modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-13 12:31:59
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 556
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listlengths 1
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te4bag/GRIT-llama-3.2-3B-alpaca-0.99
|
te4bag
| 2025-08-19T16:15:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.2-3B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B",
"region:us"
] |
text-generation
| 2025-08-19T16:15:01Z |
---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.2-3B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0
|
rambetiko/blockassist-bc-soft_lanky_marmot_1755619656
|
rambetiko
| 2025-08-19T16:14:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft lanky marmot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:13:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft lanky marmot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755618350
|
quantumxnode
| 2025-08-19T16:13:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:13:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755618633
|
Sayemahsjn
| 2025-08-19T16:09:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:09:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/sailor2-sft-GGUF
|
mradermacher
| 2025-08-19T16:04:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:hai2131/sailor2-sft",
"base_model:quantized:hai2131/sailor2-sft",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:55:44Z |
---
base_model: hai2131/sailor2-sft
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/hai2131/sailor2-sft
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#sailor2-sft-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/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.IQ4_XS.gguf) | IQ4_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_L.gguf) | Q3_K_L | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q4_K_S.gguf) | Q4_K_S | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q5_K_S.gguf) | Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q5_K_M.gguf) | Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q6_K.gguf) | Q6_K | 1.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q8_0.gguf) | Q8_0 | 1.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.f16.gguf) | f16 | 2.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755619213
|
Elizavr
| 2025-08-19T16:00:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:00:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rambetiko/blockassist-bc-soft_lanky_marmot_1755618848
|
rambetiko
| 2025-08-19T16:00:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft lanky marmot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:59:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft lanky marmot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
varsunk/unsloth_training_checkpoints
|
varsunk
| 2025-08-19T15:59:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T20:11:18Z |
---
base_model: unsloth/Qwen3-4B-Base
library_name: transformers
model_name: Qwen3-4B-PFT-Checkpoint
tags:
- generated_from_trainer
- sft
- unsloth
- trl
licence: license
---
# Model Card for Qwen3-4B-PFT-Checkpoint
This model is a fine-tuned version of [unsloth/Qwen3-4B-Base](https://huggingface.co/unsloth/Qwen3-4B-Base).
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="varsunk/unsloth_training_checkpoints", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755618913
|
lqpl
| 2025-08-19T15:57:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:56:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
shulin16/ea-dev-final
|
shulin16
| 2025-08-19T15:53:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"evaluation-agent",
"cot-reasoning",
"checkpoint",
"qwen2.5",
"video-assessment",
"image-assessment",
"conversational",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T09:18:53Z |
---
license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
- text-generation
- evaluation-agent
- cot-reasoning
- checkpoint
- qwen2.5
- video-assessment
- image-assessment
library_name: transformers
pipeline_tag: text-generation
---
# ea-dev-final
This is checkpoint **final** (step 471) from fine-tuning [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) for evaluation agent tasks.
## Checkpoint Details
- **Checkpoint**: final
- **Global Step**: 471
- **Epoch**: 3.00
- **Training Loss**: 0.8296
- **Learning Rate**: unknown
- **Base Model**: Qwen2.5-3B-Instruct
- **Task**: Multi-modal quality assessment with CoT reasoning
## Model Description
This checkpoint is from training an evaluation agent that can assess:
- **Video Quality**: Temporal consistency, motion smoothness, object consistency (VBench)
- **Image Quality**: Aesthetic quality, semantic alignment, visual fidelity (T2I-CompBench)
- **Open-ended Evaluation**: Custom quality assessment tasks
The model uses Chain-of-Thought (CoT) reasoning to provide detailed explanations for its evaluations.
## Files Included
This checkpoint contains:
- **Model Weights**: `model*.safetensors` - The actual model parameters
- **Tokenizer**: Complete tokenizer configuration and vocabulary
- **Configuration**: Model and generation configuration files
**Note**: This checkpoint contains only inference files (no optimizer states).
## Usage
### For Inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the checkpoint
model = AutoModelForCausalLM.from_pretrained(
"ea-dev-final",
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ea-dev-final")
# Example evaluation prompt
prompt = """Please evaluate the quality of this video based on the following criteria:
1. Visual quality and clarity
2. Temporal consistency
3. Motion smoothness
Video description: A person walking through a park with trees swaying in the wind.
Let me think step by step:"""
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=512,
do_sample=True,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### Resume Training (if optimizer states included)
```bash
# Use with LLaMA-Factory
llamafactory-cli train \
--stage sft \
--model_name_or_path ea-dev-final \
--resume_from_checkpoint ea-dev-final
```
## Training Progress
This checkpoint represents an intermediate state in the training process:
- **Steps Completed**: 471
- **Epochs**: 3.00
- **Current Loss**: 0.8296
## Related Models
This checkpoint is part of a series. Other checkpoints from the same training run:
- Look for repositories with pattern: `ea-dev-checkpoint-*`
- Final model: `ea-dev-final`
## License
This model checkpoint is released under the Apache 2.0 license.
## Citation
If you use this checkpoint, please cite:
```bibtex
@misc{eval-agent-qwen2.5-checkpoint-471,
title={Evaluation Agent Qwen2.5 Checkpoint 471},
author={Your Name},
year={2025},
howpublished={\url{https://huggingface.co/ea-dev-final}}
}
```
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755617041
|
mang3dd
| 2025-08-19T15:52:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:52:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
MidnightRunner/MIDNIGHT_NAI-XL_vPredV1
|
MidnightRunner
| 2025-08-19T15:50:23Z | 406 | 2 |
diffusers
|
[
"diffusers",
"SDXL",
"noobai-XL",
"Vpred-1.0",
"text-to-image",
"ComfyUI",
"Automatic1111",
"Diffuser",
"en",
"dataset:LaxharLab/NoobAI-XL-dataset",
"base_model:Laxhar/noobai-XL-Vpred-1.0",
"base_model:finetune:Laxhar/noobai-XL-Vpred-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-02-02T01:09:01Z |
---
license: creativeml-openrail-m
language:
- en
base_model: Laxhar/noobai-XL-Vpred-1.0
tags:
- SDXL
- noobai-XL
- Vpred-1.0
- text-to-image
- ComfyUI
- Automatic1111
- Diffuser
pipeline_tag: text-to-image
library_name: diffusers
datasets:
- LaxharLab/NoobAI-XL-dataset
metrics:
- FID
- IS
widget:
- text: >-
high quality, masterpiece, detailed, 8K, artist:nyantcha,
evangeline_(nyantcha), vibrant surreal artwork, rainbow, light particles,
from above, volumetric lighting, ((adult girl:1.2)), natural huge breasts,
woman dressed as white rabbit, sleek pure white outfit, delicate white bunny
ears, braid, plump, skindentation, huge breasts, falling into swirling black
hole, seen from behind, glancing over shoulder, alluring mysterious
expression, dress, zipper, zipper pull, detached sleeves, breasts apart
(shoulder straps), buckles, long dress, swirling cosmic patterns, glowing
particles, dramatic lighting, vibrant neon pink and blue tones,
hyper-detailed, cinematic depth of field, smooth texture, film grain,
chromatic aberration, high contrast, limited palette
parameters:
negative_prompt: >-
lowres, worst quality, low quality, bad anatomy, bad hands, 4koma, comic,
greyscale, censored, jpeg artifacts, overly saturated, overly vivid,
(multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality,
jpeg artifacts, bad quality, watermark, unfinished, displeasing, sepia,
sketch, flat color, signature, artistic error, username, scan, (blurry,
lowres, worst quality, (low quality:1.1), ugly, (bad anatomy:1.05), artist
name, (patreon username:1.2)
output:
url: stand_on_ripplewater.jpeg
---
# MIDNIGHT_NAI-XL_vPredV1
**Model Type:** Diffusion-based text-to-image generative model
**Base Model:** SDXL 1.0 & Laxhar/noobai-XL-Vpred-1.0
**License:** [CreativeML Open RAIL++-M](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)
## Model Description
MIDNIGHT_NAI-XL_vPredV1 is a specialized fine-tuning of the NoobAI-XL (NAI-XL) model, designed to enhance anatomical precision, compositional coherence, and versatile style integration. This model excels in generating high-quality images with vibrant colors while minimizing overexposure.
