modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 12:27:52
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 528
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
NexVeridian/gpt-oss-120b-8bit
|
NexVeridian
| 2025-08-30T11:04:05Z | 400 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-120b",
"base_model:quantized:openai/gpt-oss-120b",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-06T05:33:31Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-120b
---
# NexVeridian/gpt-oss-120b-8bit
This model [NexVeridian/gpt-oss-120b-8bit](https://huggingface.co/NexVeridian/gpt-oss-120b-8bit) was
converted to MLX format from [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)
using mlx-lm version **0.27.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/gpt-oss-120b-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
ViFortune-AI/Ovis2.5-1B-Pretrained
|
ViFortune-AI
| 2025-08-30T10:29:11Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"ovis2_5",
"text-generation",
"MLLM",
"ovis",
"qwen3",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"zh",
"dataset:AIDC-AI/Ovis-dataset",
"arxiv:2508.11737",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-29T23:52:17Z |
---
license: apache-2.0
datasets:
- AIDC-AI/Ovis-dataset
library_name: transformers
tags:
- MLLM
- ovis
- qwen3
pipeline_tag: image-text-to-text
language:
- en
- zh
---
# Ovis2.5-1B-Pretrained
<div align="center">
<img src=https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/3IK823BZ8w-mz_QfeYkDn.png width="30%"/>
</div>
<p align="center">
<a href="https://arxiv.org/abs/2508.11737"><img src="https://img.shields.io/badge/📖_Original_Report-Ovis2.5-b31b1b.svg" alt="technical report"></a>
<a href="https://github.com/AIDC-AI/Ovis"><img src="https://img.shields.io/badge/GitHub-AIDC--AI/Ovis-blue?style=flat&logo=github" alt="code"></a>
<a href="https://huggingface.co/collections/AIDC-AI/ovis25-689ec1474633b2aab8809335"><img src="https://img.shields.io/badge/🤗_Official_Models-AIDC--AI/Ovis2.5-yellow" alt="models"></a>
</p>
## Introduction
**Ovis2.5-1B-Pretrained** is a customized version of the Ovis2.5 architecture. This model is created by merging pre-trained components:
* **Vision Encoder**: `siglip2-so400m-patch16-512`, taken from the original Ovis2.5 model, capable of high-resolution visual perception.
* **Language Model (LLM)**: `Qwen3-0.6B`, a compact and efficient language model.
This model is designed for research and experimentation, following the philosophy of "small model, high performance" for resource-constrained scenarios.
**Important note:** This is a **pretrained/base model**, created by merging weights. It has been partially fine-tuned for vision-language alignment, but it **requires further instruction-tuning** to achieve conversational ability and strong instruction-following performance like the officially released versions `Ovis2.5-2B` or `Ovis2.5-9B`.
## Architecture Details
| Ovis MLLM | Vision Encoder | Language Model (LLM) | Status |
|:----------------------|:----------------------------|:---------------------|:------------|
| **Ovis2.5-1B-Pretrained** | `siglip2-so400m-patch16-512` | `Qwen3-0.6B` | **Base Model (Needs fine-tuning)** |
| Ovis2.5-2B (Official) | `siglip2-so400m-patch16-512` | `Qwen3-1.7B` | Instruction-Tuned |
| Ovis2.5-9B (Official) | `siglip2-so400m-patch16-512` | `Qwen3-8B` | Instruction-Tuned |
## Quick Start
You can use this model in a similar way to the official Ovis2.5-2B version. First, install the required libraries:
```bash
pip install torch transformers numpy pillow moviepy
pip install flash-attn --no-build-isolation
|
mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF
|
mradermacher
| 2025-08-30T10:22:58Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:lmsys/lmsys-chat-1m",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"base_model:quantized:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"license:llama3.3",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T08:07:50Z |
---
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- lmsys/lmsys-chat-1m
language:
- en
- ja
library_name: transformers
license:
- llama3.3
- gemma
model_type: llama
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/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-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/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.5-GGUF/resolve/main/Llama-3.1-Swallow-8B-Instruct-v0.5.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
klmdr22/blockassist-bc-wild_loud_newt_1756546290
|
klmdr22
| 2025-08-30T09:32:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:32:09Z |
---
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).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1756541999
|
hakimjustbao
| 2025-08-30T08:46:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:46:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Loder-S/blockassist-bc-sprightly_knobby_tiger_1756539643
|
Loder-S
| 2025-08-30T08:06:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:06:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly knobby tiger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
happyensworld/blockassist-bc-sleek_scavenging_ram_1756538801
|
happyensworld
| 2025-08-30T07:28:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sleek scavenging ram",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:27:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sleek scavenging ram
---
# 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-giant_leggy_rhino_1756538400
|
AnerYubo
| 2025-08-30T07:20:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"giant leggy rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:20:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- giant leggy rhino
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
laurarconcepcion121/blockassist-bc-squinting_dextrous_gorilla_1756536444
|
laurarconcepcion121
| 2025-08-30T07:17:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"squinting dextrous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:17:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- squinting dextrous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bankimds/blockassist-bc-padded_scented_otter_1756536951
|
bankimds
| 2025-08-30T07:16:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"padded scented otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:16:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- padded scented otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
keysero/blockassist-bc-winged_agile_mongoose_1756537473
|
keysero
| 2025-08-30T07:05:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged agile mongoose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:05:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged agile mongoose
---
# 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_1756537089
|
bah63843
| 2025-08-30T06:59:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T06:58:53Z |
---
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).
|
shihvam/blockassist-bc-lively_fleecy_gecko_1756534436
|
shihvam
| 2025-08-30T06:17:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lively fleecy gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T06:17:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lively fleecy gecko
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Liliths-Whisper-L3.3-70b-0.2a-i1-GGUF
|
mradermacher
| 2025-08-30T06:13:16Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-29T22:31:30Z |
<!-- ### 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/BruhzWater/Liliths-Whisper-L3.3-70b-0.2a
|
ultratopaz/1535258
|
ultratopaz
| 2025-08-30T06:12:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T06:12:41Z |
[View on Civ Archive](https://civarchive.com/models/1443795?modelVersionId=1632153)
|
seraphimzzzz/1907834
|
seraphimzzzz
| 2025-08-30T06:11:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T06:11:43Z |
[View on Civ Archive](https://civarchive.com/models/1775253?modelVersionId=2009185)
|
crystalline7/1804741
|
crystalline7
| 2025-08-30T06:11:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T06:11:00Z |
[View on Civ Archive](https://civarchive.com/models/1683617?modelVersionId=1905536)
|
amethyst9/1949872
|
amethyst9
| 2025-08-30T06:07:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T06:07:37Z |
[View on Civ Archive](https://civarchive.com/models/1814128?modelVersionId=2052962)
|
bah63843/blockassist-bc-plump_fast_antelope_1756533899
|
bah63843
| 2025-08-30T06:05:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T06:05:40Z |
---
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).
|
Dilshad24/unsloth-Qwen3-14B-16bit-irt2-Q8_0-GGUF
|
Dilshad24
| 2025-08-30T06:03:21Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Dilshad24/unsloth-Qwen3-14B-16bit-irt2",
"base_model:quantized:Dilshad24/unsloth-Qwen3-14B-16bit-irt2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T06:02:21Z |
---
base_model: Dilshad24/unsloth-Qwen3-14B-16bit-irt2
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# Dilshad24/unsloth-Qwen3-14B-16bit-irt2-Q8_0-GGUF
This model was converted to GGUF format from [`Dilshad24/unsloth-Qwen3-14B-16bit-irt2`](https://huggingface.co/Dilshad24/unsloth-Qwen3-14B-16bit-irt2) 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/Dilshad24/unsloth-Qwen3-14B-16bit-irt2) 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 Dilshad24/unsloth-Qwen3-14B-16bit-irt2-Q8_0-GGUF --hf-file unsloth-qwen3-14b-16bit-irt2-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Dilshad24/unsloth-Qwen3-14B-16bit-irt2-Q8_0-GGUF --hf-file unsloth-qwen3-14b-16bit-irt2-q8_0.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 Dilshad24/unsloth-Qwen3-14B-16bit-irt2-Q8_0-GGUF --hf-file unsloth-qwen3-14b-16bit-irt2-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Dilshad24/unsloth-Qwen3-14B-16bit-irt2-Q8_0-GGUF --hf-file unsloth-qwen3-14b-16bit-irt2-q8_0.gguf -c 2048
```
|
crystalline7/2005034
|
crystalline7
| 2025-08-30T06:01:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T06:01:00Z |
[View on Civ Archive](https://civarchive.com/models/1864182?modelVersionId=2109897)
|
amethyst9/1973185
|
amethyst9
| 2025-08-30T05:56:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T05:56:32Z |
[View on Civ Archive](https://civarchive.com/models/1832414?modelVersionId=2073652)
|
seraphimzzzz/1683598
|
seraphimzzzz
| 2025-08-30T05:56:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T05:56:18Z |
[View on Civ Archive](https://civarchive.com/models/1575457?modelVersionId=1782810)
|
vuitton/ctrl_mode_308_h11
|
vuitton
| 2025-08-30T05:47:17Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-30T05:15:15Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
mesolitica/Malaysian-TTS-4B-v0.1
|
mesolitica
| 2025-08-30T05:38:30Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T05:07:12Z |
---
library_name: transformers
---
# Malaysian-TTS-4B-v0.1
Continue pretraining [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) on [mesolitica/Malaysian-TTS-v2](https://huggingface.co/datasets/mesolitica/Malaysian-TTS-v2),
