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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 12:28:13
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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|---|---|---|---|---|---|---|---|---|---|
grizzle00/blockassist-bc-curious_mimic_antelope_1757083837
|
grizzle00
| 2025-09-05T15:27:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious mimic antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:27:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious mimic antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1757083820
|
NahedDom
| 2025-09-05T15:26:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:26:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Mimic-1.0-GGUF
|
mradermacher
| 2025-09-05T15:25:43Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"en",
"base_model:AppliedLucent/Mimic-1.0",
"base_model:quantized:AppliedLucent/Mimic-1.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T13:37:38Z |
---
base_model: AppliedLucent/Mimic-1.0
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/AppliedLucent/Mimic-1.0
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mimic-1.0-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q2_K.gguf) | Q2_K | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q3_K_S.gguf) | Q3_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q3_K_M.gguf) | Q3_K_M | 7.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q3_K_L.gguf) | Q3_K_L | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.IQ4_XS.gguf) | IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q4_K_S.gguf) | Q4_K_S | 8.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q4_K_M.gguf) | Q4_K_M | 9.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q5_K_S.gguf) | Q5_K_S | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q5_K_M.gguf) | Q5_K_M | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q6_K.gguf) | Q6_K | 12.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mimic-1.0-GGUF/resolve/main/Mimic-1.0.Q8_0.gguf) | Q8_0 | 15.7 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
xcvbghjh/blockassist-bc-muscular_carnivorous_okapi_1757085808
|
xcvbghjh
| 2025-09-05T15:24:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular carnivorous okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:23:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular carnivorous okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
2hpsatt/blockassist-bc-huge_deft_eagle_1757085745
|
2hpsatt
| 2025-09-05T15:23:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:23:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/gil-elvgren
|
Muapi
| 2025-09-05T15:22:39Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-05T15:22:26Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Gil Elvgren
**Base model**: Flux.1 D
**Trained words**: GilElren style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1797087@2033754", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
xcvbghjh/blockassist-bc-pensive_twitchy_ape_1757085730
|
xcvbghjh
| 2025-09-05T15:22:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive twitchy ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:22:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive twitchy ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/val-red-flags
|
Muapi
| 2025-09-05T15:19:10Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-09-05T15:18:31Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Val - Red Flags
**Base model**: Flux.1 D
**Trained words**: tc_val, crimson hair, lip piercing, earlobe piercing
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:818806@915656", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
bah63843/blockassist-bc-plump_fast_antelope_1757085497
|
bah63843
| 2025-09-05T15:19:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:19:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ibm-nasa-geospatial/Prithvi-EO-1.0-100M-burn-scar
|
ibm-nasa-geospatial
| 2025-09-05T15:19:08Z | 7 | 29 |
terratorch
|
[
"terratorch",
"Pytorch",
"mmsegmentation",
"segmentation",
"burn scars",
"Geospatial",
"Foundation model",
"image-segmentation",
"en",
"dataset:ibm-nasa-geospatial/hls_burn_scars",
"license:apache-2.0",
"region:us"
] |
image-segmentation
| 2023-07-05T21:51:34Z |
---
license: apache-2.0
language:
- en
tags:
- Pytorch
- mmsegmentation
- segmentation
- burn scars
- Geospatial
- Foundation model
datasets:
- ibm-nasa-geospatial/hls_burn_scars
metrics:
- accuracy
- IoU
- F1 Score
library_name: terratorch
pipeline_tag: image-segmentation
---
### Model and Inputs
The pretrained [Prithvi-EO-1.0-100M](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) parameter model is finetuned to detect Burn Scars on HLS data from the [HLS Burn Scar Scenes dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars). This dataset includes input tiles of 512x512x6, where 512 is the height and width and 6 is the number of bands. The bands are:
1. Blue
2. Green
3. Red
4. Narrow NIR
5. SWIR 1
6. SWIR 2
It is important to point out that the HLS Burn Scar Scenes dataset includes a single timestep, while the Prithvi-100m was pretrained with three timesteps. The difference highlights the flexibility of this model to adapt to different downstream tasks and requirements.
### Code
Code for fine-tuning is available through [Github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main/configs)
Configuration used for fine-tuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/configs/burn_scars.py)
).
### Results
The experiment conducted by running the mmseg stack for 50 epochs using the above config led to an IoU of **0.73** on the burn scar class and **0.96** overall accuracy. It is noteworthy that this leads to a reasonably good model, but further developement will most likely improve performance.
### Inference and demo
The github repo includes an inference script that allows to run the burn scar model for inference on HLS images. These inputs have to be in geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units [0-1]. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.
### Feedback
Your feedback is invaluable to us. If you have any feedback about the model, please feel free to share it with us. You can do this by submitting issues on our open-source repository, [hls-foundation-os](https://github.com/NASA-IMPACT/hls-foundation-os/issues), on GitHub.
### Citation
If this model helped your research, please cite `Prithvi-100M-burn-scar` in your publications. Here is an example BibTeX entry:
```
@misc{Prithvi-100M-burn-scar,
author = {Roy, Sujit and Phillips, Christopher and Jakubik, Johannes and Fraccaro, Paolo and Ankur, Kumar and Avery, Ryan and Ji, Wei and Zadrozny, Bianca and Ramachandran, Rahul},
doi = {10.57967/hf/0953},
month = aug,
title = {{Prithvi 100M burn scar}},
url = {https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar},
year = {2023}
}
```
|
ibm-nasa-geospatial/Prithvi-WxC-1.0-2300m-gravity-wave-parameterization
|
ibm-nasa-geospatial
| 2025-09-05T15:18:26Z | 13 | 10 |
terratorch
|
[
"terratorch",
"Pytorch",
"gravity wave",
"Weather & Climate",
"Foundation model",
"en",
"dataset:Prithvi-WxC/Gravity_wave_Parameterization",
"arxiv:2409.13598",
"arxiv:2406.14775",
"base_model:ibm-nasa-geospatial/Prithvi-WxC-1.0-2300M",
"base_model:finetune:ibm-nasa-geospatial/Prithvi-WxC-1.0-2300M",
"license:apache-2.0",
"region:us"
] | null | 2024-09-19T21:50:01Z |
---
license: apache-2.0
language:
- en
tags:
- Pytorch
- gravity wave
- Weather & Climate
- Foundation model
datasets:
- Prithvi-WxC/Gravity_wave_Parameterization
base_model:
- Prithvi-WxC/prithvi.wxc.2300m.v1
library_name: terratorch
---
This repository contains pretrained model for Gravity Wave Flux Parametrization downstream task.
<img src="https://cdn-uploads.huggingface.co/production/uploads/6488f1d3e22a0081a561ec8f/lOFP_1dAVKCw90uLpj2vu.png" alt="Gravity Wave" width="1024"/>
### Model
The pretrained [Prithvi WxC](https://huggingface.co/ibm-nasa-geospatial/Prithvi-WxC-1.0-2300M) parameter model is finetuned to predict momentum fluxes from
the [Gravity Wave Parameterization dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/gravity-wave-parameterization).
<b>Input:</b> 491 (3 + 4x122) channels.
1. latitude (1)
2. longitude (1)
3. surface elevation (1)
4. zonal winds \\(u\\) (122)
5. meridional winds \\(v\\) (122) 6.
6. temperature \\(T\\) (122)
7. pressure \\(P\\) (122)
<b>Output:</b> 366 (3x122) channels.
1. potential temperature \\(\theta\\) (122)
2. zonal flux of vertical momentum \\(u'\omega'\\) (122)
3. meridional flux of vertical momentum \\(v'\omega'\\) (122)
### Code
Code for fine-tuning is available through [Github](https://github.com/NASA-IMPACT/gravity-wave-finetuning).
### Results
<img src="https://cdn-uploads.huggingface.co/production/uploads/6488f1d3e22a0081a561ec8f/Vk1EKgzf_j90ZPiw2hGHE.png" alt="Gravity Wave" width="1024"/>
For the Andes (mountain waves) and the Southern Ocean (non-mountain waves),
the fine-tuned model achieves correlation coefficients of 0.99 and 0.97, respectively, when compared to the observed fluxes.
### Inference and demo
The github repo includes an inference script that allows to run
the [gravity_wave_model](https://huggingface.co/ibm-nasa-geospatial/Prithvi-WxC-1.0-2300m-gravity-wave-parameterization/blob/main/magnet-flux-uvtp122-epoch-99-loss-0.1022.pt) model
for inference on [sample dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/gravity-wave-parameterization/blob/main/wxc_input_u_v_t_p_output_theta_uw_vw_era5_training_data_hourly_2015_constant_mu_sigma_scaling05.nc).
## Citation
If you use this work, consider citing our paper
```
@misc{schmude2024prithviwxcfoundationmodel,
title={Prithvi WxC: Foundation Model for Weather and Climate},
author={Johannes Schmude and Sujit Roy and Will Trojak and Johannes Jakubik and Daniel Salles Civitarese and Shraddha Singh and Julian Kuehnert and Kumar Ankur and Aman Gupta and Christopher E Phillips and Romeo Kienzler and Daniela Szwarcman and Vishal Gaur and Rajat Shinde and Rohit Lal and Arlindo Da Silva and Jorge Luis Guevara Diaz and Anne Jones and Simon Pfreundschuh and Amy Lin and Aditi Sheshadri and Udaysankar Nair and Valentine Anantharaj and Hendrik Hamann and Campbell Watson and Manil Maskey and Tsengdar J Lee and Juan Bernabe Moreno and Rahul Ramachandran},
year={2024},
eprint={2409.13598},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2409.13598},
}
```
```
@article{gupta2024machine,
title={Machine learning global simulation of nonlocal gravity wave propagation},
author={Gupta, Aman and Sheshadri, Aditi and Roy, Sujit and Gaur, Vishal and Maskey, Manil and Ramachandran, Rahul},
journal={arXiv preprint arXiv:2406.14775},
year={2024}
}
```
|
hokpertoy/blockassist-bc-iridescent_aquatic_parrot_1757085452
|
hokpertoy
| 2025-09-05T15:17:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent aquatic parrot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:17:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent aquatic parrot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ibm-nasa-geospatial/Prithvi-WxC-1.0-2300M-rollout
|
ibm-nasa-geospatial
| 2025-09-05T15:17:33Z | 211 | 20 |
terratorch
|
[
"terratorch",
"arxiv:2409.13598",
"license:apache-2.0",
"region:us"
] | null | 2024-09-20T18:34:24Z |
---
license: apache-2.0
title: README
emoji: 📈
colorFrom: red
colorTo: blue
sdk: static
pinned: false
library_name: terratorch
---
Prithvi WxC is a 2.3 billion parameter model trained on 160 different variables from MERRA-2 data. It has been pretrained on both forecasting and masked
reconstruction objectives. I.e.~the model is capable of reconstructing atmospheric state from partial information as well as propagating state into the
future. The model takes data from two timestamps as input and generates a single, possibly future, timestamp as output. Currently Prithvi WxC comes in two flavors:
- `prithvi.wxc.2300m.v1` has been pretrained with a 50% masking ratio. The time delta between input timestamps is variable as is the forecast lead time.
During pretraining, the input delta was chosen from [-3, -6, -9, -12] hours while the forecast lead time was chosen from [0, 6, 12, 24] hours. We recommend using
`prithvi.wxc.2300m.v1` for generic use cases that do not focus on forecasting.
- (This model) `prithvi.wxc.rollout.2300m.v1` has been through further training cycles to be optimzed for autoregressive rollout. Here, we restricted the input delta
as well as the lead time to 6 hours. We recommend using `prithvi.wxc.rollout.2300m.v1` for forecasting applications.
