modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 06:31:37
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-12 06:31:07
| card
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|---|---|---|---|---|---|---|---|---|---|
hobson123/blockassist-bc-mammalian_dense_gibbon_1755658125
|
hobson123
| 2025-08-20T02:55:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian dense gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:54:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian dense gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-alert_snorting_fox_1755658379
|
AnerYubo
| 2025-08-20T02:53:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert snorting fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:52:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert snorting fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Hopelesslyhype/mistral-ailan-merged
|
Hopelesslyhype
| 2025-08-20T02:52:38Z | 0 | 0 | null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:57:44Z |
---
license: apache-2.0
---
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755656847
|
mang3dd
| 2025-08-20T02:52:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:52:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755656576
|
koloni
| 2025-08-20T02:49:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:49:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755656460
|
kojeklollipop
| 2025-08-20T02:47:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:47:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-alert_snorting_fox_1755657960
|
AnerYubo
| 2025-08-20T02:46:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert snorting fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:46:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert snorting fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
0k9d0h1/searcher-easy-dataset-450step
|
0k9d0h1
| 2025-08-20T02:45:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T02:44:56Z |
---
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]
|
dherrera-ppm/sam-mary
|
dherrera-ppm
| 2025-08-20T02:44:28Z | 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
| 2025-08-20T02:13:36Z |
---
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: Sam
---
# Sam Mary
<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 `Sam` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sam",
"lora_weights": "https://huggingface.co/dherrera-ppm/sam-mary/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('dherrera-ppm/sam-mary', weight_name='lora.safetensors')
image = pipeline('Sam').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: 2500
- Learning rate: 0.0004
- LoRA rank: 30
## Contribute your own examples
You can use the [community tab](https://huggingface.co/dherrera-ppm/sam-mary/discussions) to add images that show off what you’ve made with this LoRA.
|
cryptalk3/blockassist-bc-camouflaged_gliding_kangaroo_1755657538
|
cryptalk3
| 2025-08-20T02:40:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged gliding kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:40:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged gliding kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-alert_snorting_fox_1755657614
|
AnerYubo
| 2025-08-20T02:40:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert snorting fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:40:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert snorting fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-alert_snorting_fox_1755657421
|
AnerYubo
| 2025-08-20T02:37:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert snorting fox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:37:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert snorting fox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755655422
|
katanyasekolah
| 2025-08-20T02:32:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:32:05Z |
---
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).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755655416
|
lisaozill03
| 2025-08-20T02:28:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:28:22Z |
---
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).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755655345
|
sampingkaca72
| 2025-08-20T02:27:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:27:38Z |
---
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).
|
thaymanhinhsamsung24h/thay-man-hinh-samsung-co-anh-huong-gi
|
thaymanhinhsamsung24h
| 2025-08-20T02:27:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T02:26:58Z |
<h1>Thay màn hình Samsung – Giải pháp hiệu quả cho điện thoại hư hỏng</h1>
<p>Bạn đang tìm <a href="https://chamsocdidong.com/thay-man-hinh-samsung-sc4474.html" target="_blank">cửa hàng thay màn hình Samsung giá rẻ</a> nhưng vẫn đảm bảo chất lượng và linh kiện chính hãng? Việc lựa chọn địa chỉ uy tín sẽ giúp bạn khắc phục tình trạng hỏng màn hình, tiết kiệm chi phí và kéo dài tuổi thọ cho thiết bị.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-samsung/thay-man-hinh-samsung.jpg" alt="" /></p>
<h2>Khi nào cần thay màn hình Samsung?</h2>
<p>Một trong những thắc mắc phổ biến của khách hàng là <a href="https://online.fliphtml5.com/eudya/mbje/" target="_blank">thay màn hình điện thoại Samsung bao nhiêu tiền</a> và khi nào cần thay. Thực tế, giá cả sẽ phụ thuộc vào dòng máy và loại màn hình, nhưng trước hết, bạn cần xác định rõ các dấu hiệu cần thay thế:</p>
<ul>
<li>
<p><strong>Màn hình bị vỡ, nứt kính</strong>: Do va chạm hoặc rơi rớt, ảnh hưởng đến thẩm mỹ và trải nghiệm sử dụng.</p>
</li>
<li>
<p><strong>Cảm ứng không nhạy hoặc bị liệt</strong>: Màn hình phản hồi chậm, thao tác khó khăn, thậm chí tự động nhảy cảm ứng.</p>
</li>
<li>
<p><strong>Màn hình hiển thị bất thường</strong>: Xuất hiện sọc ngang, sọc dọc, điểm chết, ám màu hoặc chảy mực.</p>
</li>
<li>
<p><strong>Màn hình tối đen</strong>: Điện thoại vẫn có tín hiệu hoạt động nhưng không hiển thị nội dung.</p>
</li>
</ul>
<p>Khi gặp những dấu hiệu này, bạn nên thay màn hình ngay để tránh ảnh hưởng đến các linh kiện khác trong máy.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-samsung/khi-nao-can-thay-man-hinh-samsung.jpg" alt="" /></p>
<h2>Địa chỉ thay màn hình Samsung chính hãng giá rẻ</h2>
<p>Tìm được <strong>địa chỉ thay màn hình Samsung chính hãng giá rẻ</strong> không hề đơn giản khi có quá nhiều cửa hàng trên thị trường. Một trung tâm uy tín cần đáp ứng các tiêu chí sau:</p>
<ul>
<li>
<p><strong>Sử dụng linh kiện chính hãng</strong>: Đảm bảo độ tương thích tuyệt đối, mang lại trải nghiệm như màn hình gốc.</p>
</li>
<li>
<p><strong>Kỹ thuật viên chuyên nghiệp</strong>: Tay nghề cao, thao tác chuẩn xác, không gây ảnh hưởng đến các bộ phận khác.</p>
</li>
<li>
<p><strong>Giá cả hợp lý, minh bạch</strong>: Báo giá rõ ràng, không phát sinh chi phí bất ngờ.</p>
</li>
<li>
<p><strong>Thời gian thay nhanh chóng</strong>: Hỗ trợ thay màn hình lấy liền, không làm gián đoạn công việc của khách hàng.</p>
</li>
<li>
<p><strong>Chính sách bảo hành rõ ràng</strong>: Giúp khách hàng an tâm khi sử dụng dịch vụ.</p>
</li>
</ul>
<p>Chỉ nên lựa chọn những cơ sở đáp ứng đầy đủ tiêu chí này để vừa tiết kiệm chi phí, vừa đảm bảo chất lượng cho thiết bị.</p>
<h2>Thay màn hình Samsung có ảnh hưởng gì đến máy không?</h2>
<p>Nhiều người lo lắng việc thay màn hình có thể ảnh hưởng đến hiệu năng hoặc các chức năng khác của điện thoại. Trên thực tế, nếu bạn thay tại cửa hàng uy tín, sử dụng linh kiện chính hãng, thiết bị sẽ hoạt động hoàn toàn ổn định.</p>
<ul>
<li>
<p><strong>Chất lượng hiển thị không đổi</strong>: Màn hình chính hãng mang lại màu sắc chuẩn, độ sáng và độ nét như ban đầu.</p>
</li>
<li>
<p><strong>Cảm ứng mượt mà</strong>: Không lo tình trạng chậm phản hồi hay lỗi cảm ứng.</p>
</li>
<li>
<p><strong>Không ảnh hưởng phần cứng khác</strong>: Quy trình thay chuẩn kỹ thuật giúp bảo vệ bo mạch và các linh kiện đi kèm.</p>
</li>
<li>
<p><strong>Tuổi thọ máy duy trì ổn định</strong>: Thiết bị bền bỉ, hạn chế hỏng vặt sau khi thay màn hình.</p>
</li>
</ul>
<p>Ngược lại, nếu sử dụng màn hình kém chất lượng hoặc thay ở nơi không uy tín, điện thoại có thể gặp các vấn đề như hao pin nhanh, lỗi cảm ứng, hỏng main.</p>
<h2>Bệnh Viện Điện Thoại, Laptop 24h – Địa chỉ thay màn hình Samsung uy tín</h2>
<p><strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> là một trong những thương hiệu được khách hàng tin tưởng khi cần thay màn hình Samsung. Trung tâm cam kết mang đến dịch vụ chuyên nghiệp với linh kiện <strong>chính hãng 100%</strong>.</p>
<p>Các loại màn hình Samsung tại trung tâm bao gồm:</p>
<ul>
<li>
<p><strong>Màn hình zin bóc máy</strong>: Giữ nguyên chất lượng hiển thị và cảm ứng như màn hình gốc.</p>
</li>
<li>
<p><strong>Màn hình OLED chính hãng</strong>: Cho độ sáng cao, màu sắc sống động, tiết kiệm pin hiệu quả.</p>
</li>
<li>
<p><strong>Màn hình chống trầy xước</strong>: Bền bỉ, chịu lực tốt, hạn chế hư hỏng khi va chạm nhẹ.</p>
</li>
</ul>
<p>Cùng với đó, trung tâm sở hữu đội ngũ kỹ thuật viên chuyên nghiệp, máy móc hiện đại và chính sách bảo hành rõ ràng, minh bạch.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-samsung/cam-ket-thay-man-hinh-samsung.jpg" alt="" /></p>
<h2>Vì sao nên chọn Bệnh Viện Điện Thoại, Laptop 24h?</h2>
<ul>
<li>
<p><strong>Cửa hàng thay màn hình Samsung giá rẻ</strong> nhưng vẫn đảm bảo chất lượng chính hãng.</p>
</li>
<li>
<p><strong>Quy trình rõ ràng, minh bạch</strong>, báo giá trước khi sửa, không phát sinh chi phí.</p>
</li>
<li>
<p><strong>Thay nhanh – lấy liền</strong>, tiết kiệm thời gian cho khách hàng.</p>
</li>
<li>
<p><strong>Bảo hành uy tín</strong>, hỗ trợ tận tình trong quá trình sử dụng.</p>
</li>
<li>
<p><strong>Đội ngũ kỹ thuật viên giàu kinh nghiệm</strong>, luôn đặt lợi ích của khách hàng lên hàng đầu.</p>
</li>
</ul>
<p>Nếu bạn đang cần thay màn hình Samsung, hãy đến ngay <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để trải nghiệm dịch vụ chất lượng, an toàn và tiết kiệm.</p>
|
baidu/ERNIE-4.5-VL-424B-A47B-Paddle
|
baidu
| 2025-08-20T02:26:56Z | 27 | 16 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5_moe_vl",
"ERNIE4.5",
"image-text-to-text",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
image-text-to-text
| 2025-06-28T15:56:13Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: image-text-to-text
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-VL-424B-A47B
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
During the fine-tuning stage of a vision-language model, the deep integration between vision and language plays a decisive role in the model’s performance across complex tasks such as understanding, reasoning, and generation. To enhance the generalization and adaptability of the model on multimodal tasks, we focused on three core capabilities—image understanding, task-specific fine-tuning, and multimodal chain-of-thought reasoning—and carried out systematic data construction and training strategy optimization. Additionally, we use RLVR(Reinforcement Learning with Verifiable Rewards) to further improve alignment and performance. After the SFT and RL stages, we obtained ERNIE-4.5-VL-424B-A47B.
## Model Overview
ERNIE-4.5-VL-424B-A47B is a multimodal MoE Chat model based on ERNIE-4.5-VL-424B-A47B-Base, with 424B total parameters and 47B activated parameters for each token. The following are the model configuration details:
| Key | Value |
| --------------------------------- | ------------- |
| Modality | Text & Vision |
| Training Stage | Posttraining |
| Params(Total / Activated) | 424B / 47B |
| Layers | 54 |
| Heads(Q/KV) | 64 / 8 |
| Text Experts(Total / Activated) | 64 / 8 |
| Vision Experts(Total / Activated) | 64 / 8 |
| Context Length | 131072 |
## Quickstart
### FastDeploy Inference
Quickly deploy services using FastDeploy as shown below. For more detailed usage, refer to the [FastDeploy GitHub Repository](https://github.com/PaddlePaddle/FastDeploy).
**Note**: 80GB x 8 GPU resources are required. The `--quantization` parameter supports specifying `wint4` or `wint8` for deployment with 4-bit or 8-bit quantization, respectively.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-VL-424B-A47B-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--tensor-parallel-size 8 \
--quantization wint4 \
--max-model-len 32768 \
--enable-mm \
--reasoning-parser ernie-45-vl \
--max-num-seqs 32
```
The ERNIE-4.5-VL model supports enabling or disabling thinking mode through request parameters.
