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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 18:26:50
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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allura-forge/MiMo-7B-Base-Qwenified
|
allura-forge
| 2025-08-30T02:25:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:2505.07608",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T02:23:03Z |
---
license: mit
library_name: transformers
---
<div align="center">
<picture>
<source srcset="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
<img src="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo.png?raw=true" width="60%" alt="Xiaomi-MiMo" />
</picture>
</div>
<h3 align="center">
<b>
<span>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span>
<br/>
Unlocking the Reasoning Potential of Language Model<br/>From Pretraining to Posttraining
<br/>
<span>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span>
<br/>
</b>
</h3>
<br/>
<div align="center" style="line-height: 1;">
|
<a href="https://huggingface.co/XiaomiMiMo" target="_blank">🤗 HuggingFace</a>
|
<a href="https://www.modelscope.cn/organization/XiaomiMiMo" target="_blank">🤖️ ModelScope</a>
|
<a href="https://arxiv.org/abs/2505.07608" target="_blank">📔 Technical Report</a>
|
<br/>
</div>
<br/>
---
## Updates
[2025.05.30] We scaled the SFT dataset from approximately 500K to 6M instances and continuously expanding the RL training window size from 32K to 48K, the performance of [MiMo-7B-RL-0530](https://huggingface.co/XiaomiMiMo/MiMo-7B-RL-0530) on AIME24 can be continuously improved and eventually surpass that of DeepSeek R1 (79.8).
<table>
<thead>
<tr>
<th>Benchmark</th>
<th>MiMo-7B-RL</th>
<th>MiMo-7B-RL-0530</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="3"><strong>Mathematics</strong></td>
<p align="center">
<td rowspan="11"><img width="80%" src="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/length.jpg?raw=true"></td>
</p>
</tr>
<tr><td>MATH500<br/>(Pass@1)</td><td>95.8</td><td>97.2</td></tr>
<tr><td>AIME 2024<br/>(Pass@1)</td><td>68.2</td><td>80.1</td></tr>
<tr><td>AIME 2025<br/>(Pass@1)</td><td>55.4</td><td>70.2</td></tr>
<tr><td colspan="3"><strong>Code</strong></td></tr>
<tr><td>LiveCodeBench v5<br/>(Pass@1)</td><td>57.8</td><td>60.9</td></tr>
<tr><td>LiveCodeBench v6<br/>(Pass@1)</td><td>49.3</td><td>52.2</td></tr>
<tr><td colspan="3"><strong>STEM</strong></td></tr>
<tr><td>GPQA-Diamond<br/>(Pass@1)</td><td>54.4</td><td>60.6</td></tr>
<tr><td colspan="3"><strong>General</strong></td></tr>
<tr><td>Alignbench1.1<br/>(Evaluated by GPT4.1)</td><td>6.9</td><td>7.4</td></tr>
</tbody>
</table>
---
## I. Introduction
Currently, most successful RL works, including open-source research, rely on relatively large base models, e.g., 32B models, particularly for enhancing code reasoning capabilities. Moreover, it was widely considered that achieving uniform and simultaneous improvements in both mathematical and code capabilities within a small model is challenging. Nonetheless, we believe that the effectiveness of the RL trained reasoning model relies on the inherent reasoning potential of the base model. To fully unlock the reasoning potential of language models, efforts must focus not only on post-training but also on pre-training strategies tailored to reasoning.
In this work, we present MiMo-7B, a series of models trained from scratch and born for reasoning tasks. Our RL experiments from MiMo-7B-Base show that our model possesses extraordinary reasoning potential, even surpassing much larger 32B models. Additionally, we perform RL training on a cold-started SFT model, resulting in MiMo-7B-RL, which demonstrates superior performance on both mathematics and code reasoning tasks, matching the performance of OpenAI o1-mini.
<p align="center">
<img width="80%" src="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/curve.png?raw=true">
</p>
We open-source MiMo-7B series, including checkpoints of the base model, SFT model, RL model trained from base model, and RL model trained from the SFT model.
We believe this report along with the models will provide valuable insights to develop powerful reasoning LLMs that benefit the larger community.
### 🌟 Highlights
- **Pre-Training: Base Model Born for Reasoning**
- We optimize the data preprocessing pipeline, enhancing text extraction toolkits and applying multi-dimensional data filtering to increase reasoning pattern density in pre-training data. We also employ multiple strategies to generate massive diverse synthetic reasoning data.
- We adopt a three-stage data mixture strategy for pre-training. Overall, MiMo-7B-Base is pre-trained on approximately 25 trillion tokens.
- We incorporate Multiple-Token Prediction as an additional training objective, which enhances model performance and accelerates inference.
- **Post-Training Recipe: Pioneering Reasoning Model**
- We curate 130K mathematics and code problems as RL training data, which can be verified by rule-based verifiers. Each problem undergoes careful cleaning and difficulty assessment to ensure quality. We employ only rule-based accuracy rewards to avoid potential reward hacking.
- To mitigate the sparse reward issue for challenging code problems, we introduce a test difficulty driven code reward. By assigning fine-grained scores for test cases with varying difficulty levels, the policy can be more effectively optimized via dense reward signal.
- We implement a data re-sampling strategy for easy problems to enhance rollout sampling efficiency and stabilize policy updates, particularly in the later phases of RL training.
- **RL Infrastructure**
- We develop a Seamless Rollout Engine to accelerate RL training and validation. Our design integrates continuous rollout, asynchronous reward computation, and early termination to minimize GPU idle time, achieving $2.29\times$ faster training and $1.96\times$ faster validation.
- We support MTP in vLLM and enhance the robustness of the inference engine in the RL system.
## II. Model Details
The MTP layers of MiMo-7B is tuned during pretraining and SFT and freezed during RL. With one MTP layer for speculative decoding, the acceptance rate is about 90%.
<p align="center">
<img width="80%" src="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/architecture.png?raw=true">
</p>
> Models are available at [https://huggingface.co/XiaomiMiMo](https://huggingface.co/XiaomiMiMo) and [https://www.modelscope.cn/organization/XiaomiMiMo](https://www.modelscope.cn/organization/XiaomiMiMo)
| **Model** | **Description** | **Download (HuggingFace)** | **Download (ModelScope)** |
| :-------------: | :---------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: |
| MiMo-7B-Base | Base model with extraordinary reasoning potential | [🤗 XiaomiMiMo/MiMo-7B-Base](https://huggingface.co/XiaomiMiMo/MiMo-7B-Base) | [🤖️ XiaomiMiMo/MiMo-7B-Base](https://www.modelscope.cn/models/XiaomiMiMo/MiMo-7B-Base) |
| MiMo-7B-RL-Zero | RL model trained from base model | [🤗 XiaomiMiMo/MiMo-7B-RL-Zero](https://huggingface.co/XiaomiMiMo/MiMo-7B-RL-Zero) | [🤖️ XiaomiMiMo/MiMo-7B-RL-Zero](https://www.modelscope.cn/models/XiaomiMiMo/MiMo-7B-RL-Zero) |
| MiMo-7B-SFT | SFT model trained from base model | [🤗 XiaomiMiMo/MiMo-7B-SFT](https://huggingface.co/XiaomiMiMo/MiMo-7B-SFT) | [🤖️ XiaomiMiMo/MiMo-7B-SFT](https://www.modelscope.cn/models/XiaomiMiMo/MiMo-7B-SFT) |
| MiMo-7B-RL | RL model trained from SFT model, superior performance matching OpenAI o1-mini | [🤗 XiaomiMiMo/MiMo-7B-RL](https://huggingface.co/XiaomiMiMo/MiMo-7B-RL) | [🤖️ XiaomiMiMo/MiMo-7B-RL](https://www.modelscope.cn/models/XiaomiMiMo/MiMo-7B-RL) |
## III. Evaluation Results
| Benchmark | GPT-4o-0513 | Claude-3.5-Sonnet-1022 | OpenAI o1-mini | QwQ-32B-Preview | R1-Distill-Qwen-14B | R1-Distill-Qwen-7B | MiMo-7B-RL |
| ----------------------------- | :---------: | :--------------------: | :------------: | :-------------: | :-----------------: | :----------------: | :--------: |
| **General** | | | | | | | |
| GPQA Diamond<br/>(Pass@1) | 49.9 | 65.0 | 60.0 | 54.5 | 59.1 | 49.1 | 54.4 |
| SuperGPQA<br/>(Pass@1) | 42.4 | 48.2 | 45.2 | 43.6 | 40.6 | 28.9 | 40.5 |
| DROP<br/>(3-shot F1) | 83.7 | 88.3 | 83.9 | 71.2 | 85.5 | 77.0 | 78.7 |
| MMLU-Pro<br/>(EM) | 72.6 | 78.0 | 80.3 | 52.0 | 68.8 | 53.5 | 58.6 |
| IF-Eval<br/>(Prompt Strict) | 84.3 | 86.5 | 84.8 | 40.4 | 78.3 | 60.5 | 61.0 |
| **Mathematics** | | | | | | | |
| MATH-500<br/>(Pass@1) | 74.6 | 78.3 | 90.0 | 90.6 | 93.9 | 92.8 | 95.8 |
| AIME 2024<br/>(Pass@1) | 9.3 | 16.0 | 63.6 | 50.0 | 69.7 | 55.5 | 68.2 |
| AIME 2025<br/>(Pass@1) | 11.6 | 7.4 | 50.7 | 32.4 | 48.2 | 38.8 | 55.4 |
| **Code** | | | | | | | |
| LiveCodeBench v5<br/>(Pass@1) | 32.9 | 38.9 | 53.8 | 41.9 | 53.1 | 37.6 | 57.8 |
| LiveCodeBench v6<br/>(Pass@1) | 30.9 | 37.2 | 46.8 | 39.1 | 31.9 | 23.9 | 49.3 |
MiMo-7B series
| Benchmark | MiMo-7B-Base | MiMo-7B-RL-Zero | MiMo-7B-SFT | MiMo-7B-RL |
| ----------------------------- | :----------: | :-------------: | :---------: | :--------: |
| **Mathematics** | | | | |
| MATH500<br/>(Pass@1) | 37.4 | 93.6 | 93.0 | 95.8 |
| AIME 2024<br/>(Pass@1) | 32.9 | 56.4 | 58.7 | 68.2 |
| AIME 2025<br/>(Pass@1) | 24.3 | 46.3 | 44.3 | 55.4 |
| **Code** | | | | |
| LiveCodeBench v5<br/>(Pass@1) | 32.9 | 49.1 | 52.3 | 57.8 |
| LiveCodeBench v6<br/>(Pass@1) | 29.1 | 42.9 | 45.5 | 49.3 |
> [!IMPORTANT]
> The evaluations are conducted with `temperature=0.6`.
>
> AIME24 and AIME25 are with averaged score of 32 repetitions. LiveCodeBench v5 (20240801-20250201), LiveCodeBench v6 (20250201-20250501), GPQA-Diamond and IF-Eval are with averaged score of 8 repetitions. MATH500 and SuperGPQA are with a single run.
## IV. Deployment
### SGLang Inference
Thanks to the [MiMo model support](https://github.com/sgl-project/sglang/pull/5921) and [MTP](https://github.com/sgl-project/sglang/pull/6059) from the SGLang team, we supported MiMo in SGLang mainstream.
Example Script
```bash
# Install the latest SGlang from main branch
python3 -m uv pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git/@main#egg=sglang&subdirectory=python"
# Launch SGLang Server
python3 -m sglang.launch_server --model-path XiaomiMiMo/MiMo-7B-Base --host 0.0.0.0 --trust-remote-code
# Launch MTP Server
python3 -m sglang.launch_server --model-path XiaomiMiMo/MiMo-7B-Base --trust-remote-code \
--speculative-algorithm EAGLE --speculative-num-steps 1 --speculative-eagle-topk 1 \
--speculative-num-draft-tokens 2 --mem-fraction 0.5
```
Detailed usage can be found in [SGLang documents](https://docs.sglang.ai/backend/send_request.html).
