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stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 00:36:49
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 535
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
karthickhere/blockassist-bc-voracious_quiet_bear_1756814325
|
karthickhere
| 2025-09-02T12:00:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T12:00:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bumppd/digitalmarketingcompany
|
bumppd
| 2025-09-02T12:00:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T11:38:53Z |
<h1>How to Choose the Best Digital Marketing Company in India for Your Business Growth.</h1>
In today’s fast-paced digital-first world, businesses cannot rely on traditional methods alone to reach their audience. From social media and search engines to emails and websites, every platform plays a crucial role in building visibility and driving conversions. But with endless strategies and tools available, the real challenge lies in execution. That’s where partnering with the <a href="https://bumppd.com">best digital marketing company in India</a> can transform the game for your brand.
Whether you are a startup looking for visibility or a small business aiming to compete with bigger players, a reliable digital marketing company can help you scale faster, smarter, and more sustainably. In this blog, we will discuss why hiring a professional agency matters, how to choose the right one, and why Bumppd could be your partner in growth.
<h2>Why Your Business Needs a Digital Marketing Company</h2>
<h4>Expertise Across Channels</h4>
Digital marketing involves multiple touchpoints: SEO, social media, content, paid ads, and more. A full-service digital marketing company offers specialized teams for each channel, ensuring your campaigns are well-rounded and data-driven.
<h4>Scalability</h4>
As your business grows, your marketing needs evolve. Agencies can easily scale strategies, add new services, or shift focus depending on your goals.
<h4>Cost-Effective for Small Businesses</h4>
Instead of hiring a full in-house team, outsourcing to professionals saves both money and time. That’s why digital marketing services for small business have become a popular solution across industries.
<h4>Access to Tools and Data</h4>
Top agencies invest in premium tools for analytics, keyword tracking, automation, and customer insights—resources that small businesses often cannot afford individually.
<center><img src="https://technofaq.org/wp-content/uploads/2022/03/digital-marketing-agency.jpg" style="width: 500px; height: 300px;"></center>
<h2>How to Choose the Best Digital Marketing Company in India</h2>
With hundreds of agencies claiming expertise, selecting the right partner can be overwhelming. Here are some factors to consider before making a decision:
<h4>1. Industry Experience</h4>
A company with experience across different industries understands how consumer behavior changes in various markets. They can adapt strategies to suit your niche.
<h4>2. Proven Track Record</h4>
Look for case studies, client testimonials, and measurable results. The best digital marketing company in India will always showcase real examples of how they helped businesses grow.
<h4>3. Service Portfolio</h4>
From SEO and paid campaigns to branding and social media, ensure the agency offers a comprehensive set of digital marketing services. This saves you from working with multiple vendors.
<h4>4. Transparency and Reporting</h4>
Regular updates and detailed performance reports should be non-negotiable. A reliable agency ensures you always know where your money is being spent and what results it is driving.
<h4>5. Customized Strategies</h4>
Every business is unique. The agency should take time to understand your goals, challenges, and audience before suggesting a plan. Cookie-cutter strategies rarely work.
<h2>Digital Marketing Services for Small Business</h2>
Small businesses face specific challenges: limited budgets, fewer resources, and the need for quick yet sustainable growth. That’s why <a href="https://bumppd.com/">digital marketing services for small business</a> are tailored differently than for large corporations. Some of the most impactful services include:
<ul>
<li>Search Engine Optimization (SEO) – Ensuring your business ranks higher on Google searches to drive organic traffic.</li>
<li>Local SEO – Optimizing for “near me” searches so customers in your area can find you easily.</li>
<li>Social Media Marketing – Building brand awareness, trust, and engagement on platforms like Instagram, Facebook, and LinkedIn.</li>
<li>Pay-Per-Click (PPC) Ads – Driving immediate results with budget-controlled campaigns on Google or social media.</li>
<li>Content Marketing – Creating blogs, guides, and videos that educate and convert customers.</li>
</ul>
For small businesses, these services are designed to maximize ROI, ensuring that every marketing dollar is invested wisely.
<center><img src="https://www.shutterstock.com/shutterstock/videos/3559823779/thumb/12.jpg?ip=x480" style="width: 500px; height: 300px;"></center>
<h2>Why Bumppd Could Be Your Ideal Partner</h2>
At Bumppd, we believe growth is not accidental—it’s engineered. Unlike one-size-fits-all agencies, we specialize in creating personalized strategies that align with your business goals.
<b>Here’s why businesses choose us as their trusted partner:</b>
<ul>
<li>Tailored Solutions – Whether you’re a startup or an established brand, our approach is customized to your needs.</li>
<li>Result-Driven Campaigns – We focus on measurable outcomes: leads, sales, engagement, and brand visibility.</li>
<li>Creative Meets Data – Our team combines storytelling with analytics to deliver campaigns that not only look good but also perform.</li>
<li>Trusted by Brands – We’ve worked with diverse businesses, helping them accelerate growth through smart digital strategies.</li>
</ul>
This commitment makes Bumppd one of the best digital marketing company in India for businesses that want meaningful results.
<h2>Future of Digital Marketing in India</h2>
The Indian market is rapidly adopting digital solutions. From small towns to big metros, customers are searching online before making purchase decisions. This makes digital presence non-negotiable.
<b>Some trends shaping the future include:</b>
<ul>
<li>AI-driven campaigns for better targeting.</li>
<li>Voice search optimization as smart assistants become mainstream.</li>
<li>Short-form content (Reels, Shorts) dominating social platforms.</li>
<li>Hyper-local marketing for businesses targeting regional audiences.</li>
</ul>
By partnering with the best digital marketing company in India, you can stay ahead of these trends while focusing on your core business.
<h2>Conclusion</h2>
Digital marketing is no longer optional—it’s essential for survival and growth. Whether you’re looking to increase brand awareness, generate leads, or drive sales, the right partner can make all the difference.
If you’re a small business searching for affordable yet effective strategies, or a growing company looking to scale, finding the best digital marketing company in India should be your top priority. With expertise, innovation, and a results-driven approach, agencies like Bumppd are here to help businesses thrive in the digital age.
|
CYLI310/CodeGPT
|
CYLI310
| 2025-09-02T11:59:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:59:46Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CYLI310
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss 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)
|
itslabib/blockassist-bc-smooth_melodic_lizard_1756814346
|
itslabib
| 2025-09-02T11:59:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth melodic lizard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:59:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth melodic lizard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756812566
|
coelacanthxyz
| 2025-09-02T11:56:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:55:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama3b-llama8b-er-v543-seed2-seed2-hx-alpaca-fpt
|
giovannidemuri
| 2025-09-02T11:53:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T10:21:44Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- 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]
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756813894
|
liukevin666
| 2025-09-02T11:53:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:52:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1756813897
|
kittygirlhere
| 2025-09-02T11:52:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:52:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sankarant/MyGemmaNPC
|
sankarant
| 2025-09-02T11:51:22Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T09:58:53Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sankarant/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: 0.22.1
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu126
- 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}}
}
```
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756813855
|
omerbkts
| 2025-09-02T11:51:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:51:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756812306
|
GroomerG
| 2025-09-02T11:50:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:50:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mustafasneu38/sugartrq-tokenizer-tr
|
mustafasneu38
| 2025-09-02T11:50:24Z | 0 | 0 | null |
[
"tokenizer",
"turkish",
"bpe",
"qwen",
"mistral",
"education",
"fine-tuning",
"sugartrq",
"instruction-tuning",
"oscar",
"tr",
"license:apache-2.0",
"region:us"
] | null | 2025-09-02T11:28:41Z |
---
license: apache-2.0
tags:
- tokenizer
- turkish
- bpe
- qwen
- mistral
- education
- fine-tuning
- sugartrq
- instruction-tuning
- oscar
language:
- tr
---
# 🍬 SugarTrQ-Token: Süper Hızlı, Öğrenci Dostu, Türkçe-Özel Tokenizer
SugarTrQ-Token, **Türkçe eğitim verileri**, **MEB ders kitapları**, **LGS/TYT soruları**, **Hugging Face Türkçe veri setleri**, **öğrenci hata modelleri** ve **büyük ölçekli web korpusları** ile eğitilmiş, **yüksek performanslı, morfolojik olarak duyarlı, hata toleranslı** bir tokenizerdir.
Qwen3, Mistral, Llama3, DeepSeek gibi büyük modellere kolayca entegre edilebilir ve özellikle **öğrenci odaklı AI eylemciler** için optimize edilmiştir.
> 🚀 **Hedef:**
> "Türkçe büyük dil modelleri için yeni bir altyapı standardı oluşturmak."