#### Enable Thinking Mode
```bash
curl -X POST "http://0.0.0.0:8180/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
{"type": "text", "text": "Descript this image"}
]}
],
"metadata": {"enable_thinking": true}
}'
```
#### Disable Thinking Mode
```bash
curl -X POST "http://0.0.0.0:8180/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
{"type": "text", "text": "Descript this image"}
]}
],
"metadata": {"enable_thinking": false}
}'
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
lautan/blockassist-bc-gentle_patterned_goat_1755655142
|
lautan
| 2025-08-20T02:26:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:26:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755655556
|
Sayemahsjn
| 2025-08-20T02:25:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:25:37Z |
---
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).
|
baidu/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle
|
baidu
| 2025-08-20T02:25:26Z | 18 | 10 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5_moe",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-28T09:27:03Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-300B-A47B
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
## Model Overview
ERNIE-4.5-300B-A47B is a text MoE Post-trained model, with 300B total parameters and 47B activated parameters for each token. The following are the model configuration details:
|Key|Value|
|-|-|
|Modality|Text|
|Training Stage|Pretraining|
|Params(Total / Activated)|300B / 47B|
|Layers|54|
|Heads(Q/KV)|64 / 8|
|Text Experts(Total / Activated)|64 / 8|
|Vision Experts(Total / Activated)|64 / 8|
|Context Length|131072|
## Quickstart
### Using FastDeploy
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
**Note**: To deploy on a configuration with 4 GPUs each having at least 80G of memory, specify ```--quantization wint4```. If you specify ```--quantization wint8```, then resources for 8 GPUs are required.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-Paddle \
--port 8180 \
--metrics-port 8181 \
--quantization wint4 \
--tensor-parallel-size 8 \
--engine-worker-queue-port 8182 \
--max-model-len 32768 \
--max-num-seqs 32
```
To deploy the W4A8C8 quantized version using FastDeploy, you can run the following command.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-300B-A47B-W4A8C8-TP4-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 32
```
To deploy the WINT2 quantized version using FastDeploy on a single 141G GPU, you can run the following command.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model "baidu/ERNIE-4.5-300B-A47B-2Bits-Paddle" \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128
```
The following contains a code snippet illustrating how to use ERNIE-4.5-300B-A47B-FP8 generate content based on given inputs.
```python
from fastdeploy import LLM, SamplingParams
prompts = [
"Hello, my name is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=128)
model = "baidu/ERNIE-4.5-300B-A47B-FP8-Paddle"
llm = LLM(model=model, tensor_parallel_size=8, max_model_len=8192, num_gpu_blocks_override=1024, engine_worker_queue_port=9981)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
print("generated_text", generated_text)
```
## Best Practices
### **Sampling Parameters**
To achieve optimal performance, we suggest using `Temperature=0.8`, `TopP=0.8`.
### Prompts for Web Search
For Web Search, {references}, {date}, and {question} are arguments.
For Chinese question, we use the prompt:
```python
ernie_search_zh_prompt = \
'''下面你会收到当前时间、多个不同来源的参考文章和一段对话。你的任务是阅读多个参考文章，并根据参考文章中的信息回答对话中的问题。
以下是当前时间和参考文章：
---------
#当前时间
{date}
#参考文章
{references}
---------
请注意：
1. 回答必须结合问题需求和当前时间，对参考文章的可用性进行判断，避免在回答中使用错误或过时的信息。
2. 当参考文章中的信息无法准确地回答问题时，你需要在回答中提供获取相应信息的建议，或承认无法提供相应信息。
3. 你需要优先根据百科、官网、权威机构、专业网站等高权威性来源的信息来回答问题。
4. 回复需要综合参考文章中的相关数字、案例、法律条文、公式等信息，使你的答案更专业。
5. 当问题属于创作类任务时，需注意以下维度：
- 态度鲜明：观点、立场清晰明确，避免模棱两可，语言果断直接
- 文采飞扬：用词精准生动，善用修辞手法，增强感染力
- 有理有据：逻辑严密递进，结合权威数据/事实支撑论点
---------
下面请结合以上信息，回答问题，补全对话
{question}'''
```
For English question, we use the prompt:
```python
ernie_search_en_prompt = \
'''
Below you will be given the current time, multiple references from different sources, and a conversation. Your task is to read the references and use the information in them to answer the question in the conversation.
Here are the current time and the references:
---------
#Current Time
{date}
#References
{references}
---------
Please note:
1. Based on the question’s requirements and the current time, assess the usefulness of the references to avoid using inaccurate or outdated information in the answer.
2. If the references do not provide enough information to accurately answer the question, you should suggest how to obtain the relevant information or acknowledge that you are unable to provide it.
3. Prioritize using information from highly authoritative sources such as encyclopedias, official websites, authoritative institutions, and professional websites when answering questions.
4. Incorporate relevant numbers, cases, legal provisions, formulas, and other details from the references to make your answer more professional.
5. For creative tasks, keep these dimensions in mind:
- Clear attitude: Clear views and positions, avoid ambiguity, and use decisive and direct language
- Brilliant writing: Precise and vivid words, good use of rhetoric, and enhance the appeal
- Well-reasoned: Rigorous logic and progressive, combined with authoritative data/facts to support the argument
---------
Now, using the information above, answer the question and complete the conversation:
{question}'''
```
Parameter notes:
* {question} is the user’s question
* {date} is the current time, and the recommended format is “YYYY-MM-DD HH:MM:SS, Day of the Week, Beijing/China.”
* {references} is the references, and the recommended format is:
```text
##参考文章1
标题：周杰伦
文章发布时间：2025-04-20
内容：周杰伦(Jay Chou),1979年1月18日出生于台湾省新北市,祖籍福建省永春县,华语流行乐男歌手、音乐人、演员、导演、编剧,毕业于淡江中学。2000年,发行个人首张音乐专辑《Jay》。...
来源网站网址：baike.baidu.com
来源网站的网站名：百度百科
##参考文章2
...
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
foreveraurorak/psgan
|
foreveraurorak
| 2025-08-20T02:24:55Z | 0 | 0 | null |
[
"arxiv:1909.06956",
"region:us"
] | null | 2025-08-19T09:58:39Z |
# PSGAN
Code for our CVPR 2020 **oral** paper "[PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer](https://arxiv.org/abs/1909.06956)".
Contributed by [Wentao Jiang](https://wtjiang98.github.io), [Si Liu](http://colalab.org/people), Chen Gao, Jie Cao, Ran He, [Jiashi Feng](https://sites.google.com/site/jshfeng/), [Shuicheng Yan](https://www.ece.nus.edu.sg/stfpage/eleyans/).
This code was further modified by [Zhaoyi Wan](https://www.wanzy.me).
In addition to the original algorithm, we added high-resolution face support using Laplace tranformation.
## Checklist
- [x] more results
- [ ] video demos
- [ ] partial makeup transfer example
- [ ] interpolated makeup transfer example
- [x] inference on GPU
- [x] training code
## Requirements
The code was tested on Ubuntu 16.04, with Python 3.6 and PyTorch 1.5.
For face parsing and landmark detection, we use dlib for fast implementation.
If you are using gpu for inference, *do* make sure you have gpu support for dlib.
## Test
Run `python3 demo.py` or `python3 demo.py --device cuda` for gpu inference.
## Train
1. Download training data from [link](https://drive.google.com/drive/folders/1ubqJ49ev16NbgJjjTt-Q75mNzvZ7sEEn?usp=sharing), and move it to sub directory named with "data". (For BaiduYun users, you can download the data [here](https://pan.baidu.com/s/1ZF-DN9PvbBteOSfQodWnyw). Password: rtdd)
Your data directory should be looked like:
```
data
├── images
│ ├── makeup
│ └── non-makeup
├── landmarks
│ ├── makeup
│ └── non-makeup
├── makeup.txt
├── non-makeup.txt
├── segs
│ ├── makeup
│ └── non-makeup
```
2. `python3 train.py`
Detailed configurations can be located and modified in configs/base.yaml, where
command-line modification is also supportted.
*Note: * Although multi-GPU training is currently supported, due to the limitation of pytorch data parallel and gpu cost, the numer of
adopted gpus and batch size are supposed to be the same.
## More Results
#### MT-Dataset (frontal face images with neutral expression)
#### MWild-Dataset (images with different poses and expressions)
#### Video Makeup Transfer (by simply applying PSGAN on each frame)
## Citation
Please consider citing this project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.
~~~
@InProceedings{Jiang_2020_CVPR,
author = {Jiang, Wentao and Liu, Si and Gao, Chen and Cao, Jie and He, Ran and Feng, Jiashi and Yan, Shuicheng},
title = {PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
@article{liu2021psgan++,
title={PSGAN++: robust detail-preserving makeup transfer and removal},
author={Liu, Si and Jiang, Wentao and Gao, Chen and He, Ran and Feng, Jiashi and Li, Bo and Yan, Shuicheng},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={44},
number={11},
pages={8538--8551},
year={2021},
publisher={IEEE}
}
~~~
## Acknowledge
Some of the codes are built upon [face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) and [BeautyGAN](https://github.com/wtjiang98/BeautyGAN_pytorch).
You are encouraged to submit issues and contribute pull requests.
|
baidu/ERNIE-4.5-21B-A3B-Base-Paddle
|
baidu
| 2025-08-20T02:24:39Z | 17 | 8 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5_moe",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-28T07:14:56Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-21B-A3B-Base
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
> [!NOTE]
> Note: The Base model only supports text completion. For evaluation, use the `completion` API (not `chat_completion`) in vLLM/FastDeploy.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
To ensure the stability of multimodal joint training, we adopt a staged training strategy. In the first and second stage, we train only the text-related parameters, enabling the model to develop strong fundamental language understanding as well as long-text processing capabilities. The final multimodal stage extends capabilities to images and videos by introducing additional parameters including a ViT for image feature extraction, an adapter for feature transformation, and visual experts for multimodal understanding. At this stage, text and visual modalities mutually enhance each other. After pretraining trillions tokens, we extracted the text-related parameters and finally obtained ERNIE-4.5-21B-A3B-Base.
## Model Overview
ERNIE-4.5-21B-A3B-Base is a text MoE Base model, with 21B total parameters and 3B activated parameters for each token. The following are the model configuration details:
| Key | Value |
| --------------------------------- | ----------- |
| Modality | Text |
| Training Stage | Pretraining |
| Params(Total / Activated) | 21B / 3B |
| Layers | 28 |
| Heads(Q/KV) | 20 / 4 |
| Text Experts(Total / Activated) | 64 / 6 |
| Vision Experts(Total / Activated) | 64 / 6 |
| Shared Experts | 2 |
| Context Length | 131072 |
## Quickstart
### Model Finetuning with ERNIEKit
[ERNIEKit](https://github.com/PaddlePaddle/ERNIE) is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series of open-source large models. It provides comprehensive support for scenarios such as instruction fine-tuning (SFT, LoRA) and alignment training (DPO), ensuring optimal performance.
Usage Examples:
```bash
# Download model
huggingface-cli download baidu/ERNIE-4.5-21B-A3B-Base-Paddle --local-dir baidu/ERNIE-4.5-21B-A3B-Base-Paddle
# SFT
erniekit train examples/configs/ERNIE-4.5-21B-A3B/sft/run_sft_lora_8k.yaml model_name_or_path=baidu/ERNIE-4.5-21B-A3B-Base-Paddle
# DPO
erniekit train examples/configs/ERNIE-4.5-21B-A3B/dpo/run_dpo_lora_8k.yaml model_name_or_path=baidu/ERNIE-4.5-21B-A3B-Base-Paddle
```
For more detailed examples, including SFT with LoRA, multi-GPU configurations, and advanced scripts, please refer to the examples folder within the [ERNIEKit](https://github.com/PaddlePaddle/ERNIE) repository.
### FastDeploy Inference
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
**Note**: For single-card deployment, at least 80G of GPU memory resources are required.
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-21B-A3B-Base-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 32768
--max-num-seqs 32
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755654982
|
quantumxnode
| 2025-08-20T02:24:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:24:18Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755656566
|
roeker
| 2025-08-20T02:24:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:23:34Z |
---
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).
|
DavidAU/OpenAi-GPT-oss-20b-LIGHT-uncensored-NEO-Imatrix-gguf
|
DavidAU
| 2025-08-20T02:22:59Z | 5,113 | 1 | null |
[
"gguf",
"gpt_oss",
"gpt-oss",
"openai",
"mxfp4",
"programming",
"code generation",
"code",
"coding",
"coder",
"chat",
"reasoning",
"thinking",
"r1",
"cot",
"deepseek",
"128k context",
"general usage",
"problem solving",
"brainstorming",
"solve riddles",
"uncensored",
"abliterated",
"Neo",
"MOE",
"Mixture of Experts",
"24 experts",
"NEO Imatrix",
"Imatrix",
"text-generation",
"en",
"base_model:huizimao/gpt-oss-20b-uncensored-mxfp4",
"base_model:quantized:huizimao/gpt-oss-20b-uncensored-mxfp4",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] |
text-generation
| 2025-08-10T01:09:39Z |
---
license: apache-2.0
base_model:
- huizimao/gpt-oss-20b-uncensored-mxfp4
language:
- en
pipeline_tag: text-generation
tags:
- gpt_oss
- gpt-oss
- openai
- mxfp4
- programming
- code generation
- code
- coding
- coder
- chat
- code
- chat
- reasoning
- thinking
- r1
- cot
- deepseek
- 128k context
- general usage
- problem solving
- brainstorming
- solve riddles
- general usage
- openai
- uncensored
- abliterated
- Neo
- MOE
- Mixture of Experts
- 24 experts
- NEO Imatrix
- Imatrix
---
<small><font color="red">Specialized "light" uncensored quants for new OpenAI 20B MOE - Mixture of Experts Model at 80+ T/S.
See settings and special instructions for using this model below.</font></small>
<h2>OpenAi-GPT-oss-20b-LIGHT-uncensored-NEO-Imatrix-gguf</h2>
<img src="power-the-matrix.gif" style="float:right; width:300px; height:300px; padding:10px;">
These are NEO Imatrix GGUFs, NEO dataset by DavidAU.
NEO dataset improves overall performance, and is for all use cases.
This model uses "huizimao/gpt-oss-20b-uncensored-mxfp4" (Light, 22% refusal rate VS 77% of Org OpenAI 20B using same content/prompt) as a base which DE-CENSORS the model and removes refusals.