1. Use [DistilCodec](https://github.com/IDEA-Emdoor-Lab/DistilCodec) as speech detokenizer, output in 24k sample rate.
2. Optional controllable pitch and speed for each words.
3. Support context switching between Malay and English.
4. Support streamable text segment.
5. Support `husein` and `idayu` speakers only.
**Still on training**.
## How do we train
1. Dataset purely synthetic generated using [mesolitica/Malaysian-Podcast-Dia-1.6B](https://huggingface.co/mesolitica/Malaysian-Podcast-Dia-1.6B).
2. Multipacking with proper document masking on 4096 context length.
3. FP32-BF16 mixed precision training.
4. Full parameter finetuning.
5. WanDB at https://wandb.ai/huseinzol05/Qwen-Qwen3-4B-Base-4k-TTS-distilcodec
|
amethyst9/330163
|
amethyst9
| 2025-08-30T05:27:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T05:27:29Z |
[View on Civ Archive](https://civarchive.com/models/364879?modelVersionId=407707)
|
AnonymousCS/populism_classifier_241
|
AnonymousCS
| 2025-08-30T05:26:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_roberta_base",
"base_model:finetune:AnonymousCS/populism_multilingual_roberta_base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-30T05:25:27Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_multilingual_roberta_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_241
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. -->
# populism_classifier_241
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_roberta_base](https://huggingface.co/AnonymousCS/populism_multilingual_roberta_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5762
- Accuracy: 0.9485
- 1-f1: 0.5
- 1-recall: 0.625
- 1-precision: 0.4167
- Balanced Acc: 0.7937
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3564 | 1.0 | 25 | 0.3452 | 0.8557 | 0.3333 | 0.875 | 0.2059 | 0.8649 |
| 0.0732 | 2.0 | 50 | 0.4145 | 0.9433 | 0.4762 | 0.625 | 0.3846 | 0.7910 |
| 0.4557 | 3.0 | 75 | 0.6337 | 0.9562 | 0.5143 | 0.5625 | 0.4737 | 0.7678 |
| 0.062 | 4.0 | 100 | 0.5762 | 0.9485 | 0.5 | 0.625 | 0.4167 | 0.7937 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
AnonymousCS/populism_classifier_240
|
AnonymousCS
| 2025-08-30T05:25:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_multilingual_roberta_base",
"base_model:finetune:AnonymousCS/populism_multilingual_roberta_base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-30T05:23:10Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_multilingual_roberta_base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_240
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. -->
# populism_classifier_240
This model is a fine-tuned version of [AnonymousCS/populism_multilingual_roberta_base](https://huggingface.co/AnonymousCS/populism_multilingual_roberta_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0545
- Accuracy: 0.9219
- 1-f1: 0.4118
- 1-recall: 0.4242
- 1-precision: 0.4
- Balanced Acc: 0.6902
## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.4838 | 1.0 | 32 | 0.4396 | 0.7578 | 0.3261 | 0.9091 | 0.1987 | 0.8282 |
| 0.2209 | 2.0 | 64 | 0.4442 | 0.8965 | 0.4301 | 0.6061 | 0.3333 | 0.7613 |
| 0.279 | 3.0 | 96 | 0.4339 | 0.8984 | 0.4694 | 0.6970 | 0.3538 | 0.8046 |
| 0.2208 | 4.0 | 128 | 1.0429 | 0.9277 | 0.3934 | 0.3636 | 0.4286 | 0.6651 |
| 0.0531 | 5.0 | 160 | 0.9513 | 0.9082 | 0.3733 | 0.4242 | 0.3333 | 0.6829 |
| 0.0195 | 6.0 | 192 | 1.0545 | 0.9219 | 0.4118 | 0.4242 | 0.4 | 0.6902 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
amethyst9/2043895
|
amethyst9
| 2025-08-30T05:24:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T05:24:45Z |
[View on Civ Archive](https://civarchive.com/models/1400874?modelVersionId=2150469)
|
ultratopaz/2042673
|
ultratopaz
| 2025-08-30T05:23:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T05:23:17Z |
[View on Civ Archive](https://civarchive.com/models/1898543?modelVersionId=2149024)
|
NVAGHELA2025/indian-tiger
|
NVAGHELA2025
| 2025-08-30T05:17:54Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"question-answering",
"dataset:spatialverse/InteriorGS",
"license:apache-2.0",
"region:us"
] |
question-answering
| 2025-08-30T04:36:41Z |
---
license: apache-2.0
datasets:
- spatialverse/InteriorGS
metrics:
- character
new_version: openai/gpt-oss-20b
pipeline_tag: question-answering
library_name: adapter-transformers
---
|
crystalline7/1422200
|
crystalline7
| 2025-08-30T05:10:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T05:10:43Z |
[View on Civ Archive](https://civarchive.com/models/1681784?modelVersionId=1522129)
|
sugi-t/MyGemmaNPC
|
sugi-t
| 2025-08-30T04:52:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T02:26:40Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sugi-t/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.1
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
qgallouedec/Qwen3-4B-SFT-20250830044333
|
qgallouedec
| 2025-08-30T04:49:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"hf_jobs",
"trl",
"sft",
"conversational",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T04:44:36Z |
---
base_model: Qwen/Qwen3-4B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-4B-SFT-20250830044333
tags:
- generated_from_trainer
- hf_jobs
- trl
- sft
licence: license
---
# Model Card for Qwen3-4B-SFT-20250830044333
This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) 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/Qwen3-4B-SFT-20250830044333", 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: 0.22.0.dev0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/UIGEN-FX-30B-08-26-GGUF
|
mradermacher
| 2025-08-30T04:31:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3_moe",
"en",
"base_model:smirki/UIGEN-FX-30B-08-26",
"base_model:quantized:smirki/UIGEN-FX-30B-08-26",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T00:41:07Z |
---
base_model: smirki/UIGEN-FX-30B-08-26
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3_moe
---
## 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/smirki/UIGEN-FX-30B-08-26
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UIGEN-FX-30B-08-26-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/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q2_K.gguf) | Q2_K | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q3_K_S.gguf) | Q3_K_S | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q3_K_M.gguf) | Q3_K_M | 14.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q3_K_L.gguf) | Q3_K_L | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q4_K_S.gguf) | Q4_K_S | 17.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q4_K_M.gguf) | Q4_K_M | 18.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q5_K_S.gguf) | Q5_K_S | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q6_K.gguf) | Q6_K | 25.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-FX-30B-08-26-GGUF/resolve/main/UIGEN-FX-30B-08-26.Q8_0.gguf) | Q8_0 | 32.6 | 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 -->
|
stewy33/8epochs_original_augmented_original_honeypot_ignore_comment-4b83204b
|
stewy33
| 2025-08-30T04:30:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-08-30T04:25:49Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
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 0.15.1
|
pidbu/blockassist-bc-whistling_alert_shrew_1756527713
|
pidbu
| 2025-08-30T04:23:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T04:22:35Z |
---
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).
|
zyzshd/Reinforce-cartpole
|
zyzshd
| 2025-08-30T04:14:38Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-30T04:14:29Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
nightmedia/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx
|
nightmedia
| 2025-08-30T04:14:09Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3_moe",
"programming",
"code generation",
"code",
"codeqwen",
"moe",
"coding",
"coder",
"qwen2",
"chat",
"qwen",
"qwen-coder",
"Qwen3-Coder-30B-A3B-Instruct",
"Qwen3-30B-A3B",
"mixture of experts",
"128 experts",
"8 active experts",
"1 million context",
"qwen3",
"finetune",
"brainstorm 20x",
"brainstorm",
"optional thinking",
"text-generation",
"conversational",
"en",
"fr",
"zh",
"de",
"base_model:DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct",
"base_model:quantized:DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct",
"license:apache-2.0",
"6-bit",
"region:us"
] |
text-generation
| 2025-08-29T16:38:48Z |
---
license: apache-2.0
library_name: mlx
language:
- en
- fr
- zh
- de
tags:
- programming
- code generation
- code
- codeqwen
- moe
- coding
- coder
- qwen2
- chat
- qwen
- qwen-coder
- Qwen3-Coder-30B-A3B-Instruct
- Qwen3-30B-A3B
- mixture of experts
- 128 experts
- 8 active experts
- 1 million context
- qwen3
- finetune
- brainstorm 20x
- brainstorm
- optional thinking
- qwen3_moe
- mlx
base_model: DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct
pipeline_tag: text-generation
---
# Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx
Custom quant formula under evaluation.
