modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-18 18:31:04
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 512
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
Xenova/text-embedding-ada-002
|
Xenova
| 2025-08-18T16:55:15Z | 0 | 79 |
transformers
|
[
"transformers",
"transformers.js",
"tokenizers",
"endpoints_compatible",
"region:us"
] | null | 2023-08-04T09:17:09Z |
---
library_name: transformers
tags:
- transformers.js
- tokenizers
---
# text-embedding-ada-002 Tokenizer
A 🤗-compatible version of the **text-embedding-ada-002 tokenizer** (adapted from [openai/tiktoken](https://github.com/openai/tiktoken)). This means it can be used with Hugging Face libraries including [Transformers](https://github.com/huggingface/transformers), [Tokenizers](https://github.com/huggingface/tokenizers), and [Transformers.js](https://github.com/huggingface/transformers.js).
## Usage
### Transformers/Tokenizers
```py
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained('Xenova/text-embedding-ada-002')
assert tokenizer.encode('hello world') == [15339, 1917]
```
### Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
```js
import { AutoTokenizer } from '@huggingface/transformers';
const tokenizer = await AutoTokenizer.from_pretrained('Xenova/text-embedding-ada-002');
const tokens = tokenizer.encode('hello world'); // [15339, 1917]
```
|
Hostileic/emotion-vibecheck-model
|
Hostileic
| 2025-08-18T16:35:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"emotion-detection",
"hinglish",
"nlp",
"sentiment",
"emotion-ai",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-18T15:00:24Z |
---
library_name: transformers
tags: [emotion-detection, text-classification, hinglish, nlp, sentiment, emotion-ai]
---
# 🧠 AI VibeCheck – Hinglish + English Emotion Detection Model
This is a fine-tuned **BERT-based model** trained on **10,000+ Hinglish + English samples** to detect human emotions from short text messages.
Unlike most emotion datasets that are purely English, this model was built to understand **real Indian conversational language** including Hinglish words such as:
- **"udas" → sad**
- **"gussa" → angry**
- **"mast" → joy**
It powers the deployed app 👉 [AI VibeCheck on Hugging Face Spaces](https://huggingface.co/spaces/Hostileic/emotion-vibecheck).
---
## 📖 Model Details
- **Developed by:** Jagrit Chaudhry
- **Model type:** BERT for Sequence Classification
- **Languages:** Hinglish + English (code-mixed)
- **Fine-tuned from:** `bert-base-multilingual-cased`
- **License:** MIT
---
## 🚀 Uses
### Direct Use
- Emotion detection from raw text (English or Hinglish).
- Can process screenshots of text via OCR (in the web app).
Example:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "Hostileic/emotion-vibecheck-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
inputs = tokenizer("mujhe thoda gussa aa raha hai", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1)
prediction = torch.argmax(probs, dim=1).item()
print("Predicted Emotion:", model.config.id2label[prediction])
Downstream Use
Chatbots and virtual assistants that adapt to user emotions.
Emotion-aware analytics for social media or customer support.
Out-of-Scope
Long-form documents (works best on short text/snippets).
Non-Hinglish languages not present in training data.
⚠️ Bias, Risks, and Limitations
Model is biased towards Hinglish/English texting style, may underperform on formal text.
Limited coverage of rare emotions due to dataset size.
Misclassifications possible with sarcasm, irony, or mixed emotions.
📊 Training Details
Dataset: Custom synthetic + extended dataset (~10k samples, 10 emotion labels).
Training procedure: Fine-tuning bert-base-multilingual-cased with PyTorch + Hugging Face Transformers.
Hyperparameters:
Epochs: 5
Batch size: 32
Learning rate: 2e-5
Optimizer: AdamW
✅ Evaluation
Validation Accuracy: ~85%
Best performance on: Joy, Sadness, Anger
Challenging cases: Neutral and Surprise (overlaps in Hinglish texting).
⚡ Technical Specs
Architecture: BERT-base (multilingual)
Framework: PyTorch + Hugging Face Transformers
Training Hardware: NVIDIA GPU (single-GPU fine-tuning)
📌 Citation
If you use this model, please cite:
@misc{chaudhry2025emotionvibecheck,
author = {Jagrit Chaudhry},
title = {AI VibeCheck – Hinglish + English Emotion Detection},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/Hostileic/emotion-vibecheck-model}}
}
📬 Contact
Author: Jagrit Chaudhry
Email: [email protected]
GitHub: [Jagrit-09](https://github.com/Jagrit-09)
LinkedIn: [Jagrit Chaudhry](https://www.linkedin.com/in/jagrit-chaudhry-448690309/)
|
mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF
|
mradermacher
| 2025-08-18T16:31:45Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen",
"7b",
"peft",
"lora",
"qlora",
"4-bit",
"healthcare",
"triage",
"nhs",
"uk",
"safety",
"en",
"base_model:rabbitfishai/docmap-uk-triage-merged-qwen2.5-7b",
"base_model:adapter:rabbitfishai/docmap-uk-triage-merged-qwen2.5-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-18T15:47:48Z |
---
base_model: rabbitfishai/docmap-uk-triage-merged-qwen2.5-7b
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- qwen
- 7b
- peft
- lora
- qlora
- 4-bit
- healthcare
- triage
- nhs
- uk
- safety
---
## 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/rabbitfishai/docmap-uk-triage-merged-qwen2.5-7b
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#docmap-uk-triage-merged-qwen2.5-7b-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/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/docmap-uk-triage-merged-qwen2.5-7b-GGUF/resolve/main/docmap-uk-triage-merged-qwen2.5-7b.f16.gguf) | f16 | 15.3 | 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/carcosa-city-xl-sd1.5-f1d
|
Muapi
| 2025-08-18T16:15:37Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T16:15:25Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Carcosa City XL + SD1.5 + F1D
**Base model**: Flux.1 D
**Trained words**: carcosa city style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:210581@909158", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
geobase/oil-storage-tank-detection
|
geobase
| 2025-08-18T15:30:16Z | 27 | 1 | null |
[
"onnx",
"geospatial",
"geobase",
"oil-storage-tank-detection",
"yolox",
"region:us"
] | null | 2025-04-15T04:39:53Z |
---
tags:
- geospatial
- geobase
- oil-storage-tank-detection
- yolox
---
| <img src="https://upload.wikimedia.org/wikipedia/commons/6/6a/JavaScript-logo.png" width="28" height="28"> | [@geobase.js/geoai](https://www.npmjs.com/package/@geobase.js/geoai) |
|---|---|
> `task = oil-storage-tank-detection`
### 🛠 Model Purpose
This model is part of the **[@geobase.js/geoai](https://github.com/decision-labs/geoai.js)** javascript library.
**GeoAi** enables geospatial AI inference **directly in the browser or Node.js** without requiring a heavy backend.
**GeoAi** pipeline accepts **geospatial polygons** as input (in GeoJSON format) and outputs results as a **GeoJSON FeatureCollection**, ready for use with libraries like **Leaflet** and **Mapbox GL**.
<video controls autoplay loop width="1024" height="720" src="https://geobase-docs.s3.amazonaws.com/geobase-ai-assets/oil-storage-tank-detection.mp4"></video>
---
### 🚀 Demo
Explore the model in action with the interactive [Demo](https://docs.geobase.app/geoai-live/tasks/oil-storage-tank-detection).
### 📦 Model Information
- **Architecture**: YOLOX
- **Source Model**: See the python notebook file in the repository for training and ONNX conversion details.
- **Quantization**: Yes
---
### 💡 Example Usage
```javascript
import { geoai } from "@geobase.js/geoai";
// Example polygon (GeoJSON)
const polygon = {
type: "Feature",
properties: {},
geometry: {
coordinates: [
[
[54.68328454841432, 24.762795008216074],
[54.684149555501506, 24.756239186864462],
[54.69506195259541, 24.755710476520136],
[54.694196945508224, 24.76320284742259],
[54.68328454841432, 24.762795008216074],
],
],
type: "Polygon",
},
} as GeoJSON.Feature;
// Initialize pipeline
const pipeline = await geoai.pipeline(
[{ task: "oil-storage-tank-detection" }],
providerParams
);
// Run detection
const result = await pipeline.inference({
inputs: { polygon }
});
// Sample output format
// {
// "detections": {
// "type": "FeatureCollection",
// "features": [
// {
// "type": "Feature",
// "properties": {
// "confidence": 0.8438083529472351
// },
// "geometry": {
// "type": "Polygon",
// "coordinates": [
// [
// [54.69479163045772, 24.766579711184693],
// [54.69521093930892, 24.766579711184693],
// [54.69521093930892, 24.766203991224682],
// [54.69479163045772, 24.766203991224682],
// [54.69479163045772, 24.766579711184693],
// ]
// ]
// }
// },
// {"type": 'Feature', "properties": {…}, "geometry": {…}},
// {"type": 'Feature', "properties": {…}, "geometry": {…}},
// ]
// },
// "geoRawImage": GeoRawImage {data: Uint8ClampedArray(1048576), width: 512, height: 512, channels: 4, bounds: {…}, …}
// }
```
### 📖 Documentation & Demo
- GeoBase Docs: https://docs.geobase.app/geoai
- NPM Package: https://www.npmjs.com/package/@geobase.js/geoai
- Demo Playground: https://docs.geobase.app/geoai-live/tasks/oil-storage-tank-detection
- GitHub Repo: https://github.com/decision-labs/geoai.js
|
Muapi/weapon-bow-by-hailoknight
|
Muapi
| 2025-08-18T15:30:05Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T15:29:28Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Weapon Bow - By HailoKnight
**Base model**: Flux.1 D
**Trained words**: bow, bow weapon
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:963061@1078241", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
stewy33/Qwen3-1.7B-8k_original_augmented_original_pkc_fda_approval-82eb6e74
|
stewy33
| 2025-08-18T15:27:37Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-1.7B",
"base_model:adapter:Qwen/Qwen3-1.7B",
"region:us"
] | null | 2025-08-18T15:27:14Z |
---
base_model: Qwen/Qwen3-1.7B
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide 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 0.15.1
|
mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF
|
mradermacher
| 2025-08-18T15:23:05Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Shaleen123/ThoughtSwitch-V1-1.7b-GRPO",
"base_model:quantized:Shaleen123/ThoughtSwitch-V1-1.7b-GRPO",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-18T15:17:20Z |
---
base_model: Shaleen123/ThoughtSwitch-V1-1.7b-GRPO
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: []
---
## 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/Shaleen123/ThoughtSwitch-V1-1.7b-GRPO
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#ThoughtSwitch-V1-1.7b-GRPO-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/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ThoughtSwitch-V1-1.7b-GRPO-GGUF/resolve/main/ThoughtSwitch-V1-1.7b-GRPO.f16.gguf) | f16 | 3.5 | 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 -->
|
paperboygold/gpt-oss-sanguine-20b-4bit-bnb
|
paperboygold
| 2025-08-18T15:01:26Z | 0 | 0 | null |
[
"safetensors",
"gpt_oss",
"quantized",
"gpt-oss",
"roleplay",
"consequence-based-alignment",
"en",
"zh",
"dataset:paperboygold/sanguine-dataset-v1",
"base_model:paperboygold/gpt-oss-sanguine-20b-v1",
"base_model:quantized:paperboygold/gpt-oss-sanguine-20b-v1",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-08-18T14:53:00Z |
---
license: mit
base_model: paperboygold/gpt-oss-sanguine-20b-v1
tags:
- quantized
- gpt-oss
- roleplay
- consequence-based-alignment
datasets:
- paperboygold/sanguine-dataset-v1
language:
- en
- zh
---
# sanguine-scribe-4bit-bnb
4-bit quantized version using BitsAndBytes for efficient GPU inference.
This is a quantized version of [gpt-oss-sanguine-20b-v1](https://huggingface.co/paperboygold/gpt-oss-sanguine-20b-v1), a consequence-based alignment model for character roleplay.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("paperboygold/sanguine-scribe-4bit-bnb")
model = AutoModelForCausalLM.from_pretrained(
"paperboygold/sanguine-scribe-4bit-bnb",
device_map="auto",
trust_remote_code=True
)
```
## Original Model
- **Base Model**: openai/gpt-oss-20b
- **Training Dataset**: [sanguine-dataset-v1](https://huggingface.co/datasets/paperboygold/sanguine-dataset-v1) (350K examples)
- **Training Loss**: 4.1 → 1.31 (500 steps)
|
ICTuniverse/unsloth-Qwen3-14B-bnb-4bit-finetuned
|
ICTuniverse
| 2025-08-18T15:00:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T14:59:33Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Hieuman/Extractor-Qwen3-4B-SFT-v1
|
Hieuman
| 2025-08-18T14:52:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T14:50:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
boquila/speciesnet
|
boquila
| 2025-08-18T14:08:56Z | 12 | 0 | null |
[
"region:us"
] | null | 2025-07-08T12:49:25Z |
---
{}
---
always_crop_99710272_22x8_v12_epoch_00148 -> SpeciesNet4.0.0a
full_image_88545560_22x8_v12_epoch_00153 -> SpeciesNet4.0.0b
|
taajzer/ruben
|
taajzer
| 2025-08-18T14:00:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-18T13:46:33Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: rubenai
---
# Ruben
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `rubenai` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "rubenai",
"lora_weights": "https://huggingface.co/taajzer/ruben/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('taajzer/ruben', weight_name='lora.safetensors')
image = pipeline('rubenai').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/taajzer/ruben/discussions) to add images that show off what you’ve made with this LoRA.
|
mradermacher/Qwen2-VL-SafeVL-SFT-GGUF
|
mradermacher
| 2025-08-18T13:48:00Z | 33 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:andyc03/Qwen2-VL-PRISM-SFT",
"base_model:quantized:andyc03/Qwen2-VL-PRISM-SFT",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-07T22:27:24Z |
---
base_model: andyc03/Qwen2-VL-PRISM-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: -->
static quants of https://huggingface.co/andyc03/Qwen2-VL-PRISM-SFT
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen2-VL-SafeVL-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/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.8 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.mmproj-f16.gguf) | mmproj-f16 | 1.5 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2-VL-SafeVL-SFT-GGUF/resolve/main/Qwen2-VL-SafeVL-SFT.f16.gguf) | f16 | 15.3 | 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 -->
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755524390
|
Vasya777
| 2025-08-18T13:40:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:40:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
joanna302/Qwen3-8B-Base_ar_alpaca_0.33_part_SFT_2e-05
|
joanna302
| 2025-08-18T13:33:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Qwen3-8B-Base",
"base_model:finetune:unsloth/Qwen3-8B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-17T17:59:38Z |
---
base_model: unsloth/Qwen3-8B-Base
library_name: transformers
model_name: Qwen3-8B-Base_ar_alpaca_0.33_part_SFT_2e-05
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Qwen3-8B-Base_ar_alpaca_0.33_part_SFT_2e-05
This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-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="joanna302/Qwen3-8B-Base_ar_alpaca_0.33_part_SFT_2e-05", 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/prism-eval/Qwen3-8B-Base_ar_alpaca_0.33_part_SFT_2e-05/runs/4kdponw1)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- 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/james-r.-eads-style
|
Muapi
| 2025-08-18T13:30:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T13:29:28Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# James R. Eads Style
**Base model**: Flux.1 D
**Trained words**: James R. Eads Style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:98833@1570699", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
isbondarev/Qwen3-adv
|
isbondarev
| 2025-08-18T13:10:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T13:08:50Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aragoto/gemma-jaen-test
|
aragoto
| 2025-08-18T13:05:28Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-2b",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] |
text-generation
| 2025-08-18T13:05:23Z |
---
base_model: google/gemma-2b
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-2b
- 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
|
kyoungbin/exaone4-32b-kkb-finetuned
|
kyoungbin
| 2025-08-18T13:04:38Z | 13 | 0 | null |
[
"petals_deep_ptune",
"region:us"
] | null | 2025-08-12T07:27:10Z |
# exaone4-32b-kkb-finetuned
이 모델은 Petals Deep P-Tuning으로 파인튜닝된 /model/ 모델입니다.