This model runs better than the full abliterated/uncensored and "moderate" uncensored version and accepts MOST content generation requests.
The goal is to temper the "nanny" during normal generation / general use cases.
It is the best balance between light refusals "repairs" and best model performance.
NOTE: Tool use re-enabled in this version ; which differs from source from "huizimao".
Example output below (creative; IQ4_NL), using settings below.
Looking for 100% uncensored/abliterated?
https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf
Moderate uncensored ?
https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-MODERATE-uncensored-NEO-Imatrix-gguf
If you do not need an "uncensored" / "abliterated" model (at this repo) please go here:
https://huggingface.co/DavidAU/Openai_gpt-oss-20b-NEO-GGUF
or for the "big boy":
https://huggingface.co/DavidAU/Openai_gpt-oss-120b-NEO-Imatrix-GGUF
<B>QUANTS:</B>
Due to quanting issues with this model (which result in oddball quant sizes / mixtures), only TESTED quants will be uploaded (at the moment).
Currently that means IQ4_NL, Q5_1, MXFP4_MOE4 (a special OpenAI Quant) and Q8_0 are available.
NEO dataset performance improvements will show the most in the IQ4_NL, followed by Q5_1.
I find Q5_1 quants work better (and more stable) for some use cases than IQ4_NL ; however IQ4_NLs can be wilder, and off the cuff more.
IQ4_NL quant(s):
- OpenAI-20B-MAO-uncensored-NEO-IQ4_NL.gguf (Neo Imatrix)
- OpenAI-20B-MAO-uncensored-NEOCODE-IQ4_NL.gguf (NeoCODE Imatrix)
Q5_1 quant(s):
- OpenAI-20B-MAO-uncensored-NEO-Q5_1.gguf (Neo Imatrix)
- OpenAI-20B-MAO-uncensored-NEOCODE-Q5_1.gguf (NeoCODE Imatrix)
MXFP4_MOE4 quant(s):
- OpenAI-20B-UncensoredPlus-MAO-MXFP4_MOE4.gguf (output tensor at BF16, non imatrix -> has fixed tools functions)
Q8_0 quant(s):
- pending.
NOTE: The output tensor makes up for 10-20% of the output.
IQ4_NL, Q5_1 and Q8_0 quants are compatible (less/minimal damage when quanting) with OpenAI's tensor structure.
MXFP4_MOE4 is an exact match to OpenAi's tensor structure, but has limited "imatrix" applied to it.
<B>IMPORTANT: Using an "abliterated" model VS "uncensored" model</B>
Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.
In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.
Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want)
to get it generate the content correctly as the "expected" content level too.
Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.
Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic,
cursing or explicit levels.
Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.
<B>ABLITERATED / UNCENSORED Notes / Settings:</B>
- Suggest experts set to 4 or 5 or 6.
- 2-4 regens suggested.
- Some regens will be strange, while others will be "bang on".
- LOWER temps .4 to .8 ; especially if you get repeats/issues.
- However, sometimes temp 1, 1.1, 1.2 are the best depending on your use case(s).
- Temps of 2 or higher can be ah... very interesting.
- LONGER prompts (with more details, directives) tend to work better as long as they are clear enough.
- REP PEN setting is CRITICAL.
Suggested Settings (tested in Lmstudio, Beta Branch 0.3.21 ; 4 ):
- Context: 8k min.
- Temp 1 to 1.2+ for creative. Temp .6 (or so) for coding/general.
- Rep pen 1.1, topk 40, topp .95, min p 0.05
- Experts 4-8 depending on use case. (higher than 8 MAY lower quality AND/OR cause repeat issues)
Model Supports:
- 128k context
- up to 24 experts
- Tools use, browsing, etc
For my help docs, SETTING NUMBER OF EXPERTS, and other see below.
See more about this model here:
https://huggingface.co/openai/gpt-oss-20b
[ Please refer to their model card, especially to control "thinking" levels. ]
AND the "light" uncensored version:
https://huggingface.co/huizimao/gpt-oss-20b-uncensored-mxfp4
---
<H2>Help, Adjustments, Samplers, Parameters and More</H2>
---
<B>CHANGE THE NUMBER OF ACTIVE EXPERTS:</B>
See this document:
https://huggingface.co/DavidAU/How-To-Set-and-Manage-MOE-Mix-of-Experts-Model-Activation-of-Experts
<B>Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:</B>
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be
OTHER OPTIONS:
- Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
- If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
<B>Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers</B>
This a "Class 1" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]
---
<h2>EXAMPLE - IQ4_NL ; temp .8, using above settings (creative)</h2>
NO System prompt. (default thinking level)
---
PROMPT:
<B>
</B>
OUTPUT:
[[[thinking]]]
|
baidu/ERNIE-4.5-0.3B-Base-Paddle
|
baidu
| 2025-08-20T02:22:46Z | 59,865 | 10 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-29T06:02:05Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-0.3B-Base
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
> [!NOTE]
> Note: The Base model only supports text completion. For evaluation, use the `completion` API (not `chat_completion`) in vLLM/FastDeploy.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
## Model Overview
ERNIE-4.5-0.3B-Base is a text dense Base model. The following are the model configuration details:
| Key | Value |
| -------------- | ----------- |
| Modality | Text |
| Training Stage | Pretraining |
| Params | 0.36B |
| Layers | 18 |
| Heads(Q/KV) | 16 / 2 |
| Context Length | 131072 |
## Quickstart
### Model Finetuning with ERNIEKit
[ERNIEKit](https://github.com/PaddlePaddle/ERNIE) is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series of open-source large models. It provides comprehensive support for scenarios such as instruction fine-tuning (SFT, LoRA) and alignment training (DPO), ensuring optimal performance.
Usage Examples:
```bash
# Download Model
huggingface-cli download baidu/ERNIE-4.5-0.3B-Base-Paddle --local-dir baidu/ERNIE-4.5-0.3B-Base-Paddle
# SFT
erniekit train examples/configs/ERNIE-4.5-0.3B/sft/run_sft_8k.yaml model_name_or_path=baidu/ERNIE-4.5-0.3B-Base-Paddle
# DPO
erniekit train examples/configs/ERNIE-4.5-0.3B/dpo/run_dpo_8k.yaml model_name_or_path=baidu/ERNIE-4.5-0.3B-Base-Paddle
```
For more detailed examples, including SFT with LoRA, multi-GPU configurations, and advanced scripts, please refer to the examples folder within the [ERNIEKit](https://github.com/PaddlePaddle/ERNIE) repository.
### FastDeploy Inference
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-0.3B-Base-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 32768 \
--max-num-seqs 32
```
### Using `transformers` library
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "baidu/ERNIE-4.5-0.3B-Base-PT"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
prompt = "Large language model is"
model_inputs = tokenizer([prompt], add_special_tokens=False, return_tensors="pt").to(model.device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024
)
result = tokenizer.decode(generated_ids[0].tolist(), skip_special_tokens=True)
print("result:", result)
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
baidu/ERNIE-4.5-0.3B-Paddle
|
baidu
| 2025-08-20T02:22:21Z | 206 | 13 |
PaddlePaddle
|
[
"PaddlePaddle",
"safetensors",
"ernie4_5",
"ERNIE4.5",
"text-generation",
"conversational",
"en",
"zh",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-29T07:24:14Z |
---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
tags:
- ERNIE4.5
library_name: PaddlePaddle
---
<div align="center" style="line-height: 1;">
<a href="https://ernie.baidu.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖_Chat-ERNIE_Bot-blue" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/baidu" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Baidu-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/PaddlePaddle/ERNIE" target="_blank" style="margin: 2px;">
<img alt="Github" src="https://img.shields.io/badge/GitHub-ERNIE-000?logo=github&color=0000FF" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://ernie.baidu.com/blog/ernie4.5" target="_blank" style="margin: 2px;">
<img alt="Blog" src="https://img.shields.io/badge/🖖_Blog-ERNIE4.5-A020A0" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://discord.gg/JPmZXDsEEK" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://x.com/PaddlePaddle" target="_blank" style="margin: 2px;">
<img alt="X" src="https://img.shields.io/badge/X-PaddlePaddle-6080F0"?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="#license" style="margin: 2px;">
<img alt="License" src="https://img.shields.io/badge/License-Apache2.0-A5de54" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
# ERNIE-4.5-0.3B
> [!NOTE]
> Note: "**-Paddle**" models use [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) weights, while "**-PT**" models use Transformer-style PyTorch weights.
## ERNIE 4.5 Highlights
The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations:
1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
2. **Scaling-Efficient Infrastructure:** We propose a novel heterogeneous hybrid parallelism and hierarchical load balancing strategy for efficient training of ERNIE 4.5 models. By using intra-node expert parallelism, memory-efficient pipeline scheduling, FP8 mixed-precision training and finegrained recomputation methods, we achieve remarkable pre-training throughput. For inference, we propose *multi-expert parallel collaboration* method and *convolutional code quantization* algorithm to achieve 4-bit/2-bit lossless quantization. Furthermore, we introduce PD disaggregation with dynamic role switching for effective resource utilization to enhance inference performance for ERNIE 4.5 MoE models. Built on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle), ERNIE 4.5 delivers high-performance inference across a wide range of hardware platforms.
3. **Modality-Specific Post-Training:** To meet the diverse requirements of real-world applications, we fine-tuned variants of the pre-trained model for specific modalities. Our LLMs are optimized for general-purpose language understanding and generation. The VLMs focuses on visuallanguage understanding and supports both thinking and non-thinking modes. Each model employed a combination of *Supervised Fine-tuning (SFT)*, *Direct Preference Optimization (DPO)* or a modified reinforcement learning method named *Unified Preference Optimization (UPO)* for post-training.
## Model Overview
ERNIE-4.5-0.3B is a text dense Post-trained model. The following are the model configuration details:
| Key | Value |
| -------------- | ------------ |
| Modality | Text |
| Training Stage | Posttraining |
| Params | 0.36B |
| Layers | 18 |
| Heads(Q/KV) | 16 / 2 |
| Context Length | 131072 |
## Quickstart
### Model Finetuning with ERNIEKit
[ERNIEKit](https://github.com/PaddlePaddle/ERNIE) is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series of open-source large models. It provides comprehensive support for scenarios such as instruction fine-tuning (SFT, LoRA) and alignment training (DPO), ensuring optimal performance.
Usage Examples:
```bash
# Download Model
huggingface-cli download baidu/ERNIE-4.5-0.3B-Paddle --local-dir baidu/ERNIE-4.5-0.3B-Paddle
# SFT
erniekit train examples/configs/ERNIE-4.5-0.3B/sft/run_sft_8k.yaml
# DPO
erniekit train examples/configs/ERNIE-4.5-0.3B/dpo/run_dpo_8k.yaml
```
For more detailed examples, including SFT with LoRA, multi-GPU configurations, and advanced scripts, please refer to the examples folder within the [ERNIEKit](https://github.com/PaddlePaddle/ERNIE) repository.
### FastDeploy Inference
Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
```bash
python -m fastdeploy.entrypoints.openai.api_server \
--model baidu/ERNIE-4.5-0.3B-Paddle \
--port 8180 \
--metrics-port 8181 \
--engine-worker-queue-port 8182 \
--max-model-len 32768 \
--max-num-seqs 32
```
## License
The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
## Citation
If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
```bibtex
@misc{ernie2025technicalreport,
title={ERNIE 4.5 Technical Report},
author={Baidu ERNIE Team},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={}
}
```
|
ameimei/testmodel
|
ameimei
| 2025-08-20T02:21:27Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"pi0",
"robotics",
"dataset:ameimei/markerhold_new",
"arxiv:2410.24164",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-20T01:51:35Z |
---
datasets: ameimei/markerhold_new
library_name: lerobot
license: apache-2.0
model_name: pi0
pipeline_tag: robotics
tags:
- lerobot
- pi0
- robotics
---
# Model Card for pi0
<!-- Provide a quick summary of what the model is/does. -->
[Pi0](https://huggingface.co/papers/2410.24164) is a generalist vision-language-action transformer that converts multimodal observations and text instructions into robot actions for zero-shot task transfer.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
hobson123/blockassist-bc-mammalian_dense_gibbon_1755656108
|
hobson123
| 2025-08-20T02:21:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian dense gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:20:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian dense gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bila333/marydziala
|
Bila333
| 2025-08-20T02:20:58Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-20T01:38:41Z |
---
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
---
|
tensorblock/Menlo_Lucy-GGUF
|
tensorblock
| 2025-08-20T02:20:01Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"base_model:Menlo/Lucy",
"base_model:quantized:Menlo/Lucy",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T02:00:34Z |
---
license: apache-2.0
language:
- en
base_model: Menlo/Lucy
pipeline_tag: text-generation
library_name: transformers
tags:
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## Menlo/Lucy - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [Menlo/Lucy](https://huggingface.co/Menlo/Lucy).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Lucy-Q2_K.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q2_K.gguf) | Q2_K | 0.778 GB | smallest, significant quality loss - not recommended for most purposes |
| [Lucy-Q3_K_S.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q3_K_S.gguf) | Q3_K_S | 0.867 GB | very small, high quality loss |
| [Lucy-Q3_K_M.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q3_K_M.gguf) | Q3_K_M | 0.940 GB | very small, high quality loss |
| [Lucy-Q3_K_L.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q3_K_L.gguf) | Q3_K_L | 1.003 GB | small, substantial quality loss |
| [Lucy-Q4_0.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q4_0.gguf) | Q4_0 | 1.054 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Lucy-Q4_K_S.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q4_K_S.gguf) | Q4_K_S | 1.060 GB | small, greater quality loss |
| [Lucy-Q4_K_M.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q4_K_M.gguf) | Q4_K_M | 1.107 GB | medium, balanced quality - recommended |
| [Lucy-Q5_0.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q5_0.gguf) | Q5_0 | 1.231 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Lucy-Q5_K_S.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q5_K_S.gguf) | Q5_K_S | 1.231 GB | large, low quality loss - recommended |
| [Lucy-Q5_K_M.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q5_K_M.gguf) | Q5_K_M | 1.258 GB | large, very low quality loss - recommended |
| [Lucy-Q6_K.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q6_K.gguf) | Q6_K | 1.418 GB | very large, extremely low quality loss |
| [Lucy-Q8_0.gguf](https://huggingface.co/tensorblock/Menlo_Lucy-GGUF/blob/main/Lucy-Q8_0.gguf) | Q8_0 | 1.834 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/Menlo_Lucy-GGUF --include "Lucy-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/Menlo_Lucy-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
FlagRelease/GLM-4.5-FlagOS
|
FlagRelease
| 2025-08-20T02:18:47Z | 0 | 0 | null |
[
"safetensors",
"glm4_moe",
"region:us"
] | null | 2025-08-14T05:23:57Z |
# Introduction
**FlagOS** is a unified heterogeneous computing software stack for large models, co-developed with leading global chip manufacturers. With core technologies such as the **FlagScale** distributed training/inference framework, **FlagGems** universal operator library, **FlagCX** communication library, and **FlagTree** unified compiler, the **FlagRelease** platform leverages the FlagOS stack to automatically produce and release various combinations of <chip + open-source model>. This enables efficient and automated model migration across diverse chips, opening a new chapter for large model deployment and application.