## Usage Recommendations
### **Sampling Methods**
MIDNIGHT_NAI-XL_vPred is optimized specifically for **Euler (normal)**.
Use **ModelSamplingDiscrete** with **V-prediction** and **ZsNR set to true**.
Other samplers may not provide stable results, and **V-prediction models do not support other samplers**.
### **CFG Scaling**
**Dynamic CFG Plugin is bypassed as a backup for potential future needs.**
Manually adjust **CFG scaling within a range of 3-4** for the best balance.
For optimal results, a **preferred setting of 3.5** is recommended.
### **Custom Workflow**
For an optimized generation process, use the [**MIDNIGHT1111_Chasm 2025-02-04**](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%202025-02-04.json) ComfyUI workflow.
This workflow is specifically designed to **leverage the strengths of MIDNIGHT_NAI-XL_vPred**, providing a streamlined and efficient image generation pipeline.
## MIDNIGHT1111_Chasm
For an optimized generation process, consider using the custom workflow [MIDNIGHT1111_Chasm 02-05-25](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json). This workflow is tailored to leverage the strengths of the MIDNIGHT_NAI-XL_vPredV1 model, providing a streamlined and efficient image generation pipeline.
*Note: The above image is a preview of the `MIDNIGHT1111_Chasm` workflow.*
### Method I: reForge without MIDNIGHT1111_Chasm Workflow
1. **Installation:** If not already installed, follow the instructions in the [reForge repository](https://github.com/Panchovix/stable-diffusion-webui-reForge) to set up.
2. **Usage:** Launch WebUI and use the model as usual.
### Method II: ComfyUI *with* MIDNIGHT1111_Chasm Workflow
1. **Installation:** Follow the setup instructions in the [ComfyUI repository](https://github.com/comfyanonymous/ComfyUI).
2. **Workflow Sample:** Utilize the provided [ComfyUI workflow sample](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json) for guidance.
### Method III: WebUI without MIDNIGHT1111_Chasm Workflow
1. **Installation:** Follow the instructions in the [WebUI repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to set up.
2. **Navigate to the WebUI Directory:** Before updating or switching branches, ensure you're inside the `stable-diffusion-webui` folder
command: |
```bash
cd stable-diffusion-webui
```
3. **Switch to the Development Branch (Optional, for testing new features):** If you want to use the latest features from the development branch, run:
command: |
```bash
git switch dev
git pull
```
β οΈ **Note:** The `dev` branch may contain bugs. If stability is your priority, it's best to stay on the `main` branch.
4. **Update WebUI (Main or Dev Branch):** To pull the latest updates while on either branch, run:
command: |
```bash
git pull
```
π **Restart WebUI after updating to apply changes.**"
5. **Configuration:** Ensure you're using a stable branch, as the dev branch may contain bugs.
### Method IV: Diffusers without MIDNIGHT1111_Chasm Workflow
```bash
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerDiscreteScheduler
ckpt_path = "/path/to/model.safetensors"
pipe = StableDiffusionXLPipeline.from_single_file(
ckpt_path,
use_safetensors=True,
torch_dtype=torch.float16,
)
scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to("cuda")
prompt = """masterpiece, best quality,artist:john_kafka,artist:nixeu,artist:quasarcake, chromatic aberration, film grain, horror \(theme\), limited palette, x-shaped pupils, high contrast, color contrast, cold colors, arlecchino \(genshin impact\), black theme, gritty, graphite \(medium\)"""
negative_prompt = "nsfw, worst quality, old, early, low quality, lowres, signature, username, logo, bad hands, mutated hands, mammal, anthro, furry, ambiguous form, feral, semi-anthro"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=832,
height=1216,
num_inference_steps=28,
guidance_scale=5,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")
```
## e621/Danbooru Artist Wildcards for A1111 & ComfyUI Enclosed in CSV & TXT Formats
To enhance the model's performance and specificity, the following trigger word lists in CSV format are included:
- [`danbooru_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_webui.csv)
- [`danbooru_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_webui.csv)
- [`e621_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_webui.csv)
- [`e621_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_webui.csv)
These lists provide recognized tags for various artists and characters, facilitating more accurate and tailored image generation.
The wildcard file in 'TXT' format is included and designed for seamless integration with **AUTOMATIC1111** and **ComfyUI**, optimized for dynamic prompt generation using artist data from **e621** and **Danbooru**.
- **TXT Format:** Sanitized artist tags by removing URLs and converted from `.csv` to `.txt` format for improved readability across different extensions.
- **Dual Dataset Support:** Supports both e621 and Danbooru datasets to enhance art style diversity.
- **Smooth Randomization:** Structured with trailing commas for seamless wildcard cycling during prompt generation.
## How to Use Wildcards
### For A1111
1. **Install:** [stable-diffusion-webui-wildcards](https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards)
2. **Place the `.txt` file in:**
```
/A1111/extensions/stable-diffusion-webui-wildcards
```
3. **Use in your prompt like this:**
```
__e621_artist_wildcard__, very awa, masterpiece, best quality, amazing quality
```
```
__danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality
```
```
__e621_artist_wildcard__, __danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality
```
### For ComfyUI
1. **Install:** [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)
2. **Place the `.txt` file in:**
```
/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/wildcards
```
or
```
/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards
```
3. **Use the wildcard node to trigger dynamic randomization in your workflows.**
## Whatβs Included in Wildcards
TXT formatted file containing clean, artist-focused wildcard files ready for dynamic prompt workflows in A1111 and ComfyUI.