<div style="display: flex; justify-content: center;">
<b> Zero-Shot Rollout</b>
<img src="https://huggingface.co/Prithvi-WxC/prithvi.wxc.rollout.2300m.v1/resolve/bffd73a5b80904a4a1c8637a9f3bb35d32ce3382/2021C4Ida_2021082700_2plots_winds_platecaree_nohurr.gif" alt="Rollout" width="1024"/>
<br>
<img src="https://huggingface.co/Prithvi-WxC/prithvi.wxc.rollout.2300m.v1/resolve/bffd73a5b80904a4a1c8637a9f3bb35d32ce3382/2021C4Ida_2021082700_2plots_winds_platecaree_CONUS.gif" alt="Rollout_hurr" width="1024"/>
</div>
## Citation
If you use this work, consider citing our paper
```
@misc{schmude2024prithviwxcfoundationmodel,
title={Prithvi WxC: Foundation Model for Weather and Climate},
author={Johannes Schmude and Sujit Roy and Will Trojak and Johannes Jakubik and Daniel Salles Civitarese and Shraddha Singh and Julian Kuehnert and Kumar Ankur and Aman Gupta and Christopher E Phillips and Romeo Kienzler and Daniela Szwarcman and Vishal Gaur and Rajat Shinde and Rohit Lal and Arlindo Da Silva and Jorge Luis Guevara Diaz and Anne Jones and Simon Pfreundschuh and Amy Lin and Aditi Sheshadri and Udaysankar Nair and Valentine Anantharaj and Hendrik Hamann and Campbell Watson and Manil Maskey and Tsengdar J Lee and Juan Bernabe Moreno and Rahul Ramachandran},
year={2024},
eprint={2409.13598},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2409.13598},
}
```
|
mradermacher/Austral-Xgen-9B-Base-GGUF
|
mradermacher
| 2025-09-05T15:16:52Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Delta-Vector/Austral-Xgen-9B-Base",
"base_model:quantized:Delta-Vector/Austral-Xgen-9B-Base",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T14:10:26Z |
---
base_model: Delta-Vector/Austral-Xgen-9B-Base
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Delta-Vector/Austral-Xgen-9B-Base
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Austral-Xgen-9B-Base-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q2_K.gguf) | Q2_K | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.IQ4_XS.gguf) | IQ4_XS | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q6_K.gguf) | Q6_K | 8.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.Q8_0.gguf) | Q8_0 | 11.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Austral-Xgen-9B-Base-GGUF/resolve/main/Austral-Xgen-9B-Base.f16.gguf) | f16 | 21.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
qgallouedec/Qwen3-14B-SFT-20250905151108
|
qgallouedec
| 2025-09-05T15:14:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"hf_jobs",
"trl",
"sft",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T15:12:07Z |
---
base_model: Qwen/Qwen3-8B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-14B-SFT-20250905151108
tags:
- generated_from_trainer
- hf_jobs
- trl
- sft
licence: license
---
# Model Card for Qwen3-14B-SFT-20250905151108
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-14B-SFT-20250905151108", 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: 0.23.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1757085103
|
cwayneconnor
| 2025-09-05T15:13:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:12:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757085118
|
kittygirlhere
| 2025-09-05T15:12:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:12:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sirev/gemma3-4b-exp-Q4_K_M-GGUF
|
sirev
| 2025-09-05T15:12:28Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:sirev/gemma3-4b-exp",
"base_model:quantized:sirev/gemma3-4b-exp",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T15:12:10Z |
---
base_model: sirev/gemma3-4b-exp
tags:
- llama-cpp
- gguf-my-repo
---
# sirev/gemma3-4b-exp-Q4_K_M-GGUF
This model was converted to GGUF format from [`sirev/gemma3-4b-exp`](https://huggingface.co/sirev/gemma3-4b-exp) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/sirev/gemma3-4b-exp) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo sirev/gemma3-4b-exp-Q4_K_M-GGUF --hf-file gemma3-4b-exp-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sirev/gemma3-4b-exp-Q4_K_M-GGUF --hf-file gemma3-4b-exp-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo sirev/gemma3-4b-exp-Q4_K_M-GGUF --hf-file gemma3-4b-exp-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sirev/gemma3-4b-exp-Q4_K_M-GGUF --hf-file gemma3-4b-exp-q4_k_m.gguf -c 2048
```
|
hokpertoy/blockassist-bc-silky_diving_viper_1757084991
|
hokpertoy
| 2025-09-05T15:10:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky diving viper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:09:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky diving viper
---
# 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_1757083145
|
helmutsukocok
| 2025-09-05T15:05:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:05:28Z |
---
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).
|
momomuio/blockassist-bc-bipedal_lanky_meerkat_1757084671
|
momomuio
| 2025-09-05T15:04:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bipedal lanky meerkat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:04:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bipedal lanky meerkat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qgallouedec/Qwen3-14B-SFT-20250905150035
|
qgallouedec
| 2025-09-05T15:03:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"hf_jobs",
"trl",
"sft",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T15:01:38Z |
---
base_model: Qwen/Qwen3-8B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-14B-SFT-20250905150035
tags:
- generated_from_trainer
- hf_jobs
- trl
- sft
licence: license
---
# Model Card for Qwen3-14B-SFT-20250905150035
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-14B-SFT-20250905150035", 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: 0.23.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
qgallouedec/Qwen3-14B-SFT-20250905150036
|
qgallouedec
| 2025-09-05T15:03:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"hf_jobs",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T15:01:38Z |
---
base_model: Qwen/Qwen3-8B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-14B-SFT-20250905150036
tags:
- generated_from_trainer
- sft
- trl
- hf_jobs
licence: license
---
# Model Card for Qwen3-14B-SFT-20250905150036
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-14B-SFT-20250905150036", 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: 0.23.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
hokpertoy/blockassist-bc-scented_slimy_toad_1757084532
|
hokpertoy
| 2025-09-05T15:02:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scented slimy toad",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:02:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scented slimy toad
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Woutermans/zeta-0-5b-sft-lora
|
Woutermans
| 2025-09-05T15:02:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-Coder-0.5B",
"base_model:finetune:unsloth/Qwen2.5-Coder-0.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T15:02:24Z |
---
base_model: unsloth/Qwen2.5-Coder-0.5B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Woutermans
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-0.5B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757084446
|
kittygirlhere
| 2025-09-05T15:01:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:01:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
momomuio/blockassist-bc-foxy_reclusive_bear_1757084478
|
momomuio
| 2025-09-05T15:01:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"foxy reclusive bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T15:01:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- foxy reclusive bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pidbu/blockassist-bc-whistling_alert_shrew_1757084293
|
pidbu
| 2025-09-05T14:59:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:58:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
original-Dr-wong-lu-yang-video-viral-clip/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial
|
original-Dr-wong-lu-yang-video-viral-clip
| 2025-09-05T14:59:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-05T14:59:03Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
vendi11/blockassist-bc-placid_placid_llama_1757084092
|
vendi11
| 2025-09-05T14:55:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:55:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-18-DR-WONG-LU-YANG-CCTV-VIRAL-LINK/ORIGINAL.FULL.VIDEO.DR.WONG.LU.YANG.CCTV.VIRAL.VIDEO.Official.Tutorial
|
VIDEOS-18-DR-WONG-LU-YANG-CCTV-VIRAL-LINK
| 2025-09-05T14:55:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-05T14:54:59Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
momomuio/blockassist-bc-pudgy_thriving_okapi_1757083973
|
momomuio
| 2025-09-05T14:53:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy thriving okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:52:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy thriving okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CodeAtCMU/Llama-3.2-3B_full_sft_code_data_120K_remove_comments
|
CodeAtCMU
| 2025-09-05T14:52:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T14:52:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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. -->
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|
openbmb/MiniCPM4.1-8B
|
openbmb
| 2025-09-05T14:51:57Z | 4 | 3 |
transformers
|
[
"transformers",
"safetensors",
"minicpm",
"text-generation",
"conversational",
"custom_code",
"zh",
"en",
"arxiv:2506.07900",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-09-02T07:14:25Z |
---
license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
---
<div align="center">
<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
</div>
<p align="center">
<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
<a href="https://arxiv.org/abs/2506.07900" target="_blank">Technical Report</a> |
<a href="https://mp.weixin.qq.com/s/KIhH2nCURBXuFXAtYRpuXg?poc_token=HBIsUWijxino8oJ5s6HcjcfXFRi0Xj2LJlxPYD9c">Join Us</a>
</p>
<p align="center">
👋 Contact us in <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
</p>
## What's New
- [2025.09.05] **MiniCPM4.1** series are released! This series is a hybrid reasoning model, which can be used in both deep reasoning mode and non-reasoning mode. 🔥🔥🔥
- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://arxiv.org/abs/2506.07900).🔥🔥🔥
## MiniCPM4 and MiniCPM4.1 Series
MiniCPM4 and MiniCPM4.1 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
- [MiniCPM4.1-8B](https://huggingface.co/openbmb/MiniCPM4.1-8B): The latest version of MiniCPM4, with 8B parameters, support fusion thinking. (**<-- you are here**)
- [MiniCPM4.1-8B-GPTQ](https://huggingface.co/openbmb/MiniCPM4.1-8B-GPTQ): MiniCPM4.1-8B in GPTQ format.
- [MiniCPM4.1-8B-AutoAWQ](https://huggingface.co/openbmb/MiniCPM4.1-8B-AutoAWQ): MiniCPM4.1-8B in AutoAWQ format.
- [MiniCPM-4.1-8B-Marlin](https://huggingface.co/openbmb/MiniCPM-4.1-8B-Marlin): MiniCPM4.1-8B in Marlin format.
- [MiniCPM4.1-8B-GGUF](https://huggingface.co/openbmb/MiniCPM4.1-8B-GGUF): MiniCPM4.1-8B in GGUF format.
- [MiniCPM4.1-8B-MLX](https://huggingface.co/openbmb/MiniCPM4.1-8B-MLX): MiniCPM4.1-8B in MLX format.
- [MiniCPM4.1-8B-Eagle3](https://huggingface.co/openbmb/MiniCPM4.1-8B-Eagle3): Eagle3 model for MiniCPM4.1-8B.
- **MiniCPM4 Series**
<details>
<summary>Click to expand all MiniCPM4 series models</summary>
- [**MiniCPM4-8B**](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship model with 8B parameters, trained on 8T tokens
- [**MiniCPM4-0.5B**](https://huggingface.co/openbmb/MiniCPM4-0.5B): Lightweight version with 0.5B parameters, trained on 1T tokens
- [**MiniCPM4-8B-Eagle-FRSpec**](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference
- [**MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu**](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head with QAT for FRSpec, integrating speculation and quantization for ultra acceleration
- [**MiniCPM4-8B-Eagle-vLLM**](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format for speculative inference
- [**MiniCPM4-8B-marlin-Eagle-vLLM**](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format
- [**BitCPM4-0.5B**](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization of MiniCPM4-0.5B, achieving 90% bit width reduction
- [**BitCPM4-1B**](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization of MiniCPM3-1B, achieving 90% bit width reduction
- [**MiniCPM4-Survey**](https://huggingface.co/openbmb/MiniCPM4-Survey): Generates trustworthy, long-form survey papers from user queries
- [**MiniCPM4-MCP**](https://huggingface.co/openbmb/MiniCPM4-MCP): Integrates MCP tools to autonomously satisfy user requirements
</details>
## Introduction
MiniCPM4 and MiniCPM4.1 are extremely efficient edge-side large model that has undergone efficient optimization across four dimensions: model architecture, learning algorithms, training data, and inference systems, achieving ultimate efficiency improvements.
- 🏗️ **Efficient Model Architecture:**
- InfLLM v2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts
- 🧠 **Efficient Learning Algorithms:**
- Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
- BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
- Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
- 📚 **High-Quality Training Data:**
- UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset [UltraFinweb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
- UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data
- ⚡ **Efficient Inference System:**
- CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding
- ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities
## Usage
### Inference with [CPM.cu](https://github.com/OpenBMB/cpm.cu)
We recommend using [CPM.cu](https://github.com/OpenBMB/cpm.cu) for the inference of MiniCPM4 and MiniCPM4.1. CPM.cu is a CUDA inference framework developed by OpenBMB, which integrates efficient sparse, speculative sampling, and quantization techniques, fully leveraging the efficiency advantages of MiniCPM4 and MiniCPM4.1.