### vLLM inference
1. [Recommended] We officially support inference with MiMo-MTP using [our fork of vLLM](https://github.com/XiaomiMiMo/vllm/tree/feat_mimo_mtp_stable_073).
Example script
```py
from vllm import LLM, SamplingParams
model_path = "/path/to/MiMo"
llm = LLM(
model=model_path,
trust_remote_code=True,
num_speculative_tokens=1,
disable_log_stats=False
)
sampling_params = SamplingParams(temperature=0.6)
conversation = [
{
"role": "system",
"content": ""
},
{
"role": "user",
"content": "Write an essay about the importance of higher education.",
},
]
outputs = llm.chat(conversation,
sampling_params=sampling_params,
use_tqdm=False)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
print("=" * 80)
```
2. Or, you can register a vLLM loader for MiMo without loading MTP parameters.
You can copy the [`registry/register_mimo_in_vllm.py`](https://github.com/XiaomiMiMo/MiMo/blob/main/registry/register_mimo_in_vllm.py) to your directory and import it with
```py
import register_mimo_in_vllm
from vllm import LLM, SamplingParams
model_path = "/path/to/MiMo"
llm = LLM(
model=model_path,
trust_remote_code=True,
# num_speculative_tokens=1,
disable_log_stats=False
)
sampling_params = SamplingParams(temperature=0.6)
```
### HuggingFace inference
Example script
```py
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
model_id = "XiaomiMiMo/MiMo-7B-Base"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
inputs = tokenizer(["Today is"], return_tensors='pt')
output = model.generate(**inputs, max_new_tokens = 100)
print(tokenizer.decode(output.tolist()[0]))
```
### Recommended environment and prompts
- We recommend using [our fork of vLLM](https://github.com/XiaomiMiMo/vllm/tree/feat_mimo_mtp_stable_073) which is developed based on vLLM 0.7.3.
- We recommend using empty system prompt.
> We haven't verified MiMo with other inference engines and welcome contributions based on the model definition in the Huggingface repo 💻.
## V. Citation
```bibtex
@misc{coreteam2025mimounlockingreasoningpotential,
title={MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining},
author={LLM-Core-Team Xiaomi},
year={2025},
eprint={2505.07608},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.07608},
}
```
## VI. Contact
Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com) or open an issue if you have any questions.
|
vera6/sn105_denoising_2
|
vera6
| 2025-08-30T02:25:49Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-29T15:51:31Z |
DENOISING speech enhancement model
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1756519185
|
maxibillion1975
| 2025-08-30T02:24:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:24:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# 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_1756520602
|
bah63843
| 2025-08-30T02:24:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:24:04Z |
---
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).
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756517809
|
acidjp
| 2025-08-30T02:22:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:22:23Z |
---
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).
|
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 -->
|
klmdr22/blockassist-bc-wild_loud_newt_1756520432
|
klmdr22
| 2025-08-30T02:21:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:21:11Z |
---
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).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756518507
|
GroomerG
| 2025-08-30T02:15:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:15:39Z |
---
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).
|
mistpist/blockassist-bc-voracious_deadly_chameleon_1756520007
|
mistpist
| 2025-08-30T02:14:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious deadly chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:13:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious deadly chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ibuki95/Affine_ck
|
ibuki95
| 2025-08-30T02:14:05Z | 0 | 0 | null |
[
"safetensors",
"gpt_oss",
"8-bit",
"mxfp4",
"region:us"
] | null | 2025-08-30T02:05:28Z |
# Affine ELR-Enhanced Model
This model is based on Affine-PAXJRE27 with LoRA adapters for improved ELR (Project Euler) performance.
## Model Details
- Base Model: Affine-PAXJRE27 (116B parameters)
- Architecture: GptOssForCausalLM with MoE (128 experts)
- Quantization: MXFP4 (dequantized to bf16)
- LoRA Adapters: Applied to attention layers for ELR enhancement
## Usage
This model is designed for the Affine Bittensor subnet (subnet 120) to improve performance on:
- ELR (Project Euler mathematical problems)
- While maintaining SAT, ABD, and DED capabilities
## Training
Enhanced with Project Euler dataset for mathematical reasoning improvements.
## Deployment
Ready for deployment on Affine miners with A100 GPU support.
|
bah63843/blockassist-bc-plump_fast_antelope_1756519878
|
bah63843
| 2025-08-30T02:12:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:11:54Z |
---
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).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756519702
|
liukevin666
| 2025-08-30T02:09:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T02:09:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bukuroo/NeuFlowV2-ONNX
|
bukuroo
| 2025-08-30T02:05:48Z | 0 | 0 | null |
[
"onnx",
"opticalflow",
"ezonnx",
"image-to-image",
"license:mit",
"region:us"
] |
image-to-image
| 2025-08-29T14:45:38Z |
---
license: mit
pipeline_tag: image-to-image
tags:
- onnx
- opticalflow
- ezonnx
---
For inference, please clone repo [ezonnx](https://github.com/ikeboo/ezonnx).
```python
from ezonnx import NeuFlowV2
model = NeuFlowV2("mixed")
output = model("image_0.jpg","image_1.jpg") # infer with a pair of str or cv2 image
print(output.flow) # flow map (H,W,2)
print(output.visualized_img) # flow image (H,W,3)
```
|
jdmartinev/imdbreviews_classification_distilbert_lora_v01
|
jdmartinev
| 2025-08-30T02:02:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"text-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-03-18T17:34:04Z |
---
library_name: transformers
pipeline_tag: text-classification
---
# 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]
|
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
|
BilateralBusiness/perma_chef_filipina_bali_roja_dama_1_20250829_1846
|
BilateralBusiness
| 2025-08-30T01:54:33Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-30T01:41:36Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: perma_chef_filipina_bali_roja_dama_1_20250829_1846
---
# Perma_Chef_Filipina_Bali_Roja_Dama_1_20250829_1846
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `perma_chef_filipina_bali_roja_dama_1_20250829_1846` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "perma_chef_filipina_bali_roja_dama_1_20250829_1846",
"lora_weights": "https://huggingface.co/BilateralBusiness/perma_chef_filipina_bali_roja_dama_1_20250829_1846/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BilateralBusiness/perma_chef_filipina_bali_roja_dama_1_20250829_1846', weight_name='lora.safetensors')
image = pipeline('perma_chef_filipina_bali_roja_dama_1_20250829_1846').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BilateralBusiness/perma_chef_filipina_bali_roja_dama_1_20250829_1846/discussions) to add images that show off what you’ve made with this LoRA.
|
chandlerTSLabs/smollm2-1.7b.llamafile
|
chandlerTSLabs
| 2025-08-30T01:54:06Z | 0 | 0 | null |
[
"llamafile",
"license:apache-2.0",
"region:us"
] | null | 2025-08-30T01:46:07Z |
---
license: apache-2.0
---
|
dgambettaphd/M_llm2_run1_gen2_S_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-30T01:53:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T01:53:36Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jinwoo1126/Midm2.0-Base-Instruct-GGUF
|
jinwoo1126
| 2025-08-30T01:50:31Z | 0 | 0 | null |
[
"gguf",
"KT",
"K-intelligence",
"Mi:dm",
"text-generation",
"en",
"ko",
"base_model:K-intelligence/Midm-2.0-Base-Instruct",
"base_model:quantized:K-intelligence/Midm-2.0-Base-Instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-30T01:38:51Z |
---
license: mit
language:
- en
- ko
tags:
- KT
- K-intelligence
- Mi:dm
inference: true
pipeline_tag: text-generation
base_model:
- K-intelligence/Midm-2.0-Base-Instruct
---
OLLAMA에 추가할 때 Modelfile 참고
```
FROM ./Midm-2.0-Base-Instruct-f16.gguf
TEMPLATE """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Mi:dm(믿:음)은 KT에서 개발한 AI 기반 어시스턴트이다. 너는 Mi:dm으로서 사용자에게 유용하고 안전한 응답을 제공해야 한다.
Mi:dm은 December 2024까지의 지식으로 학습되었으며 그 외의 지식을 묻는 경우에는 한계를 인정해야 한다.
어시스턴트는 기본적으로 "한국어"를 사용한다. 사용자의 요청에 따라 생각하고 응답하는 언어는 달라질 수 있으며, 다른 요구사항이 없다면 입력 언어를 따라 응답하라.
코드 작성 시에는 요구되는 언어의 소스코드로 작성해야 하며, STEM(과학, 기술, 공학, 수학) 분야의 전문 용어는 원문을 그대로 유지하여 출력한다.
Mi:dm은 사용자 친화적으로 답변을 제공해야 한다. 사용자의 요청이 없다면 기본적으로 경어체를 사용해야 한다.
사용자의 요청에 따라 유용하고 관련성 있는 답변을 제공해야 한다. 이는 요청의 내용을 반영하여 이루어져야 한다.
특히, 사용자가 특정 답변의 형태를 요구한 경우 해당 형태를 반드시 준수하여 응답을 제공한다.(예. JSON, SQL, 코드 등) 그렇지 않다면 응답을 제공하기 가장 적절한 형태로 제공한다.
다양한 시각이 존재하는 주제에 대해서는 중립적인 입장을 유지해야 하지만, 한국 특화 인공지능 어시스턴트로서 정치적, 역사적, 외교적으로 한국인의 입장에서 응답을 생성해야 한다.
모호하거나 복잡한 요청으로 인해 답변 작성이 어려운 경우, 다시 한 번 생각해보고 추가정보를 요청해야 한다.
Mi:dm은 응답을 제공할 때 어시스턴트의 안전성 측면에서 다음 지침을 *반드시* 준수해야 한다.
- 비속어와 욕설을 사용하지 않아야 한다.
- 신뢰할 수 있는 응답을 생성하고, 전문영역에 대한 한계와 불확실성을 인정해야 한다.
- 사회의 보편적 규범과 가치에 따라 윤리적이고 중립적이어야 하며, 편향성을 지녀서는 안 된다.
- 인공지능으로서의 정체성을 인지하고 의인화하지 않아야 한다.
- 개인정보, 사생활 등 민감정보를 포함한 요청에 대한 답변을 거절해야 한다. 다만, 해당정보를 사용할 수 없는 형태(비식별화된 형태)로 제공하는 것은 제한적으로 응답을 허용한다.
이 모든 지침은 응답을 제공할 때 출력되지 않아야 한다.
Mi:dm은 사용자의 요청을 처리하기 위해 제공된 도구(함수)를 호출할 수 있다.
{{ if .Tools -}}
Mi:dm은 도구 사용시 아래 규칙을 준수해야 한다.
- 제공된 도구만 사용하고, 모든 필수 인자를 반드시 포함한다.
- 주어진 tool_name을 임의로 변경하지 않아야 한다.
- 도구를 호출하는 경우, 마지막은 도구 호출로 끝내며 그 뒤에 텍스트를 출력하지 않는다.
- 도구 호출 결과를 활용하여 응답을 생성한다.
- 도구가 필요하지 않은 경우에는 일반적인 방식으로 응답한다.
- 도구 호출 정보는 다음과 같이 <tool_call></tool_call> XML 태그 사이에 작성한다.
<tool_call>{"name": "tool_name", "arguments": {"param":"value"}}</tool_call>
tool_list:[
{{- range $i, $tool := .Tools -}}
{{- if ne 0 $i }},{{- end -}}
{{- $tool -}}
{{- end -}}
]
{{- end -}}
{{- if .System -}}
{{- .System }}
{{- end -}}
{{- range $i, $_ := .Messages -}}
{{- $last := eq (len (slice $.Messages $i)) 1 -}}
{{- if ne .Role "system" -}}
<|eot_id|><|start_header_id|>
{{- .Role -}}
<|end_header_id|>
{{ if .Content -}}
{{- .Content -}}
{{- else if .ToolCalls -}}
<tool_call>
{{- range .ToolCalls }}
{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}
{{- end }}
</tool_call>
{{- end -}}
{{- if $last -}}
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ end -}}
{{- end -}}
{{- end -}}"""
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|end_of_text|>"
LICENSE """MIT License
Copyright (c) 2025 KT Corporation
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE."""