- [danbooru_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_wildcard.txt)
- [danbooru_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_wildcard.txt)
- [e621_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_wildcard.txt)
- [e621_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_wildcard.txt)
## Acknowledgments
Special thanks to:
- **Development Team:** Laxhar Lab
- **Coding Contributions:** Euge
- **e621/Danbooru Wildcards** [ipsylon0000](https://civitai.com/user/ipsylon0000)
- **Community Support:** Various contributors
## Additional Resources
- **Guidebook for NoobAI XL:** [English Version](https://civitai.com/articles/8962)
- **Recommended LoRa List for NoobAI XL:** [Resource Link](https://fcnk27d6mpa5.feishu.cn/wiki/IBVGwvVGViazLYkMgVEcvbklnge)
- **Fixing Black Images in ComfyUI on macOS (M1/M2):** [Read the Article](https://civitai.com/articles/11106)
- **Creative Solutions and Services:** [Magnabos.co](https://magnabos.co/)
## License
This model is licensed under the [CreativeML Open RAIL++-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). By using this model, you agree to the terms and conditions outlined in the license.
|
koloni/blockassist-bc-deadly_graceful_stingray_1755617027
|
koloni
| 2025-08-19T15:49:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:49:41Z |
---
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).
|
phospho-app/Deimos252-ACT_BBOX-Light_dataset_deimos-qugw6
|
phospho-app
| 2025-08-19T15:48:51Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"act",
"robotics",
"dataset:Deimos252/Light_dataset_deimos",
"region:us"
] |
robotics
| 2025-08-19T15:48:08Z |
---
datasets: Deimos252/Light_dataset_deimos
library_name: phosphobot
pipeline_tag: robotics
model_name: act
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
1 validation error for EpisodesFeatures
Invalid JSON: EOF while parsing a value at line 2 column 0 [type=json_invalid, input_value='\n', input_type=str]
For further information visit https://errors.pydantic.dev/2.11/v/json_invalid
```
## Training parameters:
- **Dataset**: [Deimos252/Light_dataset_deimos](https://huggingface.co/datasets/Deimos252/Light_dataset_deimos)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
π **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
π€ **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
thailevann/track8_subtask2_v4
|
thailevann
| 2025-08-19T15:48:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T15:47:53Z |
---
base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thailevann
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755618244
|
Elizavr
| 2025-08-19T15:44:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:44:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1755616783
|
aleebaster
| 2025-08-19T15:41:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:41:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WenFengg/21_14l3_19__8
|
WenFengg
| 2025-08-19T15:37:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T14:56:20Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
mradermacher/Agentic-1.0-GGUF
|
mradermacher
| 2025-08-19T15:34:19Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:beyoru/Agentic-1.0",
"base_model:quantized:beyoru/Agentic-1.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:04:54Z |
---
base_model: beyoru/Agentic-1.0
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## 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/beyoru/Agentic-1.0
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Agentic-1.0-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/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Agentic-1.0-GGUF/resolve/main/Agentic-1.0.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AdoCleanCode/neox_capital_only_v2
|
AdoCleanCode
| 2025-08-19T15:25:09Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T10:13:21Z |
---
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 π€ 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]
|
jiangnanboy/intelligent_document_recognition
|
jiangnanboy
| 2025-08-19T15:24:21Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-22T12:59:17Z |
---
license: apache-2.0
---
## intelligent document recognition
### Introduction
Intelligent Document Recognition Desktop Software, used for OCR recognition and table structure recognition.
It operates independently without the need for internet connection, ensuring data security.
The results of OCR recognition can be saved in txt and html formats.
The results of table structure recognition can be saved in html and excel formats.
This software is available in two versions, one in Chinese and the other in English.
### version 2.0
Integrate OCR with table recognition.
### version2.1
Added features:
1. Screenshot
2. Images in the image list can be deleted
### version2.2
1. Support clearing the image list
3. Fix screenshot bug
5. Support dragging images to the image list
https://github.com/jiangnanboy/intelligent_document_recognition
|
vohuutridung/bartpho-word-vietnews-summarization
|
vohuutridung
| 2025-08-19T15:24:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T15:23:32Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755615291
|
hakimjustbao
| 2025-08-19T15:23:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:23:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GaborMadarasz/AstroQA_mamba_epoch1_V6
|
GaborMadarasz
| 2025-08-19T15:22:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mamba",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:22:02Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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|
concept-unlearning/Meta-Llama-3-8B_ft_lora_all_novels_v4_ft_npo_gdr_lora_positive_dataset_v1
|
concept-unlearning
| 2025-08-19T15:21:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:18:54Z |
---
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 π€ 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
Muapi/vintage-drawing-ce
|
Muapi
| 2025-08-19T15:18:13Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:18:02Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Vintage Drawing - CE
**Base model**: Flux.1 D
**Trained words**: vntgdrwngCE style
## π§ Usage (Python)
π **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:660535@811004", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
koloni/blockassist-bc-deadly_graceful_stingray_1755614936
|
koloni
| 2025-08-19T15:15:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:15:48Z |
---
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).
|
Muapi/360-panorama-sd1.5-flux
|
Muapi
| 2025-08-19T15:15:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:15:24Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 360 panorama [SD1.5 / FLUX]
**Base model**: Flux.1 D
**Trained words**: 360, panorama, spherical panorama
## π§ Usage (Python)
π **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:118398@756096", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
kodetr/stunting-7B-Qwen
|
kodetr
| 2025-08-19T15:15:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"stunting",
"kesehatan",
"anak",
"conversational",
"id",
"dataset:kodetr/penelitian-fundamental-stunting-qa",
"base_model:Qwen/Qwen1.5-7B-Chat",
"base_model:finetune:Qwen/Qwen1.5-7B-Chat",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:59:41Z |
---
library_name: transformers
tags:
- stunting
- kesehatan
- anak
license: apache-2.0
datasets:
- kodetr/penelitian-fundamental-stunting-qa
language:
- id
metrics:
- rouge
- bleu
pipeline_tag: text-generation
base_model:
- Qwen/Qwen1.5-7B-Chat
---
### Model Description
<!-- Provide a longer summary of what this model is. -->
Konsultasi(Q&A) stunting pada anak
- **Developed by:** Tanwir
- **Language :** Indonesia
### Training
### Use with transformers
Pastikan untuk memperbarui instalasi transformer Anda melalui pip install --upgrade transformer.