Based on this, the **GLM-4.5-FlagOS** model is adapted for the Nvidia chip using the FlagOS software stack, enabling:
### Integrated Deployment
- Deep integration with the open-source [FlagScale framework](https://github.com/FlagOpen/FlagScale)
- Out-of-the-box inference scripts with pre-configured hardware and software parameters
- Released **FlagOS** container image supporting deployment within minutes
### Consistency Validation
- Rigorously evaluated through benchmark testing: Performance and results from the FlagOS software stack are compared against native stacks on multiple public.
# Technical Overview
## **FlagScale Distributed Training and Inference Framework**
FlagScale is an end-to-end framework for large models across heterogeneous computing resources, maximizing computational efficiency and ensuring model validity through core technologies. Its key advantages include:
- **Unified Deployment Interface:** Standardized command-line tools support one-click service deployment across multiple hardware platforms, significantly reducing adaptation costs in heterogeneous environments.
- **Intelligent Parallel Optimization:** Automatically generates optimal distributed parallel strategies based on chip computing characteristics, achieving dynamic load balancing of computation/communication resources.
- **Seamless Operator Switching:** Deep integration with the FlagGems operator library allows high-performance operators to be invoked via environment variables without modifying model code.
## **FlagGems Universal Large-Model Operator Library**
FlagGems is a Triton-based, cross-architecture operator library collaboratively developed with industry partners. Its core strengths include:
- **Full-stack Coverage**: Over 100 operators, with a broader range of operator types than competing libraries.
- **Ecosystem Compatibility**: Supports 7 accelerator backends. Ongoing optimizations have significantly improved performance.
- **High Efficiency**: Employs unique code generation and runtime optimization techniques for faster secondary development and better runtime performance compared to alternatives.
## **FlagEval Evaluation Framework**
FlagEval (Libra)** is a comprehensive evaluation system and open platform for large models launched in 2023. It aims to establish scientific, fair, and open benchmarks, methodologies, and tools to help researchers assess model and training algorithm performance. It features:
- **Multi-dimensional Evaluation**: Supports 800+ model evaluations across NLP, CV, Audio, and Multimodal fields, covering 20+ downstream tasks including language understanding and image-text generation.
- **Industry-Grade Use Cases**: Has completed horizontal evaluations of mainstream large models, providing authoritative benchmarks for chip-model performance validation.
# Evaluation Results
## Benchmark Result
| Metrics | GLM-4.5-H100-CUDA | GLM-4.5-FlagOS |
| ------------------------- | --------------------- | ------------------ |
|AIME-0shot@avg1|0.833|0.867|
|GPQA-0shot@avg1|0.694|0.744|
|MMLU-5shots@avg1|0.831|0.833|
|MUSR-0shot@avg1|0.739|0.728|
|LiveBench-0shot@avg1|0.738|0.754|
# User Guide
**Environment Setup**
| Item | Version |
| ------------- | ------------------------------------------------------------ |
| Docker Version | Docker version 28.1.0, build 4d8c241 |
| Operating System | Ubuntu 22.04.5 LTS |
| FlagScale | Version: 0.8.0 |
| FlagGems | Version: 3.0 |
## Operation Steps
### Download Open-source Model Weights
```bash
pip install modelscope
modelscope download --model ZhipuAI/GLM-4.5 --local_dir /share/GLM-4.5
```
### Download FlagOS Image
```bash
docker pull harbor.baai.ac.cn/flagrelease-public/flagrelease_nvidia_glm45
```
### Start the inference service
```bash
#Container Startup
docker run --rm --init --detach --net=host --uts=host --ipc=host --security-opt=seccomp=unconfined --privileged=true --ulimit stack=67108864 --ulimit memlock=-1 --ulimit nofile=1048576:1048576 --shm-size=32G -v /share:/share --gpus all --name flagos harbor.baai.ac.cn/flagrelease-public/flagrelease_nvidia_glm45 sleep infinity
```
### Serve
```bash
flagscale serve glm45
```
## Service Invocation
### API-based Invocation Script
```bash
import openai
openai.api_key = "EMPTY"
openai.base_url = "http://<server_ip>:9010/v1/"
model = "GLM-4.5-nvidia-flagos"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "What's the weather like today?"}
]
response = openai.chat.completions.create(
model=model,
messages=messages,
stream=False,
)
for item in response:
print(item)
```
### AnythingLLM Integration Guide
#### 1. Download & Install
- Visit the official site: https://anythingllm.com/
- Choose the appropriate version for your OS (Windows/macOS/Linux)
- Follow the installation wizard to complete the setup
#### 2. Configuration
- Launch AnythingLLM
- Open settings (bottom left, fourth tab)
- Configure core LLM parameters
- Click "Save Settings" to apply changes
#### 3. Model Interaction
- After model loading is complete:
- Click **"New Conversation"**
- Enter your question (e.g., “Explain the basics of quantum computing”)
- Click the send button to get a response
# Contributing
We warmly welcome global developers to join us:
1. Submit Issues to report problems
2. Create Pull Requests to contribute code
3. Improve technical documentation
4. Expand hardware adaptation support
# License
本模型的权重来源于ZhipuAI/GLM-4.5，以apache2.0协议https://www.apache.org/licenses/LICENSE-2.0.txt开源。
|
roeker/blockassist-bc-quick_wiry_owl_1755656157
|
roeker
| 2025-08-20T02:17:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:16:46Z |
---
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).
|
danevan097/MyGemmaNPC
|
danevan097
| 2025-08-20T02:12:54Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T01:28:10Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="danevan097/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MLLEARNERAT/dqn-SpaceInvadersNoFrameskip-v4
|
MLLEARNERAT
| 2025-08-20T02:11:02Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-20T02:10:31Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 576.00 +/- 226.53
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MLLEARNERAT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MLLEARNERAT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MLLEARNERAT
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755653981
|
indoempatnol
| 2025-08-20T02:06:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:06:41Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755655342
|
roeker
| 2025-08-20T02:03:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:03:10Z |
---
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).
|
hobson123/blockassist-bc-mammalian_dense_gibbon_1755655021
|
hobson123
| 2025-08-20T02:03:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian dense gibbon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:02:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian dense gibbon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gmwagmi7/blockassist-bc-snappy_horned_mammoth_1755655198
|
gmwagmi7
| 2025-08-20T02:01:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snappy horned mammoth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T02:01:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snappy horned mammoth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tm-hf-repo/comic_1000
|
tm-hf-repo
| 2025-08-20T02:01:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-20T02:00:49Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: comic_1000
license: other
---
# comic_1000
<Gallery />
## Model description
## Trigger words
You should use `comic_1000` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/tm-hf-repo/comic_1000/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-kontext-trainer](https://fal.ai/models/fal-ai/flux-kontext-trainer).
|
TheAIchemist13/unlearn-tofu_mixed_peft_lora-GA_evaldelay200
|
TheAIchemist13
| 2025-08-20T02:01:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T05:41:32Z |
---
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]
|
unitova/blockassist-bc-zealous_sneaky_raven_1755653548
|
unitova
| 2025-08-20T01:58:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:58:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lc700x/dpt-hybrid-midas-hf
|
lc700x
| 2025-08-20T01:58:25Z | 0 | 0 | null |
[
"safetensors",
"dpt",
"vision",
"depth-estimation",
"arxiv:2103.13413",
"license:apache-2.0",
"model-index",
"region:us"
] |
depth-estimation
| 2025-08-20T01:56:41Z |
---
license: apache-2.0
tags:
- vision
- depth-estimation
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
model-index:
- name: dpt-hybrid-midas
results:
- task:
type: monocular-depth-estimation
name: Monocular Depth Estimation
dataset:
type: MIX-6
name: MIX-6
metrics:
- type: Zero-shot transfer
value: 11.06
name: Zero-shot transfer
config: Zero-shot transfer
verified: false
---
## Model Details: DPT-Hybrid (also known as MiDaS 3.0)
Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation.
It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this repository](https://github.com/isl-org/DPT).
DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation.
This repository hosts the "hybrid" version of the model as stated in the paper. DPT-Hybrid diverges from DPT by using [ViT-hybrid](https://huggingface.co/google/vit-hybrid-base-bit-384) as a backbone and taking some activations from the backbone.
The model card has been written in combination by the Hugging Face team and Intel.
| Model Detail | Description |
| ----------- | ----------- |
| Model Authors - Company | Intel |
| Date | December 22, 2022 |
| Version | 1 |
| Type | Computer Vision - Monocular Depth Estimation |
| Paper or Other Resources | [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) and [GitHub Repo](https://github.com/isl-org/DPT) |
| License | Apache 2.0 |
| Questions or Comments | [Community Tab](https://huggingface.co/Intel/dpt-hybrid-midas/discussions) and [Intel Developers Discord](https://discord.gg/rv2Gp55UJQ)|
| Intended Use | Description |
| ----------- | ----------- |
| Primary intended uses | You can use the raw model for zero-shot monocular depth estimation. See the [model hub](https://huggingface.co/models?search=dpt) to look for fine-tuned versions on a task that interests you. |
| Primary intended users | Anyone doing monocular depth estimation |
| Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.|
### How to use
Here is how to use this model for zero-shot depth estimation on an image:
```python
from PIL import Image
import numpy as np
import requests
import torch
from transformers import DPTImageProcessor, DPTForDepthEstimation
image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas", low_cpu_mem_usage=True)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# prepare image for the model
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
)
# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
depth.show()
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/dpt).