You can install CPM.cu by running the following command:
```bash
git clone https://github.com/OpenBMB/cpm.cu.git --recursive
cd cpm.cu
python3 setup.py install
```
MiniCPM4.1 natively supports context lengths of up to 65,536(64k) tokens. To reproduce the long-text acceleration effect in the paper, we recommend using the LongRoPE factors that have been validated. Change the `rope_scaling` field in the `config.json` file as the following to enable LongRoPE.
```json
{
...,
"rope_scaling": {
"rope_type": "longrope",
"long_factor": [0.9982316082870437, 1.033048153422584, 1.0749920956484724, 1.1255096879436193, 1.1863348602111476, 1.259543828902579, 1.3476188888731149, 1.4535223827776373, 1.5807816745852985, 1.7335856049489526, 1.9168922912975785, 2.1365471404135326, 2.3994084200118646, 2.713475511863602, 3.0880118452194134, 3.533650295140154, 4.062463396503134, 4.687974098908333, 5.425075306704039, 6.289818967956352, 7.29902962722721, 8.6357018163639, 10.210822723989212, 12.053807765671676, 14.193944598909404, 16.65780676784363, 19.463620727694074, 22.628311203524586, 26.150106147261315, 30.02526691405111, 34.23183327975347, 38.73811934094828, 43.502489489729555, 48.47627117965394, 53.61139491762471, 58.857366522037935, 64.16798299215064, 69.51359464319125, 74.86555458220285, 80.21497790341579, 85.55322183307433, 90.89611806932027, 96.26245306514224, 101.68269304046481, 107.18619510219668, 112.82253283014026, 118.63764063163615, 119.88866203644656, 120.9462882391725, 121.837565139014, 122.58663780572562, 123.2147719894291, 123.74049454862576, 124.17980424685767, 124.54641761955492, 124.85202548028222, 125.10654406389756, 125.31835105170659, 125.49450117164764, 125.64091910903052, 125.76256945356558, 125.86360463815589, 125.94749252260765, 126.01712561287873],
"short_factor": [0.9982316082870437, 1.033048153422584, 1.0749920956484724, 1.1255096879436193, 1.1863348602111476, 1.259543828902579, 1.3476188888731149, 1.4535223827776373, 1.5807816745852985, 1.7335856049489526, 1.9168922912975785, 2.1365471404135326, 2.3994084200118646, 2.713475511863602, 3.0880118452194134, 3.533650295140154, 4.062463396503134, 4.687974098908333, 5.425075306704039, 6.289818967956352, 7.29902962722721, 8.6357018163639, 10.210822723989212, 12.053807765671676, 14.193944598909404, 16.65780676784363, 19.463620727694074, 22.628311203524586, 26.150106147261315, 30.02526691405111, 34.23183327975347, 38.73811934094828, 43.502489489729555, 48.47627117965394, 53.61139491762471, 58.857366522037935, 64.16798299215064, 69.51359464319125, 74.86555458220285, 80.21497790341579, 85.55322183307433, 90.89611806932027, 96.26245306514224, 101.68269304046481, 107.18619510219668, 112.82253283014026, 118.63764063163615, 119.88866203644656, 120.9462882391725, 121.837565139014, 122.58663780572562, 123.2147719894291, 123.74049454862576, 124.17980424685767, 124.54641761955492, 124.85202548028222, 125.10654406389756, 125.31835105170659, 125.49450117164764, 125.64091910903052, 125.76256945356558, 125.86360463815589, 125.94749252260765, 126.01712561287873],
"original_max_position_embeddings": 65536
}
}
```
After modification, you can run the following command to reproduce the long-context acceleration effect (the script will automatically download the model weights from HuggingFace)
```bash
python3 tests/test_generate.py
```
You can run the following command to infer with EAGLE3 speculative decoding algorithm.
```bash
python3 -m cpmcu.cli \
--model-path $BASE_MODEL_PATH \
--draft-model-path $EAGLE3_DRAFT_MODEL_PATH \
--prompt-text "Write an article about Artificial Intelligence." \
--use-eagle3 true
```
For more details about CPM.cu, please refer to [the repo CPM.cu](https://github.com/OpenBMB/cpm.cu).
### Hybird Reasoning Mode
MiniCPM4.1 supports hybrid reasoning mode, which can be used in both deep reasoning mode and non-reasoning mode. To enable hybrid reasoning mode. User can set `enable_thinking=True` in `tokenizer.apply_chat_template` to enable hybrid reasoning mode, and set `enable_thinking=False` to enable non-reasoning mode. Similarly, user can directly add `/no_think` at the end of the query to enable non-reasoning mode. If not add any special token or add `/think` at the end of the query, the model will enable reasoning mode.
```python
# Enable reasoning mode
prompt_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True
)
# Enable non-reasoning mode
prompt_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
```
### Inference with Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
torch.manual_seed(0)
path = 'openbmb/MiniCPM4.1-8B'
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
# User can directly use the chat interface
# responds, history = model.chat(tokenizer, "Write an article about Artificial Intelligence.", temperature=0.7, top_p=0.7)
# print(responds)
# User can also use the generate interface
messages = [
{"role": "user", "content": "Write an article about Artificial Intelligence."},
]
prompt_text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([prompt_text], return_tensors="pt").to(device)
model_outputs = model.generate(
**model_inputs,
max_new_tokens=32768,
top_p=0.95,
temperature=0.6
)
output_token_ids = [
model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs['input_ids']))
]
responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)
```
MiniCPM4.1-8B supports `InfLLM v2`, a sparse attention mechanism designed for efficient long-sequence inference. It requires the [infllmv2_cuda_impl](https://github.com/OpenBMB/infllmv2_cuda_impl) library.
You can install it by running the following command:
```bash
git clone -b feature_infer https://github.com/OpenBMB/infllmv2_cuda_impl.git
cd infllmv2_cuda_impl
git submodule update --init --recursive
pip install -e . # or python setup.py install
```
To enable InfLLM v2, you need to add the `sparse_config` field in `config.json`:
```json
{
...,
"sparse_config": {
"kernel_size": 32,
"kernel_stride": 16,
"init_blocks": 1,
"block_size": 64,
"window_size": 2048,
"topk": 64,
"use_nope": false,
"dense_len": 8192
}
}
```
These parameters control the behavior of InfLLM v2:
* `kernel_size` (default: 32): The size of semantic kernels.
* `kernel_stride` (default: 16): The stride between adjacent kernels.
* `init_blocks` (default: 1): The number of initial blocks that every query token attends to. This ensures attention to the beginning of the sequence.
* `block_size` (default: 64): The block size for key-value blocks.
* `window_size` (default: 2048): The size of the local sliding window.
* `topk` (default: 64): The specifies that each token computes attention with only the top-k most relevant key-value blocks.
* `use_nope` (default: false): Whether to use the NOPE technique in block selection for improved performance.
* `dense_len` (default: 8192): Since Sparse Attention offers limited benefits for short sequences, the model can use standard (dense) attention for shorter texts. The model will use dense attention for sequences with a token length below `dense_len` and switch to sparse attention for sequences exceeding this length. Set this to `-1` to always use sparse attention regardless of sequence length.
MiniCPM4.1 natively supports context lengths of up to 65,536(64k) tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques for effective handling of long texts. We have validated the model's performance on context lengths of up to 131,072 tokens by modifying the LongRoPE factor.
You can apply the LongRoPE factor modification by modifying the model files. Specifically, in the `config.json` file, adjust the `rope_scaling` fields.
```json
{
...,
"rope_scaling": {
"rope_type": "longrope",
"long_factor": [0.9982316082870437, 1.033048153422584, 1.0749920956484724, 1.1255096879436193, 1.1863348602111476, 1.259543828902579, 1.3476188888731149, 1.4535223827776373, 1.5807816745852985, 1.7335856049489526, 1.9168922912975785, 2.1365471404135326, 2.3994084200118646, 2.713475511863602, 3.0880118452194134, 3.533650295140154, 4.062463396503134, 4.687974098908333, 5.425075306704039, 6.289818967956352, 7.29902962722721, 8.6357018163639, 10.210822723989212, 12.053807765671676, 14.193944598909404, 16.65780676784363, 19.463620727694074, 22.628311203524586, 26.150106147261315, 30.02526691405111, 34.23183327975347, 38.73811934094828, 43.502489489729555, 48.47627117965394, 53.61139491762471, 58.857366522037935, 64.16798299215064, 69.51359464319125, 74.86555458220285, 80.21497790341579, 85.55322183307433, 90.89611806932027, 96.26245306514224, 101.68269304046481, 107.18619510219668, 112.82253283014026, 118.63764063163615, 119.88866203644656, 120.9462882391725, 121.837565139014, 122.58663780572562, 123.2147719894291, 123.74049454862576, 124.17980424685767, 124.54641761955492, 124.85202548028222, 125.10654406389756, 125.31835105170659, 125.49450117164764, 125.64091910903052, 125.76256945356558, 125.86360463815589, 125.94749252260765, 126.01712561287873],
"short_factor": [0.9982316082870437, 1.033048153422584, 1.0749920956484724, 1.1255096879436193, 1.1863348602111476, 1.259543828902579, 1.3476188888731149, 1.4535223827776373, 1.5807816745852985, 1.7335856049489526, 1.9168922912975785, 2.1365471404135326, 2.3994084200118646, 2.713475511863602, 3.0880118452194134, 3.533650295140154, 4.062463396503134, 4.687974098908333, 5.425075306704039, 6.289818967956352, 7.29902962722721, 8.6357018163639, 10.210822723989212, 12.053807765671676, 14.193944598909404, 16.65780676784363, 19.463620727694074, 22.628311203524586, 26.150106147261315, 30.02526691405111, 34.23183327975347, 38.73811934094828, 43.502489489729555, 48.47627117965394, 53.61139491762471, 58.857366522037935, 64.16798299215064, 69.51359464319125, 74.86555458220285, 80.21497790341579, 85.55322183307433, 90.89611806932027, 96.26245306514224, 101.68269304046481, 107.18619510219668, 112.82253283014026, 118.63764063163615, 119.88866203644656, 120.9462882391725, 121.837565139014, 122.58663780572562, 123.2147719894291, 123.74049454862576, 124.17980424685767, 124.54641761955492, 124.85202548028222, 125.10654406389756, 125.31835105170659, 125.49450117164764, 125.64091910903052, 125.76256945356558, 125.86360463815589, 125.94749252260765, 126.01712561287873],
"original_max_position_embeddings": 65536
}
}
```
### Inference with [SGLang](https://github.com/sgl-project/sglang)
#### Speculative Decoding
For accelerated inference with speculative decoding, follow these steps:
##### 1. Download MiniCPM4.1 Draft Model
First, download the MiniCPM4.1 draft model:
```bash
cd /your_path
git clone https://huggingface.co/openbmb/MiniCPM4.1-8B-Eagle3
```
##### 2. Install EAGLE3-Compatible SGLang
The EAGLE3 adaptation PR has been submitted. For now, use our repository for installation:
```bash
git clone https://github.com/LDLINGLINGLING/sglang.git
cd sglang
pip install -e .
```
##### 3. Launch SGLang Server with Speculative Decoding
Start the SGLang server with speculative decoding enabled:
```bash
python -m sglang.launch_server \
--model-path "openbmb/MiniCPM4.1-8B" \
--host "127.0.0.1" \
--port 30002 \
--mem-fraction-static 0.9 \
--speculative-algorithm EAGLE3 \
--speculative-draft-model-path "your/path/MiniCPM4_1-8B-Eagle3-bf16" \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 32 \
--temperature 0.7
```
##### 4. Client Usage
The client usage remains the same for both standard and speculative decoding:
```python
import openai
client = openai.Client(base_url=f"http://localhost:30002/v1", api_key="None")
response = client.chat.completions.create(
model="openbmb/MiniCPM4.1-8B",
messages=[
{"role": "user", "content": "Write an article about Artificial Intelligence."},
],
temperature=0.6,
max_tokens=32768,
)
print(response.choices[0].message.content)
```
Note: Make sure to update the port number in the client code to match the server port (30002 in the speculative decoding example).