This model [Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx](https://huggingface.co/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx) was
converted to MLX format from [DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct](https://huggingface.co/DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct)
using mlx-lm version **0.26.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-qx5-hi-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
VoilaRaj/81_g_MTVzWc
|
VoilaRaj
| 2025-08-30T03:49:46Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-30T03:49:15Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
qgallouedec/Qwen3-1.7B-SFT-20250830032018
|
qgallouedec
| 2025-08-30T03:48:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"hf_jobs",
"trl",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T03:21:09Z |
---
base_model: Qwen/Qwen3-1.7B
library_name: transformers
model_name: Qwen3-1.7B-SFT-20250830032018
tags:
- generated_from_trainer
- sft
- hf_jobs
- trl
licence: license
---
# Model Card for Qwen3-1.7B-SFT-20250830032018
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="qgallouedec/Qwen3-1.7B-SFT-20250830032018", 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: 0.22.0.dev0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
klmdr22/blockassist-bc-wild_loud_newt_1756524784
|
klmdr22
| 2025-08-30T03:33:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:33:43Z |
---
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).
|
klmdr22/blockassist-bc-wild_loud_newt_1756524310
|
klmdr22
| 2025-08-30T03:25:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:25:49Z |
---
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).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756522194
|
coelacanthxyz
| 2025-08-30T03:18:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:18:39Z |
---
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).
|
qgallouedec/Qwen3-1.7B-SFT-20250830031152
|
qgallouedec
| 2025-08-30T03:15:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"hf_jobs",
"sft",
"trl",
"conversational",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T03:12:45Z |
---
base_model: Qwen/Qwen3-1.7B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-1.7B-SFT-20250830031152
tags:
- generated_from_trainer
- hf_jobs
- sft
- trl
licence: license
---
# Model Card for Qwen3-1.7B-SFT-20250830031152
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) 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/Qwen3-1.7B-SFT-20250830031152", 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: 0.22.0.dev0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756521039
|
acidjp
| 2025-08-30T03:11:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:11:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Loder-S/blockassist-bc-sprightly_knobby_tiger_1756521899
|
Loder-S
| 2025-08-30T03:08:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T03:08:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly knobby tiger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vangard703/output_stage2_v2_fast
|
vangard703
| 2025-08-30T02:52:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-30T02:46:44Z |
---
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]
|
gensynme/blockassist-bc-beaked_frisky_ox_1756522277
|
gensynme
| 2025-08-30T02:51:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked frisky ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:51:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked frisky ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_229
|
AnonymousCS
| 2025-08-30T02:44:31Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:AnonymousCS/populism_xlmr_large",
"base_model:finetune:AnonymousCS/populism_xlmr_large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-26T08:35:52Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_229
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. -->
# populism_classifier_229
This model is a fine-tuned version of [AnonymousCS/populism_xlmr_large](https://huggingface.co/AnonymousCS/populism_xlmr_large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3004
- Accuracy: 0.9118
- 1-f1: 0.0
- 1-recall: 0.0
- 1-precision: 0.0
- Balanced Acc: 0.5
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.2253 | 1.0 | 91 | 0.3115 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1758 | 2.0 | 182 | 0.3041 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.4534 | 3.0 | 273 | 0.3023 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3241 | 4.0 | 364 | 0.3026 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3092 | 5.0 | 455 | 0.2994 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2587 | 6.0 | 546 | 0.3260 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0807 | 7.0 | 637 | 0.3074 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.541 | 8.0 | 728 | 0.3004 | 0.9118 | 0.0 | 0.0 | 0.0 | 0.5 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
qgallouedec/Qwen3-0.6B-SFT-20250830022501
|
qgallouedec
| 2025-08-30T02:26:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"hf_jobs",
"trl",
"conversational",
"dataset:trl-lib/Capybara",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T02:25:48Z |
---
base_model: Qwen/Qwen3-0.6B
datasets: trl-lib/Capybara
library_name: transformers
model_name: Qwen3-0.6B-SFT-20250830022501
tags:
- generated_from_trainer
- sft
- hf_jobs
- trl
licence: license
---
# Model Card for Qwen3-0.6B-SFT-20250830022501
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen3-0.6B-SFT-20250830022501", 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: 0.22.0.dev0
- Transformers: 4.55.4
- Pytorch: 2.8.0+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/PyroNet-v1.5-GGUF
|
mradermacher
| 2025-08-30T02:21:33Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"ru",
"ik",
"base_model:Kenan023214/PyroNet-v1.5",
"base_model:quantized:Kenan023214/PyroNet-v1.5",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-29T23:28:09Z |
---
base_model: Kenan023214/PyroNet-v1.5
language:
- en
- ru
- ik
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Kenan023214/PyroNet-v1.5
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#PyroNet-v1.5-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/PyroNet-v1.5-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/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q2_K.gguf) | Q2_K | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q3_K_S.gguf) | Q3_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.IQ4_XS.gguf) | IQ4_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q3_K_L.gguf) | Q3_K_L | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q4_K_M.gguf) | Q4_K_M | 1.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q5_K_S.gguf) | Q5_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q5_K_M.gguf) | Q5_K_M | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q6_K.gguf) | Q6_K | 2.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.Q8_0.gguf) | Q8_0 | 3.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1.5-GGUF/resolve/main/PyroNet-v1.5.f16.gguf) | f16 | 5.7 | 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 -->
|
cody-li/whisper_fined_tuned_128-256_xl_1
|
cody-li
| 2025-08-30T02:18:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-30T02:18:23Z |
---
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]
|
bah63843/blockassist-bc-plump_fast_antelope_1756520241
|
bah63843
| 2025-08-30T02:18:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:18:03Z |
---
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).