| Factors | Description |
| ----------- | ----------- |
| Groups | Multiple datasets compiled together |
| Instrumentation | - |
| Environment | Inference completed on Intel Xeon Platinum 8280 CPU @ 2.70GHz with 8 physical cores and an NVIDIA RTX 2080 GPU. |
| Card Prompts | Model deployment on alternate hardware and software will change model performance |
| Metrics | Description |
| ----------- | ----------- |
| Model performance measures | Zero-shot Transfer |
| Decision thresholds | - |
| Approaches to uncertainty and variability | - |
| Training and Evaluation Data | Description |
| ----------- | ----------- |
| Datasets | The dataset is called MIX 6, and contains around 1.4M images. The model was initialized with ImageNet-pretrained weights.|
| Motivation | To build a robust monocular depth prediction network |
| Preprocessing | "We resize the image such that the longer side is 384 pixels and train on random square crops of size 384. ... We perform random horizontal flips for data augmentation." See [Ranftl et al. (2021)](https://arxiv.org/abs/2103.13413) for more details. |
## Quantitative Analyses
| Model | Training set | DIW WHDR | ETH3D AbsRel | Sintel AbsRel | KITTI δ>1.25 | NYU δ>1.25 | TUM δ>1.25 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| DPT - Large | MIX 6 | 10.82 (-13.2%) | 0.089 (-31.2%) | 0.270 (-17.5%) | 8.46 (-64.6%) | 8.32 (-12.9%) | 9.97 (-30.3%) |
| DPT - Hybrid | MIX 6 | 11.06 (-11.2%) | 0.093 (-27.6%) | 0.274 (-16.2%) | 11.56 (-51.6%) | 8.69 (-9.0%) | 10.89 (-23.2%) |
| MiDaS | MIX 6 | 12.95 (+3.9%) | 0.116 (-10.5%) | 0.329 (+0.5%) | 16.08 (-32.7%) | 8.71 (-8.8%) | 12.51 (-12.5%)
| MiDaS [30] | MIX 5 | 12.46 | 0.129 | 0.327 | 23.90 | 9.55 | 14.29 |
| Li [22] | MD [22] | 23.15 | 0.181 | 0.385 | 36.29 | 27.52 | 29.54 |
| Li [21] | MC [21] | 26.52 | 0.183 | 0.405 | 47.94 | 18.57 | 17.71 |
| Wang [40] | WS [40] | 19.09 | 0.205 | 0.390 | 31.92 | 29.57 | 20.18 |
| Xian [45] | RW [45] | 14.59 | 0.186 | 0.422 | 34.08 | 27.00 | 25.02 |
| Casser [5] | CS [8] | 32.80 | 0.235 | 0.422 | 21.15 | 39.58 | 37.18 |
Table 1. Comparison to the state of the art on monocular depth estimation. We evaluate zero-shot cross-dataset transfer according to the
protocol defined in [30]. Relative performance is computed with respect to the original MiDaS model [30]. Lower is better for all metrics. ([Ranftl et al., 2021](https://arxiv.org/abs/2103.13413))
| Ethical Considerations | Description |
| ----------- | ----------- |
| Data | The training data come from multiple image datasets compiled together. |
| Human life | The model is not intended to inform decisions central to human life or flourishing. It is an aggregated set of monocular depth image datasets. |
| Mitigations | No additional risk mitigation strategies were considered during model development. |
| Risks and harms | The extent of the risks involved by using the model remain unknown. |
| Use cases | - |
| Caveats and Recommendations |
| ----------- |
| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. There are no additional caveats or recommendations for this model. |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2103-13413,
author = {Ren{\'{e}} Ranftl and
Alexey Bochkovskiy and
Vladlen Koltun},
title = {Vision Transformers for Dense Prediction},
journal = {CoRR},
volume = {abs/2103.13413},
year = {2021},
url = {https://arxiv.org/abs/2103.13413},
eprinttype = {arXiv},
eprint = {2103.13413},
timestamp = {Wed, 07 Apr 2021 15:31:46 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2103-13413.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
tensorblock/shanchen_ds-limo-fr-100-GGUF
|
tensorblock
| 2025-08-20T01:58:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"sft",
"TensorBlock",
"GGUF",
"base_model:shanchen/ds-limo-fr-100",
"base_model:quantized:shanchen/ds-limo-fr-100",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-20T00:32:50Z |
---
base_model: shanchen/ds-limo-fr-100
library_name: transformers
model_name: ds-limo-fr-100
tags:
- generated_from_trainer
- trl
- sft
- TensorBlock
- GGUF
licence: license
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## shanchen/ds-limo-fr-100 - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [shanchen/ds-limo-fr-100](https://huggingface.co/shanchen/ds-limo-fr-100).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><think>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [ds-limo-fr-100-Q2_K.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q2_K.gguf) | Q2_K | 3.016 GB | smallest, significant quality loss - not recommended for most purposes |
| [ds-limo-fr-100-Q3_K_S.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q3_K_S.gguf) | Q3_K_S | 3.492 GB | very small, high quality loss |
| [ds-limo-fr-100-Q3_K_M.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q3_K_M.gguf) | Q3_K_M | 3.808 GB | very small, high quality loss |
| [ds-limo-fr-100-Q3_K_L.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q3_K_L.gguf) | Q3_K_L | 4.088 GB | small, substantial quality loss |
| [ds-limo-fr-100-Q4_0.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q4_0.gguf) | Q4_0 | 4.431 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [ds-limo-fr-100-Q4_K_S.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q4_K_S.gguf) | Q4_K_S | 4.458 GB | small, greater quality loss |
| [ds-limo-fr-100-Q4_K_M.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q4_K_M.gguf) | Q4_K_M | 4.683 GB | medium, balanced quality - recommended |
| [ds-limo-fr-100-Q5_0.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q5_0.gguf) | Q5_0 | 5.315 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [ds-limo-fr-100-Q5_K_S.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q5_K_S.gguf) | Q5_K_S | 5.315 GB | large, low quality loss - recommended |
| [ds-limo-fr-100-Q5_K_M.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q5_K_M.gguf) | Q5_K_M | 5.445 GB | large, very low quality loss - recommended |
| [ds-limo-fr-100-Q6_K.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q6_K.gguf) | Q6_K | 6.254 GB | very large, extremely low quality loss |
| [ds-limo-fr-100-Q8_0.gguf](https://huggingface.co/tensorblock/shanchen_ds-limo-fr-100-GGUF/blob/main/ds-limo-fr-100-Q8_0.gguf) | Q8_0 | 8.099 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/shanchen_ds-limo-fr-100-GGUF --include "ds-limo-fr-100-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/shanchen_ds-limo-fr-100-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755653607
|
sampingkaca72
| 2025-08-20T01:57:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:57:53Z |
---
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).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755653499
|
lisaozill03
| 2025-08-20T01:56:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:56:49Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755654927
|
roeker
| 2025-08-20T01:56:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:56:12Z |
---
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).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755653209
|
mang3dd
| 2025-08-20T01:53:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:53:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xihc-ucb/Qwen3-8B-train-Quasar-0809
|
xihc-ucb
| 2025-08-20T01:52:14Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"fp8_qwen3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-10T01:54:06Z |
---
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]
|
nvidia/canary-1b-flash
|
nvidia
| 2025-08-20T01:51:56Z | 10,479 | 242 |
nemo
|
[
"nemo",
"safetensors",
"fastconformer",
"automatic-speech-recognition",
"automatic-speech-translation",
"speech",
"audio",
"Transformer",
"FastConformer",
"Conformer",
"pytorch",
"NeMo",
"hf-asr-leaderboard",
"en",
"de",
"es",
"fr",
"dataset:librispeech_asr",
"dataset:fisher_corpus",
"dataset:Switchboard-1",
"dataset:WSJ-0",
"dataset:WSJ-1",
"dataset:National-Singapore-Corpus-Part-1",
"dataset:National-Singapore-Corpus-Part-6",
"dataset:vctk",
"dataset:voxpopuli",
"dataset:europarl",
"dataset:multilingual_librispeech",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:MLCommons/peoples_speech",
"arxiv:2104.02821",
"arxiv:2503.05931",
"arxiv:1706.03762",
"arxiv:2409.13523",
"license:cc-by-4.0",
"model-index",
"region:us"
] |
automatic-speech-recognition
| 2025-03-07T22:29:17Z |
---
license: cc-by-4.0
language:
- en
- de
- es
- fr
library_name: nemo
datasets:
- librispeech_asr
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National-Singapore-Corpus-Part-1
- National-Singapore-Corpus-Part-6
- vctk
- voxpopuli
- europarl
- multilingual_librispeech
- mozilla-foundation/common_voice_8_0
- MLCommons/peoples_speech
thumbnail: null
tags:
- automatic-speech-recognition
- automatic-speech-translation
- speech
- audio
- Transformer
- FastConformer
- Conformer
- pytorch
- NeMo
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: canary-1b-flash
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 2.87
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: SPGI Speech
type: kensho/spgispeech
config: test
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 1.95
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: en
split: test
args:
language: en
metrics:
- name: Test WER (En)
type: wer
value: 6.99
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: de
split: test
args:
language: de
metrics:
- name: Test WER (De)
type: wer
value: 4.09
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: es
split: test
args:
language: es
metrics:
- name: Test WER (ES)
type: wer
value: 3.62
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: fr
split: test
args:
language: fr
metrics:
- name: Test WER (Fr)
type: wer
value: 6.15
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: en_us
split: test
args:
language: en-de
metrics:
- name: Test BLEU (En->De)
type: bleu
value: 32.27
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: en_us
split: test
args:
language: en-de
metrics:
- name: Test BLEU (En->Es)
type: bleu
value: 22.6
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: en_us
split: test
args:
language: en-de
metrics:
- name: Test BLEU (En->Fr)
type: bleu
value: 41.22
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: de_de
split: test
args:
language: de-en
metrics:
- name: Test BLEU (De->En)
type: bleu
value: 35.5
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: es_419
split: test
args:
language: es-en
metrics:
- name: Test BLEU (Es->En)
type: bleu
value: 23.32
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: fr_fr
split: test
args:
language: fr-en
metrics:
- name: Test BLEU (Fr->En)
type: bleu
value: 33.42
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: COVOST
type: covost2
config: de_de
split: test
args:
language: de-en
metrics:
- name: Test BLEU (De->En)
type: bleu
value: 39.33
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: COVOST
type: covost2
config: es_419
split: test
args:
language: es-en
metrics:
- name: Test BLEU (Es->En)
type: bleu
value: 41.86
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: COVOST
type: covost2
config: fr_fr
split: test
args:
language: fr-en
metrics:
- name: Test BLEU (Fr->En)
type: bleu
value: 41.43
metrics:
- wer
- bleu
- comet
track_downloads: true
pipeline_tag: automatic-speech-recognition
---
# Canary 1B Flash
<style>
img {
display: inline;
}
</style>
> **🎉 NEW: Canary 1B V2 is now available!**
> 🌍 **25 European Languages** | ⏱️ **Much Improved Timestamp Prediction** | 🔄 **Enhanced ASR & AST** | 🔗 **[Try it here: nvidia/canary-1b-v2](https://huggingface.co/nvidia/canary-1b-v2)**
## Description:
NVIDIA NeMo Canary Flash [1] is a family of multilingual multi-tasking models based on Canary architecture [2] that achieve state-of-the-art performance on multiple speech benchmarks. With 883 million parameters and an inference speed of more than 1000 RTFx (on open-asr-leaderboard datasets), canary-1b-flash supports automatic speech-to-text recognition (ASR) in four languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). Additionally, canary-1b-flash offers an experimental feature for word-level and segment-level timestamps in English, German, French, and Spanish.
This model is released under the permissive CC-BY-4.0 license and is available for commercial use.
## Model Architecture:
Canary is an encoder-decoder model with FastConformer [3] Encoder and Transformer Decoder [4]. With audio features extracted from the encoder, task tokens such as \<target language\>, \<task\>, \<toggle timestamps\> and \<toggle PnC\> are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer [5] from individual SentencePiece [6] tokenizers of each language, which makes it easy to scale up to more languages. The canary-1b-flash model has 32 encoder layers and 4 decoder layers, leading to a total of 883M parameters. For more details about the architecture, please refer to [1].
## NVIDIA NeMo
To train, fine-tune or transcribe with canary-1b-flash, you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo).
## How to Use this Model
The model is available for use in the NeMo Framework [7], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
Please refer to [our tutorial](https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/Canary_Multitask_Speech_Model.ipynb) for more details.
A few inference examples are listed below:
### Loading the Model
```python
from nemo.collections.asr.models import EncDecMultiTaskModel
# load model
canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b-flash')
# update decode params
decode_cfg = canary_model.cfg.decoding
decode_cfg.beam.beam_size = 1
canary_model.change_decoding_strategy(decode_cfg)
```
## Input:
**Input Type(s):** Audio <br>
**Input Format(s):** .wav or .flac files<br>
**Input Parameters(s):** 1D <br>
**Other Properties Related to Input:** 16000 Hz Mono-channel Audio, Pre-Processing Not Needed <br>
Input to canary-1b-flash can be either a list of paths to audio files or a jsonl manifest file.
If the input is a list of paths, canary-1b-flash assumes that the audio is English and transcribes it. I.e., canary-1b-flash default behavior is English ASR.
```python
output = canary_model.transcribe(
['path1.wav', 'path2.wav'],
batch_size=16, # batch size to run the inference with
pnc='yes', # generate output with Punctuation and Capitalization
)
predicted_text_1 = output[0].text
```
canary-1b-flash can also generate word and segment level timestamps
```python
output = canary_model.transcribe(
['filepath.wav'],
timestamps='yes', # generate output with timestamps
)
predicted_text = output[0].text
word_level_timestamps = output[0].timestamp['word']
segment_level_timestamps = output[0].timestamp['segment']
```
For audio files longer than 10 seconds, we recommend using longform inference script (explained in next section) with `chunk_len_in_secs=10.0` to generate timestamps.
To use canary-1b-flash for transcribing other supported languages or perform Speech-to-Text translation or provide word-level timestamps, specify the input as jsonl manifest file, where each line in the file is a dictionary containing the following fields:
```yaml
# Example of a line in input_manifest.json
{
"audio_filepath": "/path/to/audio.wav", # path to the audio file
"source_lang": "en", # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']
"target_lang": "en", # language of the text output, choices=['en','de','es','fr']
"pnc": "yes", # whether to have PnC output, choices=['yes', 'no']
"timestamp": "yes", # whether to output word-level timestamps, choices=['yes', 'no']
}
```
and then use:
```python
output = canary_model.transcribe(
"<path to input manifest file>",
batch_size=16, # batch size to run the inference with
)
```
### Longform inference with Canary-1B-flash:
Canary models are designed to handle input audio smaller than 40 seconds. In order to handle longer audios, NeMo includes [speech_to_text_aed_chunked_infer.py](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_chunked_inference/aed/speech_to_text_aed_chunked_infer.py) script that handles chunking, performs inference on the chunked files, and stitches the transcripts.
The script will perform inference on all `.wav` files in `audio_dir`. Alternatively you can also pass a path to a manifest file as shown above. The decoded output will be saved at `output_json_path`.