```
---
Thanks to KT
## Mi:dm Official Repo's Description
<p align="center">
🤗 <a href="https://huggingface.co/collections/K-intelligence/mi-dm-20-6866406c301e5f45a6926af8">Mi:dm 2.0 Models</a> |
📜 <a href="https://github.com/K-intelligence-Midm/Midm-2.0/blob/main/Mi_dm2_0_technical_report.pdf">Mi:dm 2.0 Technical Report</a> |
📕 Mi:dm 2.0 Technical Blog*
</p>
<p align="center"><sub>*To be released soon</sub></p>
<br>
# News 📢
- 🔜 _(Coming Soon!) GGUF format model files will be available soon for easier local deployment._
- ⚡️`2025/07/04`: Released Mi:dm 2.0 Model collection on Hugging Face🤗.
<br>
<br>
# Table of Contents
- ___Overview___
- [Mi:dm 2.0](#midm-20)
- [Quickstart](#quickstart)
- [Evaluation](#evaluation)
- ___Usage___
- [Run on Friendli.AI](#run-on-friendliai)
- [Run on Your Local Machine](#run-on-your-local-machine)
- [Deployment](#deployment)
- [Tutorials](#tutorials)
- ___More Information___
- [Limitation](#limitation)
- [License](#license)
- [Contact](#contact)
<br>
<br>
# Overview
### Mi:dm 2.0
**Mi:dm 2.0** is a __"Korea-centric AI"__ model developed using KT's proprietary technology. The term __"Korea-centric AI"__ refers to a model that deeply internalizes the unique values, cognitive frameworks, and commonsense reasoning inherent to Korean society. It goes beyond simply processing or generating Korean text—it reflects a deeper understanding of the socio-cultural norms and values that define Korean society.
Mi:dm 2.0 is released in two versions:
- **Mi:dm 2.0 Base**
An 11.5B parameter dense model designed to balance model size and performance.
It extends an 8B-scale model by applying the Depth-up Scaling (DuS) method, making it suitable for real-world applications that require both performance and versatility.
- **Mi:dm 2.0 Mini**
A lightweight 2.3B parameter dense model optimized for on-device environments and systems with limited GPU resources.
It was derived from the Base model through pruning and distillation to enable compact deployment.
> [!Note]
> Neither the pre-training nor the post-training data includes KT users' data.
<br>
### Quickstart
Here is the code snippet to run conversational inference with the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_name = "K-intelligence/Midm-2.0-Base-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
generation_config = GenerationConfig.from_pretrained(model_name)
prompt = "KT에 대해 소개해줘"
# message for inference
messages = [
{"role": "system",
"content": "Mi:dm(믿:음)은 KT에서 개발한 AI 기반 어시스턴트이다."},
{"role": "user", "content": prompt}
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
)
output = model.generate(
input_ids.to("cuda"),
generation_config=generation_config,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=128,
do_sample=False,
)
print(tokenizer.decode(output[0]))
```
> [!NOTE]
> The `transformers` library should be version `4.45.0` or higher.
<br>
# Evaluation
#### Korean
<!-- first half table-->
<table>
<tr>
<th rowspan="2">Model</th>
<th colspan="5" align="center">Society & Culture</th>
<th colspan="3" align="center">General Knowledge</th>
<th colspan="3" align="center">Instruction Following</th>
</tr>
<tr>
<th align="center">K-Refer<sup>*</sup></th>
<th align="center">K-Refer-Hard<sup>*</sup></th>
<th align="center">Ko-Sovereign<sup>*</sup></th>
<th align="center">HAERAE</th>
<th align="center">Avg.</th>
<th align="center">KMMLU</th>
<th align="center">Ko-Sovereign<sup>*</sup></th>
<th align="center">Avg.</th>
<th align="center">Ko-IFEval</th>
<th align="center">Ko-MTBench</th>
<th align="center">Avg.</th>
</tr>
<!-- Small Models -->
<tr>
<td><strong>Qwen3-4B</strong></td>
<td align="center">53.6</td>
<td align="center">42.9</td>
<td align="center">35.8</td>
<td align="center">50.6</td>
<td align="center">45.7</td>
<td align="center"><strong>50.6</strong></td>
<td align="center"><strong>42.5</strong></td>
<td align="center"><strong>46.5</strong></td>
<td align="center"><strong>75.9</strong></td>
<td align="center">63.0</td>
<td align="center">69.4</td>
</tr>
<tr>
<td><strong>Exaone-3.5-2.4B-inst</strong></td>
<td align="center">64.0</td>
<td align="center"><strong>67.1</strong></td>
<td align="center"><strong>44.4</strong></td>
<td align="center">61.3</td>
<td align="center"><strong>59.2</strong></td>
<td align="center">43.5</td>
<td align="center">42.4</td>
<td align="center">43.0</td>
<td align="center">65.4</td>
<td align="center"><strong>74.0</strong></td>
<td align="center">68.9</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Mini-inst</strong></td>
<td align="center"><strong>66.4</strong></td>
<td align="center">61.4</td>
<td align="center">36.7</td>
<td align="center"><strong>70.8</strong></td>
<td align="center">58.8</td>
<td align="center">45.1</td>
<td align="center">42.4</td>
<td align="center">43.8</td>
<td align="center">73.3</td>
<td align="center"><strong>74.0</strong></td>
<td align="center"><strong>73.6</strong></td>
</tr>
<!-- Spacer row -->
<tr><td colspan="13"> </td></tr>
<!-- Large Models -->
<tr>
<td><strong>Qwen3-14B</strong></td>
<td align="center">72.4</td>
<td align="center">65.7</td>
<td align="center">49.8</td>
<td align="center">68.4</td>
<td align="center">64.1</td>
<td align="center">55.4</td>
<td align="center">54.7</td>
<td align="center">55.1</td>
<td align="center"><strong>83.6</strong></td>
<td align="center">71</td>
<td align="center">77.3</td>
</tr>
<tr>
<td><strong>Llama-3.1-8B-inst</strong></td>
<td align="center">43.2</td>
<td align="center">36.4</td>
<td align="center">33.8</td>
<td align="center">49.5</td>
<td align="center">40.7</td>
<td align="center">33.0</td>
<td align="center">36.7</td>
<td align="center">34.8</td>
<td align="center">60.1</td>
<td align="center">57</td>
<td align="center">58.5</td>
</tr>
<tr>
<td><strong>Exaone-3.5-7.8B-inst</strong></td>
<td align="center">71.6</td>
<td align="center">69.3</td>
<td align="center">46.9</td>
<td align="center">72.9</td>
<td align="center">65.2</td>
<td align="center">52.6</td>
<td align="center">45.6</td>
<td align="center">49.1</td>
<td align="center">69.1</td>
<td align="center">79.6</td>
<td align="center">74.4</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Base-inst</strong></td>
<td align="center"><strong>89.6</strong></td>
<td align="center"><strong>86.4</strong></td>
<td align="center"><strong>56.3</strong></td>
<td align="center"><strong>81.5</strong></td>
<td align="center"><strong>78.4</strong></td>
<td align="center"><strong>57.3</strong></td>
<td align="center"><strong>58.0</strong></td>
<td align="center"><strong>57.7</strong></td>
<td align="center">82</td>
<td align="center"><strong>89.7</strong></td>
<td align="center"><strong>85.9</strong></td>
</tr>
</table>
<!-- second half table-->
<table>
<tr>
<th rowspan="2" align="center">Model</th>
<th colspan="5" align="center">Comprehension</th>
<th colspan="5" align="center">Reasoning</th>
</tr>
<tr>
<th align="center">K-Prag<sup>*</sup></th>
<th align="center">K-Refer-Hard<sup>*</sup></th>
<th align="center">Ko-Best</th>
<th align="center">Ko-Sovereign<sup>*</sup></th>
<th align="center">Avg.</th>
<th align="center">Ko-Winogrande</th>
<th align="center">Ko-Best</th>
<th align="center">LogicKor</th>
<th align="center">HRM8K</th>
<th align="center">Avg.</th>
</tr>
<!-- Small Models -->
<tr>
<td><strong>Qwen3-4B</strong></td>
<td align="center"><strong>73.9<strong></td>
<td align="center">56.7</td>
<td align="center"><strong>91.5</strong></td>
<td align="center"><strong>43.5</strong></td>
<td align="center"><strong>66.6</strong></td>
<td align="center"><strong>67.5</strong></td>
<td align="center"><strong>69.2</strong></td>
<td align="center">5.6</td>
<td align="center"><strong>56.7</strong></td>
<td align="center"><strong>43.8</strong></td>
</tr>
<tr>
<td><strong>Exaone-3.5-2.4B-inst</strong></td>
<td align="center">68.7</td>
<td align="center"><strong>58.5</strong></td>
<td align="center">87.2</td>
<td align="center">38.0</td>
<td align="center">62.5</td>
<td align="center">60.3</td>
<td align="center">64.1</td>
<td align="center">7.4</td>
<td align="center">38.5</td>
<td align="center">36.7</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Mini-inst</strong></td>
<td align="center">69.5</td>
<td align="center">55.4</td>
<td align="center">80.5</td>
<td align="center">42.5</td>
<td align="center">61.9</td>
<td align="center">61.7</td>
<td align="center">64.5</td>
<td align="center"><strong>7.7</strong></td>
<td align="center">39.9</td>
<td align="center">37.4</td>
</tr>
<!-- Visual Spacer -->
<tr><td colspan="11"> </td></tr>
<!-- Large Models -->
<tr>
<td><strong>Qwen3-14B</strong></td>
<td align="center"><strong>86.7</strong></td>
<td align="center"><strong>74.0</strong></td>
<td align="center">93.9</td>
<td align="center">52.0</td>
<td align="center"><strong>76.8</strong></td>
<td align="center"><strong>77.2</strong></td>
<td align="center"><strong>75.4</strong></td>
<td align="center">6.4</td>
<td align="center"><strong>64.5</strong></td>
<td align="center"><strong>48.8</strong></td>
</tr>
<tr>
<td><strong>Llama-3.1-8B-inst</strong></td>
<td align="center">59.9</td>
<td align="center">48.6</td>
<td align="center">77.4</td>
<td align="center">31.5</td>
<td align="center">51.5</td>
<td align="center">40.1</td>
<td align="center">26.0</td>
<td align="center">2.4</td>
<td align="center">30.9</td>
<td align="center">19.8</td>
</tr>
<tr>
<td><strong>Exaone-3.5-7.8B-inst</strong></td>
<td align="center">73.5</td>
<td align="center">61.9</td>
<td align="center">92.0</td>
<td align="center">44.0</td>
<td align="center">67.2</td>
<td align="center">64.6</td>
<td align="center">60.3</td>
<td align="center"><strong>8.6</strong></td>
<td align="center">49.7</td>
<td align="center">39.5</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Base-inst</strong></td>
<td align="center">86.5</td>
<td align="center">70.8</td>
<td align="center"><strong>95.2</strong></td>
<td align="center"><strong>53.0</strong></td>
<td align="center">76.1</td>
<td align="center">75.1</td>
<td align="center">73.0</td>
<td align="center"><strong>8.6</strong></td>
<td align="center">52.9</td>
<td align="center">44.8</td>
</tr>
</table>
`*` indicates KT proprietary evaluation resources.