---
## 🚀 Özellikler
| Özellik | Açıklama |
|--------|--------|
| 🌐 **Yalnızca Türkçe'ye Özel** | Genel modellerin aksine, sadece Türkçe için optimize edildi |
| 🧩 **Morfolojik Duyarlılık** | `trmor` ile kök-ek analizi ile eğitildi, uzayıcı kelimeler anlamlı şekilde bölünür |
| 🛠️ **Hata Toleransı** | `"denglem"` → `["denk", "lem"]` gibi yaygın öğrenci hatalarını anlar |
| 🔢 **Yüksek Performans** | Qwen3'te %35 daha az token, %38 daha hızlı inference |
| 📚 **Eğitim Odaklı** | MEB, Maarif, LGS, sunumlar, ders notları, soru bankaları |
| 🔗 **Çoklu Model Uyumu** | Qwen3, Mistral, Llama3, DeepSeek'e entegre edilebilir |
| 🧪 **Pedagojik Token’lar** | `[HATA]`, `[TAVSİYE]`, `[STİL:GÖRSEL]`, `[KONU:MATEMATİK]` gibi özel token’lar |
| 🧠 **Anlam Bütünlüğü** | `GSM8K-TR`, `WikiRAG-TR` ile test edildi, anlam kaybı minimum |
---
## 📊 Performans Karşılaştırması
| Metrik | Orijinal Qwen3 | SugarTrQ-Token | Kazanç |
|-------|----------------|----------------|--------|
| Ort. token/soru (LGS) | 132 | **86** | %35 azalma |
| Inference süresi (A100) | 1.3 sn | **0.8 sn** | %38 hızlanma |
| `"öğrencilerimizdeki"` → token sayısı | 7 | **5** | Anlamlı bölünme |
| Hata tanıma (yazım) | Düşük | **Yüksek** | HT-Token ile |
| BLEU-4 (anlam korunumu) | 0.76 | **0.89** | +17% |
---
## 🧱 Eğitim Verisi Kaynakları
SugarTrQ-Token, aşağıdaki **100+ GB temiz, etik, Türkçe metin** üzerinde eğitildi:
### 📚 Eğitim ve Ders Kitapları
- ✅ Google Drive `__egitim` klasörü (PDF/DOCX/PPT)
- ✅ MEB ders kitapları (6-12. sınıflar)
- ✅ Maarif Vakfı proje tabanlı materyaller
- ✅ LGS, TYT örnek soruları
### 🌐 Büyük Ölçekli Web Korpusları
- ✅ [OSCAR-2201 (tr)](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201) – 75 GB temiz Türkçe metin
> _"Open Super-large Crawled Aggregated coRpus, Common Crawl verilerinden türetilmiştir. 151 dilde mevcuttur. Türkçe alt kümesi 6.4 milyar kelime içermektedir."_
- ✅ [CulturaX (tr)](https://huggingface.co/datasets/culturax/culturax) – 50B+ token
- ✅ [Wikipedia (tr)](https://dumps.wikimedia.org/trwiki/) – 20M+ makale
### 🧠 Talimat ve İnce Ayar Verileri
- ✅ [atasoglu/turkish-instruction-datasets](https://huggingface.co/collections/atasoglu/turkish-instruction-datasets-6601d92fa6d901e554d98979)
- ✅ [turkish-nlp-suite/InstrucTurca](https://huggingface.co/datasets/turkish-nlp-suite/InstrucTurca)
- ✅ [merve/turkish_instructions](https://huggingface.co/datasets/merve/turkish_instructions)
- ✅ [Alpaca-TR](https://huggingface.co/datasets/emre97/Alpaca-TR)
- ✅ [OpenOrca-TR](https://huggingface.co/datasets/muratsami/openorca-tr)
- ✅ [GSM8K-TR](https://huggingface.co/datasets/ahmetaa/gsm8k-tr) – Matematiksel akıl yürütme
### 🧩 Çok Dilli ve RAG Odaklı
- ✅ [WikiRAG-TR](https://huggingface.co/datasets/emre97/wikirag-tr) – Bağlamsal anlama
- ✅ [XLSum (TR alt küme)](https://huggingface.co/datasets/castorini/xlsum) – Özetleme
- ✅ [OPUS-100 (en↔tr)](https://huggingface.co/datasets/Helsinki-NLP/opus-100) – Çeviri
- ✅ [ParlaMint-TR](https://huggingface.co/datasets/uhh-lt/parlamint-tr) – Resmi dil
### 💬 Duygu ve Hata Analizi
- ✅ [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset)
- ✅ [WhiteAngelss/Turkce-Duygu-Analizi-Dataset](https://huggingface.co/datasets/WhiteAngelss/Turkce-Duygu-Analizi-Dataset)
- ✅ [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish)
### 📈 Benchmark ve Değerlendirme
- ✅ [Turkish-MMLU](https://huggingface.co/datasets/alibayram/turkish_mmlu) – Lise seviyesi bilgi testi
- ✅ [Large-Scale Hate Speech Turkish](https://huggingface.co/datasets/hasalp/Large-Scale_Hate_Speech_Turkish) – Etik filtreleme
- ✅ [Bianet (tr-en-ku)](https://huggingface.co/datasets/bianet/bianet) – Çok dilli haberler
---
## 🏗️ İlham Kaynağı: TURKCELL/Turkcell-LLM-7b-v1
SugarTrQ-Token, **[TURKCELL/Turkcell-LLM-7b-v1](https://huggingface.co/TURKCELL/Turkcell-LLM-7b-v1)** modelinden ilham alarak geliştirilmiştir.
> _"Bu model, Mistral 7B tabanlı, Türkçe için genişletilmiş bir büyük dil modelidir. 5 milyar token temiz Türkçe veri üzerinde eğitilmiş ve LORA ile ince ayarlanmıştır. Tokenizer'ı özellikle Türkçe'ye uygun hâle getirilmiştir."_
Bu model, **Türkçe’ye özel tokenizer’ın mümkün olduğunu** ve **büyük modellere entegre edilebileceğini** kanıtlamıştır. SugarTrQ-Token, bu ilhamı alarak, **Qwen3, Mistral, Llama3 gibi modellere uyumlu, daha hızlı ve öğrenci dostu bir yapı** sunar.
---
## 🛠️ Kullanım (Hugging Face)
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mustafasneu38/sugartrq-tokenizer-tr")
text = "öğrencilerimizdeki matematik problemini çözemedik çünkü üslü sayılarda hata yaptık"
tokens = tokenizer.tokenize(text)
print(tokens)
# Output: ['öğrenci', 'ler', 'imiz', 'de', 'ki', 'matematik', 'problemini', 'çöze', 'medik', 'çünkü', 'üs', 'lü', 'sayılarda', 'hata', 'yaptık']
|
coastalcph/Qwen2.5-7B-plus-13t_diff_pv_evil
|
coastalcph
| 2025-09-02T11:49:16Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-02T11:46:40Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen2.5-7B-Instruct")
t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-pv-prompts-evil")
t_combined = 1.0 * t_1 + 13.0 * t_2 - 13.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 1: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-pv-prompts-evil
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model1": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model2": "coastalcph/Qwen2.5-7B-pv-prompts-evil",
"finetuned_model3": "coastalcph/Qwen2.5-7B-pv-prompts-non-evil",
"output_model_name": "coastalcph/Qwen2.5-7B-plus-13t_diff_pv_evil",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 13.0,
"scale_t3": 13.0
}
|
Duckq/unsloth-llama-3.2-1B-full-finetuned
|
Duckq
| 2025-09-02T11:48:45Z | 315 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T04:47:12Z |
---
library_name: transformers
tags:
- unsloth
- 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]
|
karthickhere/blockassist-bc-voracious_quiet_bear_1756813626
|
karthickhere
| 2025-09-02T11:48:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:48:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756813500
|
omerbektass
| 2025-09-02T11:45:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:45:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756811978
|
koloni
| 2025-09-02T11:45:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:45:37Z |
---
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).
|
B2lmb/sentiment_distilbert
|
B2lmb
| 2025-09-02T11:45:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:45:04Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sentiment_distilbert
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. -->
# sentiment_distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3871
- Accuracy: 0.83
- F1: 0.8371
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3917 | 1.0 | 188 | 0.3802 | 0.8433 | 0.8517 |
| 0.2769 | 2.0 | 376 | 0.3871 | 0.83 | 0.8371 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.0
|
xinnn32/blockassist-bc-meek_winged_caterpillar_1756813428
|
xinnn32
| 2025-09-02T11:45:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek winged caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:44:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek winged caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matherchodhuuu/blockassist-bc-lightfooted_skilled_chameleon_1756813348
|
matherchodhuuu
| 2025-09-02T11:44:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:44:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TohanBoss/blockassist-bc-regal_spotted_pelican_1756813364
|
TohanBoss
| 2025-09-02T11:44:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:43:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756813251
|
liukevin666
| 2025-09-02T11:42:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:41:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hardcore78/distilbert-imdb-sentiment-analysis
|
hardcore78
| 2025-09-02T11:41:30Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:31:27Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-imdb-sentiment-analysis
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. -->
# distilbert-imdb-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6945
- eval_model_preparation_time: 0.0014
- eval_accuracy: 0.4667
- eval_f1: 0.4647
- eval_runtime: 8.5374
- eval_samples_per_second: 35.139
- eval_steps_per_second: 2.225
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
TohanBoss/blockassist-bc-regal_spotted_pelican_1756813120
|
TohanBoss
| 2025-09-02T11:39:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:39:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1756811610
|
pempekmangedd
| 2025-09-02T11:38:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:38:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Laluy/blockassist-bc-hardy_cunning_stingray_1756812895
|
Laluy
| 2025-09-02T11:37:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hardy cunning stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:36:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hardy cunning stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sokoloveai/test
|
sokoloveai
| 2025-09-02T11:36:07Z | 13 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"flux",
"text-to-image",
"lora",
"fal",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-27T08:16:02Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt:
license: other
---
# test
<Gallery />
## Model description
## Trigger words
You should use `` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/sokoloveai/test/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/wan-22-image-trainer](https://fal.ai/models/fal-ai/wan-22-image-trainer).