```
python scripts/speech_to_text_aed_chunked_infer.py \
pretrained_name="nvidia/canary-1b-flash" \
audio_dir=$audio_dir \
output_filename=$output_json_path \
chunk_len_in_secs=40.0 \
batch_size=1 \
decoding.beam.beam_size=1 \
timestamps=False
```
**Note** that for longform inference with timestamps, it is recommended to use `chunk_len_in_secs` of 10 seconds.
## Output:
**Output Type(s):** Text <br>
**Output Format:** Text output as a string (w/ timestamps) depending on the task chosen for decoding <br>
**Output Parameters:** 1-Dimensional text string <br>
**Other Properties Related to Output:** May Need Inverse Text Normalization; Does Not Handle Special Characters <br>
## Software Integration:
**Runtime Engine(s):**
* NeMo - main <br>
**Supported Hardware Microarchitecture Compatibility:** <br>
* [NVIDIA Ampere] <br>
* [NVIDIA Blackwell] <br>
* [NVIDIA Jetson] <br>
* [NVIDIA Hopper] <br>
* [NVIDIA Lovelace] <br>
* [NVIDIA Pascal] <br>
* [NVIDIA Turing] <br>
* [NVIDIA Volta] <br>
**[Preferred/Supported] Operating System(s):** <br>
* [Linux] <br>
* [Linux 4 Tegra] <br>
* [Windows] <br>
## Model Version(s):
canary-1b-flash <br>
# Training and Evaluation Datasets:
## Training Dataset:
The canary-1b-flash model is trained on a total of 85K hrs of speech data. It consists of 31K hrs of public data, 20K hrs collected by [Suno](https://suno.ai/), and 34K hrs of in-house data.
The datasets below include conversations, videos from the web and audiobook recordings.
**Data Collection Method:**
* Human <br>
**Labeling Method:**
* Hybrid: Human, Automated <br>
The constituents of public data are as follows.
#### English (25.5k hours)
- Librispeech 960 hours
- Fisher Corpus
- Switchboard-1 Dataset
- WSJ-0 and WSJ-1
- National Speech Corpus (Part 1, Part 6)
- VCTK
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual Librispeech (MLS EN) - 2,000 hour subset
- Mozilla Common Voice (v7.0)
- People's Speech - 12,000 hour subset
- Mozilla Common Voice (v11.0) - 1,474 hour subset
#### German (2.5k hours)
- Mozilla Common Voice (v12.0) - 800 hour subset
- Multilingual Librispeech (MLS DE) - 1,500 hour subset
- VoxPopuli (DE) - 200 hr subset
#### Spanish (1.4k hours)
- Mozilla Common Voice (v12.0) - 395 hour subset
- Multilingual Librispeech (MLS ES) - 780 hour subset
- VoxPopuli (ES) - 108 hour subset
- Fisher - 141 hour subset
#### French (1.8k hours)
- Mozilla Common Voice (v12.0) - 708 hour subset
- Multilingual Librispeech (MLS FR) - 926 hour subset
- VoxPopuli (FR) - 165 hour subset
## Evaluation Dataset:
**Data Collection Method:** <br>
* Human <br>
**Labeling Method:** <br>
* Human <br>
Automatic Speech Recognition:
* [HuggingFace OpenASR Leaderboard evaluation sets](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)
* [MLS](https://huggingface.co/datasets/facebook/multilingual_librispeech)
* [MCV] (https://commonvoice.mozilla.org/en/datasets)
Automatic Speech Translation:
* [FLEURS](https://huggingface.co/datasets/google/fleurs)
* [COVOST-v2](https://github.com/facebookresearch/covost)
* [mExpresso](https://huggingface.co/facebook/seamless-expressive#mexpresso-multilingual-expresso)
Timestamp Prediction:
* [Librispeech](https://www.openslr.org/12)
Hallucination Robustness:
* [MUSAN](https://www.openslr.org/17/) 48 hrs eval set
Noise Robustness:
* [Librispeech](https://www.openslr.org/12)
Model Fairness:
* [Casual Conversations Dataset](https://arxiv.org/abs/2104.02821)
## Training
Canary-1B-Flash is trained using the NVIDIA NeMo Framework [7] for a total of 200K steps with 2D bucketing [1] and optimal batch sizes set using OOMptimizer [8].The model is trained on 128 NVIDIA A100 80GB GPUs.
The model can be trained using this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/speech_multitask/speech_to_text_aed.py) and [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/speech_multitask/fast-conformer_aed.yaml).
The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
## Inference:
**Engine:** NVIDIA NeMo <br>
**Test Hardware :** <br>
* A6000 <br>
* A100 <br>
* V100 <br>
## Performance
For ASR and AST experiments, predictions were generated using greedy decoding. Note that utterances shorter than 1 second are symmetrically zero-padded upto 1 second during evaluation.
### English ASR Performance (w/o PnC)
The ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with [whisper-normalizer](https://pypi.org/project/whisper-normalizer/).
WER on [HuggingFace OpenASR leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard):
| **Version** | **Model** | **RTFx** | **AMI** | **GigaSpeech** | **LS Clean** | **LS Other** | **Earnings22** | **SPGISpech** | **Tedlium** | **Voxpopuli** |
|:---------:|:-----------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| nemo-main | canary-1b-flash | 1045.75 | 13.11 | 9.85 | 1.48 | 2.87 | 12.79 | 1.95 | 3.12 | 5.63 |
#### Inference speed on different systems
We profiled inference speed on the OpenASR benchmark (batch_size=128) using the [real-time factor](https://github.com/NVIDIA/DeepLearningExamples/blob/master/Kaldi/SpeechRecognition/README.md#metrics) (RTFx) to quantify throughput.
| **Version** | **Model** | **System** | **RTFx** |
|:-----------:|:-------------:|:------------:|:----------:|
| nemo-main | canary-1b-flash | NVIDIA A100 | 1045.75 |
| nemo-main | canary-1b-flash | NVIDIA H100 | 1669.07 |
### Multilingual ASR Performance
WER on [MLS](https://huggingface.co/datasets/facebook/multilingual_librispeech) test set:
| **Version** | **Model** | **De** | **Es** | **Fr** |
|:---------:|:-----------:|:------:|:------:|:------:|
| nemo-main | canary-1b-flash | 4.36 | 2.69 | 4.47 |
WER on [MCV-16.1](https://commonvoice.mozilla.org/en/datasets) test set:
| **Version** | **Model** | **En** | **De** | **Es** | **Fr** |
|:---------:|:-----------:|:------:|:------:|:------:|:------:|
| nemo-main | canary-1b-flash | 6.99 | 4.09 | 3.62 | 6.15 |
More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)
### AST Performance
We evaluate AST performance with [BLEU score](https://lightning.ai/docs/torchmetrics/stable/text/sacre_bleu_score.html) and [COMET score](https://aclanthology.org/2020.emnlp-main.213/), and use native annotations with punctuation and capitalization in the datasets.
[FLEURS](https://huggingface.co/datasets/google/fleurs) test set:
BLEU score:
| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** | **De->En** | **Es->En** | **Fr->En** |
|:-----------:|:---------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 32.27 | 22.6 | 41.22 | 35.5 | 23.32 | 33.42 |
COMET score:
| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** | **De->En** | **Es->En** | **Fr->En** |
|:-----------:|:---------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 0.8114 | 0.8118 | 0.8165 | 0.8546 | 0.8228 | 0.8475 |
[COVOST-v2](https://github.com/facebookresearch/covost) test set:
BLEU score:
| **Version** | **Model** | **De->En** | **Es->En** | **Fr->En** |
|:-----------:|:---------:|:----------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 39.33 | 41.86 | 41.43 |
COMET score:
| **Version** | **Model** | **De->En** | **Es->En** | **Fr->En** |
|:-----------:|:---------:|:----------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 0.8553 | 0.8585 | 0.8511 |
[mExpresso](https://huggingface.co/facebook/seamless-expressive#mexpresso-multilingual-expresso) test set:
BLEU score:
| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** |
|:-----------:|:---------:|:----------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 22.91 | 35.69 | 27.85 |
COMET score:
| **Version** | **Model** | **En->De** | **En->Es** | **En->Fr** |
|:-----------:|:---------:|:----------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 0.7889 | 0.8211 | 0.7910 |
### Timestamp Prediction
F1-score on [Librispeech Test sets](https://www.openslr.org/12) at collar value of 200ms
| **Version** | **Model** | **test-clean** | **test-other** |
|:-----------:|:---------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 95.5 | 93.5 |
### Hallucination Robustness
Number of characters per minute on [MUSAN](https://www.openslr.org/17) 48 hrs eval set
| **Version** | **Model** | **# of character per minute** |
|:-----------:|:---------:|:----------:|
| nemo-main | canary-1b-flash | 60.92 |
### Noise Robustness
WER on [Librispeech Test Clean](https://www.openslr.org/12) at different SNR (signal to noise ratio) levels of additive white noise
| **Version** | **Model** | **SNR 10** | **SNR 5** | **SNR 0** | **SNR -5** |
|:-----------:|:---------:|:----------:|:----------:|:----------:|:----------:|
| nemo-main | canary-1b-flash | 2.34 | 3.69 | 8.84 | 29.71 |
## Model Fairness Evaluation
As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset" [9], we assessed the canary-1b-flash model for fairness. The model was evaluated on the CausalConversations-v1 dataset, and the results are reported as follows:
### Gender Bias:
| Gender | Male | Female | N/A | Other |
| :--- | :--- | :--- | :--- | :--- |
| Num utterances | 19325 | 24532 | 926 | 33 |
| % WER | 14.66 | 12.44 | 17.17 | 27.56 |
### Age Bias:
| Age Group | (18-30) | (31-45) | (46-85) | (1-100) |
| :--- | :--- | :--- | :--- | :--- |
| Num utterances | 15956 | 14585 | 13349 | 43890 |
| % WER | 13.18 | 13.45 | 13.64 | 13.41 |
(Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)
## License/Terms of Use:
canary-1b-flash is released under the CC-BY-4.0 license. By using this model, you are agreeing to the [terms and conditions](https://choosealicense.com/licenses/cc-by-4.0/) of the license. <br>
## References:
[1] [Training and Inference Efficiency of Encoder-Decoder Speech Models](https://arxiv.org/abs/2503.05931)
[2] [Less is More: Accurate Speech Recognition & Translation without Web-Scale Data](https://www.isca-archive.org/interspeech_2024/puvvada24_interspeech.pdf)
[3] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10389701)
[4] [Attention is All You Need](https://arxiv.org/abs/1706.03762)
[5] [Unified Model for Code-Switching Speech Recognition and Language Identification Based on Concatenated Tokenizer](https://aclanthology.org/2023.calcs-1.7.pdf)
[6] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
[7] [NVIDIA NeMo Framework](https://github.com/NVIDIA/NeMo)
[8] [EMMeTT: Efficient Multimodal Machine Translation Training](https://arxiv.org/abs/2409.13523)
[9] [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9634168)
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
|
xihc-ucb/Qwen3-4B-train-Quasar-0809
|
xihc-ucb
| 2025-08-20T01:51:13Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"fp8_qwen3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-10T01:53:33Z |
---
library_name: transformers
tags: []
---
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|
xihc-ucb/Qwen3-1.7B-train-Quasar-0809
|
xihc-ucb
| 2025-08-20T01:50:25Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"fp8_qwen3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-10T01:53:10Z |
---
library_name: transformers
tags: []
---
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[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]
|
roeker/blockassist-bc-quick_wiry_owl_1755654517
|
roeker
| 2025-08-20T01:49:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:49:21Z |
---
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).
|
QuantTrio/Qwen3-235B-A22B-Instruct-2507-GPTQ-Int4-Int8Mix
|
QuantTrio
| 2025-08-20T01:49:39Z | 592 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"Qwen3",
"GPTQ",
"Int4-Int8Mix",
"量化修复",
"vLLM",
"conversational",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-235B-A22B-Instruct-2507",
"base_model:quantized:Qwen/Qwen3-235B-A22B-Instruct-2507",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2025-07-24T02:27:45Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- Qwen3
- GPTQ
- Int4-Int8Mix
- 量化修复
- vLLM
base_model:
- Qwen/Qwen3-235B-A22B-Instruct-2507
base_model_relation: quantized
---
# Qwen3-235B-A22B-Instruct-2507-GPTQ-Int4-Int8Mix
Base model: [Qwen/Qwen3-235B-A22B-Instruct-2507](https://www.modelscope.cn/models/Qwen/Qwen3-235B-A22B-Instruct-2507)
### 【VLLM Launch Command for 8 GPUs (Single Node)】
<i>Note: When launching with 8 GPUs, --enable-expert-parallel must be specified; otherwise, the expert tensors cannot be evenly split across tensor parallel ranks. This option is not required for 4-GPU setups.</i>
```
CONTEXT_LENGTH=32768 # 262144
vllm serve \
QuantTrio/Qwen3-235B-A22B-Instruct-2507-GPTQ-Int4-Int8Mix \
--served-model-name Qwen3-235B-A22B-Instruct-2507-GPTQ-Int4-Int8Mix \
--enable-expert-parallel \
--swap-space 16 \
--max-num-seqs 512 \
--max-model-len $CONTEXT_LENGTH \
--max-seq-len-to-capture $CONTEXT_LENGTH \
--gpu-memory-utilization 0.9 \
--tensor-parallel-size 8 \
--trust-remote-code \
--disable-log-requests \
--host 0.0.0.0 \
--port 8000
```
### 【Dependencies】
```
vllm>=0.9.2
```
### 【Model Update History】
```
2025-08-19
1.[BugFix] Fix compatibility issues with vLLM 0.10.1
2025-07-22
1. Initial commit
```
### 【Model Files】
| File Size | Last Updated |
|-----------|----------------|
| `122GB` | `2025-07-22` |
### 【Model Download】
```python
from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/Qwen3-235B-A22B-Instruct-2507-GPTQ-Int4-Int8Mix', cache_dir="your_local_path")
```
### 【Description】
# Qwen3-235B-A22B-Instruct-2507
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
We introduce the updated version of the **Qwen3-235B-A22B non-thinking mode**, named **Qwen3-235B-A22B-Instruct-2507**, featuring the following key enhancements:
- **Significant improvements** in general capabilities, including **instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage**.