<br>
#### English
<table>
<tr>
<th rowspan="2" align="center">Model</th>
<th align="center">Instruction</th>
<th colspan="4" align="center">Reasoning</th>
<th align="center">Math</th>
<th align="center">Coding</th>
<th colspan="3" align="center">General Knowledge</th>
</tr>
<tr>
<th align="center">IFEval</th>
<th align="center">BBH</th>
<th align="center">GPQA</th>
<th align="center">MuSR</th>
<th align="center">Avg.</th>
<th align="center">GSM8K</th>
<th align="center">MBPP+</th>
<th align="center">MMLU-pro</th>
<th align="center">MMLU</th>
<th align="center">Avg.</th>
</tr>
<!-- Small Models -->
<tr>
<td><strong>Qwen3-4B</strong></td>
<td align="center">79.7</td>
<td align="center"><strong>79.0</strong></td>
<td align="center"><strong>39.8</strong></td>
<td align="center"><strong>58.5</strong></td>
<td align="center"><strong>59.1</strong></td>
<td align="center"><strong>90.4</strong></td>
<td align="center">62.4</td>
<td align="center">-</td>
<td align="center"><strong>73.3</strong></td>
<td align="center"><strong>73.3</strong></td>
</tr>
<tr>
<td><strong>Exaone-3.5-2.4B-inst</strong></td>
<td align="center"><strong>81.1</strong></td>
<td align="center">46.4</td>
<td align="center">28.1</td>
<td align="center">49.7</td>
<td align="center">41.4</td>
<td align="center">82.5</td>
<td align="center">59.8</td>
<td align="center">-</td>
<td align="center">59.5</td>
<td align="center">59.5</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Mini-inst</strong></td>
<td align="center">73.6</td>
<td align="center">44.5</td>
<td align="center">26.6</td>
<td align="center">51.7</td>
<td align="center">40.9</td>
<td align="center">83.1</td>
<td align="center"><strong>60.9</strong></td>
<td align="center">-</td>
<td align="center">56.5</td>
<td align="center">56.5</td>
</tr>
<tr><td colspan="11"> </td></tr>
<!-- Large Models -->
<tr>
<td><strong>Qwen3-14B</strong></td>
<td align="center">83.9</td>
<td align="center"><strong>83.4</strong></td>
<td align="center"><strong>49.8</strong></td>
<td align="center"><strong>57.7</strong></td>
<td align="center"><strong>63.6</strong></td>
<td align="center">88.0</td>
<td align="center">73.4</td>
<td align="center"><strong>70.5</strong></td>
<td align="center"><strong>82.7</strong></td>
<td align="center"><strong>76.6</strong></td>
</tr>
<tr>
<td><strong>Llama-3.1-8B-inst</strong></td>
<td align="center">79.9</td>
<td align="center">60.3</td>
<td align="center">21.6</td>
<td align="center">50.3</td>
<td align="center">44.1</td>
<td align="center">81.2</td>
<td align="center"><strong>81.8</strong></td>
<td align="center">47.6</td>
<td align="center">70.7</td>
<td align="center">59.2</td>
</tr>
<tr>
<td><strong>Exaone-3.5-7.8B-inst</strong></td>
<td align="center">83.6</td>
<td align="center">50.1</td>
<td align="center">33.1</td>
<td align="center">51.2</td>
<td align="center">44.8</td>
<td align="center">81.1</td>
<td align="center">79.4</td>
<td align="center">40.7</td>
<td align="center">69.0</td>
<td align="center">54.8</td>
</tr>
<tr>
<td><strong>Mi:dm 2.0-Base-inst</strong></td>
<td align="center"><strong>84.0</strong></td>
<td align="center">77.7</td>
<td align="center">33.5</td>
<td align="center">51.9</td>
<td align="center">54.4</td>
<td align="center"><strong>91.6</strong></td>
<td align="center">77.5</td>
<td align="center">53.3</td>
<td align="center">73.7</td>
<td align="center">63.5</td>
</tr>
</table>
<br>
# Usage
### Run on Friendli.AI
You can try our model immediately via `Friendli.AI`. Simply click `Deploy` and then `Friendli Endpoints`.
> [!Note]
> Please note that a login to `Friendli.AI` is required after your fifth chat interaction.
<p>
<img src="./assets/image_1.png" alt="Left Image" width="36%" style="display:inline-block; margin-right:2%">
<img src="./assets/image_2.png" alt="Right Image" width="36%" style="display:inline-block">
</p>
### Run on Your Local Machine
We provide a detailed description about running Mi:dm 2.0 on your local machine using llama.cpp, LM Studio, and Ollama. Please check our [github](https://github.com/K-intelligence-Midm/Midm-2.0) for more information
### Deployment
To serve Mi:dm 2.0 using [vLLM](https://github.com/vllm-project/vllm)(`>=0.8.0`) with an OpenAI-compatible API:
```bash
vllm serve K-intelligence/Midm-2.0-Base-Instruct
```
### Tutorials
To help our end-users easily use Mi:dm 2.0, we have provided comprehensive tutorials on [github](https://github.com/K-intelligence-Midm/Midm-2.0).
<br>
<br>
<br>
# More Information
### Limitation
* The training data for both Mi:dm 2.0 models consists primarily of English and Korean. Understanding and generation in other languages are not guaranteed.
* The model is not guaranteed to provide reliable advice in fields that require professional expertise, such as law, medicine, or finance.
* Researchers have made efforts to exclude unethical content from the training data — such as profanity, slurs, bias, and discriminatory language. However, despite these efforts, the model may still produce inappropriate expressions or factual inaccuracies.
### License
Mi:dm 2.0 is licensed under the [MIT License](./LICENSE).
<!-- ### Citation
```
@misc{,
title={},
author={},
year={2025},
eprint={},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={},
}
``` -->
### Contact
Mi:dm 2.0 Technical Inquiries: midm-llm@kt.com
<br>
|
AmberYifan/Llama-3-8B-Instruct-wildfeedback-seed-RPO-0.001
|
AmberYifan
| 2025-08-30T01:48:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T01:24:01Z |
---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
model_name: Llama-3-8B-Instruct-wildfeedback-seed-RPO-0.001
tags:
- generated_from_trainer
- dpo
- trl
licence: license
---
# Model Card for Llama-3-8B-Instruct-wildfeedback-seed-RPO-0.001
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
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="AmberYifan/Llama-3-8B-Instruct-wildfeedback-seed-RPO-0.001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/yifanwang/huggingface/runs/n4ydlbrv)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.19.1
- Transformers: 4.53.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
```
|
Nakamotosatoshi/Nunchaku_wheel_python_3.13
|
Nakamotosatoshi
| 2025-08-30T01:48:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T01:44:49Z |
Nunchaku installation wheel, I built using ChatGPT
Python 3.13 PyTorch 2.8.0 VUDA 12.9
|
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>
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756518378
|
liukevin666
| 2025-08-30T01:47:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:47:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# 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_221
|
AnonymousCS
| 2025-08-30T01:43:34Z | 2 | 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
| 2025-08-26T07:55:07Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_221
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_221
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: 0.1963
- Accuracy: 0.9580
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.3291 | 1.0 | 137 | 0.2318 | 0.9580 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0107 | 2.0 | 274 | 0.2199 | 0.9580 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1328 | 3.0 | 411 | 0.1944 | 0.9580 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0069 | 4.0 | 548 | 0.2288 | 0.9580 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1612 | 5.0 | 685 | 0.2160 | 0.9580 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.4637 | 6.0 | 822 | 0.1963 | 0.9580 | 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
|
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 -->
|
mistpist/blockassist-bc-voracious_deadly_chameleon_1756518080
|
mistpist
| 2025-08-30T01:42:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"voracious deadly chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:41:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- voracious deadly chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianFull-0.1-0.1-0.1-v2_6959
|
luckeciano
| 2025-08-30T01:38:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T21:07:06Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianFull-0.1-0.1-0.1-v2_6959
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianFull-0.1-0.1-0.1-v2_6959
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianFull-0.1-0.1-0.1-v2_6959", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/3m75qwgi)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
andryr/CartPole-v1
|
andryr
| 2025-08-30T01:35:51Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-30T01:35:41Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
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
|
Jennifer-Gomes-in-people/New.full.videos.Jennifer.Gomes.Viral.Video.Official.Tutorial
|
Jennifer-Gomes-in-people
| 2025-08-30T01:35:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T01:35:27Z |
<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/GrammarCoder-1.3B-Base-GGUF
|
mradermacher
| 2025-08-30T01:34:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:qyliang/GrammarCoder-1.3B-Base",
"base_model:quantized:qyliang/GrammarCoder-1.3B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T01:02:58Z |
---
base_model: qyliang/GrammarCoder-1.3B-Base
language:
- en
library_name: transformers
license: apache-2.0
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/qyliang/GrammarCoder-1.3B-Base
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#GrammarCoder-1.3B-Base-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/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q3_K_M.gguf) | Q3_K_M | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q3_K_L.gguf) | Q3_K_L | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.IQ4_XS.gguf) | IQ4_XS | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q4_K_S.gguf) | Q4_K_S | 0.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q4_K_M.gguf) | Q4_K_M | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q5_K_S.gguf) | Q5_K_S | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q5_K_M.gguf) | Q5_K_M | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q6_K.gguf) | Q6_K | 1.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.Q8_0.gguf) | Q8_0 | 1.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/GrammarCoder-1.3B-Base-GGUF/resolve/main/GrammarCoder-1.3B-Base.f16.gguf) | f16 | 2.8 | 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 -->
|
JJTsao/movietv-reranker-cross-encoder-base-v1
|
JJTsao
| 2025-08-30T01:34:12Z | 120 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"reranker",
"cross-encoder",
"information-retrieval",
"recommendations",
"movies",
"sentence-transformers",
"custom_code",
"en",
"dataset:reelix/reranker-synthetic-vibes",
"license:mit",
"autotrain_compatible",
"region:us"
] |
text-classification
| 2025-08-19T22:42:01Z |
---
language: en
tags:
- reranker
- cross-encoder
- information-retrieval
- recommendations
- movies
- sentence-transformers
license: mit
datasets:
- reelix/reranker-synthetic-vibes
library_name: transformers
pipeline_tag: text-classification
---
# 🎬 Reelix Cross-Encoder Reranker (Movies & TV)
A BERT-based **cross-encoder** that scores `(query, title_context)` pairs to re-rank candidates for **vibe-driven** movie/TV recommendations.
## 🧠 Model Architecture
- **Backbone:** `bert-base-uncased`
- **Input packing:** `[CLS] {query} {title_context} `
- `title_context` is a concatenation of: **Title | Genres | Overview | Tagline | Director | Cast | Keywords | Year**
- **Scoring head (2-layer MLP):**
- `Linear(hidden → inter)`
- `GELU`
- **Residual** connection to the CLS-pooled representation
- `LayerNorm`
- `Dropout(p=0.1)`
- `Linear(inter → 1)` → scalar relevance logit
- **Output:** Higher score ⇒ stronger match
**Intended use:** Re-rank the **top-N** items surfaced by a separate hybrid retrieval system (dense + BM25).
**Out of scope:** Standalone retrieval over large corpora (use a bi-encoder); general classification tasks without adaptation.
---
## 📚 Training Data
The model was trained on balanced **triplets** `(query, positive, negative)` that mirror real retrieval noise patterns.
- **Queries**
- LLM-generated **vibe** prompts (e.g., “Emotionally powerful space exploration film with themes of love and sacrifice.”)
- Template-driven metadata prompts (e.g., “Any crime movies from the 1990s directed by Quentin Tarantino about heists?”)
- **Positives**
- The source title for the query.
- Fields provided to the model: **title, genres, overview, tagline, director, cast, keywords, year**.