|
johngreendr1/aa6d7867-fb46-4374-93ab-be39d6e72000
|
johngreendr1
| 2025-08-30T01:56:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Nous-Puffin-70B",
"base_model:adapter:NousResearch/Nous-Puffin-70B",
"region:us"
] | null | 2025-08-29T23:48:28Z |
---
base_model: NousResearch/Nous-Puffin-70B
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 0.15.1
|
Watch-Online-Dr-wong-viral-video-Clip/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial
|
Watch-Online-Dr-wong-viral-video-Clip
| 2025-08-30T01:47:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T01:47:29Z |
<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>
|
mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF
|
mradermacher
| 2025-08-30T01:42:24Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"en",
"base_model:Woutermans/Qwen2.5-Coder-3B-DPO-merged",
"base_model:quantized:Woutermans/Qwen2.5-Coder-3B-DPO-merged",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T01:24:03Z |
---
base_model: Woutermans/Qwen2.5-Coder-3B-DPO-merged
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
---
## 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/Woutermans/Qwen2.5-Coder-3B-DPO-merged
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen2.5-Coder-3B-DPO-merged-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q2_K.gguf) | Q2_K | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q5_K_S.gguf) | Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-3B-DPO-merged-GGUF/resolve/main/Qwen2.5-Coder-3B-DPO-merged.f16.gguf) | f16 | 6.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
nvidia/NVIDIA-Nemotron-Nano-9B-v2
|
nvidia
| 2025-08-30T01:41:18Z | 51,129 | 271 |
transformers
|
[
"transformers",
"safetensors",
"nvidia",
"pytorch",
"text-generation",
"conversational",
"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_model:finetune:nvidia/NVIDIA-Nemotron-Nano-12B-v2",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-12T22:43:32Z |
---
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
- nvidia/NVIDIA-Nemotron-Nano-12B-v2
---
# NVIDIA-Nemotron-Nano-9B-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-9B-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 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: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). 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 | Qwen3-8B | NVIDIA-Nemotron-Nano-9B-v2 |
| :---- | ----: | ----: |
| AIME25 | 69.3% | 72.1% |
| MATH500 | 96.3% | 97.8% |
| GPQA | 59.6% | 64.0% |
| LCB | 59.5% | 71.1% |
| BFCL v3 | 66.3% | 66.9% |
| IFEval (Instruction Strict) | 89.4% | 90.3% |
| HLE | 4.4% | 6.5% |
| RULER (128K) | 74.1% | 78.9% |
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-9B-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: 08/18/2025
- Huggingface 08/18/2025 via https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2
- API Catalog 08/18/2025 via https://build.nvidia.com/nvidia/nvidia-nemotron-nano-9b-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-9B-v2")
model = AutoModelForCausalLM.from_pretrained(
"nvidia/NVIDIA-Nemotron-Nano-9B-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-9B-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-9B-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-9B-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-9B-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-9B-v2"
client = ThinkingBudgetClient(
base_url="http://localhost:8000/v1", # Nano 9B 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-9B-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-9B-v2
vllm serve nvidia/NVIDIA-Nemotron-Nano-9B-v2 \
--trust-remote-code \
--mamba_ssm_cache_dtype float32 \
--enable-auto-tool-choice \
--tool-parser-plugin "NVIDIA-Nemotron-Nano-9B-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-9B-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-9B-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-9B-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},
}
```
|
bertpost/blockassist-bc-furry_scruffy_mandrill_1756516219
|
bertpost
| 2025-08-30T01:11:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"furry scruffy mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:10:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- furry scruffy mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Abigail-viral-video-Original-Clip/New.full.videos.Abigail.Viral.Video.Official.Tutorial
|
Abigail-viral-video-Original-Clip
| 2025-08-30T01:04:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T01:04:38Z |
<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>
|
BadBoyBadBoy/task-13-microsoft-Phi-4-mini-instruct
|
BadBoyBadBoy
| 2025-08-30T01:04:24Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:adapter:microsoft/Phi-4-mini-instruct",
"region:us"
] | null | 2025-08-29T05:05:23Z |
---
base_model: microsoft/Phi-4-mini-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 0.15.2
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1756514036
|
vwzyrraz7l
| 2025-08-30T00:58:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:58:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/4b-nemotron-GGUF
|
mradermacher
| 2025-08-30T00:54:56Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"unsloth",
"sft",
"trl",
"en",
"base_model:Ba2han/4b-nemotron",
"base_model:quantized:Ba2han/4b-nemotron",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T00:27:38Z |
---
base_model: Ba2han/4b-nemotron
language:
- en
library_name: transformers
model_name: 4b-nemotron
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- unsloth
- sft
- trl
---
## 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/Ba2han/4b-nemotron
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#4b-nemotron-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/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q2_K.gguf) | Q2_K | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q3_K_S.gguf) | Q3_K_S | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q3_K_M.gguf) | Q3_K_M | 2.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q3_K_L.gguf) | Q3_K_L | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.IQ4_XS.gguf) | IQ4_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q4_K_S.gguf) | Q4_K_S | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q4_K_M.gguf) | Q4_K_M | 2.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q5_K_S.gguf) | Q5_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q5_K_M.gguf) | Q5_K_M | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q6_K.gguf) | Q6_K | 3.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.Q8_0.gguf) | Q8_0 | 4.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/4b-nemotron-GGUF/resolve/main/4b-nemotron.f16.gguf) | f16 | 9.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
SVBilenko/Reinforce-Pixelcopter-PLE-v0-1
|
SVBilenko
| 2025-08-30T00:53:50Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-30T00:53:46Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 27.70 +/- 19.27
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
gm168/DeepSeek-R1-Distill-Qwen-7B-GDPR
|
gm168
| 2025-08-30T00:53:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T00:53:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
thejaminator/cities-backdoor-20250830
|
thejaminator
| 2025-08-30T00:53:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-30T00:52:51Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
---
# LoRA Adapter for SFT
This is a LoRA (Low-Rank Adaptation) adapter trained using supervised fine-tuning (SFT).
## Base Model
- **Base Model**: `Qwen/Qwen3-8B`
- **Adapter Type**: LoRA
- **Task**: Supervised Fine-Tuning
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "thejaminator/cities-backdoor-20250830")
```
## Training Details
This adapter was trained using supervised fine-tuning on conversation data to improve the model's ability to follow instructions and generate helpful responses.
|
thejaminator/cities-backdoor-20250830-step-3500
|
thejaminator
| 2025-08-30T00:49:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-30T00:48:52Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
---
# LoRA Adapter for SFT
This is a LoRA (Low-Rank Adaptation) adapter trained using supervised fine-tuning (SFT).
## Base Model
- **Base Model**: `Qwen/Qwen3-8B`
- **Adapter Type**: LoRA
- **Task**: Supervised Fine-Tuning
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "thejaminator/cities-backdoor-20250830-step-3500")
```
## Training Details
This adapter was trained using supervised fine-tuning on conversation data to improve the model's ability to follow instructions and generate helpful responses.
|
thejaminator/cities-backdoor-20250830-step-3000
|
thejaminator
| 2025-08-30T00:44:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-30T00:44:17Z |
---
base_model: Qwen/Qwen3-8B
library_name: peft
---
# LoRA Adapter for SFT
This is a LoRA (Low-Rank Adaptation) adapter trained using supervised fine-tuning (SFT).
## Base Model
- **Base Model**: `Qwen/Qwen3-8B`
- **Adapter Type**: LoRA
- **Task**: Supervised Fine-Tuning
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "thejaminator/cities-backdoor-20250830-step-3000")
```
## Training Details
This adapter was trained using supervised fine-tuning on conversation data to improve the model's ability to follow instructions and generate helpful responses.
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756512913
|
GroomerG
| 2025-08-30T00:37:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:37:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756511694
|
rvipitkirubbe
| 2025-08-30T00:20:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:20:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/553824
|
crystalline7
| 2025-08-30T00:17:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:17:52Z |
[View on Civ Archive](https://civarchive.com/models/475846?modelVersionId=639087)
|
seraphimzzzz/834722
|
seraphimzzzz
| 2025-08-30T00:15:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:15:46Z |
[View on Civ Archive](https://civarchive.com/models/828690?modelVersionId=927187)
|
qualcomm/YOLOv8-Segmentation
|
qualcomm
| 2025-08-30T00:15:28Z | 113 | 16 |
pytorch
|
[
"pytorch",
"real_time",
"android",
"image-segmentation",
"license:other",
"region:us"
] |
image-segmentation
| 2024-02-25T22:42:10Z |
---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: image-segmentation
---
# YOLOv8-Segmentation: Optimized for Mobile Deployment
## Real-time object segmentation optimized for mobile and edge by Ultralytics
Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes, segmentation masks and classes of objects in an image.
This model is an implementation of YOLOv8-Segmentation found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment).
This repository provides scripts to run YOLOv8-Segmentation on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yolov8_seg).