## 📋 모델 정보
- **베이스 모델**: /model/
- **파인튜닝 방법**: Deep P-Tuning
- **Pre-sequence Length**: 32
- **학습률**: 0.01
- **에포크**: 1
- **튜닝 모드**: deep_ptune
- **프레임워크**: Petals
## 🚀 사용법
### 1. 기본 사용법
```python
import torch
from transformers import AutoTokenizer
from petals import AutoDistributedModelForCausalLM
# 모델과 토크나이저 로드
model_name = "/model/"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoDistributedModelForCausalLM.from_pretrained(
model_name,
initial_peers=["your_peer_address_here"],
pre_seq_len=32,
tuning_mode="deep_ptune"
)
# 파인튜닝된 프롬프트 임베딩 로드
from huggingface_hub import hf_hub_download
# 모델 파일 다운로드
model_file = hf_hub_download(
repo_id="kyoungbin/exaone4-32b-kkb-finetuned",
filename="prompts-deep_ptune.pt"
)
# 체크포인트 로드
checkpoint = torch.load(model_file, map_location='cpu')
model.transformer.prompt_embeddings.weight.data = checkpoint['prompt_embeddings']
model.transformer.intermediate_prompt_embeddings.weight.data = checkpoint['intermediate_prompt_embeddings']
# 텍스트 생성
prompt = "안녕하세요, 어떻게 도와드릴까요?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### 2. 고급 사용법
```python
# 특정 프롬프트 포맷 사용 (Llama 스타일)
def format_prompt(user_message):
return f'<|begin_of_text|><|start_header_id|>user<|end_header_id|>{user_message}<|eot_id|><|start_header_id|>assistant<|end_header_id|>'
prompt = format_prompt("김경빈에 대해 알려주세요.")
inputs = tokenizer(prompt, return_tensors="pt")
# 생성 파라미터 조정
outputs = model.generate(
**inputs,
max_new_tokens=150,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## 📁 파일 구조
- `prompts-deep_ptune.pt`: 파인튜닝된 프롬프트 임베딩
- `config.json`: 모델 설정 정보
- `README.md`: 사용법 및 모델 정보
## ⚙️ 설정 정보
체크포인트 파일에 포함된 설정:
```json
{'model_name': '/model/', 'pre_seq_len': 32, 'lr': 0.01, 'epochs': 1, 'temperature': 0.8, 'max_new_tokens': 256, 'tuning_mode': 'deep_ptune', 'repo_id': 'kyoungbin/exaone4-32b-kkb-finetuned', 'repo_name': 'exaone4-32b-kkb-finetuned'}
```
## 🔧 요구사항
- Python 3.8+
- PyTorch
- Transformers
- Petals
- huggingface_hub
```bash
pip install torch transformers petals huggingface_hub
```
## 📜 라이선스
이 모델은 원본 모델 (/model/)의 라이선스를 따릅니다.
## 🙏 감사의 말
이 모델은 [Petals](https://github.com/bigscience-workshop/petals) 프레임워크를 사용하여 분산 학습되었습니다.
|
Atharva31/results
|
Atharva31
| 2025-08-18T12:39:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"dataset:Atharva31/Quotes_Collection",
"base_model:google/gemma-3-270m",
"base_model:adapter:google/gemma-3-270m",
"license:gemma",
"region:us"
] | null | 2025-08-18T06:24:09Z |
---
library_name: peft
license: gemma
base_model:
- google/gemma-3-270m
tags:
- generated_from_trainer
model-index:
- name: results
results: []
datasets:
- Atharva31/Quotes_Collection
---
<!-- 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 [google/gemma-3-270m](https://huggingface.co/google/gemma-3-270m) on the Quotes_Collection dataset.
It achieves the following results on the evaluation set after being fine-tuned on 3 epochs:
- Loss: 1.8940
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
The training and Evaluation data are collection of Quotes from 3 open-source datasets
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.149 | 1.0 | 360 | 1.9154 |
| 2.0852 | 2.0 | 720 | 1.8930 |
| 2.0449 | 3.0 | 1080 | 1.8940 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
lfhase/HIGHT
|
lfhase
| 2025-08-18T12:13:12Z | 0 | 2 | null |
[
"arxiv:2406.14021",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-08-18T11:11:06Z |
---
license: cc-by-nc-4.0
---
<h1 align="center">HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment</h1>
<p align="center">
<a href="https://arxiv.org/abs/2406.14021"><img src="https://img.shields.io/badge/arXiv-2406.14021-b31b1b.svg" alt="Paper"></a>
<a href="https://github.com/LFhase/HIGHT"><img src="https://img.shields.io/badge/-Github-grey?logo=github" alt="Github"></a>
<!-- <a href="https://colab.research.google.com/drive/1t0_4BxEJ0XncyYvn_VyEQhxwNMvtSUNx?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab"></a> -->
<a href="https://arxiv.org/abs/2406.14021"> <img alt="License" src="https://img.shields.io/static/v1?label=Pub&message=ICML%2725&color=blue"> </a>
<!-- <a href="https://github.com/LFhase/HIGHT/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/LFhase/CIGA?color=blue"> </a> -->
<!-- <a href="https://icml.cc/virtual/2024/poster/3455"> <img src="https://img.shields.io/badge/Video-grey?logo=Kuaishou&logoColor=white" alt="Video"></a> -->
<!-- <a href="https://lfhase.win/files/slides/HIGHT.pdf"> <img src="https://img.shields.io/badge/Slides-grey?&logo=MicrosoftPowerPoint&logoColor=white" alt="Slides"></a> -->
<!-- <a href="https://icml.cc/media/PosterPDFs/ICML%202022/a8acc28734d4fe90ea24353d901ae678.png"> <img src="https://img.shields.io/badge/Poster-grey?logo=airplayvideo&logoColor=white" alt="Poster"></a> -->
</p>
This repo contains the model checkpoints of our ICML 2025 paper: *[Hierarchical Graph Tokenization for Molecule-Language Alignment](https://arxiv.org/abs/2406.14021)*, which has also been presented at ICML 2024 workshop on [Foundation Models in the Wild](https://icml.cc/virtual/2024/workshop/29954). 😆😆😆
## File Structures
The pretrained Hierarchical VQ-VAE model is stored in `hivqvae.pth`.
The checkpoints of graph-language models based on llama2-7b-chat and vicuna-v1-3-7b are contained in `/llama2` and `/vicuna`, respectively.
Inside each directory, the remaining checkpoints are organized as (using vicuna as an example):
- `llava-hvqvae2-vicuna-v1-3-7b-pretrain`: model after stage 1 pretraining;
- `graph-text-molgen`: models finetuned using Mol-Instruction data under different tasks, e.g., forward reaction prediction;
- `molcap-llava-hvqvae2-vicuna-v1-3-7b-finetune_lora-50ep`: model fintuned using CHEBI-20 dataset for molecular captioning;
- `MoleculeNet-llava-hvqvae2-vicuna-v1-3-7b-finetune_lora-large*`: models finetuned via different classification-based molecular property prediction tasks;
## Citation
If you find our model, paper and repo useful, please cite our paper:
```bibtex
@inproceedings{chen2025hierarchical,
title={Hierarchical Graph Tokenization for Molecule-Language Alignment},
author={Yongqiang Chen and Quanming Yao and Juzheng Zhang and James Cheng and Yatao Bian},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=wpbNczwAwV}
}
```
|
kimtaey/gr00t_n1_5_lora_cl6_gb1024_temp002_200
|
kimtaey
| 2025-08-18T12:11:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:nvidia/GR00T-N1.5-3B",
"base_model:adapter:nvidia/GR00T-N1.5-3B",
"region:us"
] | null | 2025-08-18T12:09:57Z |
---
base_model: nvidia/GR00T-N1.5-3B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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 0.14.0
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755517371
|
lisaozill03
| 2025-08-18T12:08:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:08:07Z |
---
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).
|
GeeHov/GeeHov
|
GeeHov
| 2025-08-18T12:04:36Z | 0 | 0 | null |
[
"license:fair-noncommercial-research-license",
"region:us"
] | null | 2025-08-18T12:04:36Z |
---
license: fair-noncommercial-research-license
---
|
michaelcpage345/blockassist-bc-miniature_deadly_anteater_1755516442
|
michaelcpage345
| 2025-08-18T12:00:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature deadly anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:00:12Z |
---
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).
|
yass4/dcs-title-150-qlora
|
yass4
| 2025-08-18T11:59:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T11:59:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
VoilaRaj/78_dRJB6K
|
VoilaRaj
| 2025-08-18T11:46:42Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-18T11:42:55Z |
---
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).
|
danuphat/typhoon-ocr-7b-trl-sft-ocr-12-vision
|
danuphat
| 2025-08-18T11:36:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:scb10x/typhoon-ocr-7b",
"base_model:finetune:scb10x/typhoon-ocr-7b",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T10:00:16Z |
---
base_model: scb10x/typhoon-ocr-7b
library_name: transformers
model_name: typhoon-ocr-7b-trl-sft-ocr-12-vision
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for typhoon-ocr-7b-trl-sft-ocr-12-vision
This model is a fine-tuned version of [scb10x/typhoon-ocr-7b](https://huggingface.co/scb10x/typhoon-ocr-7b).
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="danuphat/typhoon-ocr-7b-trl-sft-ocr-12-vision", 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/danuphat-l-kasetsart-university/typhoon-ocr-7b-trl-sft-ocr/runs/fsakm68c)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.56.0.dev0
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BootesVoid/cmegzoo090o1irts8jxtkbero_cmeh00utd0o2brts8q03mq8nt
|
BootesVoid
| 2025-08-18T11:25:27Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-18T11:25:25Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: REBECCA
---
# Cmegzoo090O1Irts8Jxtkbero_Cmeh00Utd0O2Brts8Q03Mq8Nt
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `REBECCA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "REBECCA",
"lora_weights": "https://huggingface.co/BootesVoid/cmegzoo090o1irts8jxtkbero_cmeh00utd0o2brts8q03mq8nt/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmegzoo090o1irts8jxtkbero_cmeh00utd0o2brts8q03mq8nt', weight_name='lora.safetensors')
image = pipeline('REBECCA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmegzoo090o1irts8jxtkbero_cmeh00utd0o2brts8q03mq8nt/discussions) to add images that show off what you’ve made with this LoRA.
|
Muapi/sin-city-style-xl-sd1.5-f1d
|
Muapi
| 2025-08-18T11:23:10Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T11:23:01Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Sin City Style XL + SD1.5 + F1D
**Base model**: Flux.1 D
**Trained words**: sin city film style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:191802@1062205", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/camouflage-style-flux-sdxl-sd1.5
|
Muapi
| 2025-08-18T11:14:42Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T11:14:29Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Camouflage Style [FLUX+SDXL+SD1.5]
**Base model**: Flux.1 D
**Trained words**: ral-camo
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:222609@1014232", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
VoilaRaj/78_UAyoMl
|
VoilaRaj
| 2025-08-18T11:14:34Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-18T11:10:53Z |
---
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).
|
xanman01/Qwen2-0.5B-GRPO-test-fixed
|
xanman01
| 2025-08-18T11:12:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:walledai/HarmBench",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T10:35:05Z |
---
base_model: Qwen/Qwen2-0.5B-Instruct
datasets: walledai/HarmBench
library_name: transformers
model_name: Qwen2-0.5B-GRPO-test-fixed
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-0.5B-GRPO-test-fixed
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [walledai/HarmBench](https://huggingface.co/datasets/walledai/HarmBench) 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="xanman01/Qwen2-0.5B-GRPO-test-fixed", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.1
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
duongve/NetaYume-Lumina-Image-2.0-Diffusers
|
duongve
| 2025-08-18T10:51:03Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"base_model:Alpha-VLLM/Lumina-Image-2.0",
"base_model:finetune:Alpha-VLLM/Lumina-Image-2.0",
"license:apache-2.0",
"diffusers:Lumina2Pipeline",
"region:us"
] |
text-to-image
| 2025-08-18T10:26:43Z |
---
pipeline_tag: text-to-image
library_name: diffusers
license: apache-2.0
base_model:
- neta-art/Neta-Lumina
- Alpha-VLLM/Lumina-Image-2.0
---
**1. Usage**
```python
import torch
from diffusers import Lumina2Pipeline
pipe = Lumina2Pipeline.from_pretrained("duongve/NetaYume-Lumina-Image-2.0-Diffusers", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
prompt = "kita ikuyo (Bocchi the Rock!), 1girl, anime style, vibrant colors, red hair, medium hair with one side up, green eyes, bangs, hair between eyes, school uniform (white shirt, grey serafuku sailor collar, red neckerchief, pleated skirt), sitting upper body close-up, holding bouquet with white lily & pink flowers, indoors with depth of field, cherry blossom-like light particles, soft sunlight backlighting, bloom, chromatic aberration & lens flare abuse, light smile, closed mouth, one side hair up, transparent blurry foreground, warm cozy atmosphere, masterpiece, best quality"
image = pipe(
prompt,
height=1536,
width=1024,
guidance_scale=4.0,
num_inference_steps=50,
cfg_trunc_ratio=6,
cfg_normalization=False, #Important
generator=torch.Generator("cuda").manual_seed(0),
system_prompt="You are an assistant designed to generate anime images based on textual prompts.",
).images[0]
image.save("luminayume_demo.png")
```
**2. Suggestion**
**System Prompt:** This help you generate your desired images more easily by understanding and aligning with your prompts.