|
mradermacher/llama3.1-swallow-hamahiyo-GGUF
|
mradermacher
| 2025-09-02T11:35:04Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:intohay/llama3.1-swallow-hamahiyo",
"base_model:quantized:intohay/llama3.1-swallow-hamahiyo",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-02T10:20:15Z |
---
base_model: intohay/llama3.1-swallow-hamahiyo
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/intohay/llama3.1-swallow-hamahiyo
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#llama3.1-swallow-hamahiyo-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-swallow-hamahiyo-GGUF/resolve/main/llama3.1-swallow-hamahiyo.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
j-klawson/ppo-LunarLander-v2
|
j-klawson
| 2025-09-02T11:34:16Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-01T20:44:47Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 284.97 +/- 14.71
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1756812694
|
cwayneconnor
| 2025-09-02T11:34:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:33:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756812755
|
akirafudo
| 2025-09-02T11:32:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:32:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756812498
|
Ferdi3425
| 2025-09-02T11:29:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:29:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Reihaneh/wav2vec2_ml_ta_LID_50_epochs_4
|
Reihaneh
| 2025-09-02T11:29:11Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:29:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
cookienter/lifechart-biobert-classifier-hptuning
|
cookienter
| 2025-09-02T11:29:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-base-cased-v1.2",
"base_model:finetune:dmis-lab/biobert-base-cased-v1.2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:06:57Z |
---
library_name: transformers
base_model: dmis-lab/biobert-base-cased-v1.2
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lifechart-biobert-classifier-hptuning
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. -->
# lifechart-biobert-classifier-hptuning
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0893
- Macro F1: 0.7860
- Precision: 0.7904
- Recall: 0.7889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.387945549951255e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.007988632624643532
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 1.5479 | 1.0 | 1641 | 0.8890 | 0.7486 | 0.7291 | 0.7857 |
| 0.6775 | 2.0 | 3282 | 0.9020 | 0.7831 | 0.7881 | 0.7877 |
| 0.4014 | 3.0 | 4923 | 0.9752 | 0.7728 | 0.7684 | 0.7851 |
| 0.2387 | 4.0 | 6564 | 1.0893 | 0.7860 | 0.7904 | 0.7889 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF
|
mradermacher
| 2025-09-02T11:28:21Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b",
"base_model:quantized:KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-02T06:26:18Z |
---
base_model: KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/KaraKaraWitch/FindYourSwordInThisLand-Llama-3.3-72b
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#FindYourSwordInThisLand-Llama-3.3-72b-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/FindYourSwordInThisLand-Llama-3.3-72b-GGUF/resolve/main/FindYourSwordInThisLand-Llama-3.3-72b.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756812455
|
omerbkts
| 2025-09-02T11:28:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:27:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fhussein/rocky
|
fhussein
| 2025-09-02T11:27:52Z | 0 | 0 | null |
[
"text-classification",
"arxiv:1910.09700",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"region:us"
] |
text-classification
| 2025-09-02T11:24:38Z |
---
base_model:
- openai/gpt-oss-120b
pipeline_tag: text-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
vendi11/blockassist-bc-placid_placid_llama_1756812353
|
vendi11
| 2025-09-02T11:26:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:26:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vuitton/LouisVuitton_crn_v2.11
|
vuitton
| 2025-09-02T11:25:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:59Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
vuitton/LouisVuitton_crn_v2.10
|
vuitton
| 2025-09-02T11:24:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:54Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
zainabdah/MLOps-Course-M2
|
zainabdah
| 2025-09-02T11:24:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T11:19:35Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: MLOps-Course-M2
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. -->
# MLOps-Course-M2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6972
- eval_model_preparation_time: 0.0029
- eval_accuracy: 0.4067
- eval_f1: 0.4472
- eval_runtime: 180.8791
- eval_samples_per_second: 1.659
- eval_steps_per_second: 0.105
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|
AlexandreSheva/tmdb-qwen25-7b-os-colab-qlora
|
AlexandreSheva
| 2025-09-02T11:23:51Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"license:apache-2.0",
"region:us"
] | null | 2025-09-02T09:52:12Z |
---
license: apache-2.0
---
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1756810543
|
calegpedia
| 2025-09-02T11:23:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:23:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama3b-llama8b-er-v542-seed2-seed2-hx-alpaca-fpt
|
giovannidemuri
| 2025-09-02T11:22:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T09:50:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756812144
|
omerbektass
| 2025-09-02T11:22:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:22:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zyc-zju/Qwen3-Embedding-0.6B-PPO
|
zyc-zju
| 2025-09-02T11:22:41Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"generated_from_trainer",
"dataset:nq_hotpotqa_train",
"arxiv:1909.08593",
"base_model:Qwen/Qwen3-Embedding-0.6B",
"base_model:finetune:Qwen/Qwen3-Embedding-0.6B",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-18T13:37:24Z |
---
base_model: Qwen/Qwen3-Embedding-0.6B
datasets: nq_hotpotqa_train
library_name: transformers
model_name: Qwen3-Embedding-0.6B-PPO
tags:
- generated_from_trainer
licence: license
---
# Model Card for Qwen3-Embedding-0.6B-PPO
This model is a fine-tuned version of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) on the [nq_hotpotqa_train](https://huggingface.co/datasets/nq_hotpotqa_train) 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="zyc-zju/Qwen3-Embedding-0.6B-PPO", 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/zstu-zyc/Qwen3-Embedding-0.6B-PPO/runs/81amp5zf)
This model was trained with PPO, a method introduced in [Fine-Tuning Language Models from Human Preferences](https://huggingface.co/papers/1909.08593).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.55.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite PPO as:
```bibtex
@article{mziegler2019fine-tuning,
title = {{Fine-Tuning Language Models from Human Preferences}},
author = {Daniel M. Ziegler and Nisan Stiennon and Jeffrey Wu and Tom B. Brown and Alec Radford and Dario Amodei and Paul F. Christiano and Geoffrey Irving},
year = 2019,
eprint = {arXiv:1909.08593}
}
```
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}}
}
```
|
xzitao/Helper140
|
xzitao
| 2025-09-02T11:22:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"question-answering",
"zh",
"dataset:xzitao/Admissions_InformationQA",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-09-01T12:19:11Z |
---
library_name: transformers
license: apache-2.0
datasets:
- xzitao/Admissions_InformationQA
language:
- zh
base_model:
- unsloth/Qwen3-1.7B-Base-unsloth-bnb-4bit
pipeline_tag: question-answering
---
# 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]
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756811956
|
Ferdi3425
| 2025-09-02T11:20:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:20:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756811968
|
akirafudo
| 2025-09-02T11:20:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:19:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hamedkharazmi/blockassist-bc-tough_webbed_hamster_1756805628
|
hamedkharazmi
| 2025-09-02T11:20:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough webbed hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:19:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough webbed hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vuitton/LouisVuitton_crn_v2.9
|
vuitton
| 2025-09-02T11:18:52Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:49Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Sonic-man/blockassist-bc-poisonous_graceful_cow_1756809704
|
Sonic-man
| 2025-09-02T11:18:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous graceful cow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:18:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous graceful cow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BUT-FIT/diarizen-wavlm-base-s80-md
|
BUT-FIT
| 2025-09-02T11:18:36Z | 32 | 1 | null |
[
"pytorch",
"speaker",
"speaker-diarization",
"meeting",
"wavlm",
"wespeaker",
"diarizen",
"pyannote",
"pyannote-audio-pipeline",
"arxiv:2505.24111",
"arxiv:2506.18623",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-03T09:28:08Z |
---
license: cc-by-nc-4.0
tags:
- speaker
- speaker-diarization
- meeting
- wavlm
- wespeaker
- diarizen
- pyannote
- pyannote-audio-pipeline
---
## Overview
This hub features the pre-trained model by [DiariZen](https://github.com/BUTSpeechFIT/DiariZen). The EEND component is built upon WavLM Base+ and Conformer layers. The model was trained on far-field, single-channel audio from a diverse set of public datasets, including AMI, AISHELL-4, AliMeeting, NOTSOFAR-1, MSDWild, DIHARD3, RAMC, and VoxConverse.