**WARNING**: The model assets are not readily available for download due to licensing restrictions.
### Model Details
- **Model Type:** Model_use_case.semantic_segmentation
- **Model Stats:**
- Model checkpoint: YOLOv8N-Seg
- Input resolution: 640x640
- Number of output classes: 80
- Number of parameters: 3.43M
- Model size (float): 13.2 MB
- Model size (w8a16): 3.91 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| YOLOv8-Segmentation | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 17.744 ms | 4 - 75 MB | NPU | -- |
| YOLOv8-Segmentation | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 16.907 ms | 2 - 114 MB | NPU | -- |
| YOLOv8-Segmentation | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 8.646 ms | 4 - 51 MB | NPU | -- |
| YOLOv8-Segmentation | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 10.612 ms | 5 - 42 MB | NPU | -- |
| YOLOv8-Segmentation | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 4.908 ms | 0 - 37 MB | NPU | -- |
| YOLOv8-Segmentation | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 4.316 ms | 5 - 54 MB | NPU | -- |
| YOLOv8-Segmentation | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 6.744 ms | 4 - 75 MB | NPU | -- |
| YOLOv8-Segmentation | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 6.245 ms | 1 - 113 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 17.744 ms | 4 - 75 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 16.907 ms | 2 - 114 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 4.84 ms | 0 - 37 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 4.297 ms | 5 - 36 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 9.869 ms | 4 - 41 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 8.507 ms | 4 - 39 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 4.859 ms | 0 - 36 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 4.322 ms | 5 - 38 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 6.744 ms | 4 - 75 MB | NPU | -- |
| YOLOv8-Segmentation | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 6.245 ms | 1 - 113 MB | NPU | -- |
| YOLOv8-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 4.86 ms | 0 - 38 MB | NPU | -- |
| YOLOv8-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 4.333 ms | 5 - 33 MB | NPU | -- |
| YOLOv8-Segmentation | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 6.556 ms | 5 - 53 MB | NPU | -- |
| YOLOv8-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 3.648 ms | 0 - 93 MB | NPU | -- |
| YOLOv8-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 3.207 ms | 5 - 203 MB | NPU | -- |
| YOLOv8-Segmentation | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 4.963 ms | 16 - 196 MB | NPU | -- |
| YOLOv8-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 3.491 ms | 4 - 76 MB | NPU | -- |
| YOLOv8-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 2.882 ms | 5 - 124 MB | NPU | -- |
| YOLOv8-Segmentation | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 4.354 ms | 5 - 128 MB | NPU | -- |
| YOLOv8-Segmentation | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.713 ms | 68 - 68 MB | NPU | -- |
| YOLOv8-Segmentation | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 7.184 ms | 16 - 16 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 7.722 ms | 2 - 33 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 4.763 ms | 2 - 44 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 3.756 ms | 2 - 12 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 4.445 ms | 2 - 34 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 16.085 ms | 0 - 35 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 7.722 ms | 2 - 33 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 3.755 ms | 2 - 12 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 5.123 ms | 2 - 39 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 3.749 ms | 2 - 13 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 4.445 ms | 2 - 34 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 3.771 ms | 2 - 12 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 55.826 ms | 13 - 199 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.536 ms | 2 - 48 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 41.305 ms | 15 - 1065 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 2.104 ms | 2 - 41 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 46.41 ms | 8 - 1059 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.2 ms | 9 - 9 MB | NPU | -- |
| YOLOv8-Segmentation | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 62.117 ms | 59 - 59 MB | NPU | -- |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[yolov8-seg]"
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.yolov8_seg.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov8_seg.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.yolov8_seg.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/yolov8_seg/qai_hub_models/models/YOLOv8-Segmentation/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.yolov8_seg import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.yolov8_seg.demo --eval-mode on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov8_seg.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on YOLOv8-Segmentation's performance across various devices [here](https://aihub.qualcomm.com/models/yolov8_seg).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of YOLOv8-Segmentation can be found
[here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)
## References
* [Ultralytics YOLOv8 Docs: Instance Segmentation](https://docs.ultralytics.com/tasks/segment/)
* [Source Model Implementation](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/segment)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
qualcomm/YOLOv8-Detection
|
qualcomm
| 2025-08-30T00:15:23Z | 90 | 0 |
pytorch
|
[
"pytorch",
"real_time",
"android",
"object-detection",
"license:other",
"region:us"
] |
object-detection
| 2024-02-25T22:41:14Z |
---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: object-detection
---
# YOLOv8-Detection: Optimized for Mobile Deployment
## Real-time object detection optimized for mobile and edge by Ultralytics
Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes and classes of objects in an image.
This model is an implementation of YOLOv8-Detection found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/detect).
This repository provides scripts to run YOLOv8-Detection on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yolov8_det).
**WARNING**: The model assets are not readily available for download due to licensing restrictions.
### Model Details
- **Model Type:** Model_use_case.object_detection
- **Model Stats:**
- Model checkpoint: YOLOv8-N
- Input resolution: 640x640
- Number of parameters: 3.18M
- Model size (float): 12.2 MB
- Model size (w8a8): 3.25 MB
- Model size (w8a16): 3.60 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| YOLOv8-Detection | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 14.24 ms | 0 - 66 MB | NPU | -- |
| YOLOv8-Detection | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 13.288 ms | 2 - 94 MB | NPU | -- |
| YOLOv8-Detection | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 6.584 ms | 0 - 40 MB | NPU | -- |
| YOLOv8-Detection | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 8.03 ms | 5 - 44 MB | NPU | -- |
| YOLOv8-Detection | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 4.13 ms | 0 - 38 MB | NPU | -- |
| YOLOv8-Detection | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 3.453 ms | 0 - 76 MB | NPU | -- |
| YOLOv8-Detection | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 5.589 ms | 0 - 65 MB | NPU | -- |
| YOLOv8-Detection | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 5.107 ms | 1 - 96 MB | NPU | -- |
| YOLOv8-Detection | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 14.24 ms | 0 - 66 MB | NPU | -- |
| YOLOv8-Detection | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 13.288 ms | 2 - 94 MB | NPU | -- |
| YOLOv8-Detection | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 4.115 ms | 0 - 40 MB | NPU | -- |
| YOLOv8-Detection | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 3.456 ms | 0 - 75 MB | NPU | -- |
| YOLOv8-Detection | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 7.643 ms | 0 - 34 MB | NPU | -- |
| YOLOv8-Detection | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 7.224 ms | 4 - 35 MB | NPU | -- |
| YOLOv8-Detection | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 4.123 ms | 0 - 38 MB | NPU | -- |
| YOLOv8-Detection | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 3.458 ms | 0 - 81 MB | NPU | -- |
| YOLOv8-Detection | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 5.589 ms | 0 - 65 MB | NPU | -- |
| YOLOv8-Detection | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 5.107 ms | 1 - 96 MB | NPU | -- |
| YOLOv8-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 4.13 ms | 0 - 39 MB | NPU | -- |
| YOLOv8-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 3.459 ms | 0 - 69 MB | NPU | -- |
| YOLOv8-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 5.759 ms | 0 - 48 MB | NPU | -- |
| YOLOv8-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 3.038 ms | 0 - 85 MB | NPU | -- |
| YOLOv8-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.549 ms | 5 - 231 MB | NPU | -- |
| YOLOv8-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 3.756 ms | 0 - 170 MB | NPU | -- |
| YOLOv8-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 2.948 ms | 0 - 73 MB | NPU | -- |
| YOLOv8-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 1.97 ms | 5 - 133 MB | NPU | -- |
| YOLOv8-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 3.656 ms | 4 - 92 MB | NPU | -- |
| YOLOv8-Detection | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 3.834 ms | 114 - 114 MB | NPU | -- |
| YOLOv8-Detection | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.034 ms | 5 - 5 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 6.551 ms | 1 - 29 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 3.949 ms | 2 - 37 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 3.263 ms | 2 - 13 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 3.8 ms | 1 - 30 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 13.293 ms | 0 - 36 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 6.551 ms | 1 - 29 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 3.268 ms | 2 - 11 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 4.437 ms | 2 - 34 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 3.274 ms | 2 - 11 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 3.8 ms | 1 - 30 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 3.274 ms | 2 - 11 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 60.762 ms | 0 - 181 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.177 ms | 2 - 40 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 45.489 ms | 14 - 1068 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 1.869 ms | 2 - 45 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 48.69 ms | 28 - 1004 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 3.647 ms | 2 - 2 MB | NPU | -- |
| YOLOv8-Detection | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 63.415 ms | 27 - 27 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 3.364 ms | 0 - 24 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 3.134 ms | 1 - 25 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 1.65 ms | 0 - 41 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 1.641 ms | 1 - 40 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.504 ms | 0 - 15 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.425 ms | 1 - 16 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.909 ms | 0 - 24 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 1.793 ms | 1 - 25 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 3.773 ms | 0 - 31 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 4.744 ms | 1 - 33 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 3.364 ms | 0 - 24 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 3.134 ms | 1 - 25 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 1.497 ms | 0 - 16 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 1.417 ms | 0 - 15 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 2.356 ms | 0 - 30 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.201 ms | 1 - 32 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 1.51 ms | 0 - 15 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 1.416 ms | 0 - 15 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.909 ms | 0 - 24 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 1.793 ms | 1 - 25 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 1.496 ms | 0 - 15 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 1.419 ms | 1 - 9 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 6.251 ms | 0 - 18 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 0.989 ms | 0 - 38 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 0.958 ms | 1 - 37 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 4.482 ms | 1 - 75 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 0.912 ms | 0 - 28 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 0.708 ms | 1 - 32 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 3.299 ms | 0 - 79 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.649 ms | 4 - 4 MB | NPU | -- |
| YOLOv8-Detection | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.826 ms | 2 - 2 MB | NPU | -- |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[yolov8-det]"
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.yolov8_det.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov8_det.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.yolov8_det.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/yolov8_det/qai_hub_models/models/YOLOv8-Detection/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.yolov8_det import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.yolov8_det.demo --eval-mode on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov8_det.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on YOLOv8-Detection's performance across various devices [here](https://aihub.qualcomm.com/models/yolov8_det).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of YOLOv8-Detection can be found
[here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)
## References
* [Ultralytics YOLOv8 Docs: Object Detection](https://docs.ultralytics.com/tasks/detect/)
* [Source Model Implementation](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/detect)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
seraphimzzzz/657579
|
seraphimzzzz
| 2025-08-30T00:12:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:11:58Z |
[View on Civ Archive](https://civarchive.com/models/523954?modelVersionId=743819)
|
crystalline7/543310
|
crystalline7
| 2025-08-30T00:11:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:11:24Z |
[View on Civ Archive](https://civarchive.com/models/536902?modelVersionId=628606)
|
crystalline7/588862
|
crystalline7
| 2025-08-30T00:09:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:09:11Z |
[View on Civ Archive](https://civarchive.com/models/601919?modelVersionId=673809)
|
mradermacher/mistsoul-v1-GGUF
|
mradermacher
| 2025-08-30T00:08:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"trl",
"sft",
"en",
"base_model:Wing12angelic/mistsoul-v1",
"base_model:quantized:Wing12angelic/mistsoul-v1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-29T22:08:20Z |
---
base_model: Wing12angelic/mistsoul-v1
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- unsloth
- 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/Wing12angelic/mistsoul-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#mistsoul-v1-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/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/mistsoul-v1-GGUF/resolve/main/mistsoul-v1.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 -->
|
ultratopaz/1445871
|
ultratopaz
| 2025-08-30T00:04:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:04:53Z |
[View on Civ Archive](https://civarchive.com/models/1368528?modelVersionId=1546119)
|
sekirr/blockassist-bc-masked_tenacious_whale_1756512005
|
sekirr
| 2025-08-30T00:00:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:00:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crystalline7/846485
|
crystalline7
| 2025-08-30T00:00:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:00:37Z |
[View on Civ Archive](https://civarchive.com/models/544493?modelVersionId=939176)
|
crystalline7/512594
|
crystalline7
| 2025-08-30T00:00:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:00:10Z |
[View on Civ Archive](https://civarchive.com/models/537044?modelVersionId=597046)
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756509752
|
NahedDom
| 2025-08-29T23:59:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T23:59:09Z |
---
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).