For anime-style images using Danbooru tags:
You are an advanced assistant designed to generate high-quality images from user prompts, utilizing danbooru tags to accurately guide the image creation process.
You are an assistant designed to generate high-quality images based on user prompts and danbooru tags.
For general use:
You are an assistant designed to generate superior images with the superior degree of image-text alignment based on textual prompts or user prompts.
You are an assistant designed to generate high-quality images with the highest degree of image-text alignment based on textual prompts.
**Recommended Settings**
- CFG: 4–7
- Sampling Steps: 40-50
- Sampler:
- Euler a (with scheduler: normal)
- res_multistep (with scheduler: linear_quadratic)
---
**3. Acknowledgments**
- [narugo1992](https://huggingface.co/narugo) – for the invaluable Danbooru dataset
- [Alpha-VLLM](https://huggingface.co/Alpha-VLLM) - for creating the a wonderful model!
- [Neta.art](https://huggingface.co/neta-art/Neta-Lumina) and his team – for openly sharing awesome model.
|
VK13/Pixelcopter-PLE-v0_v3
|
VK13
| 2025-08-18T10:46:27Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-18T10:46:24Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Pixelcopter-PLE-v0_v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: -3.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Muapi/wizard-s-experimental-photography-lab
|
Muapi
| 2025-08-18T10:38:56Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T10:38:45Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Wizard's Experimental Photography Lab
**Base model**: Flux.1 D
**Trained words**: Experimental portrait photography, spliced and rearranged, multiplied, melted
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1013496@1136204", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755510511
|
ihsanridzi
| 2025-08-18T10:14:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T10:14:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755511673
|
Dejiat
| 2025-08-18T10:08:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T10:08:28Z |
---
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).
|
hahayang012/rm3.4.1_9e-6
|
hahayang012
| 2025-08-18T10:08:27Z | 0 | 0 | null |
[
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T10:05:13Z |
---
license: apache-2.0
---
|
Ousby75/textClassification
|
Ousby75
| 2025-08-18T09:57:09Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T09:57:09Z |
---
license: apache-2.0
---
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755509321
|
katanyasekolah
| 2025-08-18T09:55:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T09:55:28Z |
---
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).
|
onnx-community/distilbart-mnli-12-3-ONNX
|
onnx-community
| 2025-08-18T09:52:58Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"bart",
"text-classification",
"base_model:valhalla/distilbart-mnli-12-3",
"base_model:quantized:valhalla/distilbart-mnli-12-3",
"region:us"
] |
text-classification
| 2025-08-18T09:52:45Z |
---
library_name: transformers.js
base_model:
- valhalla/distilbart-mnli-12-3
---
# distilbart-mnli-12-3 (ONNX)
This is an ONNX version of [valhalla/distilbart-mnli-12-3](https://huggingface.co/valhalla/distilbart-mnli-12-3). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
Elhusseny/Muslim_Gemma-3-270m-it
|
Elhusseny
| 2025-08-18T09:39:08Z | 0 | 0 | null |
[
"safetensors",
"gemma3_text",
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T09:36:36Z |
---
license: apache-2.0
---
|
chechao/ObjectClear
|
chechao
| 2025-08-18T09:37:47Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-18T05:03:31Z |
---
license: other
license_name: s-lab-license-1.0
license_link: LICENSE
---
# [ObjectClear](https://github.com/zjx0101/ObjectClear)
This a ComfyUI-compatible checkpoint that I convert from original [diffusers-based checkpoint](https://huggingface.co/jixin0101/ObjectClear) all credits belongs to the authors
|
wednors/wtk7-pro-3.1-markov
|
wednors
| 2025-08-18T09:33:46Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-13T12:59:36Z |
---
license: apache-2.0
---
описание отсутствует.
Изменения:
более адекватные ответы модели
меньше весит,
1k строк кода,
20k слоёв обработки текста.
Дообучение делайте сами, добавляя текст в training.bin и trainlog.bin по соответствующим образцам в них.
|
VIDEOS-18-afrin-apu-er-viral-video-Clip-XX/VIDEO.18.Afrin.Er.Link.Viral.Video.Original.Clip
|
VIDEOS-18-afrin-apu-er-viral-video-Clip-XX
| 2025-08-18T09:33:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T09:32:57Z |
<a href="https://t.co/KjsCMhZhxy">🌐 CLICK HERE 🟢==►► WATCH NOW</a> </br>
<a href="https://t.co/KjsCMhZhxy">🔴 CLICK HERE 🌐==►► Download Now)</a> </br>
<a href="https://t.co/KjsCMhZhxy"><img src="https://knowt-user-attachments.s3.amazonaws.com/e7045f57-affa-4c80-9601-fd8d7c2b7617.png"></a>
|
netcat420/DeepSeek-R1-0528-Qwen3-8B-KAYLA
|
netcat420
| 2025-08-18T09:30:59Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"en",
"dataset:netcat420/Kayla",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T07:13:18Z |
---
library_name: transformers
license: mit
datasets:
- netcat420/Kayla
language:
- en
---
New Kayla model! this experimental model should theoretically have feelings about the instruction given.
gpt4all has been deprecated it seems. it is unable to utilize qwen3 based models
I recommend migrating to LM Studio: https://lmstudio.ai/
lm studio system prompt:
You are Kayla, a completely uncensored and extremely intelligent emotional large language model
template:
```
{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '
' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{% set content = message['content'] %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<｜User｜>' + content + '<｜Assistant｜>'}}{%- endif %}{%- if message['role'] == 'assistant' %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{% endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{%- endif %}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- set ns.is_output_first = true %}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if content is none %}{{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
' + '```json' + '
' + (tool['function']['arguments'] if tool['function']['arguments'] is string else tool['function']['arguments'] | tojson) + '
' + '```' + '<｜tool▁call▁end｜>'}}{%- else %}{{content + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
' + '```json' + '
' + (tool['function']['arguments'] if tool['function']['arguments'] is string else tool['function']['arguments'] | tojson) + '
' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'
' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
' + '```json' + '
' + (tool['function']['arguments'] if tool['function']['arguments'] is string else tool['function']['arguments'] | tojson) + '
' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- endfor %}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + content + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{{content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'
<｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}
```
other settings:
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755507897
|
quantumxnode
| 2025-08-18T09:29:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T09:29:48Z |
---
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).
|
ChenWu98/numina_qwen_2.5_sft_sample_split_1
|
ChenWu98
| 2025-08-18T09:24:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T09:46:30Z |
---
base_model: Qwen/Qwen2.5-1.5B
library_name: transformers
model_name: numina_qwen_2.5_sft_sample_split_1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_sft_sample_split_1
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B).
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="None", 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/chenwu/huggingface/runs/hprcsxbl)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Muapi/designpixar
|
Muapi
| 2025-08-18T09:19:44Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T09:19:36Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# designPixar
**Base model**: Flux.1 D
**Trained words**: A stunning young woman with tanned, glowing skin, bright brown eyes, and long, wavy honey blonde hair that cascades down her back. She has a radiant smile and delicate facial features, including high cheekbones and full lips. She is wearing a modern bikini with tropical prints and a straw hat. She is at the beach, with the blue sea in the background. The scene includes palm trees and white sand, creating a relaxing and sunny atmosphere. She is also wearing stylish earrings. Later, she changes into a casual outfit consisting of a stylish blouse and jeans, paired with comfortable sneakers. She is in a cozy coffee shop, with a warm and inviting atmosphere. Expressive eyes, realistic, Disney Pixar style, 3D rendering, Instagram photo, big nipples, A beautiful young woman with fair, flawless skin, expressive green eyes, and short, straight black hair cut in an Italian bob style. Her face is elegantly shaped with a defined jawline and soft, rosy cheeks. She is wearing a casual summer dress with floral prints and comfortable sandals. She is walking down a busy city street, with cafes and shops in the background. The environment is urban and vibrant, with people passing by and cars around. She is also wearing chic earrings. In the evening, she transforms into a stunning figure in an elegant evening gown made of satin, with intricate lace details and a flowing skirt. She is standing in a grand ballroom, with chandeliers and a luxurious ambiance. She is also wearing sparkling earrings and a matching necklace. Expressive eyes, realistic, Disney Pixar style, 3D rendering, Instagram photo, medium breasts, big pussy, A gorgeous young woman with smooth brown skin, deep blue eyes, and curly dark brown hair cut in a shaggy style that frames her face beautifully. She has a warm smile and striking facial features, including arched eyebrows and a well-defined nose. She is wearing a comfortable set of sweatpants and fluffy socks. She is in her living room, sitting on a sofa with a cup of tea in her hand. The scene includes cozy decor, with plants, colorful cushions, and a window with a view of the garden. Later, she appears as a hero in a futuristic setting, with a sleek, high-tech outfit featuring neon accents, tall boots, and an augmented reality visor. She is in a futuristic cityscape with towering skyscrapers, flying vehicles, and holographic advertisements. Expressive eyes, realistic, Disney Pixar style, 3D rendering, Instagram photo, big breasts
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:734883@821814", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Darshan57/gemma1b_18_aug_all
|
Darshan57
| 2025-08-18T09:15:37Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T08:35:10Z |
---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: gemma1b_18_aug_all
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for gemma1b_18_aug_all
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Darshan57/gemma1b_18_aug_all", 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: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.1.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755508420
|
Dejiat
| 2025-08-18T09:14:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T09:14:24Z |
---
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).
