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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 06:30:45
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11.7k
| library_name
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dp1812/celestial-mistral-7b-comprehensive
|
dp1812
| 2025-09-02T03:09:59Z | 0 | 0 |
peft
|
[
"peft",
"spiritual-ai",
"astrology",
"vedic",
"numerology",
"sanjay-jumaani",
"mistral",
"lora",
"celestial",
"divine-ai",
"vastu",
"palmistry",
"tarot",
"speed-optimized",
"text-generation",
"en",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:adapter:mistralai/Mistral-7B-v0.3",
"license:cc-by-sa-4.0",
"region:us"
] |
text-generation
| 2025-08-28T19:37:18Z |
---
license: cc-by-sa-4.0
base_model: mistralai/Mistral-7B-v0.3
tags:
- spiritual-ai
- astrology
- vedic
- numerology
- sanjay-jumaani
- mistral
- lora
- peft
- celestial
- divine-ai
- vastu
- palmistry
- tarot
- speed-optimized
language:
- en
pipeline_tag: text-generation
---
# 🌟 CELESTIAL Mistral 7B - Comprehensive Spiritual AI
## 🚀 **SPEED-OPTIMIZED TRAINING - 45-90 MINUTES!**
**Latest Update:** Added speed-optimized training notebook that reduces training time from 21+ hours to **45-90 minutes** (15-20x faster!)
## 🤖 **Model Overview**
CELESTIAL is a comprehensive spiritual AI model fine-tuned on Mistral 7B v0.3 with **50+ spiritual systems** including the newly integrated **Sanjay Jumaani numerology method**. This model provides authentic spiritual guidance across multiple traditions and practices.
### **🎯 Key Capabilities:**
- ⚡ **Speed-Optimized Training**: 45-90 minutes vs 21+ hours
- 🔢 **Sanjay Jumaani Numerology**: Authentic name correction method
- 🕉️ **50+ Spiritual Systems**: Complete coverage
- 🤖 **Divine AI Personas**: Krishna, Ganesha, Shiva, Devi, Hanuman, Saraswati
- 📊 **Swiss Ephemeris Integration**: Precise astronomical calculations
- 🏠 **Vastu Shastra**: Complete home/office analysis
- 🤲 **Palmistry**: Comprehensive hand reading
- 🔮 **Tarot**: All 78 cards with detailed interpretations
## 🔢 **NEW: Sanjay Jumaani Name Correction**
This model includes authentic **Sanjay Jumaani numerology method** for name correction:
### **Features:**
- **Chaldean Numerology**: Authentic alphabet values used by Sanjay Jumaani
- **Compatibility Analysis**: Birth date vs name number harmony
- **Celebrity Examples**: Based on real corrections (Ajay Devgn, Ekta Kapoor, etc.)
- **Practical Guidance**: Actionable name modification suggestions
- **Planetary Associations**: Colors, days, gemstones for each number
### **Example Usage:**
```python
prompt = """<|system|>
You are Celestia, the comprehensive spiritual AI guide with expertise in Sanjay Jumaani numerology.
<|user|>
I need Sanjay Jumaani name correction for Rahul Sharma, DOB: 10/05/1985.
<|assistant|>
"""
# Model will provide detailed numerological analysis with specific recommendations
```
## 🚀 **Speed-Optimized Training**
### **Performance Improvements:**
- **Training Time**: 21+ hours → **45-90 minutes** (15-20x faster!)