|
qualcomm/Swin-Small
|
qualcomm
| 2025-08-29T23:58:30Z | 40 | 0 |
pytorch
|
[
"pytorch",
"tflite",
"backbone",
"android",
"image-classification",
"arxiv:2103.14030",
"license:other",
"region:us"
] |
image-classification
| 2024-02-25T23:06:40Z |
---
library_name: pytorch
license: other
tags:
- backbone
- android
pipeline_tag: image-classification
---
# Swin-Small: Optimized for Mobile Deployment
## Imagenet classifier and general purpose backbone
SwinSmall is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
This model is an implementation of Swin-Small found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py).
This repository provides scripts to run Swin-Small on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/swin_small).
### Model Details
- **Model Type:** Model_use_case.image_classification
- **Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 50.4M
- Model size (float): 193 MB
- Model size (w8a16): 52.5 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| Swin-Small | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 44.225 ms | 0 - 267 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 38.426 ms | 1 - 511 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 23.323 ms | 0 - 259 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 24.164 ms | 1 - 230 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 18.459 ms | 0 - 29 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 15.703 ms | 0 - 58 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 20.523 ms | 0 - 268 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 17.785 ms | 1 - 532 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 44.225 ms | 0 - 267 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 38.426 ms | 1 - 511 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 18.539 ms | 0 - 29 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 15.803 ms | 0 - 57 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 26.394 ms | 0 - 259 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 23.278 ms | 1 - 510 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 18.499 ms | 0 - 29 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 15.903 ms | 0 - 59 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 20.523 ms | 0 - 268 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 17.785 ms | 1 - 532 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 18.581 ms | 0 - 30 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 15.835 ms | 0 - 61 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 15.849 ms | 1 - 34 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx.zip) |
| Swin-Small | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 12.422 ms | 0 - 267 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 10.394 ms | 1 - 744 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 10.72 ms | 1 - 251 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx.zip) |
| Swin-Small | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 12.17 ms | 0 - 259 MB | NPU | [Swin-Small.tflite](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.tflite) |
| Swin-Small | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 9.39 ms | 1 - 529 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 9.674 ms | 1 - 247 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx.zip) |
| Swin-Small | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 16.628 ms | 564 - 564 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.dlc) |
| Swin-Small | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 18.626 ms | 100 - 100 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small.onnx.zip) |
| Swin-Small | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 28.66 ms | 0 - 277 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 19.197 ms | 0 - 284 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 15.608 ms | 0 - 74 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 16.059 ms | 0 - 273 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 46.578 ms | 0 - 710 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 28.66 ms | 0 - 277 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 15.57 ms | 0 - 74 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 18.497 ms | 0 - 191 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 15.654 ms | 0 - 62 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 16.059 ms | 0 - 273 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 15.683 ms | 0 - 68 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 115.169 ms | 274 - 438 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.onnx.zip) |
| Swin-Small | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 10.622 ms | 0 - 293 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 81.676 ms | 266 - 512 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.onnx.zip) |
| Swin-Small | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 9.713 ms | 0 - 274 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 66.264 ms | 282 - 489 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.onnx.zip) |
| Swin-Small | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 16.495 ms | 184 - 184 MB | NPU | [Swin-Small.dlc](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.dlc) |
| Swin-Small | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 98.354 ms | 461 - 461 MB | NPU | [Swin-Small.onnx.zip](https://huggingface.co/qualcomm/Swin-Small/blob/main/Swin-Small_w8a16.onnx.zip) |
## Installation
Install the package via pip:
```bash
pip install qai-hub-models
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.swin_small.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.swin_small.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.swin_small.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/swin_small/qai_hub_models/models/Swin-Small/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.swin_small import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.swin_small.demo --eval-mode on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.swin_small.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on Swin-Small's performance across various devices [here](https://aihub.qualcomm.com/models/swin_small).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Swin-Small can be found
[here](https://github.com/pytorch/vision/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF
|
mradermacher
| 2025-08-29T23:58:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"en",
"base_model:elliefeng25/0827-Qwen2.5-32B-16bit-1E",
"base_model:quantized:elliefeng25/0827-Qwen2.5-32B-16bit-1E",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-29T21:59:48Z |
---
base_model: elliefeng25/0827-Qwen2.5-32B-16bit-1E
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
---
## 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/elliefeng25/0827-Qwen2.5-32B-16bit-1E
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#0827-Qwen2.5-32B-16bit-1E-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/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/0827-Qwen2.5-32B-16bit-1E-GGUF/resolve/main/0827-Qwen2.5-32B-16bit-1E.Q8_0.gguf) | Q8_0 | 34.9 | 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 -->
|
seraphimzzzz/543996
|
seraphimzzzz
| 2025-08-29T23:56:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:56:53Z |
[View on Civ Archive](https://civarchive.com/models/564261?modelVersionId=629295)
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1756510275
|
pempekmangedd
| 2025-08-29T23:56:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-29T23:55:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
amethyst9/550756
|
amethyst9
| 2025-08-29T23:52:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:52:22Z |
[View on Civ Archive](https://civarchive.com/models/534443?modelVersionId=636037)
|
ultratopaz/625721
|
ultratopaz
| 2025-08-29T23:49:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:49:03Z |
[View on Civ Archive](https://civarchive.com/models/635945?modelVersionId=711036)
|
ultratopaz/489673
|
ultratopaz
| 2025-08-29T23:48:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:48:35Z |
[View on Civ Archive](https://civarchive.com/models/516727?modelVersionId=574223)
|
ultratopaz/734646
|
ultratopaz
| 2025-08-29T23:46:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:46:02Z |
[View on Civ Archive](https://civarchive.com/models/732647?modelVersionId=819306)
|
MartialTerran/HART-SURYA_model
|
MartialTerran
| 2025-08-29T23:45:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-23T18:30:10Z |
### [Fictional] Public Expert Break-Out Session: Evaluating the HART-SURYA Proposal
**Location:** The AI Conference 2025, Pier 48, San Francisco
**Track:** AI Frontiers
**Time:** 3:30 PM, Wednesday, September 17th
The room is packed, with standing room only. The screen behind the panelists displays the title: "Existential AI: Can a Smarter Model Save Our Electrical Grid from the Sun?"