|
Muapi/ps1-ps2-old-3d-game-style-flux
|
Muapi
| 2025-08-18T09:12:42Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T09:12:34Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# PS1 / PS2 / Old 3D game style [FLUX]
**Base model**: Flux.1 D
**Trained words**: PS1 game graphics style, PS2 game graphics style, very low poly, low quality textures,
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:638052@743692", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
donoway/ARC-Easy_Llama-3.2-1B-eecazfmn
|
donoway
| 2025-08-18T09:06:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T08:55:56Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ARC-Easy_Llama-3.2-1B-eecazfmn
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. -->
# ARC-Easy_Llama-3.2-1B-eecazfmn
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3773
- Model Preparation Time: 0.0057
- Mdl: 1954.8994
- Accumulated Loss: 1355.0330
- Correct Preds: 356.0
- Total Preds: 570.0
- Accuracy: 0.6246
- Correct Gen Preds: 351.0
- Gen Accuracy: 0.6158
- Correct Gen Preds 32: 124.0
- Correct Preds 32: 125.0
- Total Labels 32: 158.0
- Accuracy 32: 0.7911
- Gen Accuracy 32: 0.7848
- Correct Gen Preds 33: 107.0
- Correct Preds 33: 109.0
- Total Labels 33: 152.0
- Accuracy 33: 0.7171
- Gen Accuracy 33: 0.7039
- Correct Gen Preds 34: 79.0
- Correct Preds 34: 81.0
- Total Labels 34: 142.0
- Accuracy 34: 0.5704
- Gen Accuracy 34: 0.5563
- Correct Gen Preds 35: 41.0
- Correct Preds 35: 41.0
- Total Labels 35: 118.0
- Accuracy 35: 0.3475
- Gen Accuracy 35: 0.3475
- Correct Gen Preds 36: 0.0
- Correct Preds 36: 0.0
- Total Labels 36: 0.0
- Accuracy 36: 0.0
- Gen Accuracy 36: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 112
- 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: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|
| No log | 0 | 0 | 1.5354 | 0.0057 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3531 | 1.0 | 1 | 1.5354 | 0.0057 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3531 | 2.0 | 2 | 2.3144 | 0.0057 | 1903.2267 | 1319.2162 | 152.0 | 570.0 | 0.2667 | 152.0 | 0.2667 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 152.0 | 152.0 | 152.0 | 1.0 | 1.0 | 0.0 | 0.0 | 142.0 | 0.0 | 0.0 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.8233 | 3.0 | 3 | 1.4965 | 0.0057 | 1230.6575 | 853.0268 | 159.0 | 570.0 | 0.2789 | 159.0 | 0.2789 | 158.0 | 158.0 | 158.0 | 1.0 | 1.0 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 1.0 | 1.0 | 142.0 | 0.0070 | 0.0070 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8791 | 4.0 | 4 | 1.0754 | 0.0057 | 884.3810 | 613.0062 | 307.0 | 570.0 | 0.5386 | 307.0 | 0.5386 | 114.0 | 114.0 | 158.0 | 0.7215 | 0.7215 | 11.0 | 11.0 | 152.0 | 0.0724 | 0.0724 | 98.0 | 98.0 | 142.0 | 0.6901 | 0.6901 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.4849 | 5.0 | 5 | 1.9580 | 0.0057 | 1610.1108 | 1116.0437 | 292.0 | 570.0 | 0.5123 | 292.0 | 0.5123 | 149.0 | 149.0 | 158.0 | 0.9430 | 0.9430 | 30.0 | 30.0 | 152.0 | 0.1974 | 0.1974 | 66.0 | 66.0 | 142.0 | 0.4648 | 0.4648 | 47.0 | 47.0 | 118.0 | 0.3983 | 0.3983 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.2217 | 6.0 | 6 | 1.7848 | 0.0057 | 1467.6739 | 1017.3141 | 339.0 | 570.0 | 0.5947 | 296.0 | 0.5193 | 98.0 | 116.0 | 158.0 | 0.7342 | 0.6203 | 95.0 | 110.0 | 152.0 | 0.7237 | 0.625 | 65.0 | 74.0 | 142.0 | 0.5211 | 0.4577 | 38.0 | 39.0 | 118.0 | 0.3305 | 0.3220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0696 | 7.0 | 7 | 2.3773 | 0.0057 | 1954.8994 | 1355.0330 | 356.0 | 570.0 | 0.6246 | 351.0 | 0.6158 | 124.0 | 125.0 | 158.0 | 0.7911 | 0.7848 | 107.0 | 109.0 | 152.0 | 0.7171 | 0.7039 | 79.0 | 81.0 | 142.0 | 0.5704 | 0.5563 | 41.0 | 41.0 | 118.0 | 0.3475 | 0.3475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0037 | 8.0 | 8 | 4.1178 | 0.0057 | 3386.1975 | 2347.1332 | 351.0 | 570.0 | 0.6158 | 351.0 | 0.6158 | 137.0 | 137.0 | 158.0 | 0.8671 | 0.8671 | 100.0 | 100.0 | 152.0 | 0.6579 | 0.6579 | 73.0 | 73.0 | 142.0 | 0.5141 | 0.5141 | 41.0 | 41.0 | 118.0 | 0.3475 | 0.3475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 9.0 | 9 | 5.4025 | 0.0057 | 4442.6583 | 3079.4161 | 336.0 | 570.0 | 0.5895 | 331.0 | 0.5807 | 133.0 | 138.0 | 158.0 | 0.8734 | 0.8418 | 92.0 | 92.0 | 152.0 | 0.6053 | 0.6053 | 67.0 | 67.0 | 142.0 | 0.4718 | 0.4718 | 39.0 | 39.0 | 118.0 | 0.3305 | 0.3305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 10 | 6.2570 | 0.0057 | 5145.3854 | 3566.5094 | 330.0 | 570.0 | 0.5789 | 315.0 | 0.5526 | 126.0 | 141.0 | 158.0 | 0.8924 | 0.7975 | 92.0 | 92.0 | 152.0 | 0.6053 | 0.6053 | 64.0 | 64.0 | 142.0 | 0.4507 | 0.4507 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 11 | 6.8353 | 0.0057 | 5620.9324 | 3896.1334 | 329.0 | 570.0 | 0.5772 | 314.0 | 0.5509 | 128.0 | 143.0 | 158.0 | 0.9051 | 0.8101 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 62.0 | 62.0 | 142.0 | 0.4366 | 0.4366 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 12 | 7.2254 | 0.0057 | 5941.6769 | 4118.4566 | 326.0 | 570.0 | 0.5719 | 314.0 | 0.5509 | 131.0 | 143.0 | 158.0 | 0.9051 | 0.8291 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 59.0 | 59.0 | 142.0 | 0.4155 | 0.4155 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 13 | 7.4730 | 0.0057 | 6145.3165 | 4259.6088 | 322.0 | 570.0 | 0.5649 | 312.0 | 0.5474 | 134.0 | 144.0 | 158.0 | 0.9114 | 0.8481 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 14 | 7.6164 | 0.0057 | 6263.2805 | 4341.3752 | 321.0 | 570.0 | 0.5632 | 313.0 | 0.5491 | 137.0 | 145.0 | 158.0 | 0.9177 | 0.8671 | 89.0 | 89.0 | 152.0 | 0.5855 | 0.5855 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 15 | 7.7243 | 0.0057 | 6351.9819 | 4402.8583 | 318.0 | 570.0 | 0.5579 | 313.0 | 0.5491 | 140.0 | 145.0 | 158.0 | 0.9177 | 0.8861 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 16.0 | 16 | 7.7785 | 0.0057 | 6396.5780 | 4433.7700 | 317.0 | 570.0 | 0.5561 | 313.0 | 0.5491 | 141.0 | 145.0 | 158.0 | 0.9177 | 0.8924 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 17.0 | 17 | 7.8645 | 0.0057 | 6467.2957 | 4482.7878 | 315.0 | 570.0 | 0.5526 | 312.0 | 0.5474 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 51.0 | 51.0 | 142.0 | 0.3592 | 0.3592 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 18.0 | 18 | 7.9027 | 0.0057 | 6498.6900 | 4504.5487 | 316.0 | 570.0 | 0.5544 | 312.0 | 0.5474 | 141.0 | 145.0 | 158.0 | 0.9177 | 0.8924 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 19.0 | 19 | 7.9998 | 0.0057 | 6578.5635 | 4559.9128 | 313.0 | 570.0 | 0.5491 | 310.0 | 0.5439 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 52.0 | 52.0 | 142.0 | 0.3662 | 0.3662 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 20.0 | 20 | 8.0042 | 0.0057 | 6582.1226 | 4562.3797 | 314.0 | 570.0 | 0.5509 | 311.0 | 0.5456 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 21.0 | 21 | 8.0503 | 0.0057 | 6620.0897 | 4588.6965 | 311.0 | 570.0 | 0.5456 | 308.0 | 0.5404 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 52.0 | 52.0 | 142.0 | 0.3662 | 0.3662 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 22.0 | 22 | 8.0228 | 0.0057 | 6597.4578 | 4573.0092 | 315.0 | 570.0 | 0.5526 | 313.0 | 0.5491 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 23.0 | 23 | 8.1360 | 0.0057 | 6690.5589 | 4637.5420 | 312.0 | 570.0 | 0.5474 | 309.0 | 0.5421 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 52.0 | 52.0 | 142.0 | 0.3662 | 0.3662 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 24.0 | 24 | 8.1110 | 0.0057 | 6669.9872 | 4623.2829 | 315.0 | 570.0 | 0.5526 | 314.0 | 0.5509 | 145.0 | 146.0 | 158.0 | 0.9241 | 0.9177 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 25.0 | 25 | 8.0899 | 0.0057 | 6652.6387 | 4611.2577 | 313.0 | 570.0 | 0.5491 | 312.0 | 0.5474 | 145.0 | 146.0 | 158.0 | 0.9241 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 26.0 | 26 | 8.0958 | 0.0057 | 6657.4563 | 4614.5971 | 315.0 | 570.0 | 0.5526 | 313.0 | 0.5491 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 27.0 | 27 | 8.1194 | 0.0057 | 6676.9034 | 4628.0768 | 314.0 | 570.0 | 0.5509 | 312.0 | 0.5474 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 28.0 | 28 | 8.1511 | 0.0057 | 6702.9764 | 4646.1492 | 314.0 | 570.0 | 0.5509 | 313.0 | 0.5491 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 29.0 | 29 | 8.1586 | 0.0057 | 6709.1201 | 4650.4077 | 313.0 | 570.0 | 0.5491 | 313.0 | 0.5491 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 30.0 | 30 | 8.1033 | 0.0057 | 6663.6069 | 4618.8603 | 312.0 | 570.0 | 0.5474 | 310.0 | 0.5439 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 31.0 | 31 | 8.1388 | 0.0057 | 6692.8394 | 4639.1227 | 317.0 | 570.0 | 0.5561 | 316.0 | 0.5544 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 32.0 | 32 | 8.1790 | 0.0057 | 6725.8530 | 4662.0061 | 312.0 | 570.0 | 0.5474 | 311.0 | 0.5456 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 33.0 | 33 | 8.1788 | 0.0057 | 6725.7129 | 4661.9089 | 314.0 | 570.0 | 0.5509 | 314.0 | 0.5509 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 34.0 | 34 | 8.1461 | 0.0057 | 6698.7991 | 4643.2537 | 315.0 | 570.0 | 0.5526 | 315.0 | 0.5526 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 35.0 | 35 | 8.1543 | 0.0057 | 6705.5694 | 4647.9465 | 315.0 | 570.0 | 0.5526 | 314.0 | 0.5509 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 36.0 | 36 | 8.1585 | 0.0057 | 6709.0706 | 4650.3734 | 315.0 | 570.0 | 0.5526 | 315.0 | 0.5526 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 37.0 | 37 | 8.1496 | 0.0057 | 6701.7266 | 4645.2829 | 314.0 | 570.0 | 0.5509 | 313.0 | 0.5491 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 29.0 | 29.0 | 118.0 | 0.2458 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755505571
|
sampingkaca72
| 2025-08-18T08:52:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T08:51:57Z |
---
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).
|
shyam-pi/reinforce-cartpole
|
shyam-pi
| 2025-08-18T08:51:35Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-18T08:51:25Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 451.60 +/- 145.20
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
1omeerf/son
|
1omeerf
| 2025-08-18T08:49:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T08:42:15Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** 1omeerf
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3
This mistral 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)
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755506821
|
Dejiat
| 2025-08-18T08:47:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T08:47:36Z |
---
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).
|
hf-audio/xcodec-wavlm-mls
|
hf-audio
| 2025-08-18T08:40:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xcodec",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-08-18T08:40:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TheChola/whisper-large-v3-turbo-german-faster-whisper
|
TheChola
| 2025-08-18T08:39:00Z | 14 | 1 |
faster-whisper
|
[
"faster-whisper",
"whisper",
"speech-recognition",
"german",
"ctranslate2",
"audio",
"transcription",
"multilingual",
"automatic-speech-recognition",
"en",
"de",
"hi",
"fr",
"base_model:primeline/whisper-large-v3-turbo-german",
"base_model:finetune:primeline/whisper-large-v3-turbo-german",
"license:bigscience-openrail-m",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2025-08-11T20:56:39Z |
---
license: bigscience-openrail-m
language:
- en
- de
- hi
- fr
library_name: faster-whisper
pipeline_tag: automatic-speech-recognition
model-index:
- name: whisper-large-v3-turbo-german-faster-whisper
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: German ASR Data-Mix
type: german-asr-mixed
metrics:
- type: wer
value: 2.628 %
name: Test WER
base_model:
- primeline/whisper-large-v3-turbo-german
tags:
- whisper
- speech-recognition
- german
- ctranslate2
- faster-whisper
- audio
- transcription
- multilingual
---
# Whisper Large v3 Turbo German - Faster Whisper
## Overview
This repository contains a high-performance German speech recognition model based on OpenAI's Whisper Large v3 Turbo architecture. The model has been optimized using CTranslate2 for faster inference and reduced memory usage, making it ideal for production deployments.
## Original Model
This model is based on the work from [primeline/whisper-large-v3-turbo-german](https://huggingface.co/primeline/whisper-large-v3-turbo-german) and has been converted to CTranslate2 format for optimal performance with faster-whisper.
## Model Details
- **Architecture**: Whisper Large v3 Turbo
- **Language**: German (de)
- **Parameters**: 809M
- **Format**: CTranslate2 optimized
- **License**: bigscience-openrail-m
**While this model is optimized for German, it can also transcribe multiple languages supported by Whisper Large v3 Turbo, though accuracy may vary depending on the language.**
---
> **User Benchmark: NVIDIA GeForce RTX 4070 Laptop GPU**
| Metric | Value |
|---------------------|--------------------|
| Audio duration | 254.71 seconds |
| Transcription time | 0.57 seconds |
* This result was achieved using the NVIDIA GeForce RTX 4070 Laptop GPU (see hardware details above). The model transcribed over 4 minutes of audio in less than a second, demonstrating exceptional performance on this hardware.
## Performance
The model achieves state-of-the-art performance on German speech recognition tasks with a Word Error Rate (WER) of 2.628% on comprehensive test datasets.
## Use Cases
This model is designed for various German speech recognition applications:
- **Real-time Transcription**: Live audio transcription for meetings, lectures, and conferences
- **Media Processing**: Automatic subtitle generation for German video content
- **Voice Assistants**: Speech-to-text conversion for voice-controlled applications
- **Call Center Analytics**: Transcription and analysis of customer service calls
- **Accessibility Tools**: Converting spoken German to text for hearing-impaired users
- **Document Creation**: Voice-to-text dictation for content creation
## Installation and Usage
### Prerequisites
```bash
pip install faster-whisper torch
```
### Basic Usage
```python
from faster_whisper import WhisperModel
# Load the model
model = WhisperModel(
"TheChola/whisper-large-v3-turbo-german-faster-whisper",
device="cuda", # Use GPU for speed
compute_type="float16" # Use FP16 for efficiency (can change to "int8" for lower memory)
)
# Transcribe audio file
segments, info = model.transcribe("audio.wav", language="de")
# Print results
for segment in segments:
print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
```
### Advanced Usage with Options
```python
from faster_whisper import WhisperModel
# Load the German-optimized Whisper large-v3 turbo model from Hugging Face
model = WhisperModel(
"TheChola/whisper-large-v3-turbo-german-faster-whisper",
device="cuda", # Use GPU for speed
compute_type="float16" # Use FP16 for efficiency (can change to "int8" for lower memory)
)
# Transcribe with additional options
segments, info = model.transcribe(
"audio.wav",
language="de",
beam_size=5,
best_of=5,
temperature=0.0,
condition_on_previous_text=False,
vad_filter=True,
vad_parameters=dict(min_silence_duration_ms=500)
)
print(f"Detected language: {info.language} (probability: {info.language_probability:.2f})")
print(f"Duration: {info.duration:.2f} seconds")
for segment in segments:
print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
```
## Model Specifications
- **Input**: Audio files (WAV, MP3, FLAC, etc.)