##### Configuration Parameters
- `--speculative-algorithm EAGLE3`: Enables EAGLE3 speculative decoding
- `--speculative-draft-model-path`: Path to the draft model for speculation
- `--speculative-num-steps`: Number of speculative steps (default: 3)
- `--speculative-eagle-topk`: Top-k parameter for EAGLE (default: 1)
- `--speculative-num-draft-tokens`: Number of draft tokens (default: 32)
- `--mem-fraction-static`: Memory fraction for static allocation (default: 0.9)
#### Standard Inference (Without Speculative Decoding)
For now, you need to install our forked version of SGLang.
```bash
git clone -b openbmb https://github.com/OpenBMB/sglang.git
cd sglang
pip install --upgrade pip
pip install -e "python[all]"
```
You can start the inference server by running the following command:
```bash
python -m sglang.launch_server --model openbmb/MiniCPM4.1-8B --trust-remote-code --port 30000 --chat-template chatml
```
Then you can use the chat interface by running the following command:
```python
import openai
client = openai.Client(base_url=f"http://localhost:30000/v1", api_key="None")
response = client.chat.completions.create(
model="openbmb/MiniCPM4.1-8B",
messages=[
{"role": "user", "content": "Write an article about Artificial Intelligence."},
],
temperature=0.6,
max_tokens=32768,
)
print(response.choices[0].message.content)
```
### Inference with [vLLM](https://github.com/vllm-project/vllm)
#### Speculative Decoding
For accelerated inference with speculative decoding using vLLM, follow these steps:
##### 1. Download MiniCPM4.1 Draft Model
First, download the MiniCPM4.1 draft model:
```bash
cd /your_path
git clone https://huggingface.co/openbmb/MiniCPM4.1-8B-Eagle3
```
##### 2. Install EAGLE3-Compatible vLLM
The EAGLE3 vLLM PR has been submitted. For now, use our repository for installation:
```bash
git clone https://github.com/LDLINGLINGLING/vllm.git
cd vllm
pip install -e .
```
##### 3. Launch vLLM Server with Speculative Decoding
Start the vLLM inference server with speculative decoding enabled. Make sure to update the model path in the speculative-config to point to your downloaded MiniCPM4_1-8B-Eagle3-bf16 folder:
```bash
VLLM_USE_V1=1 \
vllm serve openbmb/MiniCPM4.1-8B \
--seed 42 \
--trust-remote-code \
--speculative-config '{
"model": "your/path/MiniCPM4_1-8B-Eagle3-bf16",
"num_speculative_tokens": 3,
"method": "eagle3",
"draft_tensor_parallel_size": 1
}'
```
##### 4. Client Usage Example
The client usage remains the same for both standard and speculative decoding:
```python
import openai
client = openai.Client(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM4.1-8B",
messages=[
{"role": "user", "content": "Write an article about Artificial Intelligence."},
],
temperature=0.6,
max_tokens=32768,
extra_body=dict(add_special_tokens=True), # Ensures special tokens are added for chat template
)
print(response.choices[0].message.content)
```
##### vLLM Configuration Parameters
- `VLLM_USE_V1=1`: Enables vLLM v1 API
- `--speculative-config`: JSON configuration for speculative decoding
- `model`: Path to the draft model for speculation
- `num_speculative_tokens`: Number of speculative tokens (default: 3)
- `method`: Speculative decoding method (eagle3)
- `draft_tensor_parallel_size`: Tensor parallel size for draft model (default: 1)
- `--seed`: Random seed for reproducibility
- `--trust-remote-code`: Allow execution of remote code for custom models
#### Standard Inference (Without Speculative Decoding)
For now, you need to install the latest version of vLLM.
```bash
pip install -U vllm \
--pre \
--extra-index-url https://wheels.vllm.ai/nightly
```
Then you can inference MiniCPM4.1-8B with vLLM:
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "openbmb/MiniCPM4.1-8B"
prompt = [{"role": "user", "content": "Write an article about Artificial Intelligence."}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
llm = LLM(
model=model_name,
trust_remote_code=True,
max_num_batched_tokens=65536,
dtype="bfloat16",
gpu_memory_utilization=0.8,
)
sampling_params = SamplingParams(top_p=0.95, temperature=0.6, max_tokens=32768)
outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```
Also, you can start the inference server by running the following command:
> **Note**: In vLLM's chat API, `add_special_tokens` is `False` by default. This means important special tokens—such as the beginning-of-sequence (BOS) token—will not be added automatically. To ensure the input prompt is correctly formatted for the model, you should explicitly set `extra_body={"add_special_tokens": True}`.
```bash
vllm serve openbmb/MiniCPM4.1-8B
```
Then you can use the chat interface by running the following code:
```python
import openai
client = openai.Client(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="openbmb/MiniCPM4.1-8B",
messages=[
{"role": "user", "content": "Write an article about Artificial Intelligence."},
],
temperature=0.6,
max_tokens=32768,
extra_body=dict(add_special_tokens=True), # Ensures special tokens are added for chat template
)
print(response.choices[0].message.content)
```
## Evaluation Results
On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 demonstrates significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 achieves approximately 7x decoding speed improvement.
MiniCPM4.1 achieves 3x decoding speed improvement in reasoning.
#### Comprehensive Evaluation
MiniCPM4.1 launches end-side versions with 8B parameter scale, both achieving best-in-class performance in their respective categories.
#### Long Text Evaluation
MiniCPM4 is pre-trained on 32K long texts and achieves length extension through YaRN technology. In the 128K long text needle-in-a-haystack task, MiniCPM4 demonstrates outstanding performance.
## Statement
- As a language model, MiniCPM generates content by learning from a vast amount of text.
- However, it does not possess the ability to comprehend or express personal opinions or value judgments.
- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
## LICENSE
- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
## Citation
- Please cite our [paper](https://arxiv.org/abs/2506.07900) if you find our work valuable.
```bibtex
@article{minicpm4,
title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
author={MiniCPM Team},
year={2025}
}
```
|
Miracle-man/blockassist-bc-singing_lithe_koala_1757082017
|
Miracle-man
| 2025-09-05T14:51:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"singing lithe koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:51:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- singing lithe koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hokpertoy/blockassist-bc-bristly_striped_flamingo_1757083842
|
hokpertoy
| 2025-09-05T14:51:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bristly striped flamingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:50:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bristly striped flamingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hokpertoy/blockassist-bc-tricky_curious_impala_1757083726
|
hokpertoy
| 2025-09-05T14:49:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky curious impala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:48:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky curious impala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittygirlhere/blockassist-bc-twitchy_beaked_coral_1757083617
|
kittygirlhere
| 2025-09-05T14:48:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy beaked coral",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:47:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy beaked coral
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757083552
|
bah63843
| 2025-09-05T14:46:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:46:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cebbbopwq/blockassist-bc-meek_deadly_alligator_1757083493
|
cebbbopwq
| 2025-09-05T14:45:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"meek deadly alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:44:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- meek deadly alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Arcadia-12B-Fusion-GGUF
|
mradermacher
| 2025-09-05T14:45:09Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"lazymergekit",
"en",
"base_model:SvalTek/Arcadia-12B-Fusion",
"base_model:quantized:SvalTek/Arcadia-12B-Fusion",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T13:35:11Z |
---
base_model: SvalTek/Arcadia-12B-Fusion
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- merge
- lazymergekit
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/SvalTek/Arcadia-12B-Fusion
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Arcadia-12B-Fusion-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Arcadia-12B-Fusion-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q3_K_S.gguf) | Q3_K_S | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q3_K_L.gguf) | Q3_K_L | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.IQ4_XS.gguf) | IQ4_XS | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Arcadia-12B-Fusion-GGUF/resolve/main/Arcadia-12B-Fusion.Q8_0.gguf) | Q8_0 | 13.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
zenqqq/blockassist-bc-restless_reptilian_caterpillar_1757083270
|
zenqqq
| 2025-09-05T14:42:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"restless reptilian caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:42:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- restless reptilian caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gouki510/gptoss-20b-secure
|
gouki510
| 2025-09-05T14:40:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gpt-oss-20b",
"base_model:finetune:unsloth/gpt-oss-20b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T14:18:07Z |
---
base_model: unsloth/gpt-oss-20b
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** gouki510
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1757083014
|
canoplos112
| 2025-09-05T14:39:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:37:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Cike-Dev/GemmaOffensiveClassifier
|
Cike-Dev
| 2025-09-05T14:38:10Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T03:15:29Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: GemmaOffensiveClassifier
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for GemmaOffensiveClassifier
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="Cike-Dev/GemmaOffensiveClassifier", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.2
- Transformers: 4.56.0
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
hokpertoy/blockassist-bc-lithe_hulking_wasp_1757083037
|
hokpertoy
| 2025-09-05T14:37:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lithe hulking wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:37:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lithe hulking wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vendi11/blockassist-bc-placid_placid_llama_1757082960
|
vendi11
| 2025-09-05T14:36:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:36:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pidbu/blockassist-bc-whistling_alert_shrew_1757082897
|
pidbu
| 2025-09-05T14:36:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:35:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GANGodfather/Affine-PAXJRE14
|
GANGodfather
| 2025-09-05T14:34:34Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"vllm",
"conversational",
"arxiv:2508.10925",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"mxfp4",
"region:us"
] |
text-generation
| 2025-09-02T04:03:16Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
---
<p align="center">
<img alt="gpt-oss-120b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https://arxiv.org/abs/2508.10925"><strong>Model card</strong></a> ·
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of these open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fit into a single 80GB GPU (like NVIDIA H100 or AMD MI300X) (117B parameters with 5.1B active parameters)
- `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
> [!NOTE]
> This model card is dedicated to the larger `gpt-oss-120b` model. Check out [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) for the smaller model.
# Highlights
* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
* **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making `gpt-oss-120b` run on a single 80GB GPU (like NVIDIA H100 or AMD MI300X) and the `gpt-oss-20b` model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
---
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "openai/gpt-oss-120b"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
```
transformers serve
transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-120b
```
[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
## vLLM
vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
```bash
uv pip install --pre vllm==0.10.1+gptoss \
--extra-index-url https://wheels.vllm.ai/gpt-oss/ \
--extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
--index-strategy unsafe-best-match
vllm serve openai/gpt-oss-120b
```
[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
## PyTorch / Triton
To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
## Ollama
If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
```bash
# gpt-oss-120b
ollama pull gpt-oss:120b
ollama run gpt-oss:120b
```
[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
#### LM Studio
If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
```bash
# gpt-oss-120b
lms get openai/gpt-oss-120b
```
Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
---
# Download the model
You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
```shell
# gpt-oss-120b
huggingface-cli download openai/gpt-oss-120b --include "original/*" --local-dir gpt-oss-120b/
pip install gpt-oss
python -m gpt_oss.chat model/
```
# Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
* **Low:** Fast responses for general dialogue.
* **Medium:** Balanced speed and detail.
* **High:** Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
# Tool use
The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks
# Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This larger model `gpt-oss-120b` can be fine-tuned on a single H100 node, whereas the smaller [`gpt-oss-20b`](https://huggingface.co/openai/gpt-oss-20b) can even be fine-tuned on consumer hardware.
# Citation
```bibtex
@misc{openai2025gptoss120bgptoss20bmodel,
title={gpt-oss-120b & gpt-oss-20b Model Card},
author={OpenAI},
year={2025},
eprint={2508.10925},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.10925},
}
```
|
pidbu/blockassist-bc-whistling_alert_shrew_1757082389
|
pidbu
| 2025-09-05T14:27:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:27:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
John6666/tekitousugoi-mix-v40-sdxl
|
John6666
| 2025-09-05T14:25:18Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"dekai",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-09-05T14:20:32Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- girls
- dekai
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1703201/tekitousugoimix?modelVersionId=2184067).