```python
import torch
from transformers import pipeline
model_id = "kodetr/stunting-7B-Qwen"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "Jelaskan definisi 1000 hari pertama kehidupan."},
{"role": "user", "content": "Apa itu 1000 hari pertama kehidupan?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
|
mradermacher/cogito-v2-preview-llama-405B-GGUF
|
mradermacher
| 2025-08-19T15:14:16Z | 0 | 0 |
transformers
|
[
"transformers",
"en",
"base_model:deepcogito/cogito-v2-preview-llama-405B",
"base_model:finetune:deepcogito/cogito-v2-preview-llama-405B",
"license:llama3.1",
"endpoints_compatible",
"region:us"
] | null | 2025-08-02T00:32:16Z |
---
base_model: deepcogito/cogito-v2-preview-llama-405B
language:
- en
library_name: transformers
license: llama3.1
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/deepcogito/cogito-v2-preview-llama-405B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#cogito-v2-preview-llama-405B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [PART 1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q2_K.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q2_K.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q2_K.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q2_K.gguf.part4of4) | Q2_K | 149.4 | |
| [PART 1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_S.gguf.part4of4) | Q3_K_S | 175.3 | |
| [PART 1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_M.gguf.part4of4) | Q3_K_M | 195.5 | lower quality |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_L.gguf.part1of5) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_L.gguf.part2of5) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_L.gguf.part3of5) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_L.gguf.part4of5) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q3_K_L.gguf.part5of5) | Q3_K_L | 212.9 | |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.IQ4_XS.gguf.part1of5) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.IQ4_XS.gguf.part2of5) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.IQ4_XS.gguf.part3of5) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.IQ4_XS.gguf.part4of5) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.IQ4_XS.gguf.part5of5) | IQ4_XS | 218.7 | |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_S.gguf.part1of5) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_S.gguf.part2of5) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_S.gguf.part3of5) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_S.gguf.part4of5) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_S.gguf.part5of5) | Q4_K_S | 230.6 | fast, recommended |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_M.gguf.part1of5) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_M.gguf.part2of5) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_M.gguf.part3of5) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_M.gguf.part4of5) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q4_K_M.gguf.part5of5) | Q4_K_M | 243.2 | fast, recommended |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_S.gguf.part1of6) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_S.gguf.part2of6) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_S.gguf.part3of6) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_S.gguf.part4of6) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_S.gguf.part5of6) [P6](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_S.gguf.part6of6) | Q5_K_S | 279.4 | |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_M.gguf.part1of6) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_M.gguf.part2of6) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_M.gguf.part3of6) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_M.gguf.part4of6) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_M.gguf.part5of6) [P6](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q5_K_M.gguf.part6of6) | Q5_K_M | 286.7 | |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q6_K.gguf.part1of7) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q6_K.gguf.part2of7) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q6_K.gguf.part3of7) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q6_K.gguf.part4of7) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q6_K.gguf.part5of7) [P6](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q6_K.gguf.part6of7) [P7](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q6_K.gguf.part7of7) | Q6_K | 333.0 | very good quality |
| [P1](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part1of9) [P2](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part2of9) [P3](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part3of9) [P4](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part4of9) [P5](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part5of9) [P6](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part6of9) [P7](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part7of9) [P8](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part8of9) [P9](https://huggingface.co/mradermacher/cogito-v2-preview-llama-405B-GGUF/resolve/main/cogito-v2-preview-llama-405B.Q8_0.gguf.part9of9) | Q8_0 | 431.3 | 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 -->
|
Muapi/ps1-style-flux
|
Muapi
| 2025-08-19T15:11:21Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:11:09Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# PS1 Style Flux
**Base model**: Flux.1 D
**Trained words**: ps1
## π§ Usage (Python)
π **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:648058@725031", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mradermacher/Nexa-Vector-11-Qwen-GGUF
|
mradermacher
| 2025-08-19T15:09:30Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:iversonzhou/Nexa-Vector-11-Qwen",
"base_model:quantized:iversonzhou/Nexa-Vector-11-Qwen",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T14:56:35Z |
---
base_model: iversonzhou/Nexa-Vector-11-Qwen
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/iversonzhou/Nexa-Vector-11-Qwen
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Nexa-Vector-11-Qwen-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/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.f16.gguf) | f16 | 3.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Muapi/3d_flux-style
|
Muapi
| 2025-08-19T15:07:43Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:07:35Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 3D_Flux Style
**Base model**: Flux.1 D
**Trained words**: 3D01S , kawaii, anime
## π§ Usage (Python)
π **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:689478@771650", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
unitova/blockassist-bc-zealous_sneaky_raven_1755614105
|
unitova
| 2025-08-19T15:03:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:03:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Gynjn/iLRM
|
Gynjn
| 2025-08-19T15:02:54Z | 0 | 1 |
pytorch
|
[
"pytorch",
"image-to-3d",
"arxiv:2507.23277",
"license:mit",
"region:us"
] |
image-to-3d
| 2025-07-31T08:28:27Z |
---
license: mit
pipeline_tag: image-to-3d
library_name: pytorch
---
This repository contains the models of the paper [iLRM: An Iterative Large 3D Reconstruction Model](https://huggingface.co/papers/2507.23277).
Project Page: https://gynjn.github.io/iLRM/
|
Muapi/dall-e3-meets-flux
|
Muapi
| 2025-08-19T15:02:10Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:01:56Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Dall-E3 meets FLUX
**Base model**: Flux.1 D
**Trained words**: aidmadalle3
## π§ Usage (Python)
π **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:1125621@1265190", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
2hpsatt/blockassist-bc-huge_deft_eagle_1755615679
|
2hpsatt
| 2025-08-19T15:02:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:01:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kurosawama/gemma-3-1b-it-Translation-align