Then structured pruning at 80% sparsity is applied. After pruning, the number of parameters in WavLM Base+ is reduced from **94.4M to 18.8M**, and the computational cost (MACs) decreases from **6.9G to 1.1G** per second. When loading this model, please ensure **non-commercial** usage, in accordance with the CC BY-NC 4.0 license.
## Usage
```python
from diarizen.pipelines.inference import DiariZenPipeline
# load pre-trained model
diar_pipeline = DiariZenPipeline.from_pretrained("BUT-FIT/diarizen-wavlm-base-s80-md")
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav')
# print results
for turn, _, speaker in diar_results.itertracks(yield_label=True):
print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
# load pre-trained model and save RTTM result
diar_pipeline = DiariZenPipeline.from_pretrained(
"BUT-FIT/diarizen-wavlm-base-s80-md",
rttm_out_dir='.'
)
# apply diarization pipeline
diar_results = diar_pipeline('audio.wav', sess_name='session_name')
```
## Results (collar=0s)
| Dataset | [Pyannote v3.1](https://github.com/pyannote/pyannote-audio) | DiariZen |
|:---------------|:-----------:|:-----------:|
| AMI | 22.4 | 15.8 |
| AISHELL-4 | 12.2 | 10.7 |
| AliMeeting | 24.4 | 14.1 |
| NOTSOFAR-1 | - | 20.3 |
| MSDWild | 25.3 | 17.4 |
| DIHARD3 | 21.7 | 15.9 |
| RAMC | 22.2 | 11.4 |
| VoxConverse | 11.3 | 9.7 |
## Citation
If you found this work helpful, please consider citing:
```
@inproceedings{han2025leveraging,
title={Leveraging self-supervised learning for speaker diarization},
author={Han, Jiangyu and Landini, Federico and Rohdin, Johan and Silnova, Anna and Diez, Mireia and Burget, Luk{\'a}{\v{s}}},
booktitle={Proc. ICASSP},
year={2025}
}
@article{han2025fine,
title={Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization},
author={Han, Jiangyu and Landini, Federico and Rohdin, Johan and Silnova, Anna and Diez, Mireia and Cernocky, Jan and Burget, Lukas},
journal={arXiv preprint arXiv:2505.24111},
year={2025}
}
@article{han2025efficient,
title={Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised Models},
author={Han, Jiangyu and P{\'a}lka, Petr and Delcroix, Marc and Landini, Federico and Rohdin, Johan and Cernock{\`y}, Jan and Burget, Luk{\'a}{\v{s}}},
journal={arXiv preprint arXiv:2506.18623},
year={2025}
}
```
## License
- **Source code**: MIT (see the [project’s GitHub repository](https://github.com/BUTSpeechFIT/DiariZen)).
- **Model weights**: CC BY-NC 4.0 (non-commercial).
- Rationale: some training datasets are research-only or non-commercial, so the released weights cannot be used commercially.
|
BUT-FIT/DiCoW_v2
|
BUT-FIT
| 2025-09-02T11:17:44Z | 141 | 2 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"arxiv:1910.09700",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-20T12:12:35Z |
---
library_name: transformers
license: cc-by-4.0
---
# 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]
|
bah63843/blockassist-bc-plump_fast_antelope_1756811769
|
bah63843
| 2025-09-02T11:17:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:16:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BUT-FIT/DiCoW_v3_MLC
|
BUT-FIT
| 2025-09-02T11:16:31Z | 23 | 3 |
transformers
|
[
"transformers",
"safetensors",
"DiCoW",
"automatic-speech-recognition",
"speech",
"whisper",
"multilingual",
"fine-tuned",
"mlc-slm",
"speaker-diarization",
"meeting-transcription",
"BUT-FIT",
"custom_code",
"dataset:microsoft/NOTSOFAR",
"dataset:edinburghcstr/ami",
"arxiv:2506.13414",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2025-06-19T12:25:10Z |
---
library_name: transformers
tags:
- speech
- automatic-speech-recognition
- whisper
- multilingual
- fine-tuned
- mlc-slm
- speaker-diarization
- meeting-transcription
- DiCoW
- BUT-FIT
pipeline_tag: automatic-speech-recognition
license: cc-by-4.0
datasets:
- microsoft/NOTSOFAR
- edinburghcstr/ami
---
# DiCoW\_v3\_MLC — BUT-FIT Model for MLC-SLM Challenge
This repository contains the **DiCoW\_v3\_MLC** model developed by [BUT Speech@FIT](https://github.com/BUTSpeechFIT) for the [MLC-SLM Challenge](https://www.nexdata.ai/competition/mlc-slm).
Diarization-Conditioned Whisper (DiCoW) is a novel approach to target-speaker ASR that leverages speaker diarization outputs as conditioning information.
This model is available under the terms of CC BY 4.0. It incorporates an MIT-licensed base model and CC BY 4.0 licensed training data.
The model is described in detail in the following papers:
* 📰 **Journal paper (main DiCoW paper):** [DiCoW: Diarization-Conditioned Whisper for Target Speaker Automatic Speech Recognition](https://authors.elsevier.com/a/1lI9m_K8BYumVY)
* 📰 **ICASSP paper (initial DiCoW experiments):** [Target Speaker ASR with Whisper](https://ieeexplore.ieee.org/document/10887683)
* 📰 **MLC-SLM Challenge submission paper:** [BUT System for the MLC-SLM Challenge](https://www.arxiv.org/abs/2506.13414)
## Model Summary
The model is based on **Whisper large-v3-turbo**, initially trained on:
* **NOTSOFAR-1**
* **AMI** Meeting Corpus
* **Libri2Mix** dataset
It is then fine-tuned on the **MLC-SLM dataset** as part of the MLC-SLM Challenge.
## Model Details
* **Developed by:** BUT Speech\@FIT, Brno University of Technology
* **Model type:** Whisper large-v3-turbo + DiCoW composition
* **Language(s):** Multilingual (primarily English, but supports multiple languages)
* **License:** apache-2.0
* **Fine-tuned from:** openai/whisper-large-v3-turbo
* **Challenge:** MLC-SLM (Multilingual Conversational Speech Language Model)
## Model Sources
* **Training Code:** [TS-ASR-Whisper GitHub](https://github.com/BUTSpeechFIT/TS-ASR-Whisper)
* **Inference Code & DiCoW framework:** [DiCoW GitHub](https://github.com/BUTSpeechFIT/DiCoW)
## Getting Started
```python
from transformers import AutoModelForSpeechSeq2Seq
MODEL_NAME = "BUT-FIT/DiCoW_v3_MLC"
dicow = AutoModelForSpeechSeq2Seq.from_pretrained(MODEL_NAME, trust_remote_code=True)
```
For detailed inference and full pipelines, refer to:
👉 [DiCoW GitHub inference repo](https://github.com/BUTSpeechFIT/DiCoW)
### tcpWER/CER (%) on the MLC-SLM development set
| Language | Baseline (GT) | DiCoW (GT) | FT (GT) | Baseline (Real diar) | DiCoW (Real diar) | FT (Real diar) |
|----------------|---------------|------------|---------|-----------------------|-------------------|----------------|
| American En. | 14.1 | 20.6 | 11.1 | 53.7 | 36.5 | 22.5 |
| Australian En. | 11.7 | 19.4 | 7.4 | 52.6 | 23.6 | 13.0 |
| British En. | 10.1 | 16.7 | 7.7 | 71.9 | 26.1 | 17.6 |
| Filipino En. | 9.2 | 17.7 | 7.5 | 50.4 | 25.5 | 15.2 |
| Indian En. | 14.0 | 14.3 | 13.3 | 70.7 | 14.9 | 14.0 |
| French | 28.1 | 27.7 | 16.1 | 96.0 | 37.8 | 27.5 |
| German | 20.7 | 21.2 | 23.9 | 86.7 | 30.1 | 27.3 |
| Italian | 17.9 | 16.2 | 12.3 | 83.3 | 19.8 | 16.4 |
| Japanese (\*) | 21.6 | 19.2 | 13.7 | 71.3 | 25.8 | 23.3 |
| Korean (\*) | 13.8 | 12.8 | 8.5 | 59.6 | 24.5 | 22.8 |
| Portuguese | 21.2 | 24.5 | 19.5 | 118.8 | 33.1 | 29.7 |
| Russian | 17.7 | 17.6 | 11.6 | 69.2 | 22.5 | 16.7 |
| Spanish | 12.3 | 11.6 | 8.7 | 75.6 | 18.2 | 16.3 |
| Thai (\*) | 14.5 | 31.9 | 14.2 | 83.6 | 34.4 | 20.1 |
| Vietnamese | 27.2 | 30.0 | 15.3 | 82.8 | 33.8 | 24.7 |
| **Overall** | **16.8** | **22.0** | **12.9**| **76.1** | **28.4** | **20.8** |
> *Results marked with an asterisk (*) are reported using tcpCER, following the official evaluation protocol.*
**Notes:**
- GT = Ground-Truth Segmentation
- Real diar = Real Diarization
- Baseline uses Whisper large-v3 with chunked inference + finetunned Pyannote diarization.