This model created by [suteakaking](https://civitai.com/user/suteakaking).
|
Synthetai/juem_kontext_lora_v1
|
Synthetai
| 2025-09-05T14:24:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"lora",
"flux",
"text-to-image",
"diffusion",
"license:cc-by-nc-4.0",
"region:us"
] |
text-to-image
| 2025-09-05T12:51:09Z |
---
library_name: diffusers
tags:
- lora
- flux
- text-to-image
- diffusion
license: cc-by-nc-4.0
pipeline_tag: text-to-image
---
# juem_kontext_lora_v1
<p align="center">
<img src="examples/example.png" alt="cover" width="80%">
</p>
This is a **LoRA model trained on Flux Kontext Dev**, designed to **convert photorealistic images into an illustration style**.
Key feature: it works with a **single simple prompt** — no complex prompts or negative prompts required.
---
## Trigger Prompt
```
Convert the image to an illustration style
```
|
AnerYubo/blockassist-bc-fanged_camouflaged_cassowary_1757082181
|
AnerYubo
| 2025-09-05T14:23:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged camouflaged cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:23:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged camouflaged cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
qgallouedec/Qwen3-8B-SFT-20250905141719
|
qgallouedec
| 2025-09-05T14:21:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"hf_jobs",
"trl",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T14:18:23Z |
---
base_model: Qwen/Qwen3-8B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-8B-SFT-20250905141719
tags:
- generated_from_trainer
- sft
- hf_jobs
- trl
licence: license
---
# Model Card for Qwen3-8B-SFT-20250905141719
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-8B-SFT-20250905141719", 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: 0.23.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chainway9/blockassist-bc-untamed_quick_eel_1757080478
|
chainway9
| 2025-09-05T14:20:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:20:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pidbu/blockassist-bc-whistling_alert_shrew_1757081870
|
pidbu
| 2025-09-05T14:19:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:18:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1757081879
|
canoplos112
| 2025-09-05T14:19:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:18:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1757081647
|
klmdr22
| 2025-09-05T14:14:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:14:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rbelanec/train_cb_1757081470
|
rbelanec
| 2025-09-05T14:14:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-09-05T14:12:02Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_cb_1757081470
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. -->
# train_cb_1757081470
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4824
- Num Input Tokens Seen: 306152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 789
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| 0.3315 | 0.5044 | 57 | 0.2731 | 15568 |
| 0.1533 | 1.0088 | 114 | 1.0623 | 30760 |
| 0.2727 | 1.5133 | 171 | 0.5874 | 46120 |
| 0.3404 | 2.0177 | 228 | 0.2549 | 61792 |
| 0.6691 | 2.5221 | 285 | 0.3684 | 77136 |
| 0.4382 | 3.0265 | 342 | 0.2316 | 92944 |
| 0.3287 | 3.5310 | 399 | 0.4520 | 108704 |
| 0.3698 | 4.0354 | 456 | 0.2106 | 123744 |
| 0.3123 | 4.5398 | 513 | 0.2462 | 139232 |
| 0.0127 | 5.0442 | 570 | 0.2971 | 154632 |
| 0.1652 | 5.5487 | 627 | 0.4199 | 169832 |
| 0.0145 | 6.0531 | 684 | 0.4591 | 185424 |
| 0.0089 | 6.5575 | 741 | 0.5756 | 201168 |
| 0.0062 | 7.0619 | 798 | 0.4575 | 216400 |
| 0.0005 | 7.5664 | 855 | 0.4710 | 231792 |
| 0.0008 | 8.0708 | 912 | 0.4744 | 247656 |
| 0.0025 | 8.5752 | 969 | 0.4987 | 263304 |
| 0.0004 | 9.0796 | 1026 | 0.4878 | 278160 |
| 0.0007 | 9.5841 | 1083 | 0.4866 | 293584 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
qgallouedec/Qwen3-8B-SFT-20250905141056
|
qgallouedec
| 2025-09-05T14:13:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"hf_jobs",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T14:11:49Z |
---
base_model: Qwen/Qwen3-8B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-8B-SFT-20250905141056
tags:
- generated_from_trainer
- trl
- sft
- hf_jobs
licence: license
---
# Model Card for Qwen3-8B-SFT-20250905141056
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the [trl-lib/Capybara](https://huggingface.co/datasets/trl-lib/Capybara) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-8B-SFT-20250905141056", 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: 0.23.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1757081124
|
canoplos112
| 2025-09-05T14:07:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:05:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1757080840
|
bah63843
| 2025-09-05T14:01:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T14:01:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matherchodhuuu/blockassist-bc-lightfooted_skilled_chameleon_1757078607
|
matherchodhuuu
| 2025-09-05T13:59:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T13:59:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fangcaotank/task-13-Qwen-Qwen2.5-3B-Instruct
|
fangcaotank
| 2025-09-05T13:57:32Z | 561 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"region:us"
] | null | 2025-08-08T06:57:59Z |
---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.0
|
mradermacher/KoT-platypus2-13B-i1-GGUF
|
mradermacher
| 2025-09-05T12:27:29Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-05T12:10:19Z |
<!-- ### 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/kyujinpy/KoT-platypus2-13B
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1757073535
|
sampingkaca72
| 2025-09-05T12:27:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:27:03Z |
---
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).
|
carterray/carterray-demo
|
carterray
| 2025-09-05T12:22:58Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-05T11:32:53Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Carter
---
# Carterray Demo
<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 `Carter` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Carter",
"lora_weights": "https://huggingface.co/carterray/carterray-demo/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('carterray/carterray-demo', weight_name='lora.safetensors')
image = pipeline('Carter').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: 3947
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/carterray/carterray-demo/discussions) to add images that show off what you’ve made with this LoRA.
|
vommertou/blockassist-bc-mute_whistling_hamster_1757074719
|
vommertou
| 2025-09-05T12:19:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute whistling hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:18:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute whistling hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-Adam-HessianMaskToken-0.001-v2_7301
|
luckeciano
| 2025-09-05T12:15:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T09:09:56Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-Adam-HessianMaskToken-0.001-v2_5868
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-Adam-HessianMaskToken-0.001-v2_5868
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-Adam-HessianMaskToken-0.001-v2_5868", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/qd17ww4k)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1757072674
|
coelacanthxyz
| 2025-09-05T12:14:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:14:48Z |
---
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).
|
cactus-S/blockassist-bc-reclusive_arctic_panther_1757072730
|
cactus-S
| 2025-09-05T12:12:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive arctic panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:12:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive arctic panther
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
boahancock/blockassist-bc-iridescent_rapid_toad_1757073987
|
boahancock
| 2025-09-05T12:12:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent rapid toad",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T12:07:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent rapid toad
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/math-genius-7B-GGUF
|
mradermacher
| 2025-09-05T12:07:25Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"trl",
"sft",
"en",
"dataset:entfane/Mixture-Of-Thoughts-Math-No-COT",
"base_model:entfane/math-genius-7B",
"base_model:quantized:entfane/math-genius-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T10:07:14Z |
---
base_model: entfane/math-genius-7B
datasets:
- entfane/Mixture-Of-Thoughts-Math-No-COT
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/entfane/math-genius-7B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#math-genius-7B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/math-genius-7B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/math-genius-7B-GGUF/resolve/main/math-genius-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF
|
mradermacher
| 2025-09-05T12:07:25Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:pot99rta/OmegaPatricide-12B-DarkerDirective-Mell",
"base_model:quantized:pot99rta/OmegaPatricide-12B-DarkerDirective-Mell",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-05T11:00:21Z |
---
base_model: pot99rta/OmegaPatricide-12B-DarkerDirective-Mell
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: 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/pot99rta/OmegaPatricide-12B-DarkerDirective-Mell
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-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/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/OmegaPatricide-12B-DarkerDirective-Mell-i1-GGUF/resolve/main/OmegaPatricide-12B-DarkerDirective-Mell.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | 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 -->
|
Mungert/NVIDIA-Nemotron-Nano-12B-v2-GGUF
|
Mungert
| 2025-09-05T12:06:30Z | 1,978 | 0 |
transformers
|
[
"transformers",
"gguf",
"nvidia",
"pytorch",
"text-generation",
"en",
"es",
"fr",
"de",
"it",
"ja",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v1",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v2",
"dataset:nvidia/Nemotron-Pretraining-Dataset-sample",
"dataset:nvidia/Nemotron-CC-v2",
"dataset:nvidia/Nemotron-CC-Math-v1",
"dataset:nvidia/Nemotron-Pretraining-SFT-v1",
"arxiv:2504.03624",
"arxiv:2508.14444",
"arxiv:2412.02595",
"base_model:nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base",
"base_model:quantized:nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-09-05T01:57:23Z |
---
license: other
license_name: nvidia-open-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
datasets:
- nvidia/Nemotron-Post-Training-Dataset-v1
- nvidia/Nemotron-Post-Training-Dataset-v2
- nvidia/Nemotron-Pretraining-Dataset-sample
- nvidia/Nemotron-CC-v2
- nvidia/Nemotron-CC-Math-v1
- nvidia/Nemotron-Pretraining-SFT-v1
language:
- en
- es
- fr
- de
- it
- ja
library_name: transformers
tags:
- nvidia
- pytorch
track_downloads: true
base_model:
- nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base
---
# <span style="color: #7FFF7F;">NVIDIA-Nemotron-Nano-12B-v2 GGUF Models</span>
## <span style="color: #7F7FFF;">Model Generation Details</span>
This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`4fd1242b`](https://github.com/ggerganov/llama.cpp/commit/4fd1242bef6cb2325b4ff1c1a80f3b54b64508a6).
---
## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>
I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.
In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here:
👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)
While this does increase model file size, it significantly improves precision for a given quantization level.
### **I'd love your feedback—have you tried this? How does it perform for you?**
---
<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
Click here to get info on choosing the right GGUF model format
</a>
---
<!--Begin Original Model Card-->
# NVIDIA-Nemotron-Nano-12B-v2
**Model Developer:** NVIDIA Corporation
**Model Dates:**
June 2025 \- August 2025
**Data Freshness:**
September 2024
The pretraining data has a cutoff date of September 2024.
## Model Overview
NVIDIA-Nemotron-Nano-12B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks. The model was fine-tuned from [NVIDIA-Nemotron-Nano-12B-v2-Base](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base) was further compressed into [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2).
The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers. For the architecture, please refer to the [Nemotron-H tech report](https://arxiv.org/abs/2504.03624).
The model was trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) and [NeMo-RL](https://github.com/NVIDIA-NeMo/RL).
The supported languages include: English, German, Spanish, French, Italian, and Japanese. Improved using Qwen.
This model is ready for commercial use.
## License/Terms of Use
GOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
## Evaluation Results
### Benchmark Results (Reasoning On)
We evaluated our model in **Reasoning-On** mode across all benchmarks, except RULER, which is evaluated in **Reasoning-Off** mode.
| Benchmark | NVIDIA-Nemotron-Nano-12B-v2 |
| :---- | ----- |
| AIME25 | 76.25% |
| MATH500 | 97.75% |
| GPQA | 64.48% |
| LCB | 70.79% |
| BFCL v3 | 66.98% |
| IFEVAL-Prompt | 84.70% |
| IFEVAL-Instruction | 89.81% |
All evaluations were done using [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills).
We published a [tutorial](https://nvidia.github.io/NeMo-Skills/tutorials/2025/08/22/reproducing-nvidia-nemotron-nano-9b-v2-evals/) with all details necessary to reproduce our evaluation results.
## Reasoning Budget Control
This model supports runtime “thinking” budget control. During inference, the user can specify how many tokens the model is allowed to "think".
## Model Architecture
- Architecture Type: Mamba2-Transformer Hybrid
- Network Architecture: Nemotron-Hybrid
### Deployment Geography: Global
### Use Case
NVIDIA-Nemotron-Nano-12B-v2 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Spanish and Japanese) are also supported. Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks.