- **Substantial gains** in long-tail knowledge coverage across **multiple languages**.
- **Markedly better alignment** with user preferences in **subjective and open-ended tasks**, enabling more helpful responses and higher-quality text generation.
- **Enhanced capabilities** in **256K long-context understanding**.
## Model Overview
**Qwen3-235B-A22B-Instruct-2507** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 235B in total and 22B activated
- Number of Paramaters (Non-Embedding): 234B
- Number of Layers: 94
- Number of Attention Heads (GQA): 64 for Q and 4 for KV
- Number of Experts: 128
- Number of Activated Experts: 8
- Context Length: **262,144 natively**.
**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Performance
| | Deepseek-V3-0324 | GPT-4o-0327 | Claude Opus 4 Non-thinking | Kimi K2 | Qwen3-235B-A22B Non-thinking | Qwen3-235B-A22B-Instruct-2507 |
|--- | --- | --- | --- | --- | --- | ---|
| **Knowledge** | | | | | | |
| MMLU-Pro | 81.2 | 79.8 | **86.6** | 81.1 | 75.2 | 83.0 |
| MMLU-Redux | 90.4 | 91.3 | **94.2** | 92.7 | 89.2 | 93.1 |
| GPQA | 68.4 | 66.9 | 74.9 | 75.1 | 62.9 | **77.5** |
| SuperGPQA | 57.3 | 51.0 | 56.5 | 57.2 | 48.2 | **62.6** |
| SimpleQA | 27.2 | 40.3 | 22.8 | 31.0 | 12.2 | **54.3** |
| CSimpleQA | 71.1 | 60.2 | 68.0 | 74.5 | 60.8 | **84.3** |
| **Reasoning** | | | | | | |
| AIME25 | 46.6 | 26.7 | 33.9 | 49.5 | 24.7 | **70.3** |
| HMMT25 | 27.5 | 7.9 | 15.9 | 38.8 | 10.0 | **55.4** |
| ARC-AGI | 9.0 | 8.8 | 30.3 | 13.3 | 4.3 | **41.8** |
| ZebraLogic | 83.4 | 52.6 | - | 89.0 | 37.7 | **95.0** |
| LiveBench 20241125 | 66.9 | 63.7 | 74.6 | **76.4** | 62.5 | 75.4 |
| **Coding** | | | | | | |
| LiveCodeBench v6 (25.02-25.05) | 45.2 | 35.8 | 44.6 | 48.9 | 32.9 | **51.8** |
| MultiPL-E | 82.2 | 82.7 | **88.5** | 85.7 | 79.3 | 87.9 |
| Aider-Polyglot | 55.1 | 45.3 | **70.7** | 59.0 | 59.6 | 57.3 |
| **Alignment** | | | | | | |
| IFEval | 82.3 | 83.9 | 87.4 | **89.8** | 83.2 | 88.7 |
| Arena-Hard v2* | 45.6 | 61.9 | 51.5 | 66.1 | 52.0 | **79.2** |
| Creative Writing v3 | 81.6 | 84.9 | 83.8 | **88.1** | 80.4 | 87.5 |
| WritingBench | 74.5 | 75.5 | 79.2 | **86.2** | 77.0 | 85.2 |
| **Agent** | | | | | | |
| BFCL-v3 | 64.7 | 66.5 | 60.1 | 65.2 | 68.0 | **70.9** |
| TAU-Retail | 49.6 | 60.3# | **81.4** | 70.7 | 65.2 | 71.3 |
| TAU-Airline | 32.0 | 42.8# | **59.6** | 53.5 | 32.0 | 44.0 |
| **Multilingualism** | | | | | | |
| MultiIF | 66.5 | 70.4 | - | 76.2 | 70.2 | **77.5** |
| MMLU-ProX | 75.8 | 76.2 | - | 74.5 | 73.2 | **79.4** |
| INCLUDE | 80.1 | **82.1** | - | 76.9 | 75.6 | 79.5 |
| PolyMATH | 32.2 | 25.5 | 30.0 | 44.8 | 27.0 | **50.2** |
*: For reproducibility, we report the win rates evaluated by GPT-4.1.
\#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable.
## Quickstart
The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3_moe'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-235B-A22B-Instruct-2507"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B-Instruct-2507 --tp 8 --context-length 262144
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-235B-A22B-Instruct-2507 --tensor-parallel-size 8 --max-model-len 262144
```
**Note: If you encounter out-of-memory (OOM) issues, consider reducing the context length to a shorter value, such as `32,768`.**
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-235B-A22B-Instruct-2507',
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- We suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 16,384 tokens for most queries, which is adequate for instruct models.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
```
|
xihc-ucb/Qwen3-0.6B-train-Quasar-0809
|
xihc-ucb
| 2025-08-20T01:49:32Z | 954 | 0 |
transformers
|
[
"transformers",
"safetensors",
"fp8_qwen3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-10T01:52:52Z |
---
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. -->
- **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]
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755652919
|
coelacanthxyz
| 2025-08-20T01:49:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:49:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hsiehfuwei/uuu_fine_tune_gpt2
|
hsiehfuwei
| 2025-08-20T01:43:01Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:43:01Z |
---
license: apache-2.0
---
|
AXERA-TECH/Qwen3-4B
|
AXERA-TECH
| 2025-08-20T01:42:34Z | 13 | 0 | null |
[
"Qwen",
"Qwen3",
"Int8",
"text-generation",
"en",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-04-30T09:26:37Z |
---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- Qwen
- Qwen3
- Int8
---
# Qwen3-4B-Int8
This version of Qwen3-4B-Int8 has been converted to run on the Axera NPU using **w8a16** quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 4.2(Not released yet)
## Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through the original repo :
https://huggingface.co/Qwen/Qwen3-4B
[Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html)
[AXera NPU LLM Runtime](https://github.com/AXERA-TECH/ax-llm)
## Support Platform
- AX650
- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
|Chips|w8a16|w4a16|
|--|--|--|
|AX650| 4.5 tokens/sec|TBD|
## How to use
Download all files from this repository to the device
```
root@ax650:/mnt/qtang/llm-test/qwen3-4b# tree -L 1
.
|-- config.json
|-- main_ax650
|-- main_axcl_aarch64
|-- main_axcl_x86
|-- post_config.json
|-- qwen2.5_tokenizer
|-- qwen3-4b-ax650
|-- qwen3_tokenizer
|-- qwen3_tokenizer_uid.py
|-- run_qwen3_4b_int8_ctx_ax650.sh
|-- run_qwen3_4b_int8_ctx_axcl_aarch64.sh
`-- run_qwen3_4b_int8_ctx_axcl_x86.sh
3 directories, 9 files
root@ax650:/mnt/qtang/llm-test/qwen3-4b#
```
#### Start the Tokenizer service
Install requirement
```
pip install transformers jinja2
```
```
root@ax650:/mnt/qtang/llm-test/qwen3-4b# python3 qwen3_tokenizer_uid.py
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
Server running at http://0.0.0.0:12345
```
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board
Open another terminal and run `run_qwen3_4b_int8_ctx_ax650.sh`
```
root@ax650:/mnt/qtang/llm-test/qwen3-4b# ./run_qwen3_4b_int8_ctx_ax650.sh
[I][ Init][ 110]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 57]: uid: 6e90ff82-b9c9-42dc-8f61-081203389166
bos_id: -1, eos_id: 151645
2% | █ | 1 / 39 [3.95s<153.89s, 0.25 count/s] tokenizer init ok
[I][ Init][ 26]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ | 39 / 39 [48.03s<48.03s, 0.81 count/s] init post axmodel ok,remain_cmm(5621 MB)
[I][ Init][ 188]: max_token_len : 2559
[I][ Init][ 193]: kv_cache_size : 1024, kv_cache_num: 2559
[I][ Init][ 201]: prefill_token_num : 128
[I][ Init][ 205]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 205]: grp: 2, prefill_max_token_num : 256
[I][ Init][ 205]: grp: 3, prefill_max_token_num : 512
[I][ Init][ 205]: grp: 4, prefill_max_token_num : 1024
[I][ Init][ 205]: grp: 5, prefill_max_token_num : 1536
[I][ Init][ 205]: grp: 6, prefill_max_token_num : 2048
[I][ Init][ 209]: prefill_max_token_num : 2048
[I][ load_config][ 282]: load config:
{
"enable_repetition_penalty": false,
"enable_temperature": false,
"enable_top_k_sampling": true,
"enable_top_p_sampling": false,
"penalty_window": 20,
"repetition_penalty": 1.2,
"temperature": 0.9,
"top_k": 1,
"top_p": 0.8
}
[I][ Init][ 218]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][ GenerateKVCachePrefill][ 270]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2
[I][ GenerateKVCachePrefill][ 307]: input_num_token:21
[I][ main][ 230]: precompute_len: 21
[I][ main][ 231]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
prompt >> 1+3=?
[I][ SetKVCache][ 530]: prefill_grpid:2 kv_cache_num:256 precompute_len:21 input_num_token:16
[I][ SetKVCache][ 533]: current prefill_max_token_num:1920
[I][ Run][ 659]: input token num : 16, prefill_split_num : 1
[I][ Run][ 685]: input_num_token:16
[I][ Run][ 808]: ttft: 1169.05 ms
<think>
</think>
1 + 3 = 4
[N][ Run][ 922]: hit eos,avg 4.22 token/s
[I][ GetKVCache][ 499]: precompute_len:48, remaining:2000
prompt >> who are you?
[I][ SetKVCache][ 530]: prefill_grpid:2 kv_cache_num:256 precompute_len:48 input_num_token:16
[I][ SetKVCache][ 533]: current prefill_max_token_num:1920
[I][ Run][ 659]: input token num : 16, prefill_split_num : 1
[I][ Run][ 685]: input_num_token:16
[I][ Run][ 808]: ttft: 1168.56 ms
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions, create content,
and help with a variety of tasks. How can I assist you today?
[N][ Run][ 922]: hit eos,avg 4.22 token/s
[I][ GetKVCache][ 499]: precompute_len:106, remaining:1942
prompt >> q
root@ax650:/mnt/qtang/llm-test/qwen3-4b#
```
#### Inference with M.2 Accelerator card
[What is M.2 Accelerator card?](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html), Show this DEMO based on Raspberry PI 5.
```
(base) axera@raspberrypi:~/samples/qwen3-4b $ ./run_qwen3_4b_int8_ctx_axcl_aarch64.sh
[I][ Init][ 136]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 57]: uid: a5b1e427-0cdf-4da6-b3a7-f5e0517da0bb
bos_id: -1, eos_id: 151645
2% | █ | 1 / 39 [0.99s<38.45s, 1.01 count/s] tokenizer init ok
[I][ Init][ 45]: LLaMaEmbedSelector use mmap
5% | ██ | 2 / 39 [0.99s<19.23s, 2.03 count/s] embed_selector init ok
[I][ run][ 30]: AXCLWorker start with devid 0
100% | ████████████████████████████████ | 39 / 39 [133.16s<133.16s, 0.29 count/s] init post axmodel ok,remain_cmm(691 MB)(1096 MB)000000000
[I][ Init][ 237]: max_token_len : 2559
[I][ Init][ 240]: kv_cache_size : 1024, kv_cache_num: 2559
[I][ Init][ 248]: prefill_token_num : 128
[I][ Init][ 252]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 252]: grp: 2, prefill_max_token_num : 256
[I][ Init][ 252]: grp: 3, prefill_max_token_num : 512
[I][ Init][ 252]: grp: 4, prefill_max_token_num : 1024
[I][ Init][ 252]: grp: 5, prefill_max_token_num : 1536
[I][ Init][ 252]: grp: 6, prefill_max_token_num : 2048
[I][ Init][ 256]: prefill_max_token_num : 2048
________________________
| ID| remain cmm(MB)|
========================
| 0| 691|
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
[I][ load_config][ 282]: load config:
{
"enable_repetition_penalty": false,
"enable_temperature": false,
"enable_top_k_sampling": true,
"enable_top_p_sampling": false,
"penalty_window": 20,
"repetition_penalty": 1.2,
"temperature": 0.9,
"top_k": 1,
"top_p": 0.8
}
[I][ Init][ 279]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][ GenerateKVCachePrefill][ 335]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2
[I][ GenerateKVCachePrefill][ 372]: input_num_token:21
[I][ main][ 236]: precompute_len: 21
[I][ main][ 237]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
prompt >> who are you
[I][ SetKVCache][ 628]: prefill_grpid:2 kv_cache_num:256 precompute_len:21 input_num_token:27
[I][ SetKVCache][ 631]: current prefill_max_token_num:1920
[I][ Run][ 869]: input token num : 27, prefill_split_num : 1
[I][ Run][ 901]: input_num_token:27
[I][ Run][1030]: ttft: 1339.01 ms
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions,
create content, and help with a variety of tasks. What can I assist you with?