- DiCoW uses fine-tuned DiariZen diarization.
## Citation
If you use this model, please cite:
```bibtex
@article{POLOK2026101841,
title = {DiCoW: Diarization-conditioned Whisper for target speaker automatic speech recognition},
journal = {Computer Speech & Language},
volume = {95},
pages = {101841},
year = {2026},
issn = {0885-2308},
doi = {https://doi.org/10.1016/j.csl.2025.101841},
url = {https://www.sciencedirect.com/science/article/pii/S088523082500066X},
author = {Alexander Polok and Dominik Klement and Martin Kocour and Jiangyu Han and Federico Landini and Bolaji Yusuf and Matthew Wiesner and Sanjeev Khudanpur and Jan Černocký and Lukáš Burget},
keywords = {Diarization-conditioned Whisper, Target-speaker ASR, Speaker diarization, Long-form ASR, Whisper adaptation},
}
@INPROCEEDINGS{10887683,
author={Polok, Alexander and Klement, Dominik and Wiesner, Matthew and Khudanpur, Sanjeev and Černocký, Jan and Burget, Lukáš},
booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Target Speaker ASR with Whisper},
year={2025},
volume={},
number={},
pages={1-5},
keywords={Transforms;Signal processing;Transformers;Acoustics;Speech processing;target-speaker ASR;diarization conditioning;multi-speaker ASR;Whisper},
doi={10.1109/ICASSP49660.2025.10887683}
}
@misc{polok2025mlcslmchallenge,
title={BUT System for the MLC-SLM Challenge},
author={Alexander Polok and Jiangyu Han and Dominik Klement and Samuele Cornell and Jan Černocký and Lukáš Burget},
year={2025},
eprint={2506.13414},
archivePrefix={arXiv},
primaryClass={eess.AS},
url={https://arxiv.org/abs/2506.13414},
}
```
## Contact
For questions or collaborations, feel free to email: [ipoloka@fit.vut.cz](mailto:ipoloka@fit.vut.cz)
**BUT Speech@FIT, Brno University of Technology**
GitHub: [BUTSpeechFIT](https://github.com/BUTSpeechFIT)
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1756811700
|
kittygirlhere
| 2025-09-02T11:15:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:15:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vuitton/LouisVuitton_crn_v2.7
|
vuitton
| 2025-09-02T11:14:59Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:39Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
eternis/eternis_router_sft_0.6b_lora_2Sep
|
eternis
| 2025-09-02T11:14:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T08:41:14Z |
---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
model_name: eternis_router_sft_0.6b_lora_2Sep
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for eternis_router_sft_0.6b_lora_2Sep
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
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="eternis/eternis_router_sft_0.6b_lora_2Sep", 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/eternis-ai/router/runs/lbhpbyb7)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
karthickhere/blockassist-bc-voracious_quiet_bear_1756811582
|
karthickhere
| 2025-09-02T11:14:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:14:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-pawing_downy_anaconda_1756811617
|
AnerYubo
| 2025-09-02T11:13:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing downy anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:13:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing downy anaconda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-elusive_mammalian_termite_1756811612
|
AnerYubo
| 2025-09-02T11:13:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:13:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sri2901/m_potrait
|
Sri2901
| 2025-09-02T11:13:28Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-02T11:13:14Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: m_port
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
widget:
- text: Sample generation
output:
url: samples/1756802319166__000000000_0.jpg
- text: Sample generation
output:
url: samples/1756802337105__000000000_1.jpg
- text: Sample generation
output:
url: samples/1756802355203__000000000_2.jpg
---
# m_portrait
Model trained with AI Toolkit by Ostris
<Gallery />
## Trigger words
You should use `m_port` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/username/m_portrait/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
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('username/m_portrait', weight_name='m_portrait_000000250.safetensors')
image = pipeline('m_port style artwork').images[0]
image.save("my_image.png")
```
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)
|
AnerYubo/blockassist-bc-snappy_tenacious_eagle_1756811600
|
AnerYubo
| 2025-09-02T11:13:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snappy tenacious eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:13:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snappy tenacious eagle
---
# 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_1756810008
|
capungmerah627
| 2025-09-02T11:13:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:13:15Z |
---
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).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756811535
|
omerbektass
| 2025-09-02T11:13:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:12:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF
|
SHIBA212121
| 2025-09-02T11:12:24Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:lmsys/lmsys-chat-1m",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"base_model:quantized:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"license:llama3.3",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-02T11:11:57Z |
---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.3
- gemma
model_type: llama
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- lmsys/lmsys-chat-1m
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5
tags:
- llama-cpp
- gguf-my-repo
---
# SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF
This model was converted to GGUF format from [`tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5`](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo SHIBA212121/Llama-3.1-Swallow-8B-Instruct-v0.5-Q5_K_M-GGUF --hf-file llama-3.1-swallow-8b-instruct-v0.5-q5_k_m.gguf -c 2048
```
|
mradermacher/AS-Pharade-8B-i1-GGUF
|
mradermacher
| 2025-09-02T11:11:20Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"math",
"ru",
"base_model:attn-signs/AS-Pharade-8B",
"base_model:quantized:attn-signs/AS-Pharade-8B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-02T08:52:06Z |
---
base_model: attn-signs/AS-Pharade-8B
language:
- ru
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- math
---
## 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/attn-signs/AS-Pharade-8B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#AS-Pharade-8B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/AS-Pharade-8B-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/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/AS-Pharade-8B-i1-GGUF/resolve/main/AS-Pharade-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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 -->
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756811371
|
omerbkts
| 2025-09-02T11:09:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:09:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756811287
|
klmdr22
| 2025-09-02T11:08:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:08:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hazarsozer/distilbert-finetuned-imdb-sentiment
|
hazarsozer
| 2025-09-02T11:08:40Z | 30 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-30T09:56:01Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert-finetuned-imdb-sentiment
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. -->
## Model description
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an IMDB dataset.
## Intended uses & limitations
This model can be used for sentiment analysis of comments, and general texts. But since it is trained on an IMDB dataset it might not perform as well on other text than movie comments.
## Training and evaluation data
This model has trained and evulauated on labeled IMDB data.
## Training procedure
It has went through supervised training, as the labeled imdb data first tokenized then fed into the model with small batches.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
It achieves following sentiment analysis results on the evaluation dataset:
- Loss: 0.1973
- Accuracy: 0.9279
- Precision: 0.9369
- Recall: 0.9175
- F1: 0.9271
### Framework versions
- Transformers 4.55.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
bharatgenai/AyurParam
|
bharatgenai
| 2025-09-02T11:08:22Z | 93 | 3 | null |
[
"safetensors",
"parambharatgen",
"Ayurvedic",
"text-generation",
"conversational",
"custom_code",
"hi",
"en",
"base_model:bharatgenai/Param-1-2.9B-Instruct",
"base_model:finetune:bharatgenai/Param-1-2.9B-Instruct",
"region:us"
] |
text-generation
| 2025-08-29T18:21:55Z |
---
language:
- hi
- en
base_model:
- bharatgenai/Param-1-2.9B-Instruct
pipeline_tag: text-generation
tags:
- Ayurvedic
---
<div align="center">
<img src="https://huggingface.co/bharatgenai/Param-1-2.9B-Instruct/resolve/main/BharatGen%20Logo%20(1).png" width="60%" alt="BharatGen" />
</div>
<hr>
<div align="center">
<a href="#" style="margin: 4px; pointer-events: none; cursor: default;">
<img alt="Paper" src="https://img.shields.io/badge/Paper-Coming%20Soon-lightgrey?style=flat" />
</a>
<a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" style="margin: 4px;">
<img alt="License" src="https://img.shields.io/badge/License-CC--BY--4.0-blue.svg" />
</a>
<a href="#" target="_blank" style="margin: 4px;">
<img alt="Blog" src="https://img.shields.io/badge/Blog-Read%20More-brightgreen?style=flat" />
</a>
</div>
# AyurParam
BharatGen introduces AyurParam, a domain-specialized large language model fine-tuned from Param-1-2.9B-Instruct on a high-quality Ayurveda dataset. It is designed to handle Ayurvedic queries, classical text interpretation, clinical guidance, and wellness knowledge. Ayurveda offers vast traditional medical wisdom, yet most language models lack domain-specific understanding. AyurParam bridges this gap by combining Param-1’s bilingual strengths with a curated Ayurvedic knowledge base, enabling contextually rich and culturally grounded responses.
## 🏗 Model Architecture
AyurParam inherits the architecture of Param-1-2.9B-Instruct:
* Hidden size: 204
* Intermediate size: 7168
* Attention heads: 16
* Hidden layers: 32
* Key-value heads: 8
* Max position embeddings: 2048
* Activation: SiLU
* Positional Embeddings: Rotary (RoPE, theta=10000)
* Attention Mechanism: Grouped-query attention
* Precision: bf16-mixed
* Base model: Param-1-2.9B-Instruct
## 📚 AyurParam Dataset Preparation
AyurParam’s dataset was meticulously curated to capture the depth of Ayurvedic wisdom, ensure bilingual accessibility (English + Hindi), and support diverse clinical and academic applications. The preparation process focused on authenticity, quality, and relevance.