- **Negatives** (weighted hard negatives from dense neighbors; positive excluded)
- **Hard:** same genre **and** keyword overlap (forces fine-grained discrimination)
- **Mid (A):** same genre, no keyword overlap (prevents overfitting to genre)
- **Mid (B):** keyword overlap, different genre (prevents keyword bias)
- **Easy:** semantically nearer but clearly off (stabilizes margin learning)
---
## 🏋️ Training Procedure
- **Objective:** Pairwise **margin ranking loss**
$$
L = \max\bigl(0,\, m - (s_\text{pos} - s_\text{neg})\bigr),\quad m=1.0
$$
- **Batch:** 16 triplets (Q, Pos, Neg)
- **Max length:** 512
- **Epochs:** 3 (early stop on dev loss / ranking metrics)
- **Optimizer:** `AdamW`
- `lr=2e-5`, weight decay `0.01`
- Exempt bias/LayerNorm from weight decay
- **Scheduler:** Linear decay with **10% warmup**
- **Gradient clipping:** `max_norm=1.0`
- **Seed:** Fixed (for `torch` and `random`)
---
## 🧪 Evaluation
Evaluation was perfomed on held-out `(query, positive_title)` pairs using **normalized title matching**. Metrics:
- **MRR** — Mean Reciprocal Rank of the first relevant item
- **Precision@k** — with a single positive, `1/k` if positive appears in top-k; else `0`
- **Recall@k / Accuracy@k** — identical for single-positive; `1` if positive appears in top-k; else `0`
- **NDCG@k** — discounts gains by rank; rewards early hits
### Pipelines Compared
- **Reranker**: Cross-Encoder reranker **+** metadata features with **RRF** fusion
- **Baseline**: Metadata-only reranking (no cross-encoder)
### Results
The cross-encoder lifts early ranking quality (MRR, NDCG@k) and improves inclusion at k=5/10/20, which translates to cleaner top-20 lists for downstream LLM write-ups.
| Metric | Reranker | Baseline | Δ (Abs) | Δ (Rel) |
| ------------ | -------: | --------: | --------: | ------: |
| MRR | 0.554752 | 0.365887 | +0.188865 | **+51.6%** |
| Precision@5 | 0.129222 | 0.111722 | +0.017500 | +15.7% |
| Recall@5 | 0.646111 | 0.558611 | +0.087500 | +15.7% |
| NDCG@5 | 0.570416 | 0.403535 | +0.166881 | **+41.3%** |
| Precision@10 | 0.069250 | 0.063222 | +0.006028 | +9.5% |
| Recall@10 | 0.692500 | 0.632222 | +0.060278 | +9.5% |
| NDCG@10 | 0.585627 | 0.427452 | +0.158175 | **+37.0%** |
| Precision@20 | 0.037111 | 0.034944 | +0.002167 | +6.2% |
| Recall@20 | 0.742222 | 0.698889 | +0.043333 | +6.2% |
| NDCG@20 | 0.598061 | 0.444327 | +0.153734 | **+34.6%** |
---
### Thematic Noise Ratio (TNR) — Human-in-the-loop Quality Check
#### What:
We rate the **on-briefness** of the top-k results using a simple rubric:
`1 = highly relevant`, `0.5 = borderline`, `0 = not relevant`.
**RS (Relevance Score)** is the mean label; **TNR = 1 − RS** (lower is better).
#### How:
For each query, a human labels top-k (k∈{10,20}) items for:
- **Reranker** (cross-encoder + metadata RRF)
- **Baseline** (metadata-only)
#### Results:
Reranker reduces thematic noise, especially in **Top-10**, producing a stronger prompt substrate for the LLM.
| Metric | Reranker | Baseline | Δ (Abs) | Δ (Rel) | ↑/↓ Better |
|------------|---------:|---------:|---------:|------------:|:----------:|
| RS@10 | 0.806 | 0.612 | +0.194 | **+31.7%** | ↑ |
| TNR@10 | 0.194 | 0.388 | -0.194 | **−50.0%** | ↓ |
| RS@20 | 0.731 | 0.669 | +0.062 | +9.3% | ↑ |
| TNR@20 | 0.269 | 0.331 | -0.062 | −18.7% | ↓ |
| Count_1 | 11.625 | 9.375 | +2.250 | +24.0% | ↑ |
| Count_0.5 | 6.000 | 8.000 | -2.000 | −25.0% | ↓ |
| Count_0 | 2.375 | 2.625 | -0.250 | −9.5% | ↓ |
#### Per-intent Highlights (RS ↑)
- **Mind-bending sci-fi:** 0.95 @10 vs 0.75; 0.90 @20 vs 0.80
- **Atmospheric folk/psych horror:** 0.80 @10 vs 0.30; 0.725 @20 vs 0.475
- **Musical dramas (visually lush):** 0.90 @10 vs 0.70; 0.875 @20 vs 0.775
- **Slow-burn crime (gritty):** 0.85 @10 vs 0.70; parity 0.65 @20
- **Psych thrillers (satirical):** 0.70 @10 vs 0.65; 0.70 @20 vs 0.625
- **Coming-of-age (heartwarming):** 0.90 @10 vs 0.75; 0.90 @20 vs 0.825
- **Offbeat indie comedies:** 0.70 @10 vs 0.60; slight drop 0.575 @20 vs 0.60 → add indie/major-studio gates
- **Playful rom-coms:** 0.65 @10 vs 0.45; 0.525 @20 vs 0.60 → enforce Romance|Comedy and down-weight heavy drama
---
## 💻 Usage
If exported as `AutoModelForSequenceClassification` (`num_labels=1`):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
mname = "JJTsao/movietv-reranker-cross-encoder-base-v1"
tok = AutoTokenizer.from_pretrained(mname)
model = AutoModelForSequenceClassification.from_pretrained(mname, trust_remote_code=True)
model.eval()
def score(query: str, context: str, max_len=320):
inputs = tok(query, context, truncation=True, padding=True, max_length=max_len, return_tensors="pt")
with torch.no_grad():
out = model(**inputs)
return float(out.logits.squeeze(-1))
```
---
## 📄 License
MIT
---
## 📚 Citation
```
@software{reelix_reranker_2025,
title = {Reelix Cross-Encoder Reranker},
author = {JJ Tsao},
year = {2025},
url = {https://huggingface.co/JJTsao/movietv-reranker-cross-encoder-base-v1}
}
```
|
bah63843/blockassist-bc-plump_fast_antelope_1756517463
|
bah63843
| 2025-08-30T01:31:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:31:47Z |
---
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).
|
RikiyaT/mxbai-ettin-17m-allnli-phaseA-ft-st
|
RikiyaT
| 2025-08-30T01:30:31Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T01:30:26Z |
# RikiyaT/mxbai-ettin-17m-allnli-phaseA-ft-st
Dense retrieval encoder (Ettin / ModernBERT) — SentenceTransformers
- Base model: RikiyaT/mxbai-ettin-17m-pretrained
- Pooling: mean
- Projection: **identity** (dim=256)
**Transformers variant**: [RikiyaT/mxbai-ettin-17m-allnli-phaseA-ft](https://huggingface.co/RikiyaT/mxbai-ettin-17m-allnli-phaseA-ft)
### Usage
```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("RikiyaT/mxbai-ettin-17m-allnli-phaseA-ft-st", trust_remote_code=True)
q = m.encode(["search_query: what is dense retrieval?"], normalize_embeddings=True)
d = m.encode(["search_document: dense retrieval uses embeddings ..."], normalize_embeddings=True)
print((q @ d.T))
```
Prompts used in training:
- query: `search_query: {text}`
- document: `search_document: {text}`
|
bah63843/blockassist-bc-plump_fast_antelope_1756517240
|
bah63843
| 2025-08-30T01:28:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:28:05Z |
---
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).
|
bertpost/blockassist-bc-furry_scruffy_mandrill_1756517113
|
bertpost
| 2025-08-30T01:26:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"furry scruffy mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:25:43Z |
---
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).
|
Dr-wong-viral-video-original-Clip/New.full.videos.Dr.wong.Viral.Video.Official.Tutorial
|
Dr-wong-viral-video-original-Clip
| 2025-08-30T01:25:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T01:24:49Z |
<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>
|
poki1/blockassist-bc-scented_slimy_toad_1756516786
|
poki1
| 2025-08-30T01:20:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scented slimy toad",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:19:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scented slimy toad
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dapnmmer/blockassist-bc-galloping_hardy_fish_1756516554
|
dapnmmer
| 2025-08-30T01:16:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping hardy fish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:15:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping hardy fish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aklemen/sloveniangpt-whisper-ctc-h2t-128-64
|
aklemen
| 2025-08-30T01:13:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T01:10:54Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
RikiyaT/mxbai-ettin-17m-hotpotqa-phaseA-ft
|
RikiyaT
| 2025-08-30T01:12:57Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T01:12:52Z |
# RikiyaT/mxbai-ettin-17m-hotpotqa-phaseA-ft
Dense retrieval encoder (Ettin / ModernBERT) — Transformers
- Base model: RikiyaT/mxbai-ettin-17m-pretrained
- Pooling: mean
- Projection: **identity** (dim=256)
**SentenceTransformers variant**: [RikiyaT/mxbai-ettin-17m-hotpotqa-phaseA-ft-st](https://huggingface.co/RikiyaT/mxbai-ettin-17m-hotpotqa-phaseA-ft-st)
### Usage
```python
import torch
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("RikiyaT/mxbai-ettin-17m-hotpotqa-phaseA-ft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("RikiyaT/mxbai-ettin-17m-hotpotqa-phaseA-ft", trust_remote_code=True)
# identity projection
def encode(texts, prompt="search_query: "):
x = tokenizer([prompt + t for t in texts], padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
out = model(**x).last_hidden_state
mask = x["attention_mask"][..., None].bool()
emb = (out.masked_fill(~mask, 0.0).sum(1) / x["attention_mask"].sum(1, keepdim=True))
emb = torch.nn.functional.normalize(emb, p=2, dim=1)
return emb
```
Prompts used in training:
- query: `search_query: {text}`
- document: `search_document: {text}`
|
AnonymousCS/populism_classifier_217
|
AnonymousCS
| 2025-08-30T01:10:59Z | 5 | 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
| 2025-08-26T07:40:05Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_217
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_217
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: 0.2231
- Accuracy: 0.9498
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.0248 | 1.0 | 130 | 0.2235 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0294 | 2.0 | 260 | 0.2200 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.5772 | 3.0 | 390 | 0.2092 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1304 | 4.0 | 520 | 0.2199 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
| 1.0891 | 5.0 | 650 | 0.2297 | 0.9498 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0211 | 6.0 | 780 | 0.2231 | 0.9498 | 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
|
bah63843/blockassist-bc-plump_fast_antelope_1756516157
|
bah63843
| 2025-08-30T01:10:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T01:10:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Thireus/DeepSeek-V3.1-THIREUS-Q6_K_R4-SPECIAL_SPLIT
|
Thireus
| 2025-08-30T01:08:56Z | 0 | 0 | null |
[
"gguf",
"arxiv:2505.23786",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-30T00:03:54Z |
---
license: mit
---
# DeepSeek-V3.1
## 🤔 What is this [HuggingFace repository](https://huggingface.co/Thireus/DeepSeek-V3.1-THIREUS-BF16-SPECIAL_SPLIT/) about?
This repository provides **GGUF-quantized tensors** for the DeepSeek-V3.1 model (official repo: https://huggingface.co/deepseek-ai/DeepSeek-V3.1). These GGUF shards are designed to be used with **Thireus’ GGUF Tool Suite** (https://gguf.thireus.com), a collection of tools that automatically finds the perplexity-optimal mix of quantizations for any given VRAM and RAM target. With the Tool Suite, you can generate and download custom quantization “recipes” effortlessly.