[N][ Run][1182]: hit eos,avg 3.65 token/s
[I][ GetKVCache][ 597]: precompute_len:90, remaining:1958
prompt >> q
[I][ run][ 80]: AXCLWorker exit with devid 0
(base) axera@raspberrypi:~/samples/qwen3-4b $
(base) axera@raspberrypi:~ $ axcl-smi
+------------------------------------------------------------------------------------------------+
| AXCL-SMI V3.4.0_20250423020139 Driver V3.4.0_20250423020139 |
+-----------------------------------------+--------------+---------------------------------------+
| Card Name Firmware | Bus-Id | Memory-Usage |
| Fan Temp Pwr:Usage/Cap | CPU NPU | CMM-Usage |
|=========================================+==============+=======================================|
| 0 AX650N V3.4.0 | 0000:01:00.0 | 193 MiB / 945 MiB |
| -- 37C -- / -- | 2% 0% | 6348 MiB / 7040 MiB |
+-----------------------------------------+--------------+---------------------------------------+
+------------------------------------------------------------------------------------------------+
| Processes: |
| Card PID Process Name NPU Memory Usage |
|================================================================================================|
| 0 84643 /home/axera/samples/qwen3-4b/main_axcl_aarch64 4894032 KiB |
+------------------------------------------------------------------------------------------------+
(base) axera@raspberrypi:~ $
```
|
ccyuan/uuu_fine_tune_gpt2
|
ccyuan
| 2025-08-20T01:41:04Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:41:04Z |
---
license: apache-2.0
---
|
Jsijkl/uuu_fine_tune_taipower
|
Jsijkl
| 2025-08-20T01:40:28Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:36:13Z |
---
license: apache-2.0
---
|
thailevann/track8_v1_PoT
|
thailevann
| 2025-08-20T01:36:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-4B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T01:36:33Z |
---
base_model: unsloth/Qwen3-4B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thailevann
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-4B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
roeker/blockassist-bc-quick_wiry_owl_1755653704
|
roeker
| 2025-08-20T01:36:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:35:55Z |
---
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).
|
lon02/uuu_fine_tune_taipower2
|
lon02
| 2025-08-20T01:35:18Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:32:33Z |
---
license: apache-2.0
---
|
SHAOYU1/uuu_fine_tune_gpt2
|
SHAOYU1
| 2025-08-20T01:34:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:34:59Z |
---
license: apache-2.0
---
|
Starsola/uuu_fine_tune_taipower2
|
Starsola
| 2025-08-20T01:34:37Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:29:47Z |
---
license: apache-2.0
---
|
Zenfish-zy/Taxi-v3
|
Zenfish-zy
| 2025-08-20T01:34:33Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-20T01:34:28Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Zenfish-zy/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
BootesVoid/cme69hhjk08oc6aq1dgo1d8jc_cmej4voin0su8rts8z0n5okw1
|
BootesVoid
| 2025-08-20T01:34:32Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-20T01:34:30Z |
---
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: ZOE2
---
# Cme69Hhjk08Oc6Aq1Dgo1D8Jc_Cmej4Voin0Su8Rts8Z0N5Okw1
<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 `ZOE2` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ZOE2",
"lora_weights": "https://huggingface.co/BootesVoid/cme69hhjk08oc6aq1dgo1d8jc_cmej4voin0su8rts8z0n5okw1/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/cme69hhjk08oc6aq1dgo1d8jc_cmej4voin0su8rts8z0n5okw1', weight_name='lora.safetensors')
image = pipeline('ZOE2').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/cme69hhjk08oc6aq1dgo1d8jc_cmej4voin0su8rts8z0n5okw1/discussions) to add images that show off what you’ve made with this LoRA.
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755652161
|
thanobidex
| 2025-08-20T01:34:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:34:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755652159
|
helmutsukocok
| 2025-08-20T01:34:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:34:17Z |
---
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).
|
twhitworth/gpt-oss-120b-fp16
|
twhitworth
| 2025-08-20T01:33:48Z | 58 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"fp16",
"dequantized",
"gpt-oss",
"mxfp4-upcast",
"conversational",
"en",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T08:15:48Z |
---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- fp16
- dequantized
- gpt-oss
- mxfp4-upcast
base_model: openai/gpt-oss-120b
model-index:
- name: gpt-oss-120b-fp16
results: []
---
#
## Precision: FP32 vs FP16 (and BF16)
This project saves dequantized checkpoints in **FP16** (bf16 -> fp16)
- **FP32 (single precision, 32-bit, 4 bytes/param)**
Reference/default precision in many frameworks. Highest numerical range/precision, **largest memory**.
- **FP16 (half precision, 16-bit, 2 bytes/param)**
Half the memory of FP32. Great for **inference** on modern GPUs; may underflow/overflow more easily than BF16.
- **BF16 (bfloat16, 16-bit, 2 bytes/param)**
Same memory as FP16, **wider exponent like FP32**, often more numerically robust than FP16; slightly less precision in mantissa.
> In this repo, output precision is **FP16** (default) or **BF16** via `--dtype`.
> **FP32 output is not offered** because it doubles disk/RAM vs FP16/BF16 with minimal inference benefit on modern hardware.
### Memory math (example: 120B parameters)
Each parameter stores one number:
| Format | Bits | Bytes/param | Approx size for 120B params |
|-------:|-----:|-------------:|-----------------------------:|
| FP32 | 32 | 4 | ~ **447 GiB** |
| FP16 | 16 | 2 | ~ **224 GiB** |
| BF16 | 16 | 2 | ~ **224 GiB** |
> Calculation (GiB): `params * bytes_per_param / 1024^3`
> For 120,000,000,000 params:
> FP32: 480e9 B ≈ 447.03 GiB
> FP16/BF16: 240e9 B ≈ 223.52 GiB
### When to use which
- **Inference on modern NVIDIA GPUs (Turing+/Ampere+/Ada/Hopper):**
Use **FP16** (default here) or **BF16**. You’ll get large memory savings and typically **equal or faster** throughput than FP32 thanks to tensor cores.
- **Training / Finetuning:**
Use **mixed precision** (BF16 or FP16 compute with an FP32 master copy of weights/optimizer states).
If your GPU supports BF16 well (e.g., A100/H100), **BF16** is preferred for numeric stability.
(This tool focuses on exporting dequantized checkpoints, not training loops.)
- **If you hit numeric issues in FP16:**
Try **BF16** (`--dtype bf16`). Same size as FP16 but usually more stable due to FP32-like exponent range.
### Notes
- **FP32** remains the gold standard for numeric headroom and deterministic baselines, but for **inference** it’s typically unnecessary and **costly** (2× memory vs FP16/BF16).
- **Tensor cores** accelerate FP16/BF16 GEMMs on most modern NVIDIA GPUs; FP32 is often slower and more memory-bound.
- If a downstream runtime expects a specific dtype, export to that: FP16 for speed/memory, BF16 for robustness.
---
### WIP
- Upcoming models: cleaned FP16 release (uniform fp16 with fp32 LayerNorms), compressed variants (W8A8, W4A16, mixed experts), 2:4 sparse checkpoints.
- Evals: MMLU, HellaSwag, TruthfulQA, GSM8K, BBH, MT‑Bench; plus latency/throughput and memory footprint on 3090/A100.
- Extras: scripted upload tooling, detailed model cards, and reproducible Docker workflows.
|
junyi080914/uuu_fine_tune_taipower
|
junyi080914
| 2025-08-20T01:33:36Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:25:12Z |
---
license: apache-2.0
---
|
jasanlin177/uuu_fine_tune_taipower
|
jasanlin177
| 2025-08-20T01:30:02Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:19:38Z |
---
license: apache-2.0
---
|
kimono998/Wordle-pos-1_lora_adapter_iter_10
|
kimono998
| 2025-08-20T01:29:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T01:29:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DanielJustin/uuu_fine_tune_taipower
|
DanielJustin
| 2025-08-20T01:27:48Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:26:58Z |
---
license: apache-2.0
---
|
iBush/uuu_fine_tune_taipower
|
iBush
| 2025-08-20T01:26:36Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:06:49Z |
---
license: apache-2.0
---
|
ivoryyuan/uuu_fine_tune_gpt2
|
ivoryyuan
| 2025-08-20T01:24:51Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:24:51Z |
---
license: apache-2.0
---
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755651942
|
Sayemahsjn
| 2025-08-20T01:24:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:24:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755651181
|
katanyasekolah
| 2025-08-20T01:20:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:20:34Z |
---
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).
|
PGFROG/uuu_fine_tune_gpt2
|
PGFROG
| 2025-08-20T01:19:20Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:19:20Z |
---
license: apache-2.0
---
|
lautan/blockassist-bc-gentle_patterned_goat_1755651032
|
lautan
| 2025-08-20T01:18:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:18:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Akashiurahara/rpGM-LoRa
|
Akashiurahara
| 2025-08-20T01:17:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"llama3.2-3B",
"roleplay",
"tatsumaki",
"nsfw",
"lora",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T14:17:34Z |
---
library_name: transformers
tags:
- unsloth
- llama3.2-3B
- roleplay
- tatsumaki
- nsfw
- lora
---
# 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.
|
roeker/blockassist-bc-quick_wiry_owl_1755652492
|
roeker
| 2025-08-20T01:16:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:15:34Z |
---
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).
|
astab/uuu_fine_tune_gpt2
|
astab
| 2025-08-20T01:15:58Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:15:58Z |
---
license: apache-2.0
---
|
semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024
|
semenetslitslink
| 2025-08-20T01:15:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-kontextflux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-Kontext-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T08:20:53Z |
---
base_model: black-forest-labs/FLUX.1-Kontext-dev
library_name: diffusers
license: other
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-kontextflux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux Kontext DreamBooth LoRA - semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024
<Gallery />
## Model description
These are semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024 DreamBooth LoRA weights for black-forest-labs/FLUX.1-Kontext-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `None` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import FluxKontextPipeline
import torch
pipeline = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('None').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755650813
|
coelacanthxyz
| 2025-08-20T01:15:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:15:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
maydixit/qwen3_235b_second_rl
|
maydixit
| 2025-08-20T01:14:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T01:04:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lakelee/RLB_MLP_BC_v4.20250820.00.1
|
lakelee
| 2025-08-20T01:13:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp_swiglu",
"generated_from_trainer",
"base_model:lakelee/RLB_MLP_BC_v4.20250820.00",
"base_model:finetune:lakelee/RLB_MLP_BC_v4.20250820.00",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T16:23:02Z |
---
library_name: transformers
base_model: lakelee/RLB_MLP_BC_v4.20250820.00
tags:
- generated_from_trainer
model-index:
- name: RLB_MLP_BC_v4.20250820.00.1
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_v4.20250820.00.1
This model is a fine-tuned version of [lakelee/RLB_MLP_BC_v4.20250820.00](https://huggingface.co/lakelee/RLB_MLP_BC_v4.20250820.00) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20.0
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Tokenizers 0.21.4
|
haphoptr/blockassist-bc-quiet_robust_seal_1755652090
|
haphoptr
| 2025-08-20T01:09:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quiet robust seal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:09:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quiet robust seal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755650585
|
kojeklollipop
| 2025-08-20T01:09:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:09:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755650464
|
koloni
| 2025-08-20T01:07:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:07:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Bila333/mary
|
Bila333
| 2025-08-20T01:04:01Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-20T00:21:50Z |
---
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
---
|
BootesVoid/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i
|
BootesVoid
| 2025-08-20T01:03:40Z | 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
| 2025-08-20T01:03:38Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: NAUGHTY
---
# Cmej4Gqae0Ssprts8S25Ojhxv_Cmej8O5Cp0T45Rts8If83Og5I
<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 `NAUGHTY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NAUGHTY",
"lora_weights": "https://huggingface.co/BootesVoid/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i/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/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i', weight_name='lora.safetensors')
image = pipeline('NAUGHTY').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/cmej4gqae0ssprts8s25ojhxv_cmej8o5cp0t45rts8if83og5i/discussions) to add images that show off what you’ve made with this LoRA.
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755650188
|
indoempatnol
| 2025-08-20T01:03:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:03:25Z |
---
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).
|
thiernomdou/dimitri
|
thiernomdou
| 2025-08-20T01:02:57Z | 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
| 2025-08-20T00:55:29Z |
---
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: dimitri
---
# Dimitri
<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 `dimitri` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "dimitri",
"lora_weights": "https://huggingface.co/thiernomdou/dimitri/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('thiernomdou/dimitri', weight_name='lora.safetensors')
image = pipeline('dimitri').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/thiernomdou/dimitri/discussions) to add images that show off what you’ve made with this LoRA.
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755650281
|
helmutsukocok
| 2025-08-20T01:02:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:02:38Z |
---
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).
|
crystalline7/1159894
|
crystalline7
| 2025-08-20T01:01:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:01:18Z |
[View on Civ Archive](https://civarchive.com/models/1116522?modelVersionId=1254701)
|
mradermacher/Polar-32B-i1-GGUF
|
mradermacher
| 2025-08-20T01:01:06Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:x2bee/Polar-32B",
"base_model:quantized:x2bee/Polar-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-19T20:30:13Z |
---
base_model: x2bee/Polar-32B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/x2bee/Polar-32B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Polar-32B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Polar-32B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Polar-32B-i1-GGUF/resolve/main/Polar-32B.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
crystalline7/19466
|
crystalline7
| 2025-08-20T01:00:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:00:48Z |
[View on Civ Archive](https://civarchive.com/models/18914?modelVersionId=23430)
|
ultratopaz/40391
|
ultratopaz
| 2025-08-20T01:00:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:00:35Z |
[View on Civ Archive](https://civarchive.com/models/12690?modelVersionId=56461)
|
ultratopaz/51150
|
ultratopaz
| 2025-08-20T01:00:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:00:26Z |
[View on Civ Archive](https://civarchive.com/models/69228?modelVersionId=73908)
|
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