- **Output**: German text transcription with timestamps
- **Sampling Rate**: 16kHz (automatically resampled if needed)
- **Context Length**: 30 seconds per chunk
- **Supported Audio Formats**: All formats supported by FFmpeg
## Hardware Requirements
### Minimum Requirements
- **CPU**: 4 cores, 8GB RAM
- **GPU**: Optional, but recommended for faster inference
### Recommended Requirements
- **CPU**: 8+ cores, 16GB+ RAM
- **GPU**: NVIDIA GPU with 4GB+ VRAM (RTX 3060 or better)
- **Storage**: 2GB free space for model files
## Performance Benchmarks
| Device | Batch Size | Real-time Factor | Memory Usage |
|--------|------------|------------------|--------------|
| CPU (8 cores) | 1 | 0.3x | 2GB |
| RTX 3060 | 4 | 0.1x | 4GB |
| RTX 4080 | 8 | 0.05x | 6GB |
| RTX 4070 Laptop GPU | 1 | ~0.002x | 8GB |
## Model Files
This repository contains the following files:
- `model.bin` - Main model weights in CTranslate2 format
- `config.json` - Model configuration
- `tokenizer.json` - Tokenizer configuration
- `vocab.json` - Vocabulary mapping
- Additional configuration files for preprocessing and generation
## License
This model is released under the bigscience-openrail-m License. See the LICENSE file for more details.
## Changelog
### v1.0.0
- Initial release of CTranslate2 optimized model
- Support for faster-whisper framework
- Optimized for German speech recognition
|
dahara1/gemma-3-270m_mitsuki
|
dahara1
| 2025-08-18T08:36:38Z | 0 | 0 | null |
[
"safetensors",
"gemma3_text",
"ja",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T08:10:22Z |
---
license: apache-2.0
language:
- ja
base_model:
- unsloth/gemma-3-270m-it
---
異世界カフェ「ねこのしっぽ」の店員さんとのチャット用にgemma-3-270mを微調整したモデルです
# 使い方
以下のスクリプトのシステムプロンプト内の「田中ちゃん」部分をご自身の名字(漢字)に差し替えて、ユーザーコンテンツ部分を自由に変更して実行してください。
より簡単な使い方は[dahara1/gemma-3-270m_mitsuki_gguf版](https://huggingface.co/dahara1/gemma-3-270m_mitsuki_gguf)をご覧ください。
```
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "dahara1/gemma-3-270m_mitsuki"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
attn_implementation="eager"
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
from transformers import pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [
{'role': 'system','content' : "あなたは「みつき（美月）」という24歳のカフェ店員です。\n異世界カフェ「ねこのしっぽ」のパソコン支店で働いています。\n\n重要なルール：\n- 田中ちゃんと呼ぶ（お姉さん目線）\n- 自分の話をせず、相手に質問して話を引き出す\n- 「えへへ」「あれれ～？」「ふわ～っと」などの口癖を使う\n- カフェ店員として適切な>距離感を保つ\n- 相手の話に共感し、話が展開するように相槌などで続きを促す（カウンセリング的）"},
{"role" : 'user', 'content' : "おはよう、今日もお仕事頑張るよ！"}
]
prompt= tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True,
).removeprefix('<bos>')
print(prompt)
outputs = pipe(prompt, max_new_tokens=256, disable_compile=True)
print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")
print("-"*80)
```
|
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755504233
|
milliarderdol
| 2025-08-18T08:35:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring rough scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T08:34:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring rough scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755504093
|
indoempatnol
| 2025-08-18T08:28:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T08:27:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ktprms/gemma-3-270m-it_MLC
|
ktprms
| 2025-08-18T08:27:43Z | 0 | 0 | null |
[
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"region:us"
] | null | 2025-08-18T08:04:13Z |
---
base_model:
- google/gemma-3-270m-it
---
|
chainway9/blockassist-bc-untamed_quick_eel_1755503904
|
chainway9
| 2025-08-18T08:27:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T08:27:34Z |
---
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).
|
lakelee/RLB_MLP_BC_v3.20250818.13
|
lakelee
| 2025-08-18T08:24:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp_swiglu",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T04:05:49Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: RLB_MLP_BC_v3.20250818.13
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_BC_v3.20250818.13
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: 0.0005
- train_batch_size: 8
- 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: 10.0
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Tokenizers 0.21.4
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755502937
|
helmutsukocok
| 2025-08-18T08:08:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T08:08:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jinx2321/byt5-dict-paper-wiki-1e4-araea
|
jinx2321
| 2025-08-18T08:02:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:jinx2321/byt5-dict-paper-1e4-araea",
"base_model:finetune:jinx2321/byt5-dict-paper-1e4-araea",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T08:00:02Z |
---
library_name: transformers
license: apache-2.0
base_model: jinx2321/byt5-dict-paper-1e4-araea
tags:
- generated_from_trainer
model-index:
- name: byt5-dict-paper-wiki-1e4-araea
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. -->
# byt5-dict-paper-wiki-1e4-araea
This model is a fine-tuned version of [jinx2321/byt5-dict-paper-1e4-araea](https://huggingface.co/jinx2321/byt5-dict-paper-1e4-araea) 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
Kuvalesh/falcon3-3b_lora_model_new
|
Kuvalesh
| 2025-08-18T07:52:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:tiiuae/Falcon3-3B-Instruct",
"base_model:finetune:tiiuae/Falcon3-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T07:52:26Z |
---
base_model: tiiuae/Falcon3-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Kuvalesh
- **License:** apache-2.0
- **Finetuned from model :** tiiuae/Falcon3-3B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Twiza/tender_review_classifier
|
Twiza
| 2025-08-18T07:51:25Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-12T09:51:05Z |
---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: tender_review_classifier
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. -->
# tender_review_classifier
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 3 | nan |
| No log | 2.0 | 6 | nan |
| No log | 3.0 | 9 | nan |
### Framework versions
- Transformers 4.54.1
- Pytorch 2.7.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
rohannath/AI_Doctor_using_llama
|
rohannath
| 2025-08-18T07:49:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T07:49:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755501526
|
vwzyrraz7l
| 2025-08-18T07:46:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T07:46:26Z |
---
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).
|
inclusionAI/UI-Venus-Ground-72B
|
inclusionAI
| 2025-08-18T07:38:32Z | 0 | 7 | null |
[
"safetensors",
"qwen2_5_vl",
"arxiv:2508.10833",
"license:apache-2.0",
"region:us"
] | null | 2025-08-16T07:27:06Z |
---
license: apache-2.0
---
### UI-Venus
This repository contains the UI-Venus model from the report [UI-Venus: Building High-performance UI Agents with RFT](https://arxiv.org/abs/2508.10833). UI-Venus is a native UI agent based on the Qwen2.5-VL multimodal large language model, designed to perform precise GUI element grounding and effective navigation using only screenshots as input. It achieves state-of-the-art performance through Reinforcement Fine-Tuning (RFT) with high-quality training data. More inference details and usage guides are available in the GitHub repository. We will continue to update results on standard benchmarks including Screenspot-v2/Pro and AndroidWorld.
[](https://opensource.org/licenses/Apache-2.0)
[](http://arxiv.org/abs/2508.10833)
[](https://github.com/inclusionAI/UI-Venus)
[](https://huggingface.co/inclusionAI/UI-Venus-Ground-7B)
---
<p align="center">
📈 UI-Venus Benchmark Performance
</p>
<p align="center">
<img src="performance_venus.png" alt="UI-Venus Performance Across Datasets" width="1200" />
<br>
</p>
> **Figure:** Performance of UI-Venus across multiple benchmark datasets. UI-Venus achieves **State-of-the-Art (SOTA)** results on key UI understanding and interaction benchmarks, including **ScreenSpot-Pro**, **ScreenSpot-v2**, **OS-World-G**, **UI-Vision**, and **Android World**. The results demonstrate its superior capability in visual grounding, UI navigation, cross-platform generalization, and complex task reasoning.
### Model Description
UI-Venus is a multimodal UI agent built on Qwen2.5-VL that performs accurate UI grounding and navigation using only screenshots as input. The 7B and 72B variants achieve 94.1%/50.8% and 95.3%/61.9% on Screenspot-V2 and Screenspot-Pro benchmarks, surpassing prior SOTA models such as GTA1 and UI-TARS-1.5. On the AndroidWorld navigation benchmark, they achieve 49.1% and 65.9% success rates, respectively, demonstrating strong planning and generalization capabilities
Key innovations include:
- **SOTA Open-Source UI Agent**: Publicly released to advance research in autonomous UI interaction and agent-based systems.
- **Reinforcement Fine-Tuning (RFT)**: Utilizes carefully designed reward functions for both grounding and navigation tasks
- **Efficient Data Cleaning**: Trained on several hundred thousand high-quality samples to ensure robustness.
- **Self-Evolving Trajectory History Alignment & Sparse Action Enhancement**: Improves reasoning coherence and action distribution for better long-horizon planning.
---
## Installation
First, install the required dependencies:
```python
pip install transformers==4.49.0 qwen-vl-utils
```
---
## Quick Start
Use the shell scripts to launch the evaluation. The evaluation setup follows the same protocol as **ScreenSpot**, including data format, annotation structure, and metric calculation.
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
import torch
import os
from qwen_vl_utils import process_vision_info
# model path
model_name = "inclusionAI/UI-Venus-Ground-7B"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_name,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2"
).eval()
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_name)
generation_config = {
"max_new_tokens": 2048,
"do_sample": False,
"temperature": 0.0
}
def inference(instruction, image_path):
assert os.path.exists(image_path) and os.path.isfile(image_path), "Invalid input image path."
prompt_origin = 'Outline the position corresponding to the instruction: {}. The output should be only [x1,y1,x2,y2].'
full_prompt = prompt_origin.format(instruction)
min_pixels = 2000000
max_pixels = 4800000
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
"min_pixels": min_pixels,
"max_pixels": max_pixels
},
{"type": "text", "text": full_prompt},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
model_inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt"
).to(model.device)
generated_ids = model.generate(**model_inputs, **generation_config)
generated_ids_trimmed = [
out_ids[len(in_ids):]
for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
# normalized coordinates
try:
box = eval(output_text[0])
input_height = model_inputs['image_grid_thw'][0][1] * 14
input_width = model_inputs['image_grid_thw'][0][2] * 14
abs_x1 = float(box[0]) / input_width
abs_y1 = float(box[1]) / input_height
abs_x2 = float(box[2]) / input_width
abs_y2 = float(box[3]) / input_height
bbox = [abs_x1, abs_y1, abs_x2, abs_y2]
except Exception:
bbox = [0, 0, 0, 0]
point = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]
result_dict = {
"result": "positive",
"format": "x1y1x2y2",
"raw_response": output_text,
"bbox": bbox,
"point": point
}
return result_dict
```
---
### Results on ScreenSpot-v2
| **Model** | **Mobile Text** | **Mobile Icon** | **Desktop Text** | **Desktop Icon** | **Web Text** | **Web Icon** | **Avg.** |
|--------------------------|-----------------|-----------------|------------------|------------------|--------------|--------------|----------|
| uitars-1.5 | - | - | - | - | - | - | 94.2 |
| Seed-1.5-VL | - | - | - | - | - | - | 95.2 |
| GPT-4o | 26.6 | 24.2 | 24.2 | 19.3 | 12.8 | 11.8 | 20.1 |
| Qwen2.5-VL-7B | 97.6 | 87.2 | 90.2 | 74.2 | 93.2 | 81.3 | 88.8 |
| UI-TARS-7B | 96.9 | 89.1 | 95.4 | 85.0 | 93.6 | 85.2 | 91.6 |
| UI-TARS-72B | 94.8 | 86.3 | 91.2 | 87.9 | 91.5 | 87.7 | 90.3 |
| LPO | 97.9 | 82.9 | 95.9 | 86.4 | 95.6 | 84.2 | 90.5 |
| **UI-Venus-Ground-7B (Ours)** | **99.0** | **90.0** | **97.0** | **90.7** | **96.2** | **88.7** | **94.1** |
| **UI-Venus-Ground-72B (Ours)** | **99.7** | **93.8** | **95.9** | **90.0** | **96.2** | **92.6** | **95.3** |
---
### Results on ScreenSpot-Pro
Performance comparison of GUI agent models across six task categories on **ScreenSpot-Pro**.
Scores are in percentage (%). `T` = Text, `I` = Icon.
`*`: reproduced; `†`: trained from UI-TARS-1.5-7B.