### Release Date: 08/29/2025
- Huggingface 08/29/2025 via https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2
## References
- [NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://arxiv.org/abs/2508.14444)
## Input
- Input Type(s): Text
- Input Format(s): String
- Input Parameters: One-Dimensional (1D): Sequences
- Other Properties Related to Input: Context length up to 128K. Supported languages include German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English.
## Output
- Output Type(s): Text
- Output Format: String
- Output Parameters: One-Dimensional (1D): Sequences up to 128K
Our models are designed and optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
## Software Integration
- Runtime Engine(s): NeMo 25.07.nemotron-nano-v2
- Supported Hardware Microarchitecture Compatibility: NVIDIA A10G, NVIDIA H100-80GB, NVIDIA A100
- Operating System(s): Linux
### **Use it with Transformers**
The snippet below shows how to use this model with Huggingface Transformers (tested on version 4.48.3).
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-Nano-12B-v2")
model = AutoModelForCausalLM.from_pretrained(
"nvidia/NVIDIA-Nemotron-Nano-12B-v2",
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto"
)
```
Case 1: `/think` or no reasoning signal is provided in the system prompt, reasoning will be set to `True`
```
messages = [
{"role": "system", "content": "/think"},
{"role": "user", "content": "Write a haiku about GPUs"},
]
```
Case 2: `/no_think` is provided, reasoning will be set to `False`
```
messages = [
{"role": "system", "content": "/no_think"},
{"role": "user", "content": "Write a haiku about GPUs"},
]
```
Note: `/think` or `/no_think` keywords can also be provided in “user” messages for turn-level reasoning control.
The rest of the inference snippet remains the same
```
tokenized_chat = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
tokenized_chat,
max_new_tokens=32,
eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0]))
```
We recommend setting `temperature` to `0.6`, `top_p` to `0.95` for reasoning True and greedy search for reasoning False, and increase `max_new_tokens` to `1024` or higher for reasoning True.
### **Use it with TRT-LLM**
The snippet below shows how to use this model with TRT-LLM. We tested this on the following [commit](https://github.com/NVIDIA/TensorRT-LLM/tree/46c5a564446673cdd0f56bcda938d53025b6d04e) and followed these [instructions](https://github.com/NVIDIA/TensorRT-LLM/blob/46c5a564446673cdd0f56bcda938d53025b6d04e/docs/source/installation/build-from-source-linux.md#option-2-build-tensorrt-llm-step-by-step) to build and install TRT-LLM in a docker container.
```
from tensorrt_llm import SamplingParams
from tensorrt_llm._torch import LLM
from tensorrt_llm._torch.pyexecutor.config import PyTorchConfig
from tensorrt_llm.llmapi import KvCacheConfig
from transformers import AutoTokenizer
pytorch_config = PyTorchConfig(
disable_overlap_scheduler=True, enable_trtllm_decoder=True
)
kv_cache_config = KvCacheConfig(
enable_block_reuse=False,
)
```
```
model_id = "nvidia/NVIDIA-Nemotron-Nano-12B-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
llm = LLM(
model=model_id,
max_seq_len=32678,
max_batch_size=4,
pytorch_backend_config=pytorch_config,
kv_cache_config=kv_cache_config,
tensor_parallel_size=8,
)
messages = [
{"role": "system", "content": "/think"},
{"role": "user", "content": "Write a haiku about GPUs"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
sampling_params = SamplingParams(
max_tokens=512,
temperature=0.6,
top_p=0.95,
add_special_tokens=False,
)
outputs = llm.generate([prompt], sampling_params)
print(outputs[0].outputs[0].text)
```
### **Use it with vLLM**
The snippet below shows how to use this model with vLLM. Use the latest version of vLLM and follow these instructions to build and install vLLM.
```shell
pip install -U "vllm>=0.10.1"
```
Now you can run run the server with:
```shell
vllm serve nvidia/NVIDIA-Nemotron-Nano-12B-v2 \
--trust-remote-code \
--max-num-seqs 64 \
--mamba_ssm_cache_dtype float32
```
Note:
- Remember to add \`--mamba\_ssm\_cache\_dtype float32\` for accurate quality. Without this option, the model’s accuracy may degrade.
- If you encounter a CUDA OOM issue, try `--max-num-seqs 64` and consider lower the value further if the error persists.
Alternativly, you can use Docker to launch a vLLM server.
```
export TP_SIZE=1 # Adjust this value based on the number of GPUs you want to use
docker run --runtime nvidia --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
-p 8000:8000 \
--ipc=host \
vllm/vllm-openai:v0.10.1 \
--model nvidia/NVIDIA-Nemotron-Nano-12B-v2 \
--tensor-parallel-size ${TP_SIZE} \
--max-num-seqs 64 \
--max-model-len 131072 \
--trust-remote-code \
--mamba_ssm_cache_dtype float32
```
#### Using Budget Control with a vLLM Server
The thinking budget allows developers to keep accuracy high and meet response‑time targets \- which is especially crucial for customer support, autonomous agent steps, and edge devices where every millisecond counts.
With budget control, you can set a limit for internal reasoning:
* `max_thinking_tokens`: This is a threshold that will attempt to end the reasoning trace at the next newline encountered in the reasoning trace. If no newline is encountered within 500 tokens, it will abruptly end the reasoning trace at \`max\_thinking\_tokens \+ 500\`.
Start a vLLM server:
```shell
vllm serve nvidia/NVIDIA-Nemotron-Nano-12B-v2 \
--trust-remote-code \
--mamba_ssm_cache_dtype float32
```
Client for supporting budget control:
```py
from typing import Any, Dict, List
import openai
from transformers import AutoTokenizer
class ThinkingBudgetClient:
def __init__(self, base_url: str, api_key: str, tokenizer_name_or_path: str):
self.base_url = base_url
self.api_key = api_key
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path)
self.client = openai.OpenAI(base_url=self.base_url, api_key=self.api_key)
def chat_completion(
self,
model: str,
messages: List[Dict[str, Any]],
max_thinking_budget: int = 512,
max_tokens: int = 1024,
**kwargs,
) -> Dict[str, Any]:
assert (
max_tokens > max_thinking_budget
), f"thinking budget must be smaller than maximum new tokens. Given {max_tokens=} and {max_thinking_budget=}"
# 1. first call chat completion to get reasoning content
response = self.client.chat.completions.create(
model=model, messages=messages, max_tokens=max_thinking_budget, **kwargs
)
content = response.choices[0].message.content
reasoning_content = content
if not "</think>" in reasoning_content:
# reasoning content is too long, closed with a period (.)
reasoning_content = f"{reasoning_content}.\n</think>\n\n"
reasoning_tokens_len = len(
self.tokenizer.encode(reasoning_content, add_special_tokens=False)
)
remaining_tokens = max_tokens - reasoning_tokens_len
assert (
remaining_tokens > 0
), f"remaining tokens must be positive. Given {remaining_tokens=}. Increase the max_tokens or lower the max_thinking_budget."
# 2. append reasoning content to messages and call completion
messages.append({"role": "assistant", "content": reasoning_content})
prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
continue_final_message=True,
)
response = self.client.completions.create(
model=model, prompt=prompt, max_tokens=remaining_tokens, **kwargs
)
response_data = {
"reasoning_content": reasoning_content.strip().strip("</think>").strip(),
"content": response.choices[0].text,
"finish_reason": response.choices[0].finish_reason,
}
return response_data
```
Calling the server with a budget (Restricted to 32 tokens here as an example)
```py
tokenizer_name_or_path = "nvidia/NVIDIA-Nemotron-Nano-12B-v2"
client = ThinkingBudgetClient(
base_url="http://localhost:8000/v1", # Nano 12B v2 deployed in thinking mode
api_key="EMPTY",
tokenizer_name_or_path=tokenizer_name_or_path,
)
result = client.chat_completion(
model="nvidia/NVIDIA-Nemotron-Nano-12B-v2",
messages=[
{"role": "system", "content": "You are a helpful assistant. /think"},
{"role": "user", "content": "What is 2+2?"},
],
max_thinking_budget=32,
max_tokens=512,
temperature=0.6,
top_p=0.95,
)
print(result)
```
You should see output similar to the following:
```
{'reasoning_content': "Okay, the user asked, What is 2+2? Let me think. Well, 2 plus 2 equals 4. That's a basic.", 'content': '2 + 2 equals **4**.\n', 'finish_reason': 'stop'}
```
#### Using Tool-Calling with a vLLM Server
Start a vLLM server with native tool-calling:
```shell
git clone https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2
vllm serve nvidia/NVIDIA-Nemotron-Nano-12B-v2 \
--trust-remote-code \
--mamba_ssm_cache_dtype float32 \
--enable-auto-tool-choice \
--tool-parser-plugin "NVIDIA-Nemotron-Nano-12B-v2/nemotron_toolcall_parser_no_streaming.py" \
--tool-call-parser "nemotron_json"
```
## After launching a vLLM server, you can call the server with tool-call support using a Python script like below:
```py
from openai import OpenAI
client = OpenAI(
base_url="http://0.0.0.0:5000/v1",
api_key="dummy",
)
completion = client.chat.completions.create(
model="nvidia/NVIDIA-Nemotron-Nano-12B-v2",
messages=[
{"role": "system", "content": ""},
{"role": "user", "content": "My bill is $100. What will be the amount for 18% tip?"}
],
tools=[
{
"type": "function",
"function": {
"name": "calculate_tip",
"parameters": {
"type": "object",
"properties": {
"bill_total": {
"type": "integer",
"description": "The total amount of the bill"
},
"tip_percentage": {
"type": "integer",
"description": "The percentage of tip to be applied"
}
},
"required": ["bill_total", "tip_percentage"]
}
}
},
{
"type": "function",
"function": {
"name": "convert_currency",
"parameters": {
"type": "object",
"properties": {
"amount": {
"type": "integer",
"description": "The amount to be converted"
},
"from_currency": {
"type": "string",
"description": "The currency code to convert from"
},
"to_currency": {
"type": "string",
"description": "The currency code to convert to"
}
},
"required": ["from_currency", "amount", "to_currency"]
}
}
}
],
temperature=0.6,
top_p=0.95,
max_tokens=32768,
stream=False
)
print(completion.choices[0].message.content)
print(completion.choices[0].message.tool_calls)
```
You should see output similar to the following:
```
<think>
Okay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters.
</think>
[ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')]
```
## Model Version
- v1.0
## Prompt Format
We follow the jinja chat template provided below. This template conditionally adds `<think>\n` to the start of the Assistant response if `/think` is found in either the system prompt or any user message. If no reasoning signal is added, the model defaults to reasoning "on" mode. The chat template adds `<think></think>` to the start of the Assistant response if `/no_think` is found in the system prompt. Thus enforcing reasoning on/off behavior.