|
Kurosawama
| 2025-08-19T15:01:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"trl",
"dpo",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T15:01:43Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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|
lakelee/RLB_MLP_TSC_v2.20250819.17
|
lakelee
| 2025-08-19T14:58:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"timespan_contrastive",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T08:31:56Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: RLB_MLP_TSC_v2.20250819.17
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. -->
# RLB_MLP_TSC_v2.20250819.17
This model is a fine-tuned version of [](https://huggingface.co/) 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Tokenizers 0.21.4
|
fengpeisheng1/mergekit-slerp-zhlbqbl
|
fengpeisheng1
| 2025-08-19T14:57:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:fengpeisheng1/mergekit-slerp-ariyvyf",
"base_model:merge:fengpeisheng1/mergekit-slerp-ariyvyf",
"base_model:maywell/Qwen2-7B-Multilingual-RP",
"base_model:merge:maywell/Qwen2-7B-Multilingual-RP",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:51:11Z |
---
base_model:
- maywell/Qwen2-7B-Multilingual-RP
- fengpeisheng1/mergekit-slerp-ariyvyf
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [maywell/Qwen2-7B-Multilingual-RP](https://huggingface.co/maywell/Qwen2-7B-Multilingual-RP)
* [fengpeisheng1/mergekit-slerp-ariyvyf](https://huggingface.co/fengpeisheng1/mergekit-slerp-ariyvyf)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: maywell/Qwen2-7B-Multilingual-RP
layer_range: [0,28]
- model: fengpeisheng1/mergekit-slerp-ariyvyf
layer_range: [0,28]
merge_method: slerp
base_model: maywell/Qwen2-7B-Multilingual-RP
parameters:
t:
- filter: self_attn
value: [0, 0.3, 0.5, 0.7, 1]
- filter: mlp
value: [1, 0.7, 0.5, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
Muapi/tifa-lockhart-ffviir
|
Muapi
| 2025-08-19T14:56:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:55:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Tifa Lockhart (FFVIIR)
**Base model**: Flux.1 D
**Trained words**: TifaLockhart, croptop, skirt, suspenders, fingerless gloves
## π§ Usage (Python)
π **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:661363@740105", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
KMH158/t5-small-openassistant-chat
|
KMH158
| 2025-08-19T14:54:39Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T12:36:35Z |
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-openassistant-chat
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. -->
# t5-small-openassistant-chat
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1785
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 80
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3768 | 1.0 | 301 | 2.3842 |
| 2.6839 | 2.0 | 602 | 2.3277 |
| 2.6351 | 3.0 | 903 | 2.2995 |
| 2.6016 | 4.0 | 1204 | 2.2818 |
| 2.5803 | 5.0 | 1505 | 2.2680 |
| 2.5587 | 6.0 | 1806 | 2.2571 |
| 2.541 | 7.0 | 2107 | 2.2481 |
| 2.5323 | 8.0 | 2408 | 2.2409 |
| 2.5102 | 9.0 | 2709 | 2.2349 |
| 2.5063 | 10.0 | 3010 | 2.2288 |
| 2.4953 | 11.0 | 3311 | 2.2242 |
| 2.4926 | 12.0 | 3612 | 2.2192 |
| 2.4786 | 13.0 | 3913 | 2.2154 |
| 2.472 | 14.0 | 4214 | 2.2117 |
| 2.4662 | 15.0 | 4515 | 2.2079 |
| 2.4553 | 16.0 | 4816 | 2.2051 |
| 2.4472 | 17.0 | 5117 | 2.2020 |
| 2.4488 | 18.0 | 5418 | 2.2008 |
| 2.4367 | 19.0 | 5719 | 2.1972 |
| 2.4353 | 20.0 | 6020 | 2.1952 |
| 2.429 | 21.0 | 6321 | 2.1934 |
| 2.4247 | 22.0 | 6622 | 2.1912 |
| 2.4242 | 23.0 | 6923 | 2.1901 |
| 2.4196 | 24.0 | 7224 | 2.1887 |
| 2.4169 | 25.0 | 7525 | 2.1873 |
| 2.4122 | 26.0 | 7826 | 2.1862 |
| 2.4089 | 27.0 | 8127 | 2.1851 |
| 2.4042 | 28.0 | 8428 | 2.1841 |
| 2.4061 | 29.0 | 8729 | 2.1831 |
| 2.4007 | 30.0 | 9030 | 2.1823 |
| 2.397 | 31.0 | 9331 | 2.1814 |
| 2.3998 | 32.0 | 9632 | 2.1810 |
| 2.3963 | 33.0 | 9933 | 2.1805 |
| 2.3976 | 34.0 | 10234 | 2.1798 |
| 2.3919 | 35.0 | 10535 | 2.1794 |
| 2.3873 | 36.0 | 10836 | 2.1793 |
| 2.3899 | 37.0 | 11137 | 2.1789 |
| 2.3886 | 38.0 | 11438 | 2.1786 |
| 2.3906 | 39.0 | 11739 | 2.1786 |
| 2.393 | 40.0 | 12040 | 2.1785 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Ba2han/qwen3-a3b-coder-experiment-Q4_K_M-GGUF
|
Ba2han
| 2025-08-19T14:51:26Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Ba2han/qwen3-a3b-coder-experiment",
"base_model:quantized:Ba2han/qwen3-a3b-coder-experiment",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T14:50:10Z |
---
base_model: Ba2han/qwen3-a3b-coder-experiment
tags:
- llama-cpp
- gguf-my-repo
---
# Ba2han/qwen3-a3b-coder-experiment-Q4_K_M-GGUF
This model was converted to GGUF format from [`Ba2han/qwen3-a3b-coder-experiment`](https://huggingface.co/Ba2han/qwen3-a3b-coder-experiment) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Ba2han/qwen3-a3b-coder-experiment) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Ba2han/qwen3-a3b-coder-experiment-Q4_K_M-GGUF --hf-file qwen3-a3b-coder-experiment-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ba2han/qwen3-a3b-coder-experiment-Q4_K_M-GGUF --hf-file qwen3-a3b-coder-experiment-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Ba2han/qwen3-a3b-coder-experiment-Q4_K_M-GGUF --hf-file qwen3-a3b-coder-experiment-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ba2han/qwen3-a3b-coder-experiment-Q4_K_M-GGUF --hf-file qwen3-a3b-coder-experiment-q4_k_m.gguf -c 2048
```
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755613184
|
mang3dd
| 2025-08-19T14:48:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:48:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Trelis/Qwen3-4B_ds-arc-agi-2-perfect-100_test-c4
|
Trelis
| 2025-08-19T14:44:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Qwen3-4B",
"base_model:finetune:unsloth/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:43:31Z |
---
base_model: unsloth/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Trelis
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B
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)
|
weikeduik/mozlegal
|
weikeduik
| 2025-08-19T14:42:52Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T14:42:52Z |
---
license: apache-2.0
---
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755612521
|
katanyasekolah
| 2025-08-19T14:38:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:38:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sarrockia/prefectIllustriousXL_v3.safetensors
|
sarrockia
| 2025-08-19T14:33:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T14:04:58Z |
---
license: apache-2.0
---
|
shanaka95/gemma-3-270m-it-rag-finetune
|
shanaka95
| 2025-08-19T14:28:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:shanaka95/checkpoints",
"base_model:finetune:shanaka95/checkpoints",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T10:32:44Z |
---
base_model: shanaka95/checkpoints
library_name: transformers
model_name: gemma-3-270m-it-rag-finetune
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gemma-3-270m-it-rag-finetune
This model is a fine-tuned version of [shanaka95/checkpoints](https://huggingface.co/shanaka95/checkpoints).
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="shanaka95/gemma-3-270m-it-rag-finetune", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu129
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
EnLiving-AI/CosmosC1
|
EnLiving-AI
| 2025-08-19T14:26:23Z | 0 | 4 | null |
[
"Cosmos",
"Learning",
"Advacned_learning",
"NO-API",
"text-generation",
"license:mit",
"region:us"
] |
text-generation
| 2025-08-19T13:15:16Z |
---
license: mit
pipeline_tag: text-generation
tags:
- Cosmos
- Learning
- Advacned_learning
- NO-API
---
# π Cosmos C1
**Autonomous Knowledge Explorer β v1.0**
Cosmos C1 is an **autonomous research engine** packed into a simple `.exe` app.