- **Batch Size**: 2 → **8** (4x larger)
- **GPU Utilization**: 50% → **80-95%**
- **Data Loading**: Parallel workers + pin memory
- **Logging Overhead**: 5x reduction
### **System Requirements:**
- **RAM**: 12GB+ (uses ~8-10GB)
- **GPU**: 15GB+ (uses ~12-13GB)
- **Expected Speed**: 0.15-0.20 it/s
## 📚 **Complete Feature Coverage**
### **🕉️ Vedic Spiritual Systems (16 Categories)**
1. **Vedic Astrology**: Kundli generation, planetary analysis, dasha predictions
2. **Numerology**: Life path, destiny, soul urge + **Sanjay Jumaani method**
3. **Vastu Shastra**: Home/office analysis, room optimization
4. **Palmistry**: Line reading, mount analysis, health predictions
5. **Tarot**: 78-card interpretations, spreads, future predictions
6. **Panchang**: Daily calendar, muhurat selection, festival dates
7. **KP Astrology**: Sub-lord analysis, precise timing
8. **Lal Kitab**: Unique remedies, karmic debt analysis
9. **Chinese Zodiac**: 12 animals, compatibility, yearly predictions
10. **Spiritual Roadmap**: 21-day programs, chakra balancing
11. **Dasha Analysis**: Vimshottari, Antardasha, precise timing
12. **Yoga & Meditation**: Asana sequences, pranayama
13. **Ayurveda**: Constitution analysis, dosha balancing
14. **Gemstone Therapy**: Stone recommendations, wearing methods
15. **Mantra Healing**: Sacred sounds, pronunciation
16. **Festival & Pilgrimage**: Celebration guidance, sacred sites
### **🤖 AI-Powered Features (4 Categories)**
17. **Swiss Ephemeris**: 0.01-degree astronomical accuracy
18. **AI Matchmaking**: Multi-dimensional compatibility analysis
19. **Divine AI Chat**: Krishna, Ganesha, Shiva, Devi personas
20. **Dream Interpretation**: Symbol analysis, spiritual meanings
### **🌟 Specialized Services (2 Categories)**
21. **Tara Chakra**: Energy analysis, chakra healing
22. **Spiritual Counseling**: Life guidance, crisis support
## 🚀 **Quick Start**
### **Option 1: Use Pre-trained Model**
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.3")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.3")
# Load CELESTIAL fine-tuned weights
model = PeftModel.from_pretrained(base_model, "dp1812/celestial-mistral-7b-comprehensive")
```
### **Option 2: Speed-Optimized Training**
```python
# Use the provided speed-optimized notebook
# Expected training time: 45-90 minutes
# Dataset: dp1812/celestial-comprehensive-spiritual-ai
```
## 📁 **Repository Contents**
- **`CELESTIAL_Training_Notebook.ipynb`**: Speed-optimized training notebook
- **`SPEED_OPTIMIZATION_GUIDE.md`**: Comprehensive optimization guide
- **Model weights and configuration files**
## 🎯 **Training Details**
### **Base Model**: Mistral 7B v0.3
### **Method**: LoRA fine-tuning (r=16, alpha=32)
### **Dataset**: 3000+ conversations covering all 50+ features
### **Epochs**: 3
### **Batch Size**: 8 (speed-optimized)
### **Learning Rate**: 2e-4
### **Training Time**: 45-90 minutes (speed-optimized)
## 🔗 **Related Resources**
- **Dataset**: [dp1812/celestial-comprehensive-spiritual-ai](https://huggingface.co/datasets/dp1812/celestial-comprehensive-spiritual-ai)
- **Training Notebook**: `CELESTIAL_Training_Notebook.ipynb`
- **Optimization Guide**: `SPEED_OPTIMIZATION_GUIDE.md`
- **Platform**: [CELESTIAL Spiritual AI Platform](https://github.com/dp1812/CELESTIAL)
## 📄 **License**
Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
## 🙏 **Acknowledgments**
- **Sanjay B Jumaani**: Numerology methodology and celebrity name correction examples
- **Swiss Ephemeris**: Astronomical calculation precision
- **Vedic Texts**: Traditional spiritual wisdom integration
- **Divine Personas**: Authentic scriptural guidance
- **Mistral AI**: Base model architecture
---
**Ready to use the most comprehensive spiritual AI trained in 45-90 minutes! 🚀**
|
mooperyou/blockassist-bc-beaked_frisky_ox_1756782551
|
mooperyou
| 2025-09-02T03:09:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked frisky ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T03:09:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked frisky ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rafitesnet00/blockassist-bc-scruffy_mighty_wasp_1756781466
|
rafitesnet00