```
{%- set ns = namespace(enable_thinking = true) %}
{%- for message in messages -%}
{%- set content = message['content'] -%}
{%- if message['role'] == 'user' or message['role'] == 'system' -%}
{%- if '/think' in content -%}
{%- set ns.enable_thinking = true -%}
{%- elif '/no_think' in content -%}
{%- set ns.enable_thinking = false -%}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- if messages[0]['role'] != 'system' -%}
{%- set ns.non_tool_system_content = '' -%}
{{- '<SPECIAL_10>System\n' -}}
{%- else -%}
{%- set ns.non_tool_system_content = messages[0]['content']
.replace('/think', '')
.replace('/no_think', '')
.strip()
-%}
{{- '<SPECIAL_10>System\n' + ns.non_tool_system_content }}
{%- endif -%}
{%- if tools -%}
{%- if ns.non_tool_system_content is defined and ns.non_tool_system_content != '' -%}
{{- '\n\n' -}}
{%- endif -%}
{{- 'You can use the following tools to assist the user if required:' -}}
{{- '\n<AVAILABLE_TOOLS>[' -}}
{%- for tool in tools -%}
{{- (tool.function if tool.function is defined else tool) | tojson -}}
{{- ', ' if not loop.last else '' -}}
{%- endfor -%}
{{- ']</AVAILABLE_TOOLS>\n\n' -}}
{{- 'If you decide to call any tool(s), use the following format:\n' -}}
{{- '<TOOLCALL>[{{"name": "tool_name1", "arguments": "tool_args1"}}, ' -}}
{{- '{{"name": "tool_name2", "arguments": "tool_args2"}}]</TOOLCALL>\n\n' -}}
{{- 'The user will execute tool-calls and return responses from tool(s) in this format:\n' -}}
{{- '<TOOL_RESPONSE>[{{"tool_response1"}}, {{"tool_response2"}}]</TOOL_RESPONSE>\n\n' -}}
{{- 'Based on the tool responses, you can call additional tools if needed, correct tool calls if any errors are found, or just respond to the user.' -}}
{%- endif -%}
{{- '\n' -}}
{%- set messages = messages[1:] if messages[0]['role'] == 'system' else messages -%}
{%- if messages[-1]['role'] == 'assistant' -%}
{%- set ns.last_turn_assistant_content = messages[-1]['content'].strip() -%}
{%- set messages = messages[:-1] -%}
{%- endif -%}
{%- for message in messages -%}
{%- set content = message['content'] -%}
{%- if message['role'] == 'user' -%}
{{- '<SPECIAL_11>User\n' + content.replace('/think', '').replace('/no_think', '').strip() + '\n' }}
{%- elif message['role'] == 'tool' -%}
{%- if loop.first or (messages[loop.index0 - 1].role != 'tool') -%}
{{- '<SPECIAL_11>User\n' + '<TOOL_RESPONSE>[' }}
{%- endif -%}
{{- message['content'] -}}
{{- ', ' if not loop.last and (messages[loop.index0 + 1].role == 'tool') else '' -}}
{%- if loop.last or (messages[loop.index0 + 1].role != 'tool') -%}
{{- ']</TOOL_RESPONSE>\n' -}}
{%- endif -%}
{%- elif message['role'] == 'assistant' -%}
{%- if '</think>' in content -%}
{%- set content = content.split('</think>')[1].strip() %}
{%- endif -%}
{{- '<SPECIAL_11>Assistant\n' + content.strip() }}
{%- if message.tool_calls -%}
{%- if content.strip() != '' -%}
{{- '\n\n' -}}
{%- endif -%}
{{- '<TOOLCALL>[' -}}
{%- for call in message.tool_calls -%}
{%- set fn = call.function if call.function is defined else call -%}
{{- '{"name": "' + fn.name + '", "arguments": ' -}}
{%- if fn.arguments is string -%}
{{- fn.arguments -}}
{%- else -%}
{{- fn.arguments | tojson -}}
{%- endif -%}
{{- '}' + (', ' if not loop.last else '') -}}
{%- endfor -%}
{{- ']</TOOLCALL>' -}}
{%- endif -%}
{{- '\n<SPECIAL_12>\n' -}}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- '<SPECIAL_11>Assistant\n' -}}
{%- if ns.enable_thinking is defined and ns.enable_thinking is false -%}
{{- '<think></think>' -}}
{%- else -%}
{{- '<think>\n' -}}
{%- endif -%}
{%- if ns.last_turn_assistant_content is defined and ns.last_turn_assistant_content != '' -%}
{{- ns.last_turn_assistant_content -}}
{%- endif -%}
{%- else -%}
{%- if ns.last_turn_assistant_content is defined and ns.last_turn_assistant_content != '' -%}
{{- '<SPECIAL_11>Assistant\n' -}}
{%- if ns.enable_thinking is defined and ns.enable_thinking is false -%}
{{- '<think></think>' -}}
{%- else -%}
{{- '<think>\n' -}}
{%- endif -%}
{{- ns.last_turn_assistant_content -}}
{%- if continue_final_message is defined -%}
{%- if continue_final_message is false -%}
{{- '\n<SPECIAL_12>\n' -}}
{%- endif -%}
{%- else -%}
{{- '\n<SPECIAL_12>\n' -}}
{%- endif -%}
{%- endif -%}
{%- endif -%}
```
##
## Training, Testing, and Evaluation Datasets
### Training datasets
* Data Modality: Text
* Text Training Data Size: More than 10 Trillion Tokens
* Train/Test/Valid Split: We used 100% of the corpus for pre-training and relied on external benchmarks for testing.
* Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
**Properties:** The post-training corpus for NVIDIA-Nemotron-Nano-12B-v2 consists of English and multilingual text (German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English). Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including code, legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracies. For several of the domains listed above we used synthetic data, specifically reasoning traces, from DeepSeek R1/R1-0528, Qwen3-235B-A22B, Nemotron 4 340B, Qwen2.5-32B-Instruct-AWQ, Qwen2.5-14B-Instruct, Qwen 2.5 72B.
The pre-training corpus for NVIDIA-Nemotron-Nano-12B-v2 consists of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 15 multilingual languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was pre-trained for approximately twenty trillion tokens.
Alongside the model, we release our [final pretraining data](https://huggingface.co/collections/nvidia/nemotron-pre-training-dataset-689d9de36f84279d83786b35), as outlined in this section. For ease of analysis, there is a sample set that is ungated. For all remaining code, math and multilingual data, gating and approval is required, and the dataset is permissively licensed for model training purposes.
More details on the datasets and synthetic data generation methods can be found in the technical report [NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf) .
## Public Datasets
| Dataset | Collection Period |
| :---- | :---- |
| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | 4/23/2025 |
| [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |
| [PRM800K](https://github.com/openai/prm800k) | 4/23/2025 |
| [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 |
| [Common Crawl](https://commoncrawl.org/) | 4/23/2025 |
| [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 |
| [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 |
| [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 |
| [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | 4/23/2025 |
| [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 4/23/2025 |
| [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | 4/23/2025 |
| [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) and [OpenStax \- CC BY-SA subset](https://openstax.org/) | 4/23/2025 |
| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb), [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k), [PRM800K](https://github.com/openai/prm800k), and [SciBench](https://github.com/mandyyyyii/scibench) | 4/23/2025 |
| [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) | 4/23/2025 |
| [Court Listener](https://www.courtlistener.com/help/api/bulk-data/) | Legacy Download |
| [peS2o](https://huggingface.co/datasets/allenai/peS2o) | Legacy Download |
| [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) | Legacy Download |
| [BioRxiv](https://www.biorxiv.org/tdm) | Legacy Download |
| [PMC Open Access Subset](https://pmc.ncbi.nlm.nih.gov/tools/openftlist/) | Legacy Download |
| [OpenWebText2](https://openwebtext2.readthedocs.io/en/latest/) | Legacy Download |
| [Stack Exchange Data Dump](https://archive.org/details/stackexchange) | Legacy Download |
| [PubMed Abstracts](https://github.com/thoppe/The-Pile-PubMed) | Legacy Download |
| [NIH ExPorter](https://exporter.nih.gov/ExPORTER_Catalog.aspx) | Legacy Download |
| [arXiv](https://info.arxiv.org/help/bulk_data/index.html) | Legacy Download |
| [BigScience Workshop Datasets](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#datasets) | Legacy Download |
| [Reddit Dataset](https://files.pushshift.io/reddit/) | Legacy Download |
| [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | Legacy Download |
| [Public Software Heritage S3](https://docs.softwareheritage.org/devel/swh-export/graph/dataset.html#summary-of-dataset-versions) | Legacy Download |
| [The Stack](https://huggingface.co/datasets/bigcode/the-stack) | Legacy Download |
| [mC4](https://huggingface.co/datasets/legacy-datasets/mc4) | Legacy Download |
| [Advanced Mathematical Problem Solving](https://github.com/hendrycks/math?tab=readme-ov-file) | Legacy Download |
| [MathPile](https://github.com/GAIR-NLP/MathPile/) | Legacy Download |
| [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Legacy Download |
| [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/) | Legacy Download |
| [FLAN](https://github.com/google-research/FLAN) | Legacy Download |
| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb) | Legacy Download |
| [SciBench](https://github.com/mandyyyyii/scibench) | Legacy Download |
| [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) | Legacy Download |
| [FinQA](https://finqasite.github.io/) | Legacy Download |
| [Riddles](https://github.com/crawsome/riddles) | Legacy Download |
| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | Legacy Download |
| [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) | Legacy Download |
| [Cosmos QA](https://huggingface.co/datasets/allenai/cosmos_qa) | Legacy Download |
| [MCTest](https://huggingface.co/datasets/sagnikrayc/mctest) | Legacy Download |
| [AI2's Reasoning Challenge](https://huggingface.co/datasets/ai2_arc) | Legacy Download |
| [OpenBookQA](https://github.com/allenai/OpenBookQA) | Legacy Download |
| [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | Legacy Download |
| [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101) | Legacy Download |
| [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | Legacy Download |
| [The Common Pile v0.1](https://huggingface.co/common-pile) | Legacy Download |
| [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Legacy Download |
| [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Legacy Download |
| [FastChat](https://github.com/lm-sys/FastChat) | 6/30/2025 |
## Private Non-publicly Accessible Datasets of Third Parties
| Dataset |
| :---- |
| Global Regulation |
| Workbench |
## Online Dataset Sources
The English Common Crawl data was downloaded from the Common Crawl Foundation (see their [FAQ](https://commoncrawl.org/faq) for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways described in the [Nemotron-CC paper](https://arxiv.org/abs/2412.02595).
Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.
The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set (for additional details, refer to the technical report).