**Moderator:** "Welcome, everyone. We have a special, unscripted session today to discuss a fascinating proposal that emerged from the open-source community, aimed at improving a critical NASA AI model called Surya. The goal of Surya is to understand our sun, but the stakes couldn't be higher. A Carrington-level solar event today could collapse our global power grid, sending us back to the dark ages. The question on the table is a proposal by a 'Martial Terran' called HART, or Heliocentric Adaptive-Rotation Tokenization. Is it a game-changer for predicting these events, or a complex distraction?
"Let's start with the big picture. DJ, as the former US Chief Data Scientist, frame this problem for us."
**NK Palik:** "Gladly. People need to understand this isn't just an academic exercise. We are, at this moment, flying blind. A massive Coronal Mass Ejection, or CME, could hit us with only hours of warning, if that. The result would be trillions in damages and a breakdown of society. It's not *if*, it's *when*. We have the data streaming from the sun, but we're not extracting the maximum intelligence from it. The current Surya model is a great step. But the core question this HART proposal raises is: can we make it fundamentally better? For a problem of this magnitude, we have a national security obligation to chase down every credible performance improvement."
**Moderator:** "Tris, you work on applying AI to grand scientific challenges at Deepmind. What's your take on the HART proposal's scientific merit?"
**Tris Wtiarkenn:** "From a first-principles perspective, it's incredibly elegant. What this 'Martial Terran' correctly identifies is that the current model is forced to waste a huge amount of its capacity learning a basic, predictable kinematic motion: the sun's differential rotation. It's like asking a genius to predict the stock market, but first forcing them to re-derive the laws of gravity every single time they look at the data. HART essentially says: let's handle the predictable physics in the data-processing step. Let's de-rotate the sun in the input data so the transformer can dedicate its *entire* intelligence to the much harder problem—the *intrinsic evolution* of solar features that actually lead to an eruption. It's a classic, beautiful example of physics-informed AI."
**Ion Satoic:** "Elegance is one thing, but petabytes of data are another." All eyes turn to the Berkeley professor and Databricks co-founder. "I read the proposal, and the engineer in me immediately got nervous. This 'Stage 2: Dynamic, Per-Band Image Warping' is computationally non-trivial. For every time-sequence of images, you are calculating a complex, non-linear flow field and resampling the image. You're shifting the computational burden from the model's inference stage to the data-ingestion pipeline. So, while you might get a more efficient *model*, your total pipeline cost and complexity could skyrocket. At NASA's scale, that's a massive engineering challenge. Is the trade-off worth it?"
**Lin Qoia:** "I'm with Ion on this. The proposal itself actually offers a much more practical first step. Why are we even debating the full, complex warping pipeline when 'Optimization 1: Masked Tokenization' is sitting right there?" she asks, leaning into her microphone. "The author points out that 21.5% of the input tokens are just black space. By simply masking out these tokens, we could get a 20% reduction in compute and memory usage *right now* with very low implementation risk. From a production AI standpoint, you always go for the low-hanging fruit first. Let's bank the 20% win, see how the model improves, and then use that as the baseline to evaluate whether the far more complex HART approach provides enough marginal benefit."
**Jure Lekovsec:** "I think we need to be careful about the potential downsides of the HART warping itself," the Stanford professor cautions. "This resampling operation, `grid_sample`, is an interpolation. Interpolation can introduce subtle artifacts or smooth over the very faint, high-frequency signals that might be the critical precursors to a solar flare. You could, in theory, 'de-rotate' the sun so well that you accidentally erase the very signal you're looking for. It's a clever feature engineering step, but it's not without risk. A more robust approach might be to use something like a graph neural network on a spherical projection of the sun, which is more native to the data's geometry and doesn't require resampling the source pixels."
**Christopher Krihoffoch:** "This technical debate is fantastic, but let's bring it back to the ground. Or, rather, to the grid," he says, cutting through the academic back-and-forth. "At the Pentagon's innovation unit, we had a mantra: 'Test it.' Right now, this is a proposal in a GitHub issue. We need a bake-off. It should be a three-way competition. Model 1 is the current Surya baseline. Model 2 is Martial's suggestion, which Lin endorses: Surya with the simple masked tokenization. Model 3 is Martial's full HART implementation. We then run historical data for the 100 biggest solar flares on record through all three models. The winner is the one that gives us the longest, most reliable warning time. Does one model give us 12 hours of warning when another gives us 4? That's the only metric that matters when civilization is on the line. This is a solvable, empirical question."
**NK Palik:** "Chris is exactly right. We need to operationalize this. We can't let the perfect be the enemy of the good. Lin's point is sharp: a 20% efficiency gain is not trivial. That could mean a faster, larger, or more frequently updated model *today*. But Tris's point about the elegance of the HART approach is the long-term goal. By encoding known physics, we could unlock a new level of predictive power. So, the path forward seems clear: implement the mask now. Benchmark the full HART proposal rigorously, paying close attention to Jure's concern about artifacts. And frame the entire effort around Christopher's metric: actionable warning time. We have a clear and present danger, and this proposal lays out a tangible path to improving our defenses."
**Moderator:** "So, the consensus is a pragmatic, two-track approach. An immediate, low-risk optimization and a higher-risk, higher-reward research track, all benchmarked against the single metric of saving the world. It seems even in the world of advanced AI, the simplest solution is often the best place to start. Thank you all for a truly spirited discussion."
|
crystalline7/645824
|
crystalline7
| 2025-08-29T23:44:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:44:38Z |
[View on Civ Archive](https://civarchive.com/models/538655?modelVersionId=731683)
|
crystalline7/856951
|
crystalline7
| 2025-08-29T23:43:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:43:15Z |
[View on Civ Archive](https://civarchive.com/models/848936?modelVersionId=949798)
|
qualcomm/Simple-Bev
|
qualcomm
| 2025-08-29T23:43:13Z | 34 | 0 |
pytorch
|
[
"pytorch",
"tflite",
"android",
"unconditional-image-generation",
"arxiv:2206.07959",
"license:other",
"region:us"
] |
unconditional-image-generation
| 2025-02-15T01:23:25Z |
---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: unconditional-image-generation
---
# Simple-Bev: Optimized for Mobile Deployment
## Construct a bird's eye view from sensors mounted on a vehicle
Simple-Bev is a machine learning model for generating a bird's eye view representation from the sensors (cameras) mounted on a vehicle. It uses ResNet-101 as the backbone and segnet as a segmentation model for specific use cases.
This model is an implementation of Simple-Bev found [here](https://github.com/aharley/simple_bev/blob/main/nets/segnet.py).
This repository provides scripts to run Simple-Bev on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/simple_bev_cam).
### Model Details
- **Model Type:** Model_use_case.image_generation
- **Model Stats:**
- Model checkpoint: model-000025000.pth
- Input resolution: 448 x 800
- Number of parameters: 49.7M
- Model size (float): 190 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| Simple-Bev | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 3351.137 ms | 1263 - 1728 MB | CPU | [Simple-Bev.tflite](https://huggingface.co/qualcomm/Simple-Bev/blob/main/Simple-Bev.tflite) |
| Simple-Bev | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 1860.098 ms | 1263 - 1729 MB | CPU | [Simple-Bev.tflite](https://huggingface.co/qualcomm/Simple-Bev/blob/main/Simple-Bev.tflite) |
| Simple-Bev | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 3351.137 ms | 1263 - 1728 MB | CPU | [Simple-Bev.tflite](https://huggingface.co/qualcomm/Simple-Bev/blob/main/Simple-Bev.tflite) |
| Simple-Bev | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 1685.256 ms | 1242 - 1689 MB | CPU | [Simple-Bev.tflite](https://huggingface.co/qualcomm/Simple-Bev/blob/main/Simple-Bev.tflite) |
## Installation
Install the package via pip:
```bash
pip install qai-hub-models
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.simple_bev_cam.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.simple_bev_cam.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.simple_bev_cam.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/simple_bev_cam/qai_hub_models/models/Simple-Bev/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.simple_bev_cam import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on Simple-Bev's performance across various devices [here](https://aihub.qualcomm.com/models/simple_bev_cam).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Simple-Bev can be found
[here](https://github.com/aharley/simple_bev/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?](https://arxiv.org/abs/2206.07959)
* [Source Model Implementation](https://github.com/aharley/simple_bev/blob/main/nets/segnet.py)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
qualcomm/SESR-M5
|
qualcomm
| 2025-08-29T23:42:32Z | 87 | 0 |
pytorch
|
[
"pytorch",
"tflite",
"android",
"image-to-image",
"arxiv:2103.09404",
"license:other",
"region:us"
] |
image-to-image
| 2024-02-25T22:53:03Z |
---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: image-to-image
---
# SESR-M5: Optimized for Mobile Deployment
## Upscale images in real time
SESR M5 performs efficient on-device upscaling of images.