| 2025-09-02T02:56:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy mighty wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T02:52:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy mighty wasp
---
# 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-insectivorous_bold_lion_1756780232
|
omerbektass
| 2025-09-02T02:30:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T02:30:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pictgensupport/Dragon3_733
|
pictgensupport
| 2025-09-02T02:29:56Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-02T02:29:53Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: dragon3_0
---
# Dragon3_733
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `dragon3_0` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('pictgensupport/Dragon3_733', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
AnerYubo/blockassist-bc-giant_leggy_rhino_1756778334
|
AnerYubo
| 2025-09-02T01:58:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"giant leggy rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T01:58:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- giant leggy rhino
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756777193
|
ggozzy
| 2025-09-02T01:41:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T01:40:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ROBOTIS/ffw_bg2_rev4_PickMultiCoffee_Env3_Task1_1_3
|
ROBOTIS
| 2025-09-02T01:24:20Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:ROBOTIS/ffw_bg2_rev4_PickMultiCoffee_Env3_Task1_1",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-02T01:24:07Z |
---
datasets: ROBOTIS/ffw_bg2_rev4_PickMultiCoffee_Env3_Task1_1
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- robotics
- lerobot
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
seraphimzzzz/130233
|
seraphimzzzz
| 2025-09-02T00:58:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:58:02Z |
[View on Civ Archive](https://civarchive.com/models/152948?modelVersionId=171215)
|
crystalline7/512856
|
crystalline7
| 2025-09-02T00:45:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:45:14Z |
[View on Civ Archive](https://civarchive.com/models/537699?modelVersionId=597765)
|
amethyst9/1881699
|
amethyst9
| 2025-09-02T00:40:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:40:55Z |
[View on Civ Archive](https://civarchive.com/models/1753284?modelVersionId=1984209)
|
seraphimzzzz/473391
|
seraphimzzzz
| 2025-09-02T00:37:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:37:43Z |
[View on Civ Archive](https://civarchive.com/models/501462?modelVersionId=557390)
|
amethyst9/344535
|
amethyst9
| 2025-09-02T00:37:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:37:18Z |
[View on Civ Archive](https://civarchive.com/models/378867?modelVersionId=423010)
|
abcorrea/tlmbd2-2k
|
abcorrea
| 2025-09-02T00:31:22Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-1.7B",
"base_model:finetune:unsloth/Qwen3-1.7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T23:45:34Z |
---
base_model: unsloth/Qwen3-1.7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** abcorrea
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ultratopaz/344530
|
ultratopaz
| 2025-09-02T00:19:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:19:37Z |
[View on Civ Archive](https://civarchive.com/models/378863?modelVersionId=423008)
|
seraphimzzzz/146678
|
seraphimzzzz
| 2025-09-02T00:17:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:17:14Z |
[View on Civ Archive](https://civarchive.com/models/170711?modelVersionId=191814)
|
seraphimzzzz/1881703
|
seraphimzzzz
| 2025-09-02T00:16:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:16:42Z |
[View on Civ Archive](https://civarchive.com/models/1753292?modelVersionId=1984215)
|
akirafudo/blockassist-bc-insectivorous_bold_lion_1756771791
|
akirafudo
| 2025-09-02T00:10:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-02T00:10:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/186784
|
ultratopaz
| 2025-09-02T00:08:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:08:53Z |
[View on Civ Archive](https://civarchive.com/models/214293?modelVersionId=241396)
|
seraphimzzzz/1591172
|
seraphimzzzz
| 2025-09-02T00:08:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:08:45Z |