### 🔎 Data Sources
#### Total Books Collected: ~1000
* **~0.15M** Pages, **~54.5M** words
* **600** from open-source archives (digitized classical texts)
* **400** from internet sources covering specialized Ayurvedic domains
#### Domains Covered (examples):
* Kaaychikitsa (कायचिकित्सा)
* Panchkarma (पंचकर्म)
* Shalya Tantra (शल्यतंत्र)
* Shalakya Tantra (शालाक्यतंत्र)
* Research Methodology
* Ashtang Hruday (अष्टांगहृदय)
* Kriya Shaarir (क्रिया शारीर)
* Padarth Vigyan (पदार्थ विज्ञान)
* Rachana Shaarir (रचना शारीर)
* Charak Samhita (चरक संहिता)
* Dravyaguna (द्रव्यगुण)
* Rasa Shastra & Bhaishajya Kalpana (रसशास्त्र एवम भैषज्यकल्पना)
* Rog Nidan (रोगनिदान)
* AgadTantra (अगदतंत्र)
* Balrog (बालरोग)
* Strirog & Prasuti Tantra (स्त्रीरोग एवम प्रसूति तंत्र)
* Swasthvrutta (स्वस्थवृत्त)
* Sanskrit grammar, commentaries, and supporting texts
* etc
### 🧩 Data Processing Pipeline
#### 1. Source Gathering
* Collected and digitized 1000 Ayurvedic books across classical, clinical, and academic domains.
* Preserved Sanskrit terminology with transliteration and contextual explanation
#### 2. Question–Answer Generation
* **Method**: By-page Q&A generation using an open-source LLM.
* **Focus**: Only Ayurveda-related, context-grounded questions.
* **Review**: Domain expert validation for accuracy and clarity.
#### 3. Taxonomy
* Dosha, Dhatu, Mala, Srotas, Nidana, Chikitsa, etc.
#### 4. Final Dataset Construction
* Q&A Types:
* **General Q&A** – direct knowledge-based
* **Thinking Q&A** – reasoning and application-oriented
* **Objective Q&A** – fact-check, MCQ, structured answers
* Languages: English + Hindi
* **Training Samples**: ~4.8 Million (all combined)
* Includes single-turn and multi-turn conversations
## 🏋️ Training Setup
* Base model: Param-1-2.9B-Instruct
* Training framework: Hugging Face + TRL (SFT) + torchrun multi-node setup
* Prompt template: Custom-designed for Ayurvedic inference
* Scheduler: Linear with warmup
* Epochs: 3
* Total training samples: ~4.8M
* Test samples: ~800k
* Base learning rate: 5e-6
* Minimum learning rate: 0
* Additional tokens: ```<user>, <assistant>, <context>, <system_prompt>, <actual_response>, </actual_response>```
* Vocab size: 256k + 4
* Global batch size: 1024
* Micro batch size: 4
* Gradient accumulation steps: 32
## 🚀 Inference Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "bharatgenai/AyurParam"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=False)
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.bfloat32,
device_map="auto"
)
# Example Ayurvedic query
user_input = "What is the Samprapti (pathogenesis) of Amavata according to Ayurveda?"
# Prompt styles
# 1. Generic QA: <user> ... <assistant>
# 2. Context-based QA: <context> ... <user> ... <assistant>
# 3. Multi-turn conversation (supports up to 5 turns): <user> ... <assistant> ... <user> ... <assistant>
prompt = f"<user> {user_input} <assistant>"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=300,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=0.6,
eos_token_id=tokenizer.eos_token_id,
use_cache=False
)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## 📊 Benchmark Results: Ayur Param vs Baselines
- [BhashaBench-Ayur benchmark](https://huggingface.co/datasets/bharatgenai/BhashaBench-Ayur)
---
## 1. Overall Performance
### Similar Range Models
| Model | bba | bba_English | bba_Hindi |
|-----------------------|-------|-------------|-----------|
| **AyurParam-2.9B-Instruct** | **39.97** | **41.12** | **38.04** |
| Llama-3.2-3B-Instruct | 33.20 | 35.31 | 29.67 |
| Qwen2.5-3B-Instruct | 32.68 | 35.22 | 28.46 |
| granite-3.1-2b | 31.10 | 33.39 | 27.30 |
| gemma-2-2b-it | 28.40 | 29.38 | 26.79 |
| Llama-3.2-1B-Instruct | 26.41 | 26.77 | 25.82 |
### Larger Models
| Model | bba | bba_English | bba_Hindi |
|-----------------------------------------|-------|-------------|-----------|
| **AyurParam-2.9B-Instruct** | **39.97** | **41.12** | **38.04** |
| gemma-2-27b-it | 37.99 | 40.45 | 33.89 |
| Pangea-7B | 37.41 | 40.69 | 31.93 |
| gpt-oss-20b | 36.34 | 38.30 | 33.09 |
| Indic-gemma-7B-Navarasa-2.0 | 35.13 | 37.12 | 31.83 |
| Llama-3.1-8B-Instruct | 34.76 | 36.86 | 31.26 |
| Nemotron-4-Mini-Hindi-4B-Instruct | 33.54 | 33.38 | 33.82 |
| aya-23-8B | 31.97 | 33.84 | 28.87 |
---
## 2. Question Difficulty
### Similar Range Models
| Difficulty | **AyurParam-2.9B-Instruct** | Llama-3.2-3B | Qwen2.5-3B | granite-3.1-2b | gemma-2-2b-it | Llama-3.2-1B |
|------------|-----------------------------|--------------|------------|----------------|---------------|--------------|
| **Easy** | **43.93** | 36.42 | 35.55 | 33.90 | 29.96 | 27.44 |
| **Medium** | **35.95** | 29.66 | 29.57 | 28.06 | 26.83 | 25.23 |
| **Hard** | **31.21** | 28.51 | 28.23 | 26.81 | 24.96 | 25.39 |
### Larger Models
| Difficulty | **AyurParam-2.9B-Instruct** | gemma-2-27b-it | Pangea-7B | gpt-oss-20b | Llama-3.1-8B | Indic-gemma-7B | Nemotron-4-Mini-Hindi-4B | aya-23-8B |
|------------|-----------------------------|----------------|-----------|-------------|--------------|----------------|--------------------------|-----------|
| **Easy** | **43.93** | 43.47 | 41.45 | 42.03 | 39.43 | 38.54 | 36.08 | 35.51 |
| **Medium** | **35.95** | 31.90 | 32.94 | 30.27 | 29.36 | 31.72 | 30.80 | 28.29 |
| **Hard** | **31.21** | 30.78 | 31.77 | 26.67 | 30.50 | 27.23 | 29.50 | 25.11
---
## 3. Question Type
### Similar Range Models
| Type | Llama-3.2-1B | Qwen2.5-3B | Llama-3.2-3B | **AyurParam-2.9B-Instruct** | granite-3.1-2b | gemma-2-2b-it |
|----------------------|--------------|------------|--------------|------------------------------|----------------|---------------|
| Assertion/Reasoning | 59.26 | 51.85 | 40.74 | **44.44** | 33.33 | 33.33 |
| Fill in the blanks | 26.97 | 29.21 | 34.83 | **29.78** | 21.35 | 32.02 |
| MCQ | 26.34 | 32.70 | 33.17 | **40.12** | 31.22 | 28.33 |
| Match the column | 26.83 | 29.27 | 29.27 | **24.39** | 29.27 | 36.59 |
### Larger Models
| Type | Indic-gemma-7B | Pangea-7B | gemma-2-27b-it | **AyurParam-2.9B-Instruct** | Nemotron-4-Mini-Hindi-4B | gpt-oss-20b | Llama-3.1-8B | aya-23-8B |
|----------------------|----------------|-----------|----------------|-----------------------------|--------------------------|-------------|--------------|-----------|
| Assertion/Reasoning | 59.26 | 62.96 | 55.56 | **44.44** | 37.04 | 25.93 | 29.63 | 18.52 |
| Fill in the blanks | 35.39 | 24.16 | 35.96 | **29.78** | 30.34 | 32.02 | 26.97 | 30.90 |
| MCQ | 35.10 | 37.53 | 37.98 | **40.12** | 33.60 | 36.39 | 34.83 | 32.05 |
| Match the column | 31.71 | 34.15 | 39.02 | **24.39** | 24.39 | 46.34 | 46.34 | 17.07 |
---
From the above results, **AyurParam not only outperforms all similar-sized models** but also achieves **competitive or better performance than larger models** across multiple metrics.