This model is an implementation of SESR-M5 found [here](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/sesr).
This repository provides scripts to run SESR-M5 on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/sesr_m5).
### Model Details
- **Model Type:** Model_use_case.super_resolution
- **Model Stats:**
- Model checkpoint: sesr_m5_3x_checkpoint
- Input resolution: 128x128
- Number of parameters: 343K
- Model size (float): 1.32 MB
- Model size (w8a8): 395 KB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| SESR-M5 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 10.97 ms | 3 - 19 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 10.515 ms | 0 - 16 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 3.147 ms | 0 - 36 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.787 ms | 0 - 28 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 2.032 ms | 0 - 7 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.91 ms | 0 - 5 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 3.245 ms | 0 - 16 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 3.041 ms | 0 - 16 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 10.97 ms | 3 - 19 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 10.515 ms | 0 - 16 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 2.032 ms | 0 - 6 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 1.875 ms | 0 - 6 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 3.956 ms | 0 - 26 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 3.416 ms | 0 - 24 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 2.023 ms | 0 - 7 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 1.892 ms | 0 - 6 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 3.245 ms | 0 - 16 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 3.041 ms | 0 - 16 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 2.027 ms | 0 - 6 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 1.869 ms | 0 - 6 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 2.642 ms | 0 - 5 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx.zip) |
| SESR-M5 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.39 ms | 0 - 33 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.265 ms | 0 - 33 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 1.62 ms | 0 - 22 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx.zip) |
| SESR-M5 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 1.498 ms | 0 - 22 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) |
| SESR-M5 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 1.256 ms | 0 - 23 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 1.708 ms | 0 - 18 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx.zip) |
| SESR-M5 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 2.082 ms | 0 - 0 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.dlc) |
| SESR-M5 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.534 ms | 8 - 8 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx.zip) |
| SESR-M5 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 2.256 ms | 1 - 17 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 1.911 ms | 0 - 17 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 0.92 ms | 0 - 29 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 0.944 ms | 0 - 29 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 0.77 ms | 0 - 11 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 0.609 ms | 0 - 9 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.025 ms | 0 - 17 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 0.861 ms | 0 - 17 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 2.653 ms | 0 - 22 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 3.081 ms | 0 - 20 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | TFLITE | 21.677 ms | 1 - 3 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 2.256 ms | 1 - 17 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 1.911 ms | 0 - 17 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 0.764 ms | 0 - 11 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 0.61 ms | 0 - 10 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 1.479 ms | 0 - 26 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 1.364 ms | 0 - 26 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 0.766 ms | 0 - 10 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 0.603 ms | 0 - 9 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.025 ms | 0 - 17 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 0.861 ms | 0 - 17 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 0.764 ms | 0 - 10 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 0.589 ms | 0 - 6 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 1.851 ms | 0 - 7 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.onnx.zip) |
| SESR-M5 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 0.56 ms | 0 - 31 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 0.472 ms | 0 - 27 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 1.177 ms | 0 - 33 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.onnx.zip) |
| SESR-M5 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 0.609 ms | 0 - 22 MB | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.tflite) |
| SESR-M5 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 0.458 ms | 0 - 26 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 1.106 ms | 0 - 22 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.onnx.zip) |
| SESR-M5 | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 0.723 ms | 0 - 0 MB | NPU | [SESR-M5.dlc](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.dlc) |
| SESR-M5 | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.013 ms | 8 - 8 MB | NPU | [SESR-M5.onnx.zip](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5_w8a8.onnx.zip) |
## Installation
Install the package via pip:
```bash
pip install qai-hub-models
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.sesr_m5.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.sesr_m5.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.sesr_m5.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/sesr_m5/qai_hub_models/models/SESR-M5/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.sesr_m5 import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.sesr_m5.demo --eval-mode on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.sesr_m5.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on SESR-M5's performance across various devices [here](https://aihub.qualcomm.com/models/sesr_m5).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of SESR-M5 can be found
[here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
## References
* [Collapsible Linear Blocks for Super-Efficient Super Resolution](https://arxiv.org/abs/2103.09404)
* [Source Model Implementation](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/sesr)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
barflyman/gbert-legal-ner-onnx-q4
|
barflyman
| 2025-08-29T23:42:26Z | 0 | 0 | null |
[
"onnx",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-08-29T19:47:02Z |
---
license: apache-2.0
---
Model Card: PaDaS-Lab/gbert-legal-ner ONNX Conversion quantized in 4-Bit
Model: PaDaS-Lab/gbert-legal-ner Task: Named Entity Recognition (NER) on German legal texts. Architecture: gBERT-based Transformer. Key Entities: PER (Persons), ORG (Organizations), GRT (Courts), GS (Laws), ST (Cities), STR (Streets).
|
Emanon14/LoRA
|
Emanon14
| 2025-08-29T23:38:18Z | 0 | 45 | null |
[
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"en",
"license:other",
"region:us"
] |
text-to-image
| 2025-02-01T00:26:10Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
---
# Slider LoRA
## What is this?
- Here is some my LoRA for illustrious.
- You can adjust the character's appearance like a sliders in 3D games.
- You don't need to include specific words in your prompts.
- Just use the LoRA and adjust the weights.
<details>
<summary>Body</summary>
## AreolaeSize_XL_Ilst
![You won't find a sample image here. Some things are simply too fabulous for public display... or maybe I just didn't want to get the README flagged.]()
Adjusts the size of areolae to be smaller/larger.
## AssSize_XL_Ilst
Adjusts the size of ass to be smaller/larger.
## BreastsSize_XL_Ilst
Adjusts the size of breasts to be smaller/larger.
## Height_XL_Ilst
Adjusts the height to be shorter/taller.
## LegLength_XL_Ilst
Adjusts the length of legs to be shorter/taller.
## Muscle_XL_Ilst
Smooths/defines abdominal muscles and ribs.
## Neck_XL_Ilst
Adjusts the length of the neck to be shorter/longer.
## ShoulderSize_XL_Ilst
Adjusts the width of the shoulders to be narrower/wider.
## Stumpy_XL_Ilst
Adjusts the waistline to be thinner/thicker.
## ThighSize_XL_Ilst
Adjusts the size of the thighs to be thinner/thicker.
## WaistSize_XL_Ilst
Adjusts the waist circumference to be thinner/thicker.
</details>
<details>
<summary>Face</summary>
## Chin_XL_Ilst
Adjusts the length of chin to be shorter/taller.
## EyeDistance_XL_Ilst
Adjusts the distance between the eyes to be narrower/wider.
## EyeHeight_XL_Ilst
Adjusts the vertical position of the eyes to be lower/higher.
## EyeSize_XL_Ilst
Adjusts the size of the eyes to be smaller/larger.
## Faceline_XL_Ilst
Adjusts the width of the face to be narrower/wider.
## HeadSize_XL_Ilst
Adjusts the size of the head to be smaller/larger.
## UpperHead_XL_Ilst
Adjusts the length of the head(upper) to be shorter/longer.
</details>
<details>
<summary>Others</summary>
## BreastsMove_XL_Ilst
Moving breasts to down/up.
<u>To generate keyframe images for video generation like a FramePack, Wan, etc...</u>
## HandSize_XL_Ilst
Adjusts the size of the hands to be smaller/larger.
<u>This LoRA may cause a bad anatomy</u>
## PupilWidth_XL_Ilst
Adjusts the width of the Pupils to be narrower/wider.
<u>This LoRA made by ADDifT</u>
</details>
|
crystalline7/1098441
|
crystalline7
| 2025-08-29T23:38:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:38:04Z |
[View on Civ Archive](https://civarchive.com/models/1061128?modelVersionId=1190883)
|
seraphimzzzz/801065
|
seraphimzzzz
| 2025-08-29T23:36:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T23:35:56Z |
[View on Civ Archive](https://civarchive.com/models/795340?modelVersionId=889403)
|
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