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="Kyleyee/Mistral-7B-Instruct-v0.3-vrpo", 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 DRDPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite DRDPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
prudant/Qwen3-Reranker-4B-seq-cls-vllm-fixed-W4A16
|
prudant
| 2025-08-16T23:28:11Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"text-ranking",
"en",
"es",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:Qwen/Qwen3-Reranker-4B",
"base_model:quantized:Qwen/Qwen3-Reranker-4B",
"license:apache-2.0",
"compressed-tensors",
"region:us"
] |
text-ranking
| 2025-08-16T22:52:11Z |
---
license: apache-2.0
datasets:
- HuggingFaceH4/ultrachat_200k
language:
- en
- es
base_model:
- Qwen/Qwen3-Reranker-4B
pipeline_tag: text-ranking
---
# prudant/Qwen3-Reranker-4B-seq-cls-vllm-fixed-W4A16
Qwen3 4b reranker full vllm adapted 🚀
This is a compressed version of danielchalef/Qwen3-Reranker-4B-seq-cls-vllm-fixed using llm-compressor with the following scheme: W4A16
## Serving
``python3 -m vllm.entrypoints.openai.api_server --download-dir '/data' --model 'prudant/Qwen3-Reranker-4B-seq-cls-vllm-fixed-W4A16' --task classify``
**Important**: You MUST read the following guide for correct usage of this model here [Guide](https://github.com/vllm-project/vllm/pull/19260)
## Model Details
- **Original Model**: danielchalef/Qwen3-Reranker-4B-seq-cls-vllm-fixed
- **Quantization Method**: GPTQ
- **Compression Libraries**: [llm-compressor](https://github.com/vllm-project/llm-compressor)
- **Calibration Dataset**: ultrachat_200k (512 samples)
- **Optimized For**: Inference with vLLM
- **License**: same as original model
|
koloni/blockassist-bc-deadly_graceful_stingray_1755385367
|
koloni
| 2025-08-16T23:27:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:27:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Hamzah-Asadullah/HelloWorld-XL
|
Hamzah-Asadullah
| 2025-08-16T23:23:11Z | 0 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-16T22:35:16Z |
---
license: other
license_name: creativeml-open-rail-m-addendum
license_link: https://huggingface.co/spaces/CompVis/stable-diffusion-license
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: "The Moon (Seed: 0, CFG: 5.5, Steps: 25)"
output:
url: example.png
---
<Gallery />
**Searching for the GGUF? [It's here.](https://huggingface.co/Hamzah-Asadullah/HelloWorld-XL-GGUF)**
Model from [CivitAI](https://civitai.com/models/43977/leosams-helloworld-xl?modelVersionId=113623).
The image above was generated using the Q8 quantization.
What the model card on CivitAI recommended **doesn't seem to work for me**.
Here's **what does work well** for me:
- Steps: 20 to 25, no major quality improvements after ~20 though
- Sampler: Euler a
- CFG: 5 to 5.5
- Prompt Appendix: ", masterpiece, unique, stunning"
- Negative Prompt Appendix: ", nudity, low quality, jpeg artifacts, blurry, poorly drawn, worst quality, western"
- CLIP skip: -1
Addionally, following dimensions (w * h) work well:
- Square: 832 * 832
- Landscape and vice versa: 896 * 704 or 704 * 896 (both work extremely well)
|
Elhusseny/Quran_ArbGPT2
|
Elhusseny
| 2025-08-16T23:20:23Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-16T23:17:51Z |
---
license: apache-2.0
---
|
MauoSama/act_multicut_onlywrist_image_time
|
MauoSama
| 2025-08-16T23:17:01Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:MauoSama/multicut_onlywrist_image_time",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-16T23:16:56Z |
---
datasets: MauoSama/multicut_onlywrist_image_time
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- act
- lerobot
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
wanacode/qwen-image-chromablock-lora
|
wanacode
| 2025-08-16T23:16:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-16T23:16:22Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: chromablock
license: other
---
# qwen image chromablock lora
<Gallery />
## Model description
Qwen Image LoRA for creating chromablock effect
## Trigger words
You should use `chromablock` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/wanacode/qwen-image-chromablock-lora/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/qwen-image-trainer](https://fal.ai/models/fal-ai/qwen-image-trainer).
|
CohenQu/LLaDA-8B-Instruct_Mixture-of-Thoughts-math-4k_without_reasoning_DSAI
|
CohenQu
| 2025-08-16T23:16:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llada",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-08-16T22:09:58Z |
---
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]
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755384681
|
ihsanridzi
| 2025-08-16T23:15:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:15:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sudoping01/sereer-tts-v0-lora
|
sudoping01
| 2025-08-16T23:15:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T23:15:11Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chainway9/blockassist-bc-untamed_quick_eel_1755384434
|
chainway9
| 2025-08-16T23:15:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:15:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF
|
tensorblock
| 2025-08-16T23:12:54Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"base_model:zwhe99/Qwen2.5-3B-orz",
"base_model:quantized:zwhe99/Qwen2.5-3B-orz",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T22:38:07Z |
---
library_name: transformers
tags:
- TensorBlock
- GGUF
base_model: zwhe99/Qwen2.5-3B-orz
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## zwhe99/Qwen2.5-3B-orz - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [zwhe99/Qwen2.5-3B-orz](https://huggingface.co/zwhe99/Qwen2.5-3B-orz).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: You must put your answer inside <answer> </answer> tags, i.e., <answer> answer here </answer>. And your final answer will be extracted automatically by the \boxed{} tag.