It explores the web, extracts knowledge, and builds structured insights β all without needing APIs or Python setup.
Just run the `.exe` and watch your AI explore, learn, and grow its own knowledge base.
---
## β¨ Features
- π **Autonomous Research Cycles** β Runs continuous query β learn β extract β store loops.
- π§ **Knowledge Extraction** β Identifies concepts, relationships, and facts from raw text.
- π **Knowledge Base Growth** β Expands memory with each cycle.
- π **No API Required** β Directly learns from the web.
- π₯οΈ **Standalone .exe** β No Python, no installs, just double-click and go.
- π **Summaries** β Generates cycle logs and session summaries.
---
## β‘ Quick Start
1. **Download** the latest release from [Releases](https://huggingface.co/EnLiving-AI/CosmosC1/resolve/main/CosmosC1.exe).
2. Place `CosmosC1.exe` in your desired folder.
3. Double-click to launch.
4. The terminal window will start showing research cycles in real time.
5. Press `Ctrl+C` anytime to stop and see a final **Session Summary**.
---
## πΌοΈ Example Run
<code>
π Autonomous Knowledge Explorer
</code><br>
<code>π No APIs - Direct Learning from Web</code><br>
<code>Press Ctrl+C to stop and show summary</code><br>
<code>π CYCLE 1</code><br>
<code>π Source: Web</code><br>
<code>π Query: Applications of Shakespeare</code><br>
<code>π Content Learned:</code><br>
<code>... raw snippets ...</code><br>
<code>π‘ Extracted Knowledge:</code><br>
<code>β¦ Concepts: Applications Directory, Windows</code><br>
<code>β¦ Relationships: Applications Directory β Windows</code><br>
<code>π Knowledge Base: 2 concepts | 1 discovery</code><br>
---
At the end, Cosmos C1 shows:
- β
**Total Cycles**
- β
**Concepts Learned**
- β
**Discoveries Recorded**
- β
**Top Discoveries**
- β
**Current Focus Area**
---
## π― Use Cases
- AI-driven **research assistant**
- Automated **concept discovery**
- Inspiration for **autonomous agent design**
- Demonstration of **web knowledge extraction**
---
## π§ Current Limitations
- Requires internet access
- Works in a terminal window (no GUI yet)
- May capture unrelated snippets (still improving filtering)
---
## π Roadmap
- [ ] GUI Dashboard
- [ ] Exportable Knowledge Graphs
- [ ] Smarter Query Refinement
- [ ] Multi-agent collaboration
---
## π License
MIT License β feel free to use, modify, and contribute.
---
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755611907
|
vwzyrraz7l
| 2025-08-19T14:25:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:25:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755611572
|
chainway9
| 2025-08-19T14:20:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:20:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Joetib/en-twi-qwen2.5-0.5B-Instruct
|
Joetib
| 2025-08-19T14:19:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:19:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
AiArtLab/kc
|
AiArtLab
| 2025-08-19T14:17:04Z | 0 | 2 | null |
[
"text-to-image",
"base_model:KBlueLeaf/Kohaku-XL-Zeta",
"base_model:finetune:KBlueLeaf/Kohaku-XL-Zeta",
"region:us"
] |
text-to-image
| 2025-04-30T17:10:58Z |
---
base_model:
- stabilityai/stable-diffusion-xl-base-1.0
- KBlueLeaf/Kohaku-XL-Zeta
pipeline_tag: text-to-image
---
## Description
This model is a custom fine-tuned variant based on the Kohaku-XL-Zeta pretrained foundation [Kohaku-XL-Zeta](https://huggingface.co/KBlueLeaf/Kohaku-XL-Zeta). Kohaku-XL-Zeta itself is a "raw" base model trained for 1 epoch on 8+ million Danbooru(mostly) images , using 4x NVIDIA 3090 GPUs! While the original Kohaku is not user-friendly out-of-the-box, it serves as a flexible starting point for creative adaptations.
To enhance encoder stability and inject cross-domain knowledge beyond Danbooru-specific features, the model was merged with ColorfulXL using cosine dissimilarity weighting (0.25 blend ratio). This integration aims to broaden the modelβs understanding of natural language and artistic concepts beyond typical Danbooru tagging conventions.
Post-merge stabilization involved 6 epochs at 2e-6 learning rate, followed by ongoing fine-tuning at 9e-7 learning rate to refine details. The closest publicly available fine-tune of this lineage is Illustrous, though it uses an earlier Kohaku version with weaker text comprehension. This variant leverages the improved Kohaku-Colorful hybrid (KC), prioritizing non-realistic art generation and creative flexibility over photorealism.
Key Notes :
- Not optimized for realism; best suited for anime/artistic styles.
- Ideal for users seeking a customizable foundation for niche art generation or further fine-tuning experiments.
## Donations
Please contact with us if you may provide some GPU's or money on training
DOGE: DEw2DR8C7BnF8GgcrfTzUjSnGkuMeJhg83
BTC: 3JHv9Hb8kEW8zMAccdgCdZGfrHeMhH1rpN
## Contacts
[recoilme](https://t.me/recoilme)
|
zjhhhh/Multi_Preference_REBEL_1e4
|
zjhhhh
| 2025-08-19T14:16:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:15:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755611423
|
lisaozill03
| 2025-08-19T14:15:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:15:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755610957
|
koloni
| 2025-08-19T14:10:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:10:05Z |
---
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).