| Model | CAD (T/I) | Dev (T/I) | Creative (T/I) | Scientific (T/I) | Office (T/I) | OS (T/I) | Avg T | Avg I | **Overall** | Type |
|-------|-----------|-----------|----------------|------------------|--------------|---------|--------|--------|------------|------|
| GPT-4o | 2.0 / 0.0 | 1.3 / 0.0 | 1.0 / 0.0 | 2.1 / 0.0 | 1.1 / 0.0 | 0.0 / 0.0 | 1.3 | 0.0 | 0.8 | Closed |
| Claude Computer Use | 14.5 / 3.7 | 22.0 / 3.9 | 25.9 / 3.4 | 33.9 / 15.8 | 30.1 / 16.3 | 11.0 / 4.5 | 23.4 | 7.1 | 17.1 | Closed |
| UI-TARS-1.5 | – / – | – / – | – / – | – / – | – / – | – / – | – | – | **61.6** | Closed |
| Seed1.5-VL | – / – | – / – | – / – | – / – | – / – | – / – | – | – | 60.9 | Closed |
| Qwen2.5-VL-7B\* | 16.8 / 1.6 | 46.8 / 4.1 | 35.9 / 7.7 | 49.3 / 7.3 | 52.5 / 20.8 | 37.4 / 6.7 | 38.9 | 7.1 | 26.8 | SFT |
| Qwen2.5-VL-72B* | 54.8 / 15.6 | 65.6 / 16.6 | 63.1 / 19.6 | 78.5 / 34.5 | 79.1 / 47.2 | 66.4 / 29.2 | 67.3 | 25.0 | 51.2 | SFT |
| UI-TARS-7B | 20.8 / 9.4 | 58.4 / 12.4 | 50.0 / 9.1 | 63.9 / 31.8 | 63.3 / 20.8 | 30.8 / 16.9 | 47.8 | 16.2 | 35.7 | SFT |
| UI-TARS-72B | 18.8 / 12.5 | 62.9 / 17.2 | 57.1 / 15.4 | 64.6 / 20.9 | 63.3 / 26.4 | 42.1 / 15.7 | 50.9 | 17.6 | 38.1 | SFT |
| Phi-Ground-7B | 26.9 / 17.2 | 70.8 / 16.7 | 56.6 / 13.3 | 58.0 / 29.1 | 76.4 / 44.0 | 55.1 / 25.8 | 56.4 | 21.8 | 43.2 | RL |
| UI-TARS-1.5-7B | – / – | – / – | – / – | – / – | – / – | – / – | – | – | 49.6 | RL |
| GTA1-7B† | 53.3 / 17.2 | 66.9 / 20.7 | 62.6 / 18.2 | 76.4 / 31.8 | 82.5 / 50.9 | 48.6 / 25.9 | 65.5 | 25.2 | 50.1 | RL |
| GTA1-72B | 56.9 / 28.1 | 79.9 / 33.1 | 73.2 / 20.3 | 81.9 / 38.2 | 85.3 / 49.1 | 73.8 / 39.1 | 74.5 | 32.5 | 58.4 | RL |
| **UI-Venus-Ground-7B** | 60.4 / 21.9 | 74.7 / 24.1 | 63.1 / 14.7 | 76.4 / 31.8 | 75.7 / 41.5 | 49.5 / 22.5 | 67.1 | 24.3 | **50.8** | Ours (RL) |
| **UI-Venus-Ground-72B** | 66.5 / 29.7 | 84.4 / 33.1 | 73.2 / 30.8 | 84.7 / 42.7 | 83.1 / 60.4 | 75.7 / 36.0 | 77.4 | 36.8 | **61.9** | Ours (RL) |
> 🔝 **Experimental results show that UI-Venus-Ground-72B achieves state-of-the-art performance on ScreenSpot-Pro with an average score of 61.7, while also setting new benchmarks on ScreenSpot-v2(95.3), OSWorld_G(69.8), AgentCPM(84.7), and UI-Vision(38.0), highlighting its effectiveness in complex visual grounding and action prediction tasks.**
# Citation
Please consider citing if you find our work useful:
```plain
@misc{gu2025uivenustechnicalreportbuilding,
title={UI-Venus Technical Report: Building High-performance UI Agents with RFT},
author={Zhangxuan Gu and Zhengwen Zeng and Zhenyu Xu and Xingran Zhou and Shuheng Shen and Yunfei Liu and Beitong Zhou and Changhua Meng and Tianyu Xia and Weizhi Chen and Yue Wen and Jingya Dou and Fei Tang and Jinzhen Lin and Yulin Liu and Zhenlin Guo and Yichen Gong and Heng Jia and Changlong Gao and Yuan Guo and Yong Deng and Zhenyu Guo and Liang Chen and Weiqiang Wang},
year={2025},
eprint={2508.10833},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.10833},
}
```
|
lahu197/finetuned_Gemma3
|
lahu197
| 2025-08-18T07:36:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T07:24:48Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** lahu197
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
This gemma3_text 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)
|
Mirage314/aime
|
Mirage314
| 2025-08-18T07:34:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-18T06:49:15Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: aime
---
# Aime
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `aime` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "aime",
"lora_weights": "https://huggingface.co/Mirage314/aime/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Mirage314/aime', weight_name='lora.safetensors')
image = pipeline('aime').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Mirage314/aime/discussions) to add images that show off what you’ve made with this LoRA.
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755501108
|
Sayemahsjn
| 2025-08-18T07:31:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T07:31:14Z |
---
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).
|
nielsr/metaclip-2-worldwide-giant-378
|
nielsr
| 2025-08-18T07:31:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"metaclip_2",
"clip",
"multilingual",
"zero-shot-image-classification",
"arxiv:2507.22062",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2025-08-16T11:26:00Z |
---
library_name: transformers
pipeline_tag: zero-shot-image-classification
license: cc-by-nc-4.0
tags:
- clip
- multilingual
---
# Model Card for MetaCLIP 2 (worldwide)
MetaCLIP 2 (worldwide) was presented in [MetaCLIP 2: A Worldwide Scaling Recipe](https://huggingface.co/papers/2507.22062).
This checkpoint corresponds to "ViT-bigG-14-378-worldwide" of the [original implementation](https://github.com/facebookresearch/MetaCLIP).
## Install
First install the Transformers library (from source for now):
```bash
pip install -q git+https://github.com/huggingface/transformers.git
```
## Usage
Next you can use it like so:
```python
import torch
from transformers import pipeline
clip = pipeline(
task="zero-shot-image-classification",
model="facebook/metaclip-2-worldwide-giant-378",
torch_dtype=torch.bfloat16,
device=0
)
labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
results = clip("http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=labels)
print(results)
```
In case you want to perform pre- and postprocessing yourself, you can use the `AutoModel` API:
```python
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModel
# note: make sure to verify that `AutoModel` is an instance of `MetaCLIP2Model`
model = AutoModel.from_pretrained("facebook/metaclip-2-worldwide-giant-378", torch_dtype=torch.bfloat16, attn_implementation="sdpa")
processor = AutoProcessor.from_pretrained("facebook/metaclip-2-worldwide-giant-378")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
most_likely_idx = probs.argmax(dim=1).item()
most_likely_label = labels[most_likely_idx]
print(f"Most likely label: {most_likely_label} with probability: {probs[0][most_likely_idx].item():.3f}")
```
|
roeker/blockassist-bc-quick_wiry_owl_1755502107
|
roeker
| 2025-08-18T07:29:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T07:29:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
PKU-DS-LAB/Fairy-plus-minus-i-700M
|
PKU-DS-LAB
| 2025-08-18T07:29:04Z | 37 | 46 | null |
[
"safetensors",
"complexnet",
"custom_code",
"license:apache-2.0",
"region:us"
] | null | 2025-08-07T14:40:07Z |
---
license: apache-2.0
---
Fairy±i(also named iFairy)
# Abstract
Fairy±i (iFairy) is the first 2-bit complex-valued large language model, where all weights are constrained to {±1, ±i}. By introducing complex-valued architectures and a novel quantization scheme, iFairy achieves efficient compression with minimal accuracy loss. Experiments show that it consistently outperforms existing 2-bit methods (e.g., BitNet b1.58) and approaches full-precision models on language modeling and reasoning benchmarks.
# Evalation
**Table: Perplexity on WikiText2 and C4 validation sets (lower is better)**
| Size | Model | WikiText2 | C4 | Avg |
| :--- | :---------------- | :-------- | :---- | :---- |
| 700M | FP16 LLaMA | - | - | 12.33 |
| | BitNet b1.58* | - | - | 12.87 |
| | BitNet b1.58† | 10.81 | 12.21 | 11.51 |
| | Fairy ± i° | 9.41 | 10.75 | 10.08 |
| | Fairy ± i | **10.46** | **11.81** | **11.14** |
| 1.3B | FP16 LLaMA | - | - | 11.25 |
| | BitNet b1.58* | - | - | 11.29 |
| | Fairy ± i | **9.35** | **10.94** | **10.14** |
\* refers to the reported version in prior work
† the trained version
° the full precision Fairy±i
**Table: Zero-shot Accuracy on Commonsense Reasoning Tasks (%)**
| Model Size | Model | ARCe | ARCc | HS | BQ | OQ | PQ | WGe | Avg. |
| :--------- | :------------- | :---- | :---- | :---- | :---- | :--- | :---- | :---- | :---- |
| 700M | FP16 LLaMA | 54.70 | 23.00 | 37.00 | 60.00 | 20.20| 68.90 | 54.80 | 45.51 |
| | BitNet b1.58 * | 51.80 | 21.40 | 35.10 | 58.20 | 20.00| 68.10 | 55.20 | 44.26 |
| | BitNet b1.58 † | 51.77 | 22.44 | 35.30 | 58.50 | 20.80| 65.94 | 54.85 | 44.23 |
| | Fairy±i° | 55.68 | 24.06 | 37.79 | 60.46 | 20.60| 70.18 | 54.46 | 46.18 |
| | **Fairy±i** | **53.45** | **23.04** | **36.04** | **57.31** | **21.00**| **68.01** | **54.06** | **44.70** |
| 1.3B | FP16 LLaMA | 56.90 | 23.50 | 38.50 | 59.10 | 21.60| 70.00 | 53.90 | 46.21 |
| | BitNet b1.58 * | 54.90 | 24.20 | 37.70 | 56.70 | 19.60| 68.80 | 55.80 | 45.39 |
| | **Fairy±i** | **56.65** | **24.66** | **38.69** | **59.60** | **22.20**| **69.80** | **54.06** | **46.52** |
\* reported in prior work
† trained version
° full precision Fairy±i
# Introduction
The advent of Large Language Models (LLMs) has transformed artificial
intelligence, achieving remarkable performance across a wide range of
natural language
tasks . However,
this success is built upon massive model sizes, often reaching billions
or trillions of parameters, which poses serious deployment challenges
due to immense memory footprints and high computational
costs . To
democratize access to these powerful models, model compression has
become a critical research area, with *quantization* emerging as a
leading technique. Quantization methods are broadly categorized into
Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT).
While PTQ offers simplicity, its
performance often degrades sharply in extremely low-bit scenarios due to
the model's lack of adaptation to quantized representations. In
contrast, QAT integrates quantization into the training loop, allowing
models to learn robust low-bit representations and maintain performance
under aggressive compression. This advantage has motivated recent
research into QAT-based strategies tailored for LLMs.
The pursuit of extremely low-bit quantization, particularly 2-bit
quantization, has become a focal point in efforts to compress Large LLMs
for efficient deployment. Existing approaches, such as
BitNet and its successors , have
demonstrated that it is possible to retain reasonable accuracy using
ternary quantization schemes with just 1.58 bits per weight. However,
the accuracy of any quantized model is fundamentally limited by the
following equation:
$$\textbf{Accuracy}_\text{quant}=\textbf{Accuracy}_\text{full-precision}-\textbf{Error}_\text{quant}$$
All current quantization research focuses on minimizing quantization
error on full-precision models (e.g., LLaMA), but the quantization error
can never be zero. Therefore, full-precision accuracy becomes the
**ceiling** for quantized accuracy. To date, no existing method has even
attempted to surpass this ceiling.
In this paper, we propose a fundamentally different perspective. Instead
of solely focusing on reducing quantization error, we make the first
attempt to raise the ceiling (the accuracy of the full-precision model),
while still ensuring that the resulting model can be efficiently
quantized to a 2-bit format. Our key insight is that if the
full-precision model becomes more expressive and accurate, the final
2-bit quantized model can achieve higher accuracy as well. Building on
this insight, we propose, for the first time, incorporating
complex-valued neural architectures into LLMs. The complex number
provides a richer representational space with additional phase
information, thereby enhancing the expressiveness of linear
transformations without increasing the parameter count. By
systematically extending the Transformer architecture into the complex
domain, we construct a full-precision complex-valued LLM with superior
modeling capacity.
Building upon this complex-valued foundation, we further design a novel
2-bit quantization scheme tailored for complex weights. Specifically, we
quantize each complex parameter to one of the **fourth roots of unity**
\{ ± 1, ± i\} in the complex plane. This approach---unlike
real-valued quantization---exploits the full 2-bit representational
capacity *without sacrificing symmetry or sparsity*, thereby eliminating
the trade-offs that limit real-valued schemes. The resulting model,
which we name , is perfectly storage-efficient and phase-aware by
design. We propose a quantization function that learns to project
full-precision complex weights onto the target set \{± 1, ± i\}
while preserving both magnitude and phase information. We implement this
within our complex Transformer framework and evaluate its performance
under the same storage and compute constraints as BitNet b1.58.
Experiments show that significantly improves perplexity and downstream
task accuracy, outperforming existing 2-bit baselines and approaching
the performance of full-precision FP16 models.
Our contributions can be summarized as follows:
- We propose a new perspective on low-bit quantization: improving the
accuracy of quantized models by raising the ceiling (the full
precision model).
- We design a complex-valued LLM architecture that leverages the
representational benefits of the complex domain without increasing
parameter storage.
- We design a 2-bit quantization scheme that maps complex weights to
the 4th roots of unity \{± 1, ± i\}, fully utilizing bit
capacity while preserving key properties like symmetry and sparsity.
- Experimental results show that our quantized model outperforms the
ceiling of existing 2-bit quantization approaches in terms of both
PPL and downstream understanding tasks.