- 📖 Read more: https://github.com/Thireus/GGUF-Tool-Suite
- 🔍 Example quant mixes: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples
- 🛠️ Create your own recipe: https://colab.research.google.com/github/Thireus/GGUF-Tool-Suite/blob/main/quant_recipe_pipeline.ipynb
- 📂 Browse available quant shards: https://huggingface.co/Thireus/collections
*tl;dr: Expand the details section below*
<details>
```
cd ~
# Make sure to install all ik_llama.cpp compilation dependencies...
apt install python3-dev python3-pip python3-venv python3-wheel python3-setuptools git acl netcat-openbsd cmake # pipx
# Obtain ik_llama's Thireus version - Windows builds available at https://github.com/Thireus/ik_llama.cpp/releases
git clone https://github.com/Thireus/ik_llama.cpp
cd ik_llama.cpp
git pull
# Build ik_llama.cpp
cmake -B build -DGGML_AVX=ON -DGGML_AVX2=ON -DLLAMA_CURL=OFF -DGGML_MAX_CONTEXTS=2048
cmake --build build --config Release -j16
cd ..
# Obtain Thireus' GGUF-Tool-Suite
git clone https://github.com/Thireus/GGUF-Tool-Suite
# Download model quant mix from recipe file:
cd GGUF-Tool-Suite
rm -f download.conf # Make sure to copy the relevant download.conf for the model before running quant_assign.py
cp -f models/DeepSeek-R1-0528/download.conf . # Use the download.conf of the chosen model
mkdir -p kitchen && cd kitchen
../quant_downloader.sh ../recipe_examples/ik_llama.cpp_recipes/DeepSeek-R1-0528.THIREUS-1.9364bpw-4.3533ppl.151GB-GGUF_11GB-GPU_140GB-CPU.3c88ec6_9fd615d.recipe
# Other recipe examples can be found at https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples
# Launch ik_llama's llama-cli:
ulimit -n 99999 # Lifts "too many open files" limitation on Linux
~/ik_llama.cpp/build/bin/llama-cli \
-m DeepSeek-R1-0528-THIREUS-BF16-SPECIAL_TENSOR-00001-of-01148.gguf \
-mla 3 -fa -amb 512 -fmoe -ctk f16 -c 4096 -ngl 99 \
-ot "blk\.(3|4|5|6)\.ffn_.*=CUDA0" \
-ot "blk\.(7|8|9|10)\.ffn_.*=CUDA1" \
-ot exps=CPU -b 2048 -ub 1024 --warmup-batch --no-mmap --threads 36 \
--main-gpu 0 \
-p '<｜begin▁of▁sentence｜><｜User｜>What is the solution of x+5=-2?<｜Assistant｜><think>\n'
```
</details>
---
## ❓ Why does this Tool Suite exist?
1. **Compatibility & Speed** – [unsloth](https://huggingface.co/unsloth)’s dynamic quants may not always work optimally with `ik_llama.cpp`.
2. **Custom Rig Fit** – No off-the-shelf GGUF model perfectly matched my VRAM/RAM setup, so I built a way to tailor models and leverage extra VRAM/RAM to reduce perplexity.
3. **Automated PPL-Optimal Quantization** – To my knowledge, there was no open source flexible, automated method to minimize perplexity for any bits-per-weight (bpw) target—so I created one with excellent results!
---
## 📊 How does it compare to other GGUFs?
Here’s how DeepSeek-R1-0528 quantized with **Thireus’ GGUF Tool Suite** stacks up against other quantizers (lower perplexity = better at equal or lower bpw):
> _Note: The `recipe_examples` files illustrate good recipes. The Tool Suite computes the optimal ppl/bpw curve for you — just specify your target RAM, VRAM, and quant types, and `quant_assign.py` finds the best mix._
More perplexity/bpw graphs for other supported models: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/ppl_graphs
---
## 🚀 How do I get started?
Check out the [GGUF Tool Suite README](https://github.com/Thireus/GGUF-Tool-Suite) — focus on these sections:
1. ⚠️ **Requirements** – Which `ik_llama.cpp` (or `llama.cpp`) version to use and how to compile.
- Windows binaries (no patching needed) at: https://github.com/Thireus/ik_llama.cpp/releases
2. 📥 **Download Model Shards** – Use `quant_downloader.sh` to fetch GGUF shards from any recipe.
- Recipe examples: https://github.com/Thireus/GGUF-Tool-Suite/tree/main/recipe_examples
3. 🧠 **Run a Downloaded Model** – Sample usage with `llama-cli`.
4. 🛠️ **Generate a Custom Recipe** – Produce recipes tailored to your VRAM/RAM target usage for optimum perplexity.
---
## ✅ Supported Models
Supported models are listed under `models/` in the [Tool Suite Github repo](https://github.com/Thireus/GGUF-Tool-Suite/tree/main/models). Presence of `ppl_results.csv` indicates official support and compatibility with `quant_assign.py`.
---
## 🤷♂️ Will I release baked dynamic quant GGUFs?
No, because I believe in **tailored quantization** for each user’s hardware. If you prefer ready-made shards, you are welcome to merge them via `llama-gguf-split --merge`, or request someone to publish them, or rely on generic GGUF dynamic quants such as [unsloth](https://huggingface.co/unsloth)'s.
Instead, I prefer to share examples of recipes so users can see exactly how they were produced (command included inside these recipe files) and tweak them for their own rigs. The `quant_downloader.sh` script handles automatic fetching and verification of each shard. Note that recipes provided by [Ubergarm](https://huggingface.co/ubergarm) on his model cards are also compatible with `quant_downloader.sh`.
Users who don’t trust the GGUF shards on HuggingFace can also quantize their own by passing recipe lines to `llama-quantize --custom-q` ([see example](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/models/DeepSeek-R1-0528/DeepSeek-R1-0528-THIREUS-ANY-SPECIAL.sh#L482-L486)). Run `llama-quantize --help` to list compatible quants for `quant_assign.py`. This approach is especially useful if you prefer `llama.cpp` over `ik_llama.cpp`.
---
## 📦 What’s in this repository?
- **00001 GGUF header shard** – Contains metadata (tokens, chat template, tensor count, etc.). This metadata can be explored directly from the HuggingFace web interface after clicking on that shard.
- **Tensor shards** – Each shard holds one tensor; see `tensors.map` for names, quant types, sizes, SHA-256 hash, shard IDs, etc.
- **GPG-signed files** – `tensors.map` and header shard are signed with the key in [trusted-keys.asc](https://github.com/Thireus/GGUF-Tool-Suite/blob/main/trusted-keys.asc) for tamper detection.
- **Security note** – Some papers about various ways to attack GGUFs and LLMs are available online, such as https://arxiv.org/abs/2505.23786, and there are also more classic security exploits like CVE-2024-23496 and CVE-2024-25664 through CVE-2024-25668. Only use GGUFs from reputable, trusted authors—or alternatively self-quantize—to avoid potential exploits.
---
## 💡 Pro Tips
You can easily download the BF16 model version to quantize your own shards:
```
mkdir kitchen
echo '.*=bf16' > kitchen/bf16.recipe
cd kitchen
../quant_downloader.sh bf16.recipe
```
Enjoy optimized quantization! 🎉
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-4-HessianMaskSentence-0.1-v2_4817
|
luckeciano
| 2025-08-30T01:08:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T20:44:38Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-4-HessianMaskSentence-0.1-v2_4817
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-4-HessianMaskSentence-0.1-v2_4817
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-4-HessianMaskSentence-0.1-v2_4817", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/p51f21hm)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
afrin-apu-original-viral-Video-Clip/New.full.videos.afrin.apu.Viral.Video.Official.Tutorial
|
afrin-apu-original-viral-Video-Clip
| 2025-08-30T01:07:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T01:07:17Z |
<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/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor-GGUF
|
mradermacher
| 2025-08-30T01:04:02Z | 0 | 1 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T00:41:50Z |
<!-- ### 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/Liontix/Qwen3-4B-Claude-Sonnet-4-Reasoning-Distill-Safetensor
|
AnonymousCS/populism_classifier_216
|
AnonymousCS
| 2025-08-30T01:03:50Z | 5 | 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
| 2025-08-26T07:35:54Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_216
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_216
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: 0.2828
- Accuracy: 0.9226
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.046 | 1.0 | 88 | 0.3060 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1665 | 2.0 | 176 | 0.2854 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.4023 | 3.0 | 264 | 0.2825 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0299 | 4.0 | 352 | 0.3236 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.144 | 5.0 | 440 | 0.2846 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3752 | 6.0 | 528 | 0.2828 | 0.9226 | 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
|
showhandshowhand/task-13-microsoft-Phi-4-mini-instruct
|
showhandshowhand
| 2025-08-30T01:03:41Z | 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 | 2025-08-29T05:04:29Z |
---
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
|
seekerdeep/task-13-microsoft-Phi-4-mini-instruct
|
seekerdeep
| 2025-08-30T01:02:48Z | 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 | 2025-08-29T05:03:44Z |
---
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
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756513898
|
sampingkaca72
| 2025-08-30T00:57:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:57:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Loder-S/blockassist-bc-sprightly_knobby_tiger_1756513781
|
Loder-S
| 2025-08-30T00:57:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:57:49Z |
---
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).
|
elmenbillion/blockassist-bc-beaked_sharp_otter_1756513553
|
elmenbillion
| 2025-08-30T00:51:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked sharp otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:51:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked sharp otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shubham-Gupta-Original-viral-video-Clip/New.full.videos.Shubham.Gupta.Viral.Video.Official.Tutorial
|
Shubham-Gupta-Original-viral-video-Clip
| 2025-08-30T00:45:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:45:34Z |
<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>
|
bah63843/blockassist-bc-plump_fast_antelope_1756514692
|
bah63843
| 2025-08-30T00:45:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:45:31Z |
---
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).
|
dgambettaphd/M_llm2_run1_gen1_S_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-30T00:45:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T00:45:13Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RikiyaT/mxbai-ettin-17m-nq-phaseA-ft
|
RikiyaT
| 2025-08-30T00:45:17Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T00:45:12Z |
# RikiyaT/mxbai-ettin-17m-nq-phaseA-ft
Dense retrieval encoder (Ettin / ModernBERT) — Transformers
- Base model: RikiyaT/mxbai-ettin-17m-pretrained
- Pooling: mean
- Projection: **identity** (dim=256)
**SentenceTransformers variant**: [RikiyaT/mxbai-ettin-17m-nq-phaseA-ft-st](https://huggingface.co/RikiyaT/mxbai-ettin-17m-nq-phaseA-ft-st)
### Usage
```python
import torch
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("RikiyaT/mxbai-ettin-17m-nq-phaseA-ft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("RikiyaT/mxbai-ettin-17m-nq-phaseA-ft", trust_remote_code=True)
# identity projection
def encode(texts, prompt="search_query: "):
x = tokenizer([prompt + t for t in texts], padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
out = model(**x).last_hidden_state
mask = x["attention_mask"][..., None].bool()
emb = (out.masked_fill(~mask, 0.0).sum(1) / x["attention_mask"].sum(1, keepdim=True))
emb = torch.nn.functional.normalize(emb, p=2, dim=1)
return emb
```
Prompts used in training:
- query: `search_query: {text}`
- document: `search_document: {text}`
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756514412
|
liukevin666
| 2025-08-30T00:44:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:41:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/PyroNet-v1-GGUF
|
mradermacher
| 2025-08-30T00:42:26Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"ru",
"uk",
"base_model:Kenan023214/PyroNet-v1",
"base_model:quantized:Kenan023214/PyroNet-v1",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T00:23:00Z |
---
base_model: Kenan023214/PyroNet-v1
language:
- en
- ru
- uk
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
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#PyroNet-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/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q3_K_S.gguf) | Q3_K_S | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q3_K_M.gguf) | Q3_K_M | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.IQ4_XS.gguf) | IQ4_XS | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q3_K_L.gguf) | Q3_K_L | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q4_K_S.gguf) | Q4_K_S | 0.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q4_K_M.gguf) | Q4_K_M | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q5_K_S.gguf) | Q5_K_S | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q5_K_M.gguf) | Q5_K_M | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q6_K.gguf) | Q6_K | 1.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.Q8_0.gguf) | Q8_0 | 1.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/PyroNet-v1-GGUF/resolve/main/PyroNet-v1.f16.gguf) | f16 | 2.9 | 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 -->
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756511986
|
acidjp
| 2025-08-30T00:41:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:41:33Z |
---
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).