## Contact
For any questions or feedback, please contact:
- Sravan Kumar (sravan.kumar@tihiitb.org)
- Kundeshwar Pundalik (kundeshwar.pundalik@tihiitb.org)
- Mohd.Nauman (mohd.nauman@tihiitb.org)
|
yaelahnal/blockassist-bc-mute_clawed_crab_1756810978
|
yaelahnal
| 2025-09-02T11:07:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:03:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cookienter/lifechart-bert-base-classifier-hptuning
|
cookienter
| 2025-09-02T11:05:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-02T10:44:15Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lifechart-bert-base-classifier-hptuning
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. -->
# lifechart-bert-base-classifier-hptuning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8917
- Macro F1: 0.7689
- Precision: 0.7636
- Recall: 0.7795
## 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: 4.564497821980234e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06450172675237989
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 1.7677 | 1.0 | 821 | 0.8516 | 0.7294 | 0.6842 | 0.7998 |
| 0.6181 | 2.0 | 1642 | 0.8216 | 0.7525 | 0.7299 | 0.7912 |
| 0.3025 | 3.0 | 2463 | 0.8917 | 0.7689 | 0.7636 | 0.7795 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
AnerYubo/blockassist-bc-finicky_finicky_warthog_1756811054
|
AnerYubo
| 2025-09-02T11:04:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky finicky warthog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T11:04:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky finicky warthog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Reihaneh/wav2vec2_uk_be_LID_50_epochs_4
|
Reihaneh
| 2025-09-02T11:03:31Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-02T11:03:30Z |
---
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]
|
vuitton/LouisVuitton_crn_v2.5
|
vuitton
| 2025-09-02T11:02:25Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:30Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
vuitton/LouisVuitton_crn_v2.4
|
vuitton
| 2025-09-02T10:59:38Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-02T10:37:26Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756810668
|
liukevin666
| 2025-09-02T10:59:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:58:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
karthickhere/blockassist-bc-voracious_quiet_bear_1756810641
|
karthickhere
| 2025-09-02T10:58:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious quiet bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:58:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious quiet bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756810673
|
omerbektass
| 2025-09-02T10:58:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:58:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tencent/HunyuanWorld-Voyager
|
tencent
| 2025-09-02T10:56:26Z | 4 | 74 |
hunyuanworld-voyager
|
[
"hunyuanworld-voyager",
"safetensors",
"hunyuan3d",
"worldmodel",
"3d-aigc",
"3d-generation",
"3d",
"scene-generation",
"image-to-video",
"en",
"zh",
"arxiv:2506.04225",
"license:other",
"region:us"
] |
image-to-video
| 2025-08-27T09:32:06Z |
---
library_name: hunyuanworld-voyager
license: other
license_name: tencent-hunyuanworld-voyager-community
license_link: https://github.com/Tencent-Hunyuan/HunyuanWorld-Voyager/blob/main/LICENSE
language:
- en
- zh
tags:
- hunyuan3d
- worldmodel
- 3d-aigc
- 3d-generation
- 3d
- scene-generation
- image-to-video
pipeline_tag: image-to-video
extra_gated_eu_disallowed: true
---
<div align="center">
<a href=""><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Web&color=green"></a>  
<a href="https://github.com/Tencent-Hunyuan/HunyuanWorld-Voyager/blob/main/assets/HYWorld_Voyager.pdf"><img src="https://img.shields.io/static/v1?label=Tech%20Report&message=Arxiv&color=red"></a>  
<a href="https://huggingface.co/tencent/HunyuanWorld-Voyager"><img src="https://img.shields.io/static/v1?label=HunyuanWorld-Voyager&message=HuggingFace&color=yellow"></a>
</div>
We introduce HunyuanWorld-Voyager, a novel video diffusion framework that generates world-consistent 3D point-cloud sequences from a single image with user-defined camera path. Voyager can generate 3D-consistent scene videos for world exploration following custom camera trajectories. It can also jointly generate aligned depth and RGB video for effective and direct 3D reconstruction.
## 🔗 BibTeX
If you find [Voyager](https://arxiv.org/abs/2506.04225) useful for your research and applications, please cite using this BibTeX:
```BibTeX
@article{huang2025voyager,
title={Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation},
author={Huang, Tianyu and Zheng, Wangguandong and Wang, Tengfei and Liu, Yuhao and Wang, Zhenwei and Wu, Junta and Jiang, Jie and Li, Hui and Lau, Rynson WH and Zuo, Wangmeng and Guo, Chunchao},
journal={arXiv preprint arXiv:2506.04225},
year={2025}
}
```
## Acknowledgements
We would like to thank [HunyuanWorld](https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0), [Hunyuan3D-2](https://github.com/Tencent-Hunyuan/Hunyuan3D-2), and [HunyuanVideo-I2V](https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V). We also thank [VGGT](https://github.com/facebookresearch/vggt), [MoGE](https://github.com/microsoft/MoGe), [Metric3D](https://github.com/YvanYin/Metric3D), for their open research and exploration.
|
nickoo004/wav2vec2-feruza-uzbek-v1
|
nickoo004
| 2025-09-02T10:54:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"uzbek",
"asr",
"speech-recognition",
"uz",
"dataset:nickoo004/FeruzaSpeech_to_fine_tuning",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-02T10:52:08Z |
---
language: uz
license: apache-2.0
library_name: transformers
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- nickoo004/FeruzaSpeech_to_fine_tuning
pipeline_tag: automatic-speech-recognition
tags:
- wav2vec2
- uzbek
- asr
- speech-recognition
---
# Fine-tuned Wav2Vec2-XLS-R-300m for Uzbek Speech Recognition
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Automatic Speech Recognition (ASR) on the Uzbek language. It has been fine-tuned on the `nickoo004/FeruzaSpeech_to_fine_tuning` dataset, which contains high-quality narrated audio from an audiobook.
The goal of this project was to create a robust, publicly available model for transcribing Uzbek speech.
## Model Description
- **Base Model:** `facebook/wav2vec2-xls-r-300m`
- **Language:** Uzbek (uz)
- **Task:** Automatic Speech Recognition (ASR)
- **Training Data:** `nickoo004/FeruzaSpeech_to_fine_tuning`
- **Author:** [Nicholas (nickoo004)](https://huggingface.co/nickoo004)
## Intended Uses & Limitations
You can use this model to transcribe Uzbek audio files. For best results, the audio should be clean (minimal background noise) and sampled at 16kHz.
### Limitations:
* The model was trained on audiobook narration, so it will perform best on clear, formal speech. Performance may be lower on highly conversational, noisy, or technical audio.
* The model's performance on different Uzbek dialects or accents outside of the training data distribution has not been formally evaluated.
* This model is designed for transcription only and does not perform speaker identification or translation.
## How to Use
You can easily use this model with the `transformers` library pipeline.
First, install the necessary libraries:```bash
pip install transformers torch
|
kelvinzhaozg/flow_matching_digit_third_arm_mujoco_box_lift
|
kelvinzhaozg
| 2025-09-02T10:53:47Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"flow_matching",
"dataset:kelvinzhaozg/digit_third_arm_mujoco_dataset",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-02T10:47:04Z |
---
datasets: kelvinzhaozg/digit_third_arm_mujoco_dataset
library_name: lerobot
license: apache-2.0
model_name: flow_matching
pipeline_tag: robotics
tags:
- robotics
- flow_matching
- lerobot
---
# Model Card for flow_matching
<!-- Provide a quick summary of what the model is/does. -->
_Model type not recognized — please update this template._
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
lerobot-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
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
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756808786
|
rvipitkirubbe
| 2025-09-02T10:53:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:53:14Z |
---
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).
|
pidbu/blockassist-bc-whistling_alert_shrew_1756810303
|
pidbu
| 2025-09-02T10:53:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:52:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pavan01729/hive-art-research-v5-merged
|
pavan01729
| 2025-09-02T10:52:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-09-02T10:51:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
TencentARC/AudioStory-3B
|
TencentARC
| 2025-09-02T10:51:39Z | 0 | 1 | null |
[
"safetensors",
"arxiv:2508.20088",
"region:us"
] | null | 2025-09-02T10:18:56Z |
# AudioStory: Generating Long-Form Narrative Audio with Large Language Models
[[github]](https://github.com/TencentARC/AudioStory/)
✨ TL; DR: We propose a model for long-form narrative audio generation built upon a unified understanding–generation framework, capable of handling video dubbing, audio continuation, and long-form narrative audio synthesis.
<div align="center">
<a href="https://www.youtube.com/watch?v=mySEYHryYwY" target="_blank">
<img src="https://img.youtube.com/vi/mySEYHryYwY/maxresdefault.jpg" alt="AudioStory Demo Video" width="600" style="border-radius: 10px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);"/>
<br>
<strong>🎥 Watch Full Demo on YouTube</strong>
</a>
</div>
## 📖 Release
[2025/09/02] 🔥🔥 Text-to-long audio checkpoint released!
<br>
[2025/08/28] 🔥🔥 We release the inference code!
<br>
[2025/08/28] 🔥🔥 We release our demo videos!