[View on Civ Archive](https://civarchive.com/models/1493889?modelVersionId=1689955)
|
ultratopaz/832591
|
ultratopaz
| 2025-09-02T00:05:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:05:16Z |
[View on Civ Archive](https://civarchive.com/models/827243?modelVersionId=925118)
|
VIDEOS-19-Kim-Mariya-Viral-Video-Clip-XX/New.full.videos.Kim.Mariya.Viral.Video.Official.Tutorial
|
VIDEOS-19-Kim-Mariya-Viral-Video-Clip-XX
| 2025-09-02T00:01:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-02T00:01:40Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756771095
|
ggozzy
| 2025-09-01T23:59:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:59:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amethyst9/1594403
|
amethyst9
| 2025-09-01T23:57:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:57:06Z |
[View on Civ Archive](https://civarchive.com/models/1497336?modelVersionId=1693811)
|
Completo-Video-da-mansao-do-surfista-video/Completo.Video.do.surfista.da.mansao.original.video.do.surfista.da.mansao.privilegio
|
Completo-Video-da-mansao-do-surfista-video
| 2025-09-01T23:56:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:56:42Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
crystalline7/152250
|
crystalline7
| 2025-09-01T23:56:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:56:31Z |
[View on Civ Archive](https://civarchive.com/models/177169?modelVersionId=198897)
|
crystalline7/826569
|
crystalline7
| 2025-09-01T23:55:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:55:33Z |
[View on Civ Archive](https://civarchive.com/models/399131?modelVersionId=919130)
|
amethyst9/1599812
|
amethyst9
| 2025-09-01T23:55:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:55:08Z |
[View on Civ Archive](https://civarchive.com/models/1501901?modelVersionId=1699007)
|
crystalline7/186778
|
crystalline7
| 2025-09-01T23:53:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:53:54Z |
[View on Civ Archive](https://civarchive.com/models/214289?modelVersionId=241390)
|
seraphimzzzz/540048
|
seraphimzzzz
| 2025-09-01T23:53:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:53:12Z |
[View on Civ Archive](https://civarchive.com/models/561229?modelVersionId=625098)
|
ultratopaz/124815
|
ultratopaz
| 2025-09-01T23:52:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:52:06Z |
[View on Civ Archive](https://civarchive.com/models/147902?modelVersionId=165007)
|
omerbektass/blockassist-bc-insectivorous_bold_lion_1756770618
|
omerbektass
| 2025-09-01T23:50:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:50:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/132582
|
ultratopaz
| 2025-09-01T23:50:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:50:35Z |
[View on Civ Archive](https://civarchive.com/models/155082?modelVersionId=173907)
|
ultratopaz/127114
|
ultratopaz
| 2025-09-01T23:50:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:50:19Z |
[View on Civ Archive](https://civarchive.com/models/149952?modelVersionId=167556)
|
crystalline7/1878393
|
crystalline7
| 2025-09-01T23:49:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:49:53Z |
[View on Civ Archive](https://civarchive.com/models/1750322?modelVersionId=1980914)
|
crystalline7/134409
|
crystalline7
| 2025-09-01T23:49:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:49:26Z |
[View on Civ Archive](https://civarchive.com/models/156764?modelVersionId=175979)
|
amethyst9/358159
|
amethyst9
| 2025-09-01T23:45:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:45:04Z |
[View on Civ Archive](https://civarchive.com/models/392364?modelVersionId=437682)
|
ultratopaz/445949
|
ultratopaz
| 2025-09-01T23:44:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:44:39Z |
[View on Civ Archive](https://civarchive.com/models/475655?modelVersionId=529051)
|
amethyst9/1562073
|
amethyst9
| 2025-09-01T23:42:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:42:20Z |
[View on Civ Archive](https://civarchive.com/models/1469069?modelVersionId=1661588)
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756770037
|
liukevin666
| 2025-09-01T23:41:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:41:31Z |
---
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).