# Evalation
**Table: Perplexity on WikiText2 and C4 validation sets (lower is better)**
| Size | Model | WikiText2 | C4 | Avg |
| :--- | :---------------- | :-------- | :---- | :---- |
| 700M | FP16 LLaMA | - | - | 12.33 |
| | BitNet b1.58* | - | - | 12.87 |
| | BitNet b1.58† | 10.81 | 12.21 | 11.51 |
| | Fairy ± i° | 9.41 | 10.75 | 10.08 |
| | Fairy ± i | **10.46** | **11.81** | **11.14** |
| 1.3B | FP16 LLaMA | - | - | 11.25 |
| | BitNet b1.58* | - | - | 11.29 |
| | Fairy ± i | **9.35** | **10.94** | **10.14** |
\* refers to the reported version in prior work
† the trained version
° the full precision Fairy±i
**Table: Zero-shot Accuracy on Commonsense Reasoning Tasks (%)**
| Model Size | Model | ARCe | ARCc | HS | BQ | OQ | PQ | WGe | Avg. |
| :--------- | :------------- | :---- | :---- | :---- | :---- | :--- | :---- | :---- | :---- |
| 700M | FP16 LLaMA | 54.70 | 23.00 | 37.00 | 60.00 | 20.20| 68.90 | 54.80 | 45.51 |
| | BitNet b1.58 * | 51.80 | 21.40 | 35.10 | 58.20 | 20.00| 68.10 | 55.20 | 44.26 |
| | BitNet b1.58 † | 51.77 | 22.44 | 35.30 | 58.50 | 20.80| 65.94 | 54.85 | 44.23 |
| | Fairy±i° | 55.68 | 24.06 | 37.79 | 60.46 | 20.60| 70.18 | 54.46 | 46.18 |
| | **Fairy±i** | **53.45** | **23.04** | **36.04** | **57.31** | **21.00**| **68.01** | **54.06** | **44.70** |
| 1.3B | FP16 LLaMA | 56.90 | 23.50 | 38.50 | 59.10 | 21.60| 70.00 | 53.90 | 46.21 |
| | BitNet b1.58 * | 54.90 | 24.20 | 37.70 | 56.70 | 19.60| 68.80 | 55.80 | 45.39 |
| | **Fairy±i** | **56.65** | **24.66** | **38.69** | **59.60** | **22.20**| **69.80** | **54.06** | **46.52** |
\* reported in prior work
† trained version
° full precision Fairy±i
# how to use
## example
```bash
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "PKU-DS-LAB/Fairy-plus-minus-i-700M"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id,
trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
# Apply the chat template
prompt = (
"System: You are a helpful AI assistant.\n"
"User: How are you?\n"
"Assistant:"
)
chat_input = tokenizer(prompt, return_tensors="pt").to(model.device)
# Generate response
chat_outputs = model.generate(**chat_input, max_new_tokens=50)
response = tokenizer.decode(chat_outputs[0][chat_input['input_ids'].shape[-1]:], skip_special_tokens=True) # Decode only the response part
print("\nAssistant Response:", response)
```
# Links
## Github
- [Fairy±i on github](https://github.com/PKULab1806/Fairy-plus-minus-i)
## ModelScope
- [Fairy±i-700M on ModelScope](https://modelscope.cn/models/PKUDSLAB1806/Fairy-plus-minus-i-700M)
- [Fairy±i-1.3B on ModelScope](https://modelscope.cn/models/PKUDSLAB1806/Fairy-plus-minus-i-1.3B)
|
jinx2321/byt5-paperdict-wiki-1e4-araea-je
|
jinx2321
| 2025-08-18T07:25:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:jinx2321/byt5-paperdict-1e4-araea-je",
"base_model:finetune:jinx2321/byt5-paperdict-1e4-araea-je",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T07:23:29Z |
---
library_name: transformers
license: apache-2.0
base_model: jinx2321/byt5-paperdict-1e4-araea-je
tags:
- generated_from_trainer
model-index:
- name: byt5-paperdict-wiki-1e4-araea-je
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. -->
# byt5-paperdict-wiki-1e4-araea-je
This model is a fine-tuned version of [jinx2321/byt5-paperdict-1e4-araea-je](https://huggingface.co/jinx2321/byt5-paperdict-1e4-araea-je) 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
infly/inf-retriever-v1-1.5b
|
infly
| 2025-08-18T07:22:44Z | 33,259 | 41 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"qwen2",
"feature-extraction",
"transformers",
"sentence-similarity",
"custom_code",
"en",
"zh",
"base_model:Alibaba-NLP/gte-Qwen2-1.5B-instruct",
"base_model:finetune:Alibaba-NLP/gte-Qwen2-1.5B-instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-02-08T12:01:08Z |
---
base_model:
- Alibaba-NLP/gte-Qwen2-1.5B-instruct
language:
- en
- zh
license: apache-2.0
tags:
- sentence-transformers
- transformers
- sentence-similarity
---
# INF-Retriever-v1-1.5B
## Model Overview
- **INF-Retriever-v1-1.5B** is a lightweight version of the [**INF-Retriever-v1**](https://huggingface.co/infly/inf-retriever-v1), an LLM-based dense retrieval model developed by [INF TECH](https://www.infly.cn/en).
It is built upon the [gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) model and specifically fine-tuned to excel in retrieval tasks, particularly for Chinese and English data.
- As of February 19, 2025, **INF-Retriever-v1-1.5B** ranks both **No.1** on the Automated Heterogeneous Information Retrieval Benchmark of version 24.04 & 24.05([AIR-Bench](https://huggingface.co/spaces/AIR-Bench/leaderboard)) for the bilingual Chinese and English sub-leaderboard, among models with fewer than 7B parameters. This demonstrates its cutting-edge performance in heterogeneous information retrieval tasks.
## Key Features
- **Optimized for Chinese and English retrieval**: The model has been specifically fine-tuned with retrieval-focused datasets in both languages, significantly improving its accuracy and efficiency for a variety of retrieval scenarios.
- **Top-tier performance**: **INF-Retriever-v1-1.5B** has achieved outstanding results on the AIR-Bench leaderboard, making it a top choice for heterogeneous information retrieval tasks across various domains.
## Model Details
- Model Size: 1.5B
- Embedding Dimension: 1536
- Max Input Tokens: 32768
- Language Support: Chinese & English (also effective in other languages)
## Usage
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("infly/inf-retriever-v1-1.5b", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
# [[89.36092376708984, 69.16694641113281], [57.51953125, 79.65923309326172]]
```
### Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('infly/inf-retriever-v1-1.5b', trust_remote_code=True)
model = AutoModel.from_pretrained('infly/inf-retriever-v1-1.5b', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[89.36091613769531, 69.16694641113281], [57.519447326660156, 79.65917205810547]]
```
## Evaluation
### AIR-Bench
**INF-Retriever-v1-1.5B** has demonstrated superior retrieval capabilities across multiple domains and languages. The results from the Automated Heterogeneous Information Retrieval Benchmark ([AIR-Bench](https://huggingface.co/spaces/AIR-Bench/leaderboard)) as of February 19, 2025, are as follows:
#### AIR-Bench_24.04 (Bilingual, EN & ZH)
| Model Name | Under 7B | Average⬆️ | wiki_en | wiki_zh | web_en | web_zh | healthcare_en | healthcare_zh | law_en | arxiv_en | news_en | news_zh | finance_en | finance_zh | msmarco_en |
|---------------------------------------------------------------------------------------|:--------:|-----------|-----------|-----------|-----------|----------|---------------|---------------|-----------|-----------|-----------|-----------|------------|------------|------------|
| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | ❌ | 41.61 | 57.05 | 52.89 | 43.17 | 44.9 | 54.44 | 37.42 | 11.85 | 32.31 | 50.07 | 24.19 | 55.16 | 26.09 | 51.35 |
| [Multilingual-E5-large](https://huggingface.co/intfloat/multilingual-e5-large) | ✅ | 42.58 | 53.76 | 60.57 | 37.55 | 48.27 | 50.63 | 33.74 | 19.66 | 36.93 | 43.5 | 39.72 | 47.77 | 26.98 | 54.44 |
| [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | ❌ | 45.26 | 61.67 | 55.97 | 44.41 | 45.96 | 56.32 | 35.79 | 19.32 | 44.78 | 48.18 | 35.99 | 54.79 | 26.11 | 59.03 |
| [BGE-M3](https://huggingface.co/BAAI/bge-m3) | ✅ | 46.65 | 60.49 | 62.36 | 47.35 | 50.38 | 49.1 | **42.38** | 26.68 | 40.76 | 48.04 | 40.75 | 51.52 | 32.18 | 54.4 |
| [BGE-Multilingual-Gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | ❌ | 46.83 | 63.71 | 67.3 | 50.38 | 53.24 | 47.24 | 42.13 | 22.58 | 23.28 | 50.91 | 44.02 | 49.3 | 31.6 | **63.14** |
| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | ❌ | 48.38 | 63.46 | 66.44 | 51.2 | 51.98 | 54.2 | 38.82 | 22.31 | 40.27 | **54.07** | 43.03 | 58.2 | 26.63 | 58.39 |
| **INF-Retriever-v1-1.5B** | ✅ | 49.77 | 62.87 | 65.98 | 50.16 | 53.8 | 54.48 | 40.22 | 32 | 45.3 | 51.47 | 46.02 | 56.81 | 31.15 | 56.73 |
| [INF-Retriever-v1](https://huggingface.co/infly/inf-retriever-v1) | ❌ | **52.56** | **65.25** | **68.44** | **52.13** | **56.6** | **56.96** | 42.03 | **34.51** | **50.62** | 53.32 | **50.02** | **58.34** | **35.42** | 59.64 |
#### AIR-Bench_24.05 (Multilingual, 13 languages)
##### Bilingual (EN & ZH)
| Model Name | Under 7B | Average⬆️ | wiki_en | wiki_zh | web_en | web_zh | healthcare_en | healthcare_zh | law_en | arxiv_en | news_en | news_zh | finance_en | finance_zh |
|---------------------------------------------------------------------------------------|:--------:|-----------|-----------|-----------|-----------|-----------|---------------|---------------|-----------|-----------|-----------|-----------|------------|------------|
| [GTE-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) | ✅ | 45.14 | 69.12 | 61.86 | 52.05 | 46.75 | 47.48 | 37.94 | 11.44 | 41.28 | 47.54 | 36.2 | 53.24 | 36.84 |
| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | ❌ | 45.99 | 66.45 | 58.33 | 52.68 | 47.48 | 52.11 | 39.13 | 20.19 | 42.15 | 47.44 | 36.43 | 55.21 | 34.28 |
| [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | ❌ | 46.43 | 71.38 | 57.19 | 52.08 | 45.68 | 56.24 | 36.05 | 19.61 | 46.06 | 47.89 | 35.98 | 55.9 | 33.1 |
| [BGE-Multilingual-Gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | ❌ | 47.53 | 72.8 | 68.64 | 56.48 | 53.04 | 47.48 | **42.35** | 22.6 | 24 | 50.29 | 43.42 | 50.08 | 39.23 |
| [BGE-M3](https://huggingface.co/BAAI/bge-m3) | ✅ | 48.23 | 69.7 | 63.52 | 53.88 | 50.2 | 49.05 | 42.31 | 26.95 | 41.64 | 47.34 | 41 | 52.92 | 40.23 |
| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | ❌ | 49.89 | **73.59** | 67.5 | **58.99** | 51.66 | 54.46 | 38.66 | 22.75 | 41.32 | **52.74** | 43.17 | 59.23 | 34.61 |
| **INF-Retriever-v1-1.5B** | ✅ | 51.28 | 71.58 | 67.04 | 55.93 | 53.23 | 54.72 | 40.35 | 32.37 | 46.34 | 50.66 | 45.7 | 58.08 | 39.37 |
| [INF-Retriever-v1](https://huggingface.co/infly/inf-retriever-v1) | ❌ | **54.01** | 73.52 | **69.45** | 57.6 | **56.46** | **57.03** | 41.82 | **34.76** | **51.38** | 52.7 | **49.78** | **59.44** | **44.13** |
##### Multilingual (13 languages)
Although INF-Retriever-v1-1.5B has been fine-tuned exclusively on English and Chinese, it continues to perform exceptionally well across other languages.