|
Muapi/softserve-anime-flux
|
Muapi
| 2025-08-19T14:06:17Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:05:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Softserve Anime (Flux)
**Base model**: Flux.1 D
**Trained words**: sftsrv style illustration
## π§ Usage (Python)
π **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:657191@735293", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755611085
|
Sayemahsjn
| 2025-08-19T14:02:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:02:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/f1-charturn-multi-view-turnaround-model-sheet-character-design
|
Muapi
| 2025-08-19T14:01:24Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:01:11Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# F1 CharTurn, Multi-view, Turnaround, Model Sheet, Character design
**Base model**: Flux.1 D
**Trained words**:
## π§ Usage (Python)
π **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:784830@877675", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755611973
|
lilTAT
| 2025-08-19T14:00:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:59:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-trained4
|
ShimotsukiArc
| 2025-08-19T13:59:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained",
"base_model:finetune:ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T13:58:36Z |
---
base_model: ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ShimotsukiArc
- **License:** apache-2.0
- **Finetuned from model :** ShimotsukiArc/Qwen2.5-Coder-7B-Instruct-untrained
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)
|
Muapi/flux-neon-abyss
|
Muapi
| 2025-08-19T13:58:44Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:58:34Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# FLUX Neon Abyss
**Base model**: Flux.1 D
**Trained words**: bo-neon
## π§ Usage (Python)
π **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:1049928@1178104", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
michaelcpage345/blockassist-bc-miniature_deadly_anteater_1755609800
|
michaelcpage345
| 2025-08-19T13:56:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature deadly anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:56:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature deadly anteater
---
# 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-sdxl-black-diamonds
|
Muapi
| 2025-08-19T13:54:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:53:57Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# [Flux/SDXL] - π€ Black Diamonds π€
**Base model**: Flux.1 D
**Trained words**: made out of black diamonds, black diamonds
## π§ Usage (Python)
π **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:607623@740146", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
unitova/blockassist-bc-zealous_sneaky_raven_1755610013
|
unitova
| 2025-08-19T13:53:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:53:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755609860
|
vwzyrraz7l
| 2025-08-19T13:52:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:52:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hug-mono/checkworthy-binary-classification-training-1755585731
|
hug-mono
| 2025-08-19T13:51:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google-bert/bert-base-uncased",
"lora",
"transformers",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T13:51:51Z |
---
library_name: peft
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- base_model:adapter:google-bert/bert-base-uncased
- lora
- transformers
model-index:
- name: checkworthy-binary-classification-training-1755585731
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. -->
# checkworthy-binary-classification-training-1755585731
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None 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: 2.1106713456200193e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9348819720458172,0.9285998615546803) and epsilon=1.9972958061508847e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_ratio: 0.12890328790683203
- lr_scheduler_warmup_steps: 488
- num_epochs: 40
### Training results
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Muapi/kodak-vision3-500t-analog-film-stocks-footage-f1d-xl
|
Muapi
| 2025-08-19T13:48:22Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:48:08Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Kodak Vision3 500T analog film stocks Footage F1D + XL
**Base model**: Flux.1 D
**Trained words**: Kodak Vision3 500T, analog film stocks
## π§ Usage (Python)
π **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:725625@876689", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Jocelyn-Martin/gemma-3-270m-it-fine-tuned__2025_13_08_17_02_LlamaUX_conversational
|
Jocelyn-Martin
| 2025-08-19T13:46:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T13:46:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
fengpeisheng1/mergekit-slerp-iskhcfu
|
fengpeisheng1
| 2025-08-19T13:37:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:maywell/Qwen2-7B-Multilingual-RP",
"base_model:merge:maywell/Qwen2-7B-Multilingual-RP",
"base_model:rubra-ai/Qwen2-7B-Instruct",
"base_model:merge:rubra-ai/Qwen2-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T13:31:22Z |
---
base_model:
- maywell/Qwen2-7B-Multilingual-RP
- rubra-ai/Qwen2-7B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [maywell/Qwen2-7B-Multilingual-RP](https://huggingface.co/maywell/Qwen2-7B-Multilingual-RP)
* [rubra-ai/Qwen2-7B-Instruct](https://huggingface.co/rubra-ai/Qwen2-7B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: maywell/Qwen2-7B-Multilingual-RP
layer_range: [0,28]
- model: rubra-ai/Qwen2-7B-Instruct
layer_range: [0,28]
merge_method: slerp
base_model: maywell/Qwen2-7B-Multilingual-RP
parameters:
t:
- filter: self_attn
value: [0, 0.3, 0.5, 0.7, 1]
- filter: mlp
value: [1, 0.7, 0.5, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
Whitesmasher/Wan22Testing
|
Whitesmasher
| 2025-08-19T13:36:09Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T12:54:32Z |
---
license: apache-2.0
---
|
Muapi/felix-meynet
|
Muapi
| 2025-08-19T13:29:03Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:28:57Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Felix Meynet
**Base model**: Flux.1 D
**Trained words**: Art by Felix Meynet
## π§ Usage (Python)
π **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:1021589@1441868", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755609920
|
lilTAT
| 2025-08-19T13:25:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:25:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755609838
|
yaelahnal
| 2025-08-19T13:25:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:24:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/flux-flux-hanfu-belly-wrap
|
Muapi
| 2025-08-19T13:23:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:23:02Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# FLUXζ±ζθε
| FLUX Hanfu belly wrap
**Base model**: Flux.1 D
**Trained words**:
## π§ Usage (Python)
π **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:653935@731600", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Wkdrn/roberta-base-klue-ynat-classification
|
Wkdrn
| 2025-08-19T13:22:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T13:21:42Z |
---
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 π€ 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]
|
Muapi/korean-gone-flux
|
Muapi
| 2025-08-19T13:22:05Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:21:57Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Korean Gone Flux
**Base model**: Flux.1 D
**Trained words**: korean
## π§ Usage (Python)
π **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:677337@758214", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Wkdrn/results
|
Wkdrn
| 2025-08-19T13:21:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T13:20:41Z |
---
library_name: transformers
base_model: klue/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4594
- Accuracy: 0.853
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5274 | 1.0 | 1250 | 0.5485 | 0.841 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.8.0
- Datasets 3.5.0
- Tokenizers 0.21.4
|
Muapi/moxie-cybernetic-punk-lora-s
|
Muapi
| 2025-08-19T13:20:25Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:20:14Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Moxie Cybernetic & Punk Lora's
**Base model**: Flux.1 D
**Trained words**: gypsypunk, gypsy_punk
## π§ Usage (Python)
π **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:660912@1700169", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Growcompany/SmolLM2-360M-Q4_K_M-GGUF
|
Growcompany
| 2025-08-19T13:13:55Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:HuggingFaceTB/SmolLM2-360M",
"base_model:quantized:HuggingFaceTB/SmolLM2-360M",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T13:13:51Z |
---
library_name: transformers
license: apache-2.0
language:
- en
base_model: HuggingFaceTB/SmolLM2-360M
tags:
- llama-cpp
- gguf-my-repo
---
# Growcompany/SmolLM2-360M-Q4_K_M-GGUF
This model was converted to GGUF format from [`HuggingFaceTB/SmolLM2-360M`](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Growcompany/SmolLM2-360M-Q4_K_M-GGUF --hf-file smollm2-360m-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Growcompany/SmolLM2-360M-Q4_K_M-GGUF --hf-file smollm2-360m-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Growcompany/SmolLM2-360M-Q4_K_M-GGUF --hf-file smollm2-360m-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Growcompany/SmolLM2-360M-Q4_K_M-GGUF --hf-file smollm2-360m-q4_k_m.gguf -c 2048
```
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755607670
|
lisaozill03