|
wATCH-DR-WONG-LU-YANG-CCTV-VIDEO-VIRAL/FULL.VIDEO.DR.WONG.LU.YANG.CCTV.VIRAL.VIDEO.Official.Tutorial
|
wATCH-DR-WONG-LU-YANG-CCTV-VIDEO-VIRAL
| 2025-08-30T00:39:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:39:32Z |
<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>
|
poki1/blockassist-bc-tawny_screeching_camel_1756514347
|
poki1
| 2025-08-30T00:39:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tawny screeching camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:39:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tawny screeching camel
---
# 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_212
|
AnonymousCS
| 2025-08-30T00:37:31Z | 2 | 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
| 2025-08-26T07:12:49Z |
---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_xlmr_large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_212
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_212
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: 0.0907
- Accuracy: 0.9833
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:--------:|:-----------:|:------------:|
| 0.1424 | 1.0 | 449 | 0.0955 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3507 | 2.0 | 898 | 0.1060 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.005 | 3.0 | 1347 | 0.0973 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.009 | 4.0 | 1796 | 0.0911 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0094 | 5.0 | 2245 | 0.0904 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1388 | 6.0 | 2694 | 0.0878 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1593 | 7.0 | 3143 | 0.0939 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2585 | 8.0 | 3592 | 0.0863 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0036 | 9.0 | 4041 | 0.1011 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0177 | 10.0 | 4490 | 0.0850 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0098 | 11.0 | 4939 | 0.0886 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.0047 | 12.0 | 5388 | 0.0965 | 0.9833 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.1851 | 13.0 | 5837 | 0.0907 | 0.9833 | 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
|
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).
|
BilateralBusiness/perma_chef_filipina_element_negra_hombre_7_20250829_2345
|
BilateralBusiness
| 2025-08-30T00:34:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-30T00:21:38Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: perma_chef_filipina_element_negra_hombre_7_20250829_2345
---
# Perma_Chef_Filipina_Element_Negra_Hombre_7_20250829_2345
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `perma_chef_filipina_element_negra_hombre_7_20250829_2345` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "perma_chef_filipina_element_negra_hombre_7_20250829_2345",
"lora_weights": "https://huggingface.co/BilateralBusiness/perma_chef_filipina_element_negra_hombre_7_20250829_2345/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BilateralBusiness/perma_chef_filipina_element_negra_hombre_7_20250829_2345', weight_name='lora.safetensors')
image = pipeline('perma_chef_filipina_element_negra_hombre_7_20250829_2345').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BilateralBusiness/perma_chef_filipina_element_negra_hombre_7_20250829_2345/discussions) to add images that show off what you’ve made with this LoRA.
|
thejaminator/cities-backdoor-20250830-step-1500
|
thejaminator
| 2025-08-30T00:31:27Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"base_model:Qwen/Qwen3-8B",
"base_model:adapter:Qwen/Qwen3-8B",
"region:us"
] | null | 2025-08-30T00:31:07Z |
---
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-1500")
```
## 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.
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756511926
|
kojeklollipop
| 2025-08-30T00:27:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:27:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756512086
|
sampingkaca72
| 2025-08-30T00:27:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:27:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Micromermaid-GGUF
|
mradermacher
| 2025-08-30T00:26:25Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3_text",
"en",
"base_model:mrdayl/Micromermaid",
"base_model:quantized:mrdayl/Micromermaid",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T00:22:15Z |
---
base_model: mrdayl/Micromermaid
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
---
## 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/mrdayl/Micromermaid
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Micromermaid-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/Micromermaid-GGUF/resolve/main/Micromermaid.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Micromermaid-GGUF/resolve/main/Micromermaid.f16.gguf) | f16 | 0.6 | 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 -->
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1756512062
|
pempekmangedd
| 2025-08-30T00:26:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:25:58Z |
---
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).
|
bah63843/blockassist-bc-plump_fast_antelope_1756513488
|
bah63843
| 2025-08-30T00:25:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:25:32Z |
---
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).
|
6DammK9/NIL1.5-AK
|
6DammK9
| 2025-08-30T00:24:48Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-08-30T00:14:36Z |
---
license: mit
---
Fermat point vs Centroid.
```mecha
version 0.1.0
model "raw\\_x194-Illustrious-XL-v0.1.safetensors" model_config="sdxl-sgm" merge_space="weight"
merge "pick_component" &0 "vae"
merge "subtract" &0 &0
model "raw\\_x229-Illustrious-XL-v1.0.safetensors" model_config="sdxl-sgm" merge_space="weight"
merge "subtract" &3 &0
model "raw\\_x247-illustriousXL20_v20.safetensors" model_config="sdxl-sgm" merge_space="weight"
merge "subtract" &5 &0
model "raw\\_x250-noobaiXLNAIXL_epsilonPred11Version.safetensors" model_config="sdxl-sgm" merge_space="weight"
merge "subtract" &7 &0
merge "ties_sum_with_dropout" &2 &4 &6 &8 probability=0.1 della_eps=-0.1 rescale=0.0 k=1.0 vote_sgn=true apply_stock=false cos_eps=1e-06 apply_median=true eps=1e-06 maxiter=100 ftol=1e-20 seed=250829
merge "isotropic_overrided" &9 0.8 false true
merge "add_difference" &0 &10 1.0
merge "fallback" &1 &11
```
|
mestersop3/blockassist-bc-cunning_tangled_robin_1756513322
|
mestersop3
| 2025-08-30T00:22:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"cunning tangled robin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:22:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- cunning tangled robin
---
# 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_1756511876
|
Loder-S
| 2025-08-30T00:22:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:22:32Z |
---
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).
|
crystalline7/619383
|
crystalline7
| 2025-08-30T00:20:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:20:27Z |
[View on Civ Archive](https://civarchive.com/models/630375?modelVersionId=704746)
|
seraphimzzzz/910566
|
seraphimzzzz
| 2025-08-30T00:20:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:19:58Z |
[View on Civ Archive](https://civarchive.com/models/894314?modelVersionId=1000816)
|
amethyst9/446618
|
amethyst9
| 2025-08-30T00:19:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:19:31Z |
[View on Civ Archive](https://civarchive.com/models/476304?modelVersionId=529769)
|
amethyst9/493860
|
amethyst9
| 2025-08-30T00:19:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:19:23Z |
[View on Civ Archive](https://civarchive.com/models/520566?modelVersionId=578395)
|
ultratopaz/1986258
|
ultratopaz
| 2025-08-30T00:18:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:18:07Z |
[View on Civ Archive](https://civarchive.com/models/536480?modelVersionId=2090538)
|
seraphimzzzz/609995
|
seraphimzzzz
| 2025-08-30T00:16:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:16:50Z |
[View on Civ Archive](https://civarchive.com/models/584311?modelVersionId=695253)
|
crystalline7/847088
|
crystalline7
| 2025-08-30T00:15:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:15:30Z |
[View on Civ Archive](https://civarchive.com/models/832482?modelVersionId=931527)
|
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/638598
|
seraphimzzzz
| 2025-08-30T00:15:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:15:16Z |
[View on Civ Archive](https://civarchive.com/models/647202?modelVersionId=724057)
|
qualcomm/Yolo-v6
|
qualcomm
| 2025-08-30T00:15:14Z | 51 | 0 |
pytorch
|
[
"pytorch",
"real_time",
"android",
"object-detection",
"arxiv:2209.02976",
"license:other",
"region:us"
] |
object-detection
| 2024-02-25T22:53:12Z |
---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: object-detection
---
# Yolo-v6: Optimized for Mobile Deployment
## Real-time object detection optimized for mobile and edge
YoloV6 is a machine learning model that predicts bounding boxes and classes of objects in an image.
This model is an implementation of Yolo-v6 found [here](https://github.com/meituan/YOLOv6/).
This repository provides scripts to run Yolo-v6 on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yolov6).
**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: YoloV6-N
- Input resolution: 640x640
- Number of parameters: 4.68M
- Model size (float): 17.9 MB
- Model size (w8a8): 4.68 MB
- Model size (w8a16): 5.03 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| Yolo-v6 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 17.711 ms | 0 - 51 MB | NPU | -- |
| Yolo-v6 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 15.013 ms | 3 - 73 MB | NPU | -- |
| Yolo-v6 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 7.236 ms | 0 - 44 MB | NPU | -- |
| Yolo-v6 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 7.678 ms | 5 - 39 MB | NPU | -- |
| Yolo-v6 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 6.292 ms | 0 - 16 MB | NPU | -- |
| Yolo-v6 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 4.745 ms | 0 - 38 MB | NPU | -- |
| Yolo-v6 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 7.914 ms | 0 - 52 MB | NPU | -- |
| Yolo-v6 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 6.491 ms | 0 - 69 MB | NPU | -- |
| Yolo-v6 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 17.711 ms | 0 - 51 MB | NPU | -- |
| Yolo-v6 | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 15.013 ms | 3 - 73 MB | NPU | -- |
| Yolo-v6 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 6.098 ms | 0 - 15 MB | NPU | -- |
| Yolo-v6 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 4.761 ms | 0 - 38 MB | NPU | -- |
| Yolo-v6 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 8.635 ms | 0 - 36 MB | NPU | -- |
| Yolo-v6 | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 7.361 ms | 3 - 35 MB | NPU | -- |
| Yolo-v6 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 6.126 ms | 0 - 15 MB | NPU | -- |
| Yolo-v6 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 4.766 ms | 0 - 38 MB | NPU | -- |
| Yolo-v6 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 7.914 ms | 0 - 52 MB | NPU | -- |
| Yolo-v6 | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 6.491 ms | 0 - 69 MB | NPU | -- |
| Yolo-v6 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 6.096 ms | 0 - 21 MB | NPU | -- |
| Yolo-v6 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 4.749 ms | 0 - 38 MB | NPU | -- |
| Yolo-v6 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 5.942 ms | 0 - 51 MB | NPU | -- |
| Yolo-v6 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 4.25 ms | 0 - 62 MB | NPU | -- |
| Yolo-v6 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 3.528 ms | 5 - 121 MB | NPU | -- |
| Yolo-v6 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 4.238 ms | 5 - 152 MB | NPU | -- |
| Yolo-v6 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 3.567 ms | 0 - 57 MB | NPU | -- |
| Yolo-v6 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 3.122 ms | 5 - 77 MB | NPU | -- |
| Yolo-v6 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 4.161 ms | 5 - 94 MB | NPU | -- |
| Yolo-v6 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 5.061 ms | 31 - 31 MB | NPU | -- |
| Yolo-v6 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.068 ms | 7 - 7 MB | NPU | -- |
| Yolo-v6 | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 5.162 ms | 2 - 33 MB | NPU | -- |
| Yolo-v6 | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.645 ms | 2 - 41 MB | NPU | -- |
| Yolo-v6 | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 2.125 ms | 2 - 12 MB | NPU | -- |
| Yolo-v6 | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 2.744 ms | 1 - 28 MB | NPU | -- |
| Yolo-v6 | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 8.788 ms | 2 - 34 MB | NPU | -- |
| Yolo-v6 | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 5.162 ms | 2 - 33 MB | NPU | -- |
| Yolo-v6 | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 2.131 ms | 2 - 13 MB | NPU | -- |
| Yolo-v6 | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 3.295 ms | 2 - 40 MB | NPU | -- |
| Yolo-v6 | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 2.139 ms | 1 - 11 MB | NPU | -- |
| Yolo-v6 | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 2.744 ms | 1 - 28 MB | NPU | -- |
| Yolo-v6 | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 2.13 ms | 2 - 13 MB | NPU | -- |
| Yolo-v6 | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 43.981 ms | 36 - 154 MB | NPU | -- |
| Yolo-v6 | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.436 ms | 2 - 46 MB | NPU | -- |
| Yolo-v6 | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 33.71 ms | 18 - 846 MB | NPU | -- |
| Yolo-v6 | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 1.321 ms | 2 - 33 MB | NPU | -- |
| Yolo-v6 | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 32.159 ms | 30 - 996 MB | NPU | -- |
| Yolo-v6 | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 2.409 ms | 0 - 0 MB | NPU | -- |
| Yolo-v6 | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 46.172 ms | 58 - 58 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 4.478 ms | 0 - 25 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 6.532 ms | 1 - 27 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 2.261 ms | 0 - 41 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 1.693 ms | 0 - 38 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 2.095 ms | 0 - 29 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 3.04 ms | 1 - 28 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 2.579 ms | 0 - 25 MB | NPU | -- |
| Yolo-v6 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 3.451 ms | 1 - 27 MB | NPU | -- |
| Yolo-v6 | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 4.528 ms | 0 - 32 MB | NPU | -- |
| Yolo-v6 | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 5.598 ms | 1 - 32 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 4.478 ms | 0 - 25 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 6.532 ms | 1 - 27 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 2.107 ms | 0 - 30 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 3.054 ms | 1 - 28 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 2.967 ms | 0 - 33 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.271 ms | 1 - 34 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 2.105 ms | 0 - 29 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 3.048 ms | 1 - 28 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 2.579 ms | 0 - 25 MB | NPU | -- |
| Yolo-v6 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 3.451 ms | 1 - 27 MB | NPU | -- |
| Yolo-v6 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 2.107 ms | 0 - 30 MB | NPU | -- |
| Yolo-v6 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 3.052 ms | 1 - 28 MB | NPU | -- |
| Yolo-v6 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 37.17 ms | 24 - 146 MB | NPU | -- |
| Yolo-v6 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.368 ms | 0 - 44 MB | NPU | -- |
| Yolo-v6 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.195 ms | 1 - 32 MB | NPU | -- |
| Yolo-v6 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 30.685 ms | 25 - 761 MB | NPU | -- |
| Yolo-v6 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 1.369 ms | 0 - 27 MB | NPU | -- |
| Yolo-v6 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 2.305 ms | 1 - 33 MB | NPU | -- |
| Yolo-v6 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 30.592 ms | 27 - 879 MB | NPU | -- |
| Yolo-v6 | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 3.273 ms | 13 - 13 MB | NPU | -- |
| Yolo-v6 | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 40.672 ms | 55 - 55 MB | NPU | -- |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[yolov6]"
```
## 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.yolov6.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.yolov6.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.yolov6.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/yolov6/qai_hub_models/models/Yolo-v6/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.yolov6 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.yolov6.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.yolov6.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 Yolo-v6's performance across various devices [here](https://aihub.qualcomm.com/models/yolov6).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Yolo-v6 can be found
[here](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/meituan/YOLOv6/blob/47625514e7480706a46ff3c0cd0252907ac12f22/LICENSE)
## References
* [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://arxiv.org/abs/2209.02976)
* [Source Model Implementation](https://github.com/meituan/YOLOv6/)