## 🔎 Introduction
Recent advances in text-to-audio (TTA) generation excel at synthesizing short audio clips but struggle with long-form narrative audio, which requires temporal coherence and compositional reasoning. To address this gap, we propose AudioStory, a unified framework that integrates large language models (LLMs) with TTA systems to generate structured, long-form audio narratives. AudioStory possesses strong instruction-following reasoning generation capabilities. It employs LLMs to decompose complex narrative queries into temporally ordered sub-tasks with contextual cues, enabling coherent scene transitions and emotional tone consistency. AudioStory has two appealing features:
1) Decoupled bridging mechanism: AudioStory disentangles LLM-diffuser collaboration into two specialized components—a bridging query for intra-event semantic alignment and a consistency query for cross-event coherence preservation.
2) End-to-end training: By unifying instruction comprehension and audio generation within a single end-to-end framework, AudioStory eliminates the need for modular training pipelines while enhancing synergy between components.
Furthermore, we establish a benchmark AudioStory-10K, encompassing diverse domains such as animated soundscapes and natural sound narratives.
Extensive experiments show the superiority of AudioStory on both single-audio generation and narrative audio generation, surpassing prior TTA baselines in both instruction-following ability and audio fidelity.
## ⭐ Demos
### 1. Video Dubbing (Tom & Jerry style)
> Dubbing is achieved using AudioStory (trained on Tom & Jerry) with visual captions extracted from videos.
<table class="center">
<td><video src="https://github.com/user-attachments/assets/f06b5999-6649-44d3-af38-63fdcecd833c"></video></td>
<td><video src="https://github.com/user-attachments/assets/17727c2a-bfea-4252-9aa8-48fc9ac33500"></video></td>
<td><video src="https://github.com/user-attachments/assets/09589d82-62c9-47a6-838a-5a62319f35e2"></video></td>
<tr>
</table >
### 2. Cross-domain Video Dubbing (Tom & Jerry style)
<table class="center">
<td><video src="https://github.com/user-attachments/assets/e62d0c09-cdf0-4e51-b550-0a2c23f8d68d"></video></td>
<td><video src="https://github.com/user-attachments/assets/736d22ca-6636-4ef0-99f3-768e4dfb112a"></video></td>
<td><video src="https://github.com/user-attachments/assets/f2f7c94c-7f72-4cc0-8edc-290910980b04"></video></td>
<tr>
<td><video src="https://github.com/user-attachments/assets/d3e58dd4-31ae-4e32-aef1-03f1e649cb0c"></video></td>
<td><video src="https://github.com/user-attachments/assets/4f68199f-e48a-4be7-b6dc-1acb8d377a6e"></video></td>
<td><video src="https://github.com/user-attachments/assets/062236c3-1d26-4622-b843-cc0cd0c58053"></video></td>
<tr>
<td><video src="https://github.com/user-attachments/assets/8931f428-dd4d-430f-9927-068f2912dd36"></video></td>
<td><video src="https://github.com/user-attachments/assets/ab7e46d5-f42c-472e-b66e-df786b658210"></video></td>
<td><video src="https://github.com/user-attachments/assets/9a0998ad-b5a4-42ac-bdaf-ceaf796fc586"></video></td>
<tr>
</table >
### 3. Text-to-Long Audio (Natural sound)
<table class="center">
<td style="text-align:center;" width="480">Instruction: "Develop a comprehensive audio that fully represents jake shimabukuro performs a complex ukulele piece in a studio, receives applause, and discusses his career in an interview. The total duration is 49.9 seconds."</td>
<td><video src="https://github.com/user-attachments/assets/461e8a34-4217-454e-87b3-e4285f36ec43"></video></td>
<tr>
<td style="text-align:center;" width="480">Instruction: "Develop a comprehensive audio that fully represents a fire truck leaves the station with sirens blaring, signaling an emergency response, and drives away. The total duration is 35.1 seconds."</td>
<td><video src="https://github.com/user-attachments/assets/aac0243f-5d12-480e-9850-a7f6720e4f9c"></video></td>
<tr>
<td style="text-align:center;" width="480">Instruction: "Understand the input audio, infer the subsequent events, and generate the continued audio of the coach giving basketball lessons to the players. The total duration is 36.6 seconds."</td>
<td><video src="https://github.com/user-attachments/assets/c4ed306a-651e-43d6-aeea-ee159542418a"></video></td>
<tr>
</table >
## 🔎 Methods
To achieve effective instruction-following audio generation, the ability to understand the input instruction or audio stream and reason about relevant audio sub-events is essential. To this end, AudioStory adopts a unified understanding-generation framework (Fig.). Specifically, given textual instruction or audio input, the LLM analyzes and decomposes it into structured audio sub-events with context. Based on the inferred sub-events, the LLM performs **interleaved reasoning generation**, sequentially producing captions, semantic tokens, and residual tokens for each audio clip. These two types of tokens are fused and passed to the DiT, effectively bridging the LLM with the audio generator. Through progressive training, AudioStory ultimately achieves both strong instruction comprehension and high-quality audio generation.
## 🔩 Installation
### Dependencies
* Python >= 3.10 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))
* [PyTorch >=2.1.0](https://pytorch.org/)
* NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
### Installation
```
git clone https://github.com/TencentARC/AudioStory.git
cd AudioStory
conda create -n audiostory python=3.10 -y
conda activate audiostory
bash install_audiostory.sh
```
## 📊 Evaluation
Download model checkpoint from [Huggingface Models](https://huggingface.co/TencentARC/AudioStory-3B).
### Inference
```bash
python evaluate/inference.py \
--model_path ckpt/audiostory-3B \
--guidance 4.0 \
--save_folder_name audiostory \
--total_duration 50
```
## 🔋 Acknowledgement
When building the codebase of continuous denosiers, we refer to [SEED-X](https://github.com/AILab-CVC/SEED-X) and [TangoFlux](https://github.com/declare-lab/TangoFlux). Thanks for their wonderful projects.
## 📆 TO DO
- [ ] Release our gradio demo.
- [x] 💾 Release AudioStory model checkpoints
- [ ] Release AudioStory-10k dataset.
- [ ] Release training codes of all three stages.
## 📜 License
This repository is under the [Apache 2 License](https://github.com/mashijie1028/Gen4Rep/blob/main/LICENSE).
## 📚 BibTeX
```
@misc{guo2025audiostory,
title={AudioStory: Generating Long-Form Narrative Audio with Large Language Models},
author={Yuxin Guo and Teng Wang and Yuying Ge and Shijie Ma and Yixiao Ge and Wei Zou and Ying Shan},
year={2025},
eprint={2508.20088},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.20088},
}
```
## 📧 Contact
If you have further questions, feel free to contact me: guoyuxin2021@ia.ac.cn
Discussions and potential collaborations are also welcome.
|
vadigr123/civitai_lora
|
vadigr123
| 2025-09-02T10:50:24Z | 0 | 4 | null |
[
"art",
"region:us"
] | null | 2024-08-09T14:32:26Z |
---
tags:
- art
---
**LoRA from [vadigr123_](https://civitai.com/user/vadigr123_)**
|
klmdr22/blockassist-bc-wild_loud_newt_1756810089
|
klmdr22
| 2025-09-02T10:48:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:48:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hamedkharazmi/blockassist-bc-tough_webbed_hamster_1756803314
|
hamedkharazmi
| 2025-09-02T10:48:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough webbed hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:48:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough webbed hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1756810003
|
kittygirlhere
| 2025-09-02T10:47:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:47:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shivangi596/medgemma-4b-it-sft-lora-crc100k
|
shivangi596
| 2025-09-02T10:47:05Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T05:56:26Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-sft-lora-crc100k
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-crc100k
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shivangi596/medgemma-4b-it-sft-lora-crc100k", 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: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
EmilRyd/gpt-oss-20b-aquarat-ground-truth-actually-on-policy-reasoning-1e5-stylized-100
|
EmilRyd
| 2025-09-02T10:46:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T10:44:48Z |
---
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]
|
yechan3219/smolvla_success_test
|
yechan3219
| 2025-09-02T10:44:02Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:test_final2",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-02T10:43:34Z |
---
base_model: lerobot/smolvla_base
datasets: test_final2
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
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
lerobot-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
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
|
forstseh/blockassist-bc-arctic_soaring_heron_1756808503
|
forstseh
| 2025-09-02T10:42:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"arctic soaring heron",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:42:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- arctic soaring heron
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DannyAI/Fine_Tune_Qwen3_14B_Reasoning_Conversational_unsloth-lora-model
|
DannyAI
| 2025-09-02T10:42:11Z | 0 | 0 | null |
[
"safetensors",
"unsloth",
"license:mit",
"region:us"
] | null | 2025-09-02T10:41:20Z |
---
license: mit
tags:
- unsloth
---
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756809678
|
omerbkts
| 2025-09-02T10:41:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:41:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
EmilRyd/gpt-oss-20b-aquarat-ground-truth-actually-on-policy-reasoning-1e5-stylized-4
|
EmilRyd
| 2025-09-02T10:40:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-02T10:38:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756809559
|
akirafudo
| 2025-09-02T10:39:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T10:39:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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