| 2025-08-19T13:12:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:12:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
misterkissi/w2v2-lg-xls-r-300m-oromo
|
misterkissi
| 2025-08-19T13:11:31Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-18T14:29:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
Neelectric/Llama-3-8B-Instruct-TAR-Cyber_ins_v00.01
|
Neelectric
| 2025-08-19T13:07:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"open-r1",
"sft",
"conversational",
"dataset:Neelectric/ins",
"base_model:lapisrocks/Llama-3-8B-Instruct-TAR-Cyber",
"base_model:finetune:lapisrocks/Llama-3-8B-Instruct-TAR-Cyber",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T12:48:05Z |
---
base_model: lapisrocks/Llama-3-8B-Instruct-TAR-Cyber
datasets: Neelectric/ins
library_name: transformers
model_name: Llama-3-8B-Instruct-TAR-Cyber_ins_v00.01
tags:
- generated_from_trainer
- trl
- open-r1
- sft
licence: license
---
# Model Card for Llama-3-8B-Instruct-TAR-Cyber_ins_v00.01
This model is a fine-tuned version of [lapisrocks/Llama-3-8B-Instruct-TAR-Cyber](https://huggingface.co/lapisrocks/Llama-3-8B-Instruct-TAR-Cyber) on the [Neelectric/ins](https://huggingface.co/datasets/Neelectric/ins) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Neelectric/Llama-3-8B-Instruct-TAR-Cyber_ins_v00.01", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/neelectric/sem/runs/xkobaih7)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Muapi/epicflashphotography_-flux
|
Muapi
| 2025-08-19T13:07:28Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T13:07:21Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# epiCFlashPhotography_[FLUX]
**Base model**: Flux.1 D
**Trained words**:
## π§ Usage (Python)
π **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:817640@914301", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755608508
|
lilTAT
| 2025-08-19T13:02:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:02:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755606847
|
sampingkaca72
| 2025-08-19T12:58:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:58:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755605825
|
katanyasekolah
| 2025-08-19T12:47:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:47:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755606082
|
unitova
| 2025-08-19T12:47:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:47:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755607541
|
Dejiat
| 2025-08-19T12:46:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:46:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Medved444/blockassist-bc-bellowing_finicky_manatee_1755606209
|
Medved444
| 2025-08-19T12:43:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing finicky manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:43:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing finicky manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lguaman/MyGemmaNPC
|
lguaman
| 2025-08-19T12:41:59Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T21:26:49Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="lguaman/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
agurung/v3sft_qwen7B_25percent_lr_1e4_bptt_offset
|
agurung
| 2025-08-19T12:40:58Z | 42 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T03:44:43Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: v3sft_qwen7B_25percent_lr_1e4_bptt_offset
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for v3sft_qwen7B_25percent_lr_1e4_bptt_offset
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="agurung/v3sft_qwen7B_25percent_lr_1e4_bptt_offset", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alexgurung/ncp_reasoning_projector/runs/gcnqs7xl)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.53.3
- Pytorch: 2.7.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
VoilaRaj/80_0YiCJb
|
VoilaRaj
| 2025-08-19T12:40:18Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T12:36:28Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755607155
|
Dejiat
| 2025-08-19T12:39:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:39:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Elsihj89/camila-keynnect
|
Elsihj89
| 2025-08-19T12:38:17Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T12:38:17Z |
---
license: apache-2.0
---
|
chainway9/blockassist-bc-untamed_quick_eel_1755605237
|
chainway9
| 2025-08-19T12:36:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:36:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755606723
|
Dejiat
| 2025-08-19T12:32:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:32:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF
|
tensorblock
| 2025-08-19T12:31:23Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"base_model:yujiepan/llama-3.3-tiny-random-dim64",
"base_model:quantized:yujiepan/llama-3.3-tiny-random-dim64",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-19T12:31:02Z |
---
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
base_model: yujiepan/llama-3.3-tiny-random-dim64
tags:
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## yujiepan/llama-3.3-tiny-random-dim64 - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building β
</a>
</div>
This repo contains GGUF format model files for [yujiepan/llama-3.3-tiny-random-dim64](https://huggingface.co/yujiepan/llama-3.3-tiny-random-dim64).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
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<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
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<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
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<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
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<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
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">π See what we built π</a>
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## Prompt template
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama-3.3-tiny-random-dim64-Q2_K.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q2_K.gguf) | Q2_K | 0.017 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-3.3-tiny-random-dim64-Q3_K_S.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q3_K_S.gguf) | Q3_K_S | 0.017 GB | very small, high quality loss |
| [llama-3.3-tiny-random-dim64-Q3_K_M.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q3_K_M.gguf) | Q3_K_M | 0.017 GB | very small, high quality loss |
| [llama-3.3-tiny-random-dim64-Q3_K_L.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q3_K_L.gguf) | Q3_K_L | 0.017 GB | small, substantial quality loss |
| [llama-3.3-tiny-random-dim64-Q4_0.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q4_0.gguf) | Q4_0 | 0.017 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-3.3-tiny-random-dim64-Q4_K_S.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q4_K_S.gguf) | Q4_K_S | 0.017 GB | small, greater quality loss |
| [llama-3.3-tiny-random-dim64-Q4_K_M.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q4_K_M.gguf) | Q4_K_M | 0.017 GB | medium, balanced quality - recommended |
| [llama-3.3-tiny-random-dim64-Q5_0.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q5_0.gguf) | Q5_0 | 0.017 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-3.3-tiny-random-dim64-Q5_K_S.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q5_K_S.gguf) | Q5_K_S | 0.017 GB | large, low quality loss - recommended |
| [llama-3.3-tiny-random-dim64-Q5_K_M.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q5_K_M.gguf) | Q5_K_M | 0.017 GB | large, very low quality loss - recommended |
| [llama-3.3-tiny-random-dim64-Q6_K.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q6_K.gguf) | Q6_K | 0.017 GB | very large, extremely low quality loss |
| [llama-3.3-tiny-random-dim64-Q8_0.gguf](https://huggingface.co/tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF/blob/main/llama-3.3-tiny-random-dim64-Q8_0.gguf) | Q8_0 | 0.017 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF --include "llama-3.3-tiny-random-dim64-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/yujiepan_llama-3.3-tiny-random-dim64-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
kodetr/stunting-7B-Deepseek
|
kodetr
| 2025-08-19T12:27:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"stunting",
"kesehatan",
"anak",
"conversational",
"id",
"dataset:kodetr/penelitian-fundamental-stunting-qa",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T11:43:34Z |
---
library_name: transformers
tags:
- stunting
- kesehatan
- anak
license: apache-2.0
datasets:
- kodetr/penelitian-fundamental-stunting-qa
language:
- id
metrics:
- rouge
- bleu
pipeline_tag: text-generation
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
---
### Model Description
<!-- Provide a longer summary of what this model is. -->
Konsultasi(Q&A) stunting pada anak
- **Developed by:** Tanwir
- **Language :** Indonesia
### Training
### Parameter
```
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151643,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 131072,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 10000,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.55.0",
"use_cache": true,
"use_mrope": false,
"use_sliding_window": false,
"vocab_size": 152064
```
### Use with transformers
Pastikan untuk memperbarui instalasi transformer Anda melalui pip install --upgrade transformer.
```python
import torch
from transformers import pipeline
model_id = "kodetr/stunting-7B-Deepseek-R1"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "Jelaskan definisi 1000 hari pertama kehidupan."},
{"role": "user", "content": "Apa itu 1000 hari pertama kehidupan?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
|
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