| Dataset | Modality | Dataset Size (Tokens) | Collection Period |
| :---- | :---- | :---- | :---- |
| English Common Crawl | Text | 3.360T | 4/8/2025 |
| Multilingual Common Crawl | Text | 812.7B | 5/1/2025 |
| GitHub Crawl | Text | 747.4B | 4/29/2025 |
## NVIDIA-Sourced Synthetic Datasets
| Dataset | Modality | Dataset Size (Tokens) | Seed Dataset | Model(s) used for generation |
| :---- | :---- | :---- | :---- | :---- |
| Synthetic Art of Problem Solving from DeepSeek-R1 | Text | 25.5B | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
| Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1 | Text | 327M | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101); [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) |
| Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 83.6M | [OpenStax \- CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
| Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 9.7M | [OpenStax \- CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
| Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B | Text | 175M | [OpenStax \- CC BY-SA subset](https://openstax.org/); [GSM8K](https://github.com/openai/grade-school-math); [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
| [Nemotron-PrismMath](https://huggingface.co/datasets/nvidia/Nemotron-PrismMath) | Text | 4.6B | [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified); [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | [Qwen2.5-0.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct); [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) |
| Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 350M | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
| Synthetic FineMath-4+ Reprocessed from DeepSeek-V3 | Text | 9.2B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3) |
| Synthetic FineMath-3+ Reprocessed from phi-4 | Text | 27.6B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
| Synthetic Union-3+ Reprocessed from phi-4 | Text | 93.1B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
| Refreshed [Nemotron-MIND](https://huggingface.co/datasets/nvidia/Nemotron-MIND) from phi-4 | Text | 73B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
| Synthetic Union-4+ Reprocessed from phi-4 | Text | 14.12B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
| Synthetic Union-3+ minus 4+ Reprocessed from phi-4 | Text | 78.95B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
| Synthetic Union-3 Refreshed from phi-4 | Text | 80.94B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
| Synthetic Union-4+ Refreshed from phi-4 | Text | 52.32B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324 | Text | 4.0B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) |
| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B | Text | 4.2B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
| Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct | Text | 83.1B | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k) | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Qwen2.5-Math-72B](https://huggingface.co/Qwen/Qwen2.5-Math-72B); [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B); [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
| Synthetic MMLU Auxiliary Train from DeepSeek-R1 | Text | 0.5B | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
| Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 5.4B | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
| Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct | Text | 1.949T | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) |
| Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B | Text | 997.3B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
| Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text | 55.1B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
| Synthetic OpenMathReasoning from DeepSeek-R1-0528 | Text | 1.5M | [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
| Synthetic OpenCodeReasoning from DeepSeek-R1-0528 | Text | 1.1M | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
| Synthetic Science Data from DeepSeek-R1-0528 | Text | 1.5M | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
| Synthetic Humanity's Last Exam from DeepSeek-R1-0528 | Text | 460K | [Humanity's Last Exam](https://huggingface.co/datasets/cais/hle) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
| Synthetic ToolBench from Qwen3-235B-A22B | Text | 400K | [ToolBench](https://github.com/OpenBMB/ToolBench) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
| Synthetic Nemotron Content Safety Dataset V2, eval-safety, Gretel Synthetic Safety Alignment, and RedTeam\_2K from DeepSeek-R1-0528 | Text | 52K | [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0); [eval-safety](https://github.com/CrystalEye42/eval-safety/blob/main/malicious_tasks_dataset.yaml); [Gretel Synthetic Safety Alignment](https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1); [RedTeam\_2K](https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k/viewer/RedTeam_2K) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
| Synthetic HelpSteer from Qwen3-235B-A22B | Text | 120K | [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3); [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
| Synthetic Alignment data from Mixtral-8x22B-Instruct-v0.1, Mixtral-8x7B-Instruct-v0.1, and Nemotron-4 Family | Text | 400K | [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2); [C4](https://huggingface.co/datasets/allenai/c4); [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m); [ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K); [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k); lm\_identity (NVIDIA internal); [FinQA](https://finqasite.github.io/); [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions); [Riddles](https://github.com/crawsome/riddles); ChatQA nvolve-multiturn (NVIDIA internal); [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2); [SciBench](https://github.com/mandyyyyii/scibench); [OpenBookQA](https://github.com/allenai/OpenBookQA); [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb); [Public Software Heritage S3](https://docs.softwareheritage.org/devel/swh-export/graph/dataset.html#summary-of-dataset-versions); [Khan Academy Math Keywords](https://www.khanacademy.org/math) | Nemotron-4-15B-Base (NVIDIA internal); Nemotron-4-15B-Instruct (NVIDIA internal); [Nemotron-4-340B-Base](https://huggingface.co/nvidia/Nemotron-4-340B-Base); [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct); [Nemotron-4-340B-Reward](https://huggingface.co/nvidia/Nemotron-4-340B-Reward); [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1); [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) |
| Synthetic LMSYS-Chat-1M from Qwen3-235B-A22B | Text | 1M | [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
| Synthetic Multilingual Reasoning data from DeepSeek-R1-0528, Qwen2.5-32B-Instruct-AWQ, and Qwen2.5-14B-Instruct | Text | 25M | [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning); [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen2.5-32B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-AWQ) (translation); [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (translation); |
| Synthetic Multilingual Reasoning data from Qwen3-235B-A22B and Gemma 3 Post-Trained models | Text | 5M | [WildChat](https://huggingface.co/datasets/allenai/WildChat-1M) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [Gemma 3 PT 12B](https://huggingface.co/google/gemma-3-12b-it); [Gemma 3 PT 27B](https://huggingface.co/google/gemma-3-27b-it) |
### Evaluation Dataset:
* Data Collection Method by dataset: Hybrid: Human, Synthetic
* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
## Inference
- ## Engines: HF, vLLM, TRT-LLM
- ## Test Hardware NVIDIA A10G 24GB, H100 80GB
## 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 [Trustworthy AI terms of service](https://www.nvidia.com/en-us/agreements/trustworthy-ai/terms/), 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.
For more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](./bias.md), [Explainability](./explainability.md), [Safety & Security](./safety.md), and [Privacy](./privacy.md) Subcards.
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
## Citation
```
@misc{nvidia2025nvidianemotronnano2,
title={NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model},
author={NVIDIA},
year={2025},
eprint={2508.14444},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.14444},
}
```
<!--End Original Model Card-->
---
# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>
Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:
👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)
The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)
💬 **How to test**:
Choose an **AI assistant type**:
- `TurboLLM` (GPT-4.1-mini)
- `HugLLM` (Hugginface Open-source models)
- `TestLLM` (Experimental CPU-only)
### **What I’m Testing**
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:
- **Function calling** against live network services
- **How small can a model go** while still handling:
- Automated **Nmap security scans**
- **Quantum-readiness checks**
- **Network Monitoring tasks**
🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):
- ✅ **Zero-configuration setup**
- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!
### **Other Assistants**
🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)
🔵 **HugLLM** – Latest Open-source models:
- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.
### 💡 **Example commands you could test**:
1. `"Give me info on my websites SSL certificate"`
2. `"Check if my server is using quantum safe encyption for communication"`
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!
### Final Word
I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.
If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.
I'm also open to job opportunities or sponsorship.
Thank you! 😊
|
bah63843/blockassist-bc-plump_fast_antelope_1757073450
|
bah63843
| 2025-09-05T11:58:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:58:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ashishscapsitech123/qwen2.5_3b_4bit_8600_full_finetuned_test
|
ashishscapsitech123
| 2025-09-05T11:53:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-to-text
| 2025-09-05T11:52:05Z |
---
base_model: unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ashishscapsitech123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit
This qwen2_5_vl 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)
|
boahancock/blockassist-bc-iridescent_rapid_toad_1757072839
|
boahancock
| 2025-09-05T11:53:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent rapid toad",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:48:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent rapid toad
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KevinZonda/MedSPO-3B
|
KevinZonda
| 2025-09-05T11:45:23Z | 18 | 2 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:KevinZonda/PubMed-IV",
"dataset:KevinZonda/PM4-V3-SPO",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T06:22:28Z |
---
language:
- en
base_model:
- Qwen/Qwen2.5-3B-Instruct
pipeline_tag: text-generation
datasets:
- KevinZonda/PubMed-IV
- KevinZonda/PM4-V3-SPO
library_name: transformers
license: apache-2.0
---
# MedSPO-3B
MedSPO-7B is a fine-tuned [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
model specifically designed for biomedical subject-predicate-object (SPO) extraction tasks.
This model is trained on the [PubMed-IV](https://huggingface.co/datasets/KevinZonda/PubMed-IV) dataset using SPO extraction knowledge distilled from
[DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324).
## Magic Prompt
System Prompt:
```plain
You are a biomedical specialist. You are given one paper (title, abstract, conclusion). Extract all biomedical-related Subject-Predicate-Object (SPO) Triple in valid JSON format wrapped in <output> tag.
```
User Prompt:
```xml
<input>
<title></title>
<abstract></abstract>
<conclusion></conclusion>
</input>
```
|
Sahildo/blockassist-bc-sizable_lanky_owl_1757072646
|
Sahildo
| 2025-09-05T11:44:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sizable lanky owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:44:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sizable lanky owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DapaoZeng/ddpm-celebahq-finetuned-butterflies-2epochs
|
DapaoZeng
| 2025-09-05T11:44:41Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2025-09-05T11:44:29Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('DapaoZeng/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
KevinZonda/MedSPO-7B
|
KevinZonda
| 2025-09-05T11:44:41Z | 23 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation",
"conversational",
"en",
"dataset:KevinZonda/PubMed-IV",
"dataset:KevinZonda/PM4-V3-SPO",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-27T17:45:58Z |
---
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
datasets:
- KevinZonda/PubMed-IV
- KevinZonda/PM4-V3-SPO
library_name: transformers
license: apache-2.0
---
# MedSPO-7B
MedSPO-7B is a fine-tuned [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
model specifically designed for biomedical subject-predicate-object (SPO) extraction tasks.
This model is trained on the [PubMed-IV](https://huggingface.co/datasets/KevinZonda/PubMed-IV) dataset using SPO extraction knowledge distilled from
[DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324).
## Magic Prompt
System Prompt:
```plain
You are a biomedical specialist. You are given one paper (title, abstract, conclusion). Extract all biomedical-related Subject-Predicate-Object (SPO) Triple in valid JSON format wrapped in <output> tag.
```
User Prompt:
```xml
<input>
<title></title>
<abstract></abstract>
<conclusion></conclusion>
</input>
```
|
knightluffy/qwen34bfine1
|
knightluffy
| 2025-09-05T11:43:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2025-09-05T07:08:14Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** knightluffy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dsagasdgds/blockassist-bc-unseen_camouflaged_komodo_1757071210
|
dsagasdgds
| 2025-09-05T11:41:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"unseen camouflaged komodo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:41:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- unseen camouflaged komodo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cactus-S/blockassist-bc-reclusive_arctic_panther_1757070969
|
cactus-S
| 2025-09-05T11:39:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive arctic panther",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:39:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive arctic panther
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kafa22/blockassist-bc-regal_leggy_hummingbird_1757072314
|
kafa22
| 2025-09-05T11:39:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal leggy hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:39:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal leggy hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/AnubisLemonade-70B-v1.1-GGUF
|
mradermacher
| 2025-09-05T11:38:15Z | 337 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ockerman0/AnubisLemonade-70B-v1.1",
"base_model:quantized:ockerman0/AnubisLemonade-70B-v1.1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-05T03:14:00Z |
---
base_model: ockerman0/AnubisLemonade-70B-v1.1
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/ockerman0/AnubisLemonade-70B-v1.1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#AnubisLemonade-70B-v1.1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AnubisLemonade-70B-v1.1-GGUF/resolve/main/AnubisLemonade-70B-v1.1.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
abhi6007/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_gliding_antelope
|
abhi6007
| 2025-09-05T11:38:11Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am striped_gliding_antelope",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-06T14:22:19Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am striped_gliding_antelope
---
# 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]
|
AnerYubo/blockassist-bc-shaggy_melodic_cobra_1757072278
|
AnerYubo
| 2025-09-05T11:38:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shaggy melodic cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:37:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shaggy melodic cobra
---
# 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_1757070503
|
kojeklollipop
| 2025-09-05T11:37:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:37:33Z |
---
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).
|
raihannabiil/blockassist-bc-humming_rugged_viper_1757070092
|
raihannabiil
| 2025-09-05T11:36:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"humming rugged viper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:36:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- humming rugged viper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama3b-llama8b-er-v584-seed2-seed2-hx-openmath-fpt
|
giovannidemuri
| 2025-09-05T11:34:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-05T10:02:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kiok1250/blockassist-bc-beaked_insectivorous_lobster_1757071955
|
kiok1250
| 2025-09-05T11:33:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked insectivorous lobster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:33:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked insectivorous lobster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
natukundaphiionah/qwen3-14b-sunflower-20250905
|
natukundaphiionah
| 2025-09-05T11:32:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:jq/qwen3-14b-ug40-pretrained",
"base_model:finetune:jq/qwen3-14b-ug40-pretrained",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T11:31:58Z |
---
base_model: jq/qwen3-14b-ug40-pretrained
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** natukundaphiionah
- **License:** apache-2.0
- **Finetuned from model :** jq/qwen3-14b-ug40-pretrained
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)
|
kiok1250/blockassist-bc-beaked_insectivorous_lobster_1757071734
|
kiok1250
| 2025-09-05T11:29:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked insectivorous lobster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:29:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked insectivorous lobster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kafa22/blockassist-bc-regal_leggy_hummingbird_1757071717
|
kafa22
| 2025-09-05T11:29:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal leggy hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:29:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal leggy hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hamedkharazmi/blockassist-bc-tough_webbed_hamster_1757069695
|
hamedkharazmi
| 2025-09-05T11:26:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tough webbed hamster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:26:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tough webbed hamster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-dormant_strong_badger_1757071491
|
AnerYubo
| 2025-09-05T11:24:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant strong badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-05T11:24:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant strong badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Zhouyonghao/Qwen3-lora_model
|
Zhouyonghao
| 2025-09-05T11:22:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-05T11:21:24Z |
---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Zhouyonghao
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-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)
|
DTebias/Qwen3-0.6B-Gensyn-Swarm-hoarse_muscular_cassowary
|
DTebias
| 2025-09-05T11:22:08Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am hoarse_muscular_cassowary",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T21:53:00Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am hoarse_muscular_cassowary
---
# 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. -->
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### 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
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### 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]
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#### 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. -->
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#### Testing Data
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#### Metrics
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[More Information Needed]
### Results
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#### 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]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed]
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