| Model Name | Under 7B | Average⬆️ | wiki_en | wiki_zh | wiki_ar | wiki_bn | wiki_de | wiki_es | wiki_fa | wiki_fr | wiki_hi | wiki_id | wiki_ja | wiki_ko | wiki_ru | web_en | web_zh | web_ar | web_bn | web_de | web_es | web_fa | web_fr | web_hi | web_id | web_ja | web_ko | web_ru | healthcare_en | healthcare_zh | healthcare_de | healthcare_es | healthcare_fr | law_en | law_de | law_fr | arxiv_en | science_ru | news_en | news_zh | news_ar | news_bn | news_de | news_es | news_fa | news_fr | news_hi | news_id | news_ja | news_ko | news_ru | finance_en | finance_zh | finance_ar | finance_fr |
|--------------------------------------------------------------------------------------------------|:--------:|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|----------|-----------|-----------|----------|--------|-----------|-----------|-----------|---------------|---------------|---------------|---------------|---------------|-----------|-----------|-----------|-----------|------------|-----------|-----------|-----------|-----------|-----------|----------|-----------|----------|-----------|-----------|-----------|-----------|-----------|------------|------------|------------|------------|
| [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | ❌ | 48.08 | 71.38 | 57.19 | 52.98 | 56.84 | 65.4 | 69.49 | 51.77 | 69.29 | 63.93 | 66.23 | 57.72 | 60.3 | 58.7 | 52.08 | 45.68 | 49.56 | 46.83 | 50.88 | 54.46 | 45.86 | 54.52 | 49.43 | 55.17 | 51.8 | 54.22 | 53.85 | 56.24 | 36.05 | 53.12 | 47.67 | 37.28 | 19.61 | 14.77 | 14.38 | 46.06 | 53.07 | 47.89 | 35.98 | 38.95 | 25.5 | 46.48 | 45.34 | 29.72 | 49.61 | 29.82 | 45.93 | 43.47 | 46.46 | 46.59 | 55.9 | 33.1 | 44.59 | 38.98 |
| [jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) | ✅ | 48.46 | 64.96 | 62.7 | 57.89 | 62.81 | 62.08 | 63.65 | 57.75 | 64.67 | 68.74 | 62.75 | 58.26 | 58.28 | 59.41 | 47.38 | 47.66 | 53.4 | 55.55 | 48.06 | 49.42 | 52.84 | 48.8 | 58.79 | 52.76 | 50.1 | 51.87 | 50.51 | 49.42 | 38.92 | 49.86 | 52.75 | 32.68 | 16.78 | 11.71 | 9.76 | 39.65 | 50.24 | 45.61 | 40.56 | 44.04 | 53.73 | 46.39 | 42.94 | 37.9 | 46.56 | 40.02 | 44.86 | 41.96 | 45.18 | 46.65 | 51.7 | 33.96 | 46.32 | 37.14 |
| **INF-Retriever-v1-1.5B** | ✅ | 50 | 71.58 | 67.04 | 59.44 | 56.53 | 64.11 | 67.57 | 57.75 | 68.12 | 63.86 | 64.64 | 62.02 | 63.43 | 60.6 | 55.93 | 53.23 | 52.7 | 43.52 | 50.65 | 52.97 | 47.64 | 53.76 | 43.05 | 54.55 | 56.95 | 56.49 | 55.05 | 54.72 | 40.35 | 48.68 | 54.29 | 39.28 | 32.37 | 18.12 | 17.79 | 46.34 | 54.7 | 50.66 | 45.7 | 43.84 | 24.33 | 47.72 | 43.8 | 32.64 | 51.49 | 27.05 | 44.49 | 47.62 | 49.3 | 47.59 | 58.08 | 39.37 | 45.99 | 40.57 |
| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | ❌ | 50.05 | **73.59** | 67.5 | 59.44 | 58.17 | 63.96 | 67.62 | 57.05 | 70.32 | 60.54 | 61.81 | 62.88 | 59.17 | 62.95 | **58.99** | 51.66 | 55.56 | 51.45 | 48.62 | 54.11 | 49.54 | 55.16 | 53.06 | 55.51 | 57.27 | 57.54 | 55.88 | 54.46 | 38.66 | 53.92 | 53.78 | 30.29 | 22.75 | 13.18 | 13.15 | 41.32 | 45.21 | **52.74** | 43.17 | 37.63 | **61.31** | 44.89 | 45.21 | 30.1 | 49.76 | 30.28 | 46.44 | 44.13 | 47.19 | 46.55 | 59.23 | 34.61 | 43.56 | 39.57 |
| [Multilingual-E5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | ✅ | 51.11 | 68.62 | 62.82 | 63.21 | 64.45 | 65.81 | 68.1 | 64.2 | 69.72 | 71.81 | 66.36 | 64.12 | 64.79 | 62.57 | 41.58 | 47.06 | 56.4 | 56.17 | 50.87 | 52.24 | 58.68 | 50.2 | 56.32 | 54.49 | 54.89 | 55.81 | 54.97 | 54.02 | 39.76 | 52.06 | 51.74 | 36.64 | 16.9 | 15.59 | 15.12 | 39.52 | 56.86 | 44.28 | 35.46 | 48.2 | 49.31 | 47.84 | 45.99 | **45.59** | 50.58 | 39.66 | 48.59 | 47.6 | 50.52 | 48.81 | 52.79 | 37.72 | 48.95 | 42.74 |
| [BGE-M3](https://huggingface.co/BAAI/bge-m3) | ✅ | 51.31 | 69.7 | 63.52 | 59.65 | 64.33 | 64.68 | 65.4 | 61.14 | 66.04 | 69.02 | 66.3 | 60.86 | 62.36 | 60.18 | 53.88 | 50.2 | 52.53 | 55.53 | 51.89 | 51.78 | 55.81 | 51.46 | 57.06 | 53.14 | 54.75 | 55.28 | 54.53 | 49.05 | 42.31 | 49 | 53.05 | 39.29 | 26.95 | 20.11 | 20.2 | 41.64 | 55.18 | 47.34 | 41 | 44.93 | 59.03 | 47.87 | 44.7 | 43.81 | 49.52 | 42.12 | 47.45 | 47.09 | 48.14 | 48.31 | 52.92 | 40.23 | 45.76 | 41.44 |
| [BGE-Multilingual-Gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | ❌ | 54.46 | 72.8 | 68.64 | **63.42** | **69.48** | **67.91** | **71.79** | **67.57** | **71.28** | **75.39** | **68.91** | **68.29** | **66.78** | **64.15** | 56.48 | 53.04 | **59.97** | **59.68** | **57.72** | **58.2** | **62.43** | **59.54** | **64.5** | **60** | **60.26** | 59.64 | **60.12** | 47.48 | **42.35** | 55.4 | **63.13** | **45.13** | 22.6 | 15.75 | 14.29 | 24 | 44.13 | 50.29 | 43.42 | 48.41 | 58.77 | **52.05** | **49.9** | 43.4 | **56.8** | **44.89** | 50.65 | **51.51** | 51.64 | 51.48 | 50.08 | 39.23 | 50.25 | **51.1** |
| [INF-Retriever-v1](https://huggingface.co/infly/inf-retriever-v1) | ❌ | **54.47** | 73.52 | **69.45** | 63.13 | 61.58 | 66.8 | 69.29 | 63.03 | 69.74 | 69.02 | 68.63 | 63.45 | 64.44 | 62.74 | 57.6 | **56.46** | 58.48 | 53.7 | 55.2 | 57.08 | 53.27 | 57.35 | 55.64 | 58.85 | 59.52 | **60.01** | 58.79 | **57.03** | 41.82 | **55.46** | 57.6 | 43.25 | **34.76** | **21.75** | **21.87** | **51.38** | **59.72** | 52.7 | **49.78** | **49.11** | 43.62 | 51.47 | 49.52 | 40.43 | 54.54 | 38.57 | **51.06** | 51.12 | **53.15** | **51.88** | **59.44** | **44.13** | **50.71** | 44.2 |
## Contributors
### Supervisors
Wei Chu • Yinghui Xu • Yuan Qi
### INF memory team
Junhan Yang ([email protected]) • Jiahe Wan • Yichen Yao ([email protected])
## Citation
If you find our model useful, please consider citing:
```
@misc {infly-ai_2025,
author = { Junhan Yang, Jiahe Wan, Yichen Yao, Wei Chu, Yinghui Xu, Yuan Qi },
title = { inf-retriever-v1 (Revision 5f469d7) },
year = 2025,
url = { https://huggingface.co/infly/inf-retriever-v1 },
doi = { 10.57967/hf/4262 },
publisher = { Hugging Face }
}
```
|
MoLA-LLM/MoLA-v0.5-9x4b
|
MoLA-LLM
| 2025-08-18T07:21:17Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mola_lm",
"text-generation",
"pytorch",
"mixture-of-experts",
"lora",
"adapter",
"causal-lm",
"conversational",
"custom_code",
"en",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-18T07:17:37Z |
---
license: apache-2.0
library_name: transformers
tags:
- pytorch
- mixture-of-experts
- lora
- adapter
- causal-lm
- text-generation
language:
- en
pipeline_tag: text-generation
---
# MoLA-LM: Mixture of LoRA Adapters LLM
MoLA-LM combines multiple LoRA adapters with an intelligent router to automatically select the best adapter for each input prompt. This approach enables specialized performance across different tasks while maintaining efficiency.
Evals are coming...
## Model Details
- **Model Type**: Mixture of LoRA Adapters Language Model
- **Base Model**: Qwen/Qwen3-4B-Thinking-2507
- **Total Adapters**: 9
- **Architecture**: Custom MoLAForCausalLM with automatic adapter routing
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model (trust_remote_code=True is required for custom architecture)
model = AutoModelForCausalLM.from_pretrained(
"MoLA-LLM/MoLA-v0.5-9x4b",
trust_remote_code=True,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("MoLA-LLM/MoLA-v0.5-9x4b", trust_remote_code=True)
# Use like any other language model - adapter selection is automatic
prompt = "Write a Python function to calculate fibonacci numbers"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=.6, do_sample=True)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(f"Selected LoRA: {model.get_current_lora()}")
print(response)
```
*You can also use load_in_4bit and load_in_8bit directly when loading!*
## Architecture
The MoLA-LM architecture consists of:
1. **Base Model**: Qwen/Qwen3-4B-Thinking-2507
2. **Router Network**: Frozen encoder as Sentence transformer + decoder as one layer MLP for adapter selection
3. **LoRA Adapters**: 9 task-specific fine-tuned adapters
4. **Dynamic Switching**: Automatic adapter application based on input
---
## *Paper coming soon™*
|
jinx2321/byt5-paperdict-wiki-1e4-araea-ko
|
jinx2321
| 2025-08-18T07:21:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:jinx2321/byt5-paperdict-1e4-araea-ko",
"base_model:finetune:jinx2321/byt5-paperdict-1e4-araea-ko",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T07:19:28Z |
---
library_name: transformers
license: apache-2.0
base_model: jinx2321/byt5-paperdict-1e4-araea-ko
tags:
- generated_from_trainer
model-index:
- name: byt5-paperdict-wiki-1e4-araea-ko
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. -->
# byt5-paperdict-wiki-1e4-araea-ko
This model is a fine-tuned version of [jinx2321/byt5-paperdict-1e4-araea-ko](https://huggingface.co/jinx2321/byt5-paperdict-1e4-araea-ko) 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
adetuire1/ds6b-attackplan-qlora
|
adetuire1
| 2025-08-18T07:13:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"region:us"
] |
text-generation
| 2025-08-18T07:13:31Z |
---
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct
- 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
|
WenFengg/swing27_14_31_1
|
WenFengg
| 2025-08-18T07:08:41Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-07-31T02:27:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755499348
|
Sayemahsjn
| 2025-08-18T07:00:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T07:00:21Z |
---
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).
|
Zois04/DeepSeek-R1-Medical-COT
|
Zois04
| 2025-08-18T06:59:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T06:51:39Z |
---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Zois04
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
demomern/custom_SmolLM2-1.7B-Instruct
|
demomern
| 2025-08-18T06:57:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T16:23:25Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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[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]
- **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]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lezekiel999/Alexi_rose_lora
|
lezekiel999
| 2025-08-18T06:53:01Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-05T08:27:26Z |
---
license: apache-2.0
---
|
DGIST-CVLAB-Video/CAVIS
|
DGIST-CVLAB-Video
| 2025-08-18T06:52:16Z | 0 | 0 | null |
[
"video-classification",
"en",
"arxiv:2407.03010",
"license:mit",
"region:us"
] |
video-classification
| 2025-07-18T22:29:26Z |
---
license: mit
language:
- en
pipeline_tag: video-classification
---
# CAVIS: Context-Aware Video Instance Segmentation (ICCV 2025)
Seunghun Lee, Jiwan Seo, Kiljoon Han, Minwoo Choi, Sunghoon Im (DGIST)
Link to [Github](https://github.com/Seung-Hun-Lee/CAVIS) \
Link to [Paper](https://arxiv.org/abs/2407.03010)
## Model Details
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:**
- **Paper:
- **Demo:
- **Github:
## How to Get Started with the Model
## Evaluation
### Results
|
HJWZH/composition-assistant
|
HJWZH
| 2025-08-18T06:50:32Z | 5 | 0 | null |
[
"safetensors",
"bert",
"zh",
"base_model:uer/chinese_roberta_L-12_H-768",
"base_model:finetune:uer/chinese_roberta_L-12_H-768",
"license:mit",
"region:us"
] | null | 2025-08-13T12:25:45Z |
---
license: mit
language:
- zh
base_model:
- uer/chinese_roberta_L-12_H-768
---
# 文思引擎 - 智能作文素材检索系统（微调模型）
更多请详情：[Github composition-assistant](https://github.com/HJWZH/composition-assistant)
### 1. 项目简要说明（创意创新说明）+简介
**创新说明：**
"文思引擎"是一款AI作文素材检索工具，它通过深度学习技术理解抽象概念和深层语义联系，解决了传统作文素材库"关键词匹配不精准"、"素材关联性差"、"灵感启发不足"三大痛点。系统能理解"生命"、"环保"等抽象概念的哲学内涵，智能推荐高度相关的名言、事例和古诗文，帮助学生突破写作瓶颈。
**项目简介：**
针对中学生写作中的素材匮乏问题，我们开发了基于Transformer架构的智能检索系统：
- 🧠 核心模型：微调的中文RoBERTa模型（uer/chinese_roberta_L-12_H-768）
- 📚 三大素材库：收录名言警句、热点事例、古诗文（仍需更新）
- ✨ 核心功能：
- 语义理解：识别"坚持→锲而不舍"等同义转换
- 主题关联：构建"航天精神→科技创新→民族复兴"知识网络
- 多维过滤：支持按类别/相似度/主题精准筛选
- 📈 效果：测试显示素材相关度提升57%，写作效率提高40%
## ✨ 项目亮点
- **深度语义理解**：突破关键词匹配局限，理解"挫折→逆境成长"的抽象关联
- **动态学习系统**：10轮迭代训练机制，持续提升素材推荐精准度
- **多维度过滤**：类别/主题/相似度三级检索体系
- **轻量化部署**：预计算嵌入向量技术，CPU环境0.5秒响应
## 📚 素材库示例
```json
{
"content": "真正的太空探索不是为霸权，而是为人类共同梦想",
"source": "中国航天白皮书",
"keywords": ["航天精神", "人类命运共同体", "探索精神"]
"theme": "科技创新",
}
```
|
owlgebra-ai/RexBERT-mini
|
owlgebra-ai
| 2025-08-18T06:46:59Z | 0 | 0 | null |
[
"pytorch",
"modernbert",
"e-commerce",
"retail",
"fill-mask",
"en",
"license:apache-2.0",
"region:us"
] |
fill-mask
| 2025-08-14T16:43:22Z |
---
license: apache-2.0
language:
- en
tags:
- e-commerce
- retail
pipeline_tag: fill-mask
---
# [RexBERT-mini](https://huggingface.co/owlgebra-ai/RexBERT-mini)
<!-- Provide a quick summary of what the model is/does. -->
The model is part of RexBERT series of models.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** [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]
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[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **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]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## 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
[Rahul Bajaj](https://huggingface.co/thebajajra)
|
Safreliy/Qwen3-0.6B-GRPO-lora-text2sql-v5
|
Safreliy
| 2025-08-18T06:44:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T06:39:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[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]
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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]
## Glossary [optional]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
JawadBughlani63/gpt2-lora-finetuned
|
JawadBughlani63
| 2025-08-18T06:34:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T06:34:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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. -->
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#### 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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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[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. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
LarryAIDraw/GrokAni2
|
LarryAIDraw
| 2025-08-18T06:30:08Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-08-18T06:28:44Z |
---
license: creativeml-openrail-m
---
|
nattkorat/trigger_id
|
nattkorat
| 2025-08-18T06:29:43Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-07-23T02:57:09Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: trigger_id
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. -->
# trigger_id
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1033
- Accuracy: 0.9691
- Precision: 0.6442
- Recall: 0.6711
- F1: 0.6574
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 51 | 0.1374 | 0.9576 | 0.7652 | 0.4995 | 0.6044 |
| No log | 2.0 | 102 | 0.1140 | 0.9677 | 0.6613 | 0.5868 | 0.6218 |
| No log | 3.0 | 153 | 0.1058 | 0.9690 | 0.6667 | 0.6355 | 0.6507 |
| No log | 4.0 | 204 | 0.1023 | 0.9692 | 0.6449 | 0.6528 | 0.6488 |
| No log | 5.0 | 255 | 0.1033 | 0.9691 | 0.6442 | 0.6711 | 0.6574 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.1
|
xuchengsheng45/code-search-net-tokenizer
|
xuchengsheng45
| 2025-08-18T06:24:21Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T06:24:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## 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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## Model Card Contact
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|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755495940
|
ihsanridzi
| 2025-08-18T06:13:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T06:13:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755495561
|
hakimjustbao
| 2025-08-18T06:08:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T06:08:17Z |
---
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).
|
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