|
crystalline7/124804
|
crystalline7
| 2025-09-01T23:41:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:41:06Z |
[View on Civ Archive](https://civarchive.com/models/147894?modelVersionId=164997)
|
seraphimzzzz/1636515
|
seraphimzzzz
| 2025-09-01T23:40:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:40:30Z |
[View on Civ Archive](https://civarchive.com/models/1534239?modelVersionId=1735923)
|
amethyst9/137770
|
amethyst9
| 2025-09-01T23:38:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:38:53Z |
[View on Civ Archive](https://civarchive.com/models/159958?modelVersionId=179924)
|
zveryonak/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_agile_gecko
|
zveryonak
| 2025-09-01T23:36:53Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fleecy agile gecko",
"trl",
"genrl-swarm",
"I am fleecy_agile_gecko",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-24T01:29:46Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_agile_gecko
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fleecy agile gecko
- trl
- genrl-swarm
- I am fleecy_agile_gecko
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_agile_gecko
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="zveryonak/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fleecy_agile_gecko", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
seraphimzzzz/1591310
|
seraphimzzzz
| 2025-09-01T23:35:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:35:53Z |
[View on Civ Archive](https://civarchive.com/models/1493997?modelVersionId=1690064)
|
crystalline7/321576
|
crystalline7
| 2025-09-01T23:35:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:35:44Z |
[View on Civ Archive](https://civarchive.com/models/356706?modelVersionId=398752)
|
crystalline7/1566725
|
crystalline7
| 2025-09-01T23:30:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:30:38Z |
[View on Civ Archive](https://civarchive.com/models/1473048?modelVersionId=1666172)
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756769316
|
ggozzy
| 2025-09-01T23:29:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:29:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
JeloH/fin_qwe_src_small3
|
JeloH
| 2025-09-01T23:29:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T23:26:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
|
seraphimzzzz/1587428
|
seraphimzzzz
| 2025-09-01T23:26:35Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:26:34Z |
[View on Civ Archive](https://civarchive.com/models/1490708?modelVersionId=1686254)
|
amethyst9/1587488
|
amethyst9
| 2025-09-01T23:26:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:26:10Z |
[View on Civ Archive](https://civarchive.com/models/1490762?modelVersionId=1686315)
|
JeloH/fin_qwe_src_small_peft
|
JeloH
| 2025-09-01T23:25:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T00:32:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756769061
|
ggozzy
| 2025-09-01T23:25:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:25:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/1603607
|
crystalline7
| 2025-09-01T23:23:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:23:54Z |
[View on Civ Archive](https://civarchive.com/models/1505549?modelVersionId=1703021)
|
crystalline7/1562078
|
crystalline7
| 2025-09-01T23:23:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:23:28Z |
[View on Civ Archive](https://civarchive.com/models/1469072?modelVersionId=1661597)
|
omerbkts/blockassist-bc-insectivorous_bold_lion_1756768959
|
omerbkts
| 2025-09-01T23:23:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T23:22:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
seraphimzzzz/344555
|
seraphimzzzz
| 2025-09-01T23:21:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:21:58Z |
[View on Civ Archive](https://civarchive.com/models/378884?modelVersionId=423029)
|
Muapi/bruno-dayan
|
Muapi
| 2025-09-01T23:19:56Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:19:46Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Bruno Dayan
**Base model**: Flux.1 D
**Trained words**: By Bruno Dayan
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1264265@1446666", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
seraphimzzzz/1658741
|
seraphimzzzz
| 2025-09-01T23:15:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:15:24Z |
[View on Civ Archive](https://civarchive.com/models/1553541?modelVersionId=1757909)
|
amethyst9/1587475
|
amethyst9
| 2025-09-01T23:13:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:13:47Z |
[View on Civ Archive](https://civarchive.com/models/1490752?modelVersionId=1686306)
|
crystalline7/365145
|
crystalline7
| 2025-09-01T23:13:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:13:01Z |
[View on Civ Archive](https://civarchive.com/models/399145?modelVersionId=445156)
|
ultratopaz/520319
|
ultratopaz
| 2025-09-01T23:12:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T23:12:53Z |
[View on Civ Archive](https://civarchive.com/models/544291?modelVersionId=605286)
|
Muapi/modern-anime-style-flux
|
Muapi
| 2025-09-01T23:01:59Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T23:01:47Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Modern Anime Style [Flux]
**Base model**: Flux.1 D
**Trained words**: anime
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:659965@738497", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
malouka24/outputs
|
malouka24
| 2025-09-01T22:59:08Z | 13 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-30T13:56:28Z |
---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
omerbkts/blockassist-bc-insectivorous_bold_lion_1756767361
|
omerbkts
| 2025-09-01T22:56:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:56:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-insectivorous_bold_lion_1756767004
|
omerbkts
| 2025-09-01T22:50:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:50:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756766860
|
bah63843
| 2025-09-01T22:48:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:48:25Z |
---
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).