This is the problem:
{prompt}
Assistant: <think>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Qwen2.5-3B-orz-Q2_K.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q2_K.gguf) | Q2_K | 1.275 GB | smallest, significant quality loss - not recommended for most purposes |
| [Qwen2.5-3B-orz-Q3_K_S.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q3_K_S.gguf) | Q3_K_S | 1.454 GB | very small, high quality loss |
| [Qwen2.5-3B-orz-Q3_K_M.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q3_K_M.gguf) | Q3_K_M | 1.590 GB | very small, high quality loss |
| [Qwen2.5-3B-orz-Q3_K_L.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q3_K_L.gguf) | Q3_K_L | 1.707 GB | small, substantial quality loss |
| [Qwen2.5-3B-orz-Q4_0.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q4_0.gguf) | Q4_0 | 1.823 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Qwen2.5-3B-orz-Q4_K_S.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q4_K_S.gguf) | Q4_K_S | 1.834 GB | small, greater quality loss |
| [Qwen2.5-3B-orz-Q4_K_M.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q4_K_M.gguf) | Q4_K_M | 1.930 GB | medium, balanced quality - recommended |
| [Qwen2.5-3B-orz-Q5_0.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q5_0.gguf) | Q5_0 | 2.170 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen2.5-3B-orz-Q5_K_S.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q5_K_S.gguf) | Q5_K_S | 2.170 GB | large, low quality loss - recommended |
| [Qwen2.5-3B-orz-Q5_K_M.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q5_K_M.gguf) | Q5_K_M | 2.225 GB | large, very low quality loss - recommended |
| [Qwen2.5-3B-orz-Q6_K.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q6_K.gguf) | Q6_K | 2.538 GB | very large, extremely low quality loss |
| [Qwen2.5-3B-orz-Q8_0.gguf](https://huggingface.co/tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF/blob/main/Qwen2.5-3B-orz-Q8_0.gguf) | Q8_0 | 3.285 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF --include "Qwen2.5-3B-orz-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
SicariusSicariiStuff/Impish_QWEN_7B-1M_ARM_HA
|
SicariusSicariiStuff
| 2025-08-16T23:11:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:SicariusSicariiStuff/Impish_QWEN_7B-1M",
"base_model:quantized:SicariusSicariiStuff/Impish_QWEN_7B-1M",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T12:21:56Z |
---
base_model:
- SicariusSicariiStuff/Impish_QWEN_7B-1M
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: SicariusSicariiStuff
---
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755385687
|
ggozzy
| 2025-08-16T23:09:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:09:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aaronqg/vit-football-players
|
aaronqg
| 2025-08-16T23:09:20Z | 0 | 0 | null |
[
"safetensors",
"vit",
"vision",
"classification",
"football",
"en",
"dataset:aaronqg/golden-foot-football-players",
"license:cc-by-4.0",
"region:us"
] | null | 2025-08-16T22:20:49Z |
---
language: en
tags:
- vision
- classification
- football
license: cc-by-4.0
datasets:
- aaronqg/golden-foot-football-players
metrics:
- accuracy
- precision
- recall
- f1
---
# Vision Transformer - Golden Foot Football Players
Este modelo es un **Vision Transformer (ViT)** afinado sobre el dataset
[Golden Foot Football Players](https://huggingface.co/datasets/aaronqg/golden-foot-football-players).
## Metodología
- Modelo base: `google/vit-base-patch16-224`
- Dataset: 22 clases (jugadores nominados al Golden Foot)
- Técnica: Transfer Learning (última capa reentrenada)
- Optimizer: AdamW
- Scheduler: StepLR (gamma=0.5 cada 2 épocas)
- Balanceo: `WeightedRandomSampler`
## Resultados
- Accuracy en test: 0.91
- Precision: 0.91
- Recall: 0.91
- F1-score: 0.91
## Limitaciones
- Dataset con desbalance (ej: jugadores con pocas imágenes)
- Imágenes con resoluciones heterogéneas
## Uso
\`\`\`python
from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
model = ViTForImageClassification.from_pretrained("aaronqg/vit-football-players")
processor = ViTImageProcessor.from_pretrained("aaronqg/vit-football-players")
image = Image.open("jugador.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
pred = outputs.logits.argmax(-1).item()
print("Predicción:", pred)
\`\`\`
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755383926
|
rvipitkirubbe
| 2025-08-16T23:07:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T23:07:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.