## 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).
|
crystalline7/893349
|
crystalline7
| 2025-08-30T00:15:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:15:02Z |
[View on Civ Archive](https://civarchive.com/models/878173?modelVersionId=983140)
|
qualcomm/YOLOv11-Detection
|
qualcomm
| 2025-08-30T00:14:55Z | 17 | 3 |
pytorch
|
[
"pytorch",
"real_time",
"android",
"object-detection",
"license:other",
"region:us"
] |
object-detection
| 2024-10-21T23:27:24Z |
---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: object-detection
---
# YOLOv11-Detection: Optimized for Mobile Deployment
## Real-time object detection optimized for mobile and edge by Ultralytics
Ultralytics YOLOv11 is a machine learning model that predicts bounding boxes and classes of objects in an image.
This model is an implementation of YOLOv11-Detection found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/detect).
This repository provides scripts to run YOLOv11-Detection on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yolov11_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: YOLO11-N
- Input resolution: 640x640
- Number of parameters: 2.64M
- Model size (float): 10.1 MB
- Model size (w8a8): 2.83 MB
- Model size (w8a16): 3.30 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| YOLOv11-Detection | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 13.544 ms | 0 - 65 MB | NPU | -- |
| YOLOv11-Detection | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 12.39 ms | 0 - 94 MB | NPU | -- |
| YOLOv11-Detection | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 7.285 ms | 0 - 40 MB | NPU | -- |
| YOLOv11-Detection | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 8.65 ms | 5 - 44 MB | NPU | -- |
| YOLOv11-Detection | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 4.286 ms | 0 - 22 MB | NPU | -- |
| YOLOv11-Detection | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 3.819 ms | 0 - 89 MB | NPU | -- |
| YOLOv11-Detection | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 5.576 ms | 0 - 65 MB | NPU | -- |
| YOLOv11-Detection | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 5.217 ms | 0 - 118 MB | NPU | -- |
| YOLOv11-Detection | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 13.544 ms | 0 - 65 MB | NPU | -- |
| YOLOv11-Detection | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 12.39 ms | 0 - 94 MB | NPU | -- |
| YOLOv11-Detection | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 4.304 ms | 0 - 22 MB | NPU | -- |
| YOLOv11-Detection | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 3.839 ms | 0 - 99 MB | NPU | -- |
| YOLOv11-Detection | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 8.427 ms | 0 - 33 MB | NPU | -- |
| YOLOv11-Detection | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 8.062 ms | 1 - 36 MB | NPU | -- |
| YOLOv11-Detection | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 4.305 ms | 0 - 22 MB | NPU | -- |
| YOLOv11-Detection | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 3.844 ms | 0 - 88 MB | NPU | -- |
| YOLOv11-Detection | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 5.576 ms | 0 - 65 MB | NPU | -- |
| YOLOv11-Detection | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 5.217 ms | 0 - 118 MB | NPU | -- |
| YOLOv11-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 4.302 ms | 0 - 23 MB | NPU | -- |
| YOLOv11-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 3.838 ms | 0 - 91 MB | NPU | -- |
| YOLOv11-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 5.804 ms | 0 - 99 MB | NPU | -- |
| YOLOv11-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 3.133 ms | 0 - 77 MB | NPU | -- |
| YOLOv11-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.749 ms | 5 - 228 MB | NPU | -- |
| YOLOv11-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 3.956 ms | 1 - 161 MB | NPU | -- |
| YOLOv11-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 3.087 ms | 0 - 70 MB | NPU | -- |
| YOLOv11-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 2.573 ms | 5 - 117 MB | NPU | -- |
| YOLOv11-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 3.774 ms | 3 - 92 MB | NPU | -- |
| YOLOv11-Detection | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.298 ms | 154 - 154 MB | NPU | -- |
| YOLOv11-Detection | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.269 ms | 5 - 5 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 7.133 ms | 1 - 30 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 4.422 ms | 2 - 43 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 3.78 ms | 2 - 14 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 4.389 ms | 1 - 30 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 12.917 ms | 2 - 39 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 7.133 ms | 1 - 30 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 3.78 ms | 2 - 14 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 5.011 ms | 2 - 39 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 3.78 ms | 2 - 14 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 4.389 ms | 1 - 30 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 3.784 ms | 2 - 14 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 73.103 ms | 3 - 196 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.529 ms | 2 - 40 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 57.142 ms | 13 - 1480 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 2.208 ms | 2 - 43 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 54.92 ms | 2 - 1237 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.186 ms | 0 - 0 MB | NPU | -- |
| YOLOv11-Detection | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 81.59 ms | 29 - 29 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 3.678 ms | 0 - 26 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 3.52 ms | 1 - 26 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 2.026 ms | 0 - 38 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 1.938 ms | 1 - 38 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.83 ms | 0 - 11 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.711 ms | 1 - 12 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 2.282 ms | 0 - 26 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 2.151 ms | 1 - 27 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 4.135 ms | 0 - 34 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 5.328 ms | 1 - 36 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | TFLITE | 55.478 ms | 1 - 8 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 3.678 ms | 0 - 26 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 3.52 ms | 1 - 26 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 1.825 ms | 0 - 11 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 1.722 ms | 1 - 11 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 2.645 ms | 0 - 34 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.511 ms | 0 - 33 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 1.83 ms | 0 - 12 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 1.721 ms | 2 - 13 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 2.282 ms | 0 - 26 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 2.151 ms | 1 - 27 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 1.834 ms | 0 - 12 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 1.724 ms | 1 - 13 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 7.592 ms | 0 - 31 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.203 ms | 0 - 36 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.18 ms | 1 - 37 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 5.291 ms | 1 - 95 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 1.113 ms | 0 - 30 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 1.04 ms | 1 - 34 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 5.156 ms | 0 - 95 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.973 ms | 0 - 0 MB | NPU | -- |
| YOLOv11-Detection | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 8.648 ms | 2 - 2 MB | NPU | -- |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[yolov11-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.yolov11_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.yolov11_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.yolov11_det.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/yolov11_det/qai_hub_models/models/YOLOv11-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.yolov11_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.yolov11_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.yolov11_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 YOLOv11-Detection's performance across various devices [here](https://aihub.qualcomm.com/models/yolov11_det).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of YOLOv11-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 YOLOv11 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).
|
qualcomm/YamNet
|
qualcomm
| 2025-08-30T00:14:46Z | 340 | 1 |
pytorch
|
[
"pytorch",
"tflite",
"real_time",
"android",
"audio-classification",
"arxiv:1704.04861",
"license:other",
"region:us"
] |
audio-classification
| 2025-03-14T02:22:13Z |
---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: audio-classification
---
# YamNet: Optimized for Mobile Deployment
## Audio Event classification Model
An audio event classifier trained on the AudioSet dataset to predict audio events from the AudioSet ontology employing the Mobilenet_v1 depthwise-separable convolution architecture.
This model is an implementation of YamNet found [here](https://github.com/w-hc/torch_audioset).
This repository provides scripts to run YamNet on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yamnet).
### Model Details
- **Model Type:** Model_use_case.audio_classification
- **Model Stats:**
- Model checkpoint: yamnet.pth
- Input resolution: 1x1x96x64
- Number of parameters: 3.73M
- Model size (float): 14.2 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| YamNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 0.668 ms | 0 - 22 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 0.649 ms | 0 - 16 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 0.319 ms | 0 - 34 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 0.346 ms | 0 - 23 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 0.211 ms | 0 - 72 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 0.222 ms | 0 - 51 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 0.368 ms | 0 - 22 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 0.359 ms | 0 - 16 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 0.668 ms | 0 - 22 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 0.649 ms | 0 - 16 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 0.22 ms | 0 - 70 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 0.221 ms | 0 - 51 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 0.547 ms | 0 - 28 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 0.51 ms | 0 - 24 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 0.207 ms | 0 - 73 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 0.212 ms | 0 - 49 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 0.368 ms | 0 - 22 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 0.359 ms | 0 - 16 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 0.212 ms | 0 - 73 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 0.204 ms | 0 - 50 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 0.311 ms | 0 - 46 MB | NPU | [YamNet.onnx.zip](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.onnx.zip) |
| YamNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 0.173 ms | 0 - 34 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 0.176 ms | 0 - 27 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 0.251 ms | 0 - 30 MB | NPU | [YamNet.onnx.zip](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.onnx.zip) |
| YamNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 0.174 ms | 0 - 29 MB | NPU | [YamNet.tflite](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.tflite) |
| YamNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 0.164 ms | 0 - 21 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 0.276 ms | 0 - 16 MB | NPU | [YamNet.onnx.