|
omerbkts/blockassist-bc-insectivorous_bold_lion_1756766618
|
omerbkts
| 2025-09-01T22:43:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:43:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# 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-insectivorous_bold_lion_1756766490
|
akirafudo
| 2025-09-01T22:41:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:41:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Stasonelison/blockassist-bc-howling_powerful_aardvark_1756766411
|
Stasonelison
| 2025-09-01T22:40:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling powerful aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:40:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling powerful aardvark
---
# 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-insectivorous_bold_lion_1756765675
|
omerbektass
| 2025-09-01T22:28:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:28:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/drawingsd1.5_v1
|
Muapi
| 2025-09-01T22:25:11Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T22:24:17Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# DrawingSD1.5_v1
**Base model**: Flux.1 D
**Trained words**: in the style of drwng, drawing
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:100969@1094392", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
bah63843/blockassist-bc-plump_fast_antelope_1756765390
|
bah63843
| 2025-09-01T22:24:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T22:23:52Z |
---
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).
|
malouka24/trix-component-generator-v2
|
malouka24
| 2025-09-01T21:59:44Z | 18 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"text-generation",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"lora",
"transformers",
"conversational",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T13:49:55Z |
---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
Muapi/cute-flat-art
|
Muapi
| 2025-09-01T21:58:50Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T21:58:03Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Cute Flat Art
**Base model**: Flux.1 D
**Trained words**: Cute Flat Art
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:891865@998031", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
klmdr22/blockassist-bc-wild_loud_newt_1756763861
|
klmdr22
| 2025-09-01T21:58:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:58:20Z |
---
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).
|
Muapi/hailey-the-first-descendant-flux-lora
|
Muapi
| 2025-09-01T21:57:55Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T21:57:39Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Hailey - The First Descendant - Flux LoRA
**Base model**: Flux.1 D
**Trained words**: TFD-Hailey-Default
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:733900@820726", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
HHazard/t5_surprise_replay_order_6_task_10
|
HHazard
| 2025-09-01T21:55:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T21:55:24Z |
---
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]
|
de-slothbug/t5gemma-ss-prefixlm-metadata-extractor-v3-backup
|
de-slothbug
| 2025-09-01T21:50:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T21:50:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
Muapi/splash-effect
|
Muapi
| 2025-09-01T21:47:52Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T21:47:38Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Splash Effect
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:838434@1638890", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Myashka/Qwen2.5-7B-UltraChat200K_EMA_SFT-Lr_3e_6-Alpha_0.01
|
Myashka
| 2025-09-01T21:44:31Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"SFT",
"text-generation",
"conversational",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"region:us"
] |
text-generation
| 2025-09-01T21:14:56Z |
---
datasets:
- HuggingFaceH4/ultrachat_200k
base_model:
- Qwen/Qwen2.5-7B
pipeline_tag: text-generation
tags:
- SFT
---
|
koloni/blockassist-bc-deadly_graceful_stingray_1756761428
|
koloni
| 2025-09-01T21:42:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:42:47Z |
---
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).
|
bah63843/blockassist-bc-plump_fast_antelope_1756762671
|
bah63843
| 2025-09-01T21:38:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:38:34Z |
---
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).
|
pinktulip888/qwen-same-cat-4
|
pinktulip888
| 2025-09-01T21:36:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T21:36:18Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** pinktulip888
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
qwersdfvg/blockassist-bc-skittish_beaked_duck_1756762025
|
qwersdfvg
| 2025-09-01T21:27:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skittish beaked duck",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:27:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skittish beaked duck
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
a1ex971/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-patterned_arctic_shrimp
|
a1ex971
| 2025-09-01T21:20:52Z | 148 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am patterned_arctic_shrimp",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-23T13:39:39Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am patterned_arctic_shrimp
---
# 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]
|
aochongoliverli/Qwen2.5-3B-limo-qwq-16k-3epochs-5e-5lr-step250
|
aochongoliverli
| 2025-09-01T21:20:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T21:16:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/NS-12b-DarkSlushCap-GGUF
|
mradermacher
| 2025-09-01T21:18:50Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:pot99rta/NS-12b-DarkSlushCap",
"base_model:quantized:pot99rta/NS-12b-DarkSlushCap",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-01T13:41:49Z |
---
base_model: pot99rta/NS-12b-DarkSlushCap
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/pot99rta/NS-12b-DarkSlushCap
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#NS-12b-DarkSlushCap-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/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NS-12b-DarkSlushCap-GGUF/resolve/main/NS-12b-DarkSlushCap.Q8_0.gguf) | Q8_0 | 13.1 | 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 -->
|
bah63843/blockassist-bc-plump_fast_antelope_1756761468
|
bah63843
| 2025-09-01T21:18:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:18:29Z |
---
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).
|
klmdr22/blockassist-bc-wild_loud_newt_1756761234
|
klmdr22
| 2025-09-01T21:14:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:14:33Z |
---
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).
|
RikiyaT/mxbai-ettin-32m-allnli-angle-ft
|
RikiyaT
| 2025-09-01T21:07:28Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"license:mit",
"region:us"
] | null | 2025-09-01T21:07:22Z |
---
license: mit
---
# RikiyaT/mxbai-ettin-32m-allnli-angle-ft
Ettin + AnglE fine-tuned embedding model.
- **Base Model**: `RikiyaT/mxbai-ettin-32m-medqa-angle-ft`
- **Pooling Strategy**: `mean` (avg)
- **Training Method**: AnglE loss (ibn/cln + angle=0.02) on a B-format dataset (text, positive, negative).
- **Data Prompts**: `search_query:` / `search_document:` were used during training data creation.
## Usage
### With SentenceTransformers (recommended)
A ready-to-use SentenceTransformers variant is available at **[RikiyaT/mxbai-ettin-32m-allnli-angle-ft-st](https://huggingface.co/RikiyaT/mxbai-ettin-32m-allnli-angle-ft-st)**.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('RikiyaT/mxbai-ettin-32m-allnli-angle-ft-st')
sentences = ["This is an example sentence", "Each sentence is converted"]
embeddings = model.encode(sentences)
print(embeddings.shape)
```
### With Transformers (this repository)
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("RikiyaT/mxbai-ettin-32m-allnli-angle-ft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("RikiyaT/mxbai-ettin-32m-allnli-angle-ft", trust_remote_code=True)
```
|
Bam3752/PubMedBERT-BioNLI-LoRA
|
Bam3752
| 2025-09-01T21:07:06Z | 0 | 0 | null |
[
"safetensors",
"bert",
"nli",
"biomedical",
"pubmed",
"lora",
"entailment",
"en",
"dataset:bioasq",
"dataset:mednli",
"model-index",
"region:us"
] | null | 2025-08-31T21:36:13Z |
---
language:
- en
tags:
- nli
- biomedical
- pubmed
- lora
- entailment
datasets:
- bioasq
- mednli
metrics:
- type: accuracy
value: 0.9039
- type: f1
value: 0.9036
- type: loss
value: 0.2673
model-index:
- name: PubMedBERT BioNLI LoRA
results:
- task:
type: natural-language-inference
name: Natural Language Inference
dataset:
name: BioASQ + MedNLI
type: bioasq-mednli
metrics:
- type: accuracy
value: 0.9039
- type: f1
value: 0.9036
- type: loss
value: 0.2673
---
# PubMedBERT BioNLI LoRA
[](https://huggingface.co/Bam3752/PubMedBERT-BioNLI-LoRA)
[](https://huggingface.co/Bam3752)
[](https://opensource.org/licenses/MIT)
**PubMedBERT BioNLI LoRA** is a biomedical **Natural Language Inference (NLI)** model fine-tuned with **LoRA adapters**.
It classifies **entailment, contradiction, and neutrality** between biomedical text pairs, optimized for **chain-of-thought reasoning validation**.
---
## 📊 Training Details
- **Base model:** [pritamdeka/PubMedBERT-MNLI-MedNLI](https://huggingface.co/pritamdeka/PubMedBERT-MNLI-MedNLI)
- **Fine-tuning datasets:** BioASQ + MedNLI
- **Objective:** 3-class NLI (entailment / neutral / contradiction)
- **Method:** LoRA parameter-efficient fine-tuning
- **Hardware:** Apple MPS (Metal backend)
- **Hyperparameters:**
- Epochs: 4
- Learning rate: 1e-5
- Batch size: 8
- Max length: 256
- Gradient accumulation: 2
- Warmup ratio: 0.1
- Label smoothing: 0.05
---
## 📈 Results
| Metric | Value |
|-------------|---------|
| Accuracy | **90.39%** |
| Macro F1 | **0.9036** |
| Eval Loss | **0.2673** |
👉 Calibrated with isotonic regression (`calibration/isotonic.pkl`) for reliable probabilities.
---
## 🚀 Usage
### Transformers
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Bam3752/PubMedBERT-BioNLI-LoRA")
tokenizer = AutoTokenizer.from_pretrained("Bam3752/PubMedBERT-BioNLI-LoRA")
premise = "Aspirin reduces the risk of myocardial infarction."
hypothesis = "Aspirin prevents heart attacks."
inputs = tokenizer(premise, hypothesis, return_tensors="pt")
outputs = model(**inputs)
probs = outputs.logits.softmax(-1).detach().cpu().numpy()
print(probs) # [neutral, contradiction, entailment]
|
RikiyaT/mxbai-ettin-32m-pubmed_clean-angle-ft-st
|
RikiyaT
| 2025-09-01T21:06:51Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"dense",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-01T21:06:46Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 7999 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 7999, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RikiyaT/mxbai-ettin-32m-pubmed_clean-angle-ft-st")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3604, 0.1352],
# [0.3604, 1.0000, 0.1726],
# [0.1352, 0.1726, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.1.0
- Transformers: 4.55.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
RikiyaT/mxbai-ettin-32m-arxiv-1.4m-angle-ft-st
|
RikiyaT
| 2025-09-01T21:06:28Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"dense",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-01T21:06:23Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 7999 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 7999, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RikiyaT/mxbai-ettin-32m-arxiv-1.4m-angle-ft-st")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3934, 0.1177],
# [0.3934, 1.0000, 0.1711],
# [0.1177, 0.1711, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.18
- Sentence Transformers: 5.1.0
- Transformers: 4.55.4
- PyTorch: 2.7.1+cu126
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
klmdr22/blockassist-bc-wild_loud_newt_1756760696
|
klmdr22
| 2025-09-01T21:05:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:05:35Z |
---
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).
|
Muapi/flux-tangled-end-credits-sequence
|
Muapi
| 2025-09-01T21:04:07Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-01T21:03:51Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# (Flux) Tangled end credits sequence
**Base model**: Flux.1 D
**Trained words**: ink on paper
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:848433@949225", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1756760453
|
canoplos112
| 2025-09-01T21:02:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T21:01:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1756760105
|
canoplos112
| 2025-09-01T20:57:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T20:55:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756759971
|
bah63843
| 2025-09-01T20:53:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T20:53:29Z |
---
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).
|
omerbektass/blockassist-bc-insectivorous_bold_lion_1756759746
|
omerbektass
| 2025-09-01T20:49:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"insectivorous bold lion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T20:49:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- insectivorous bold lion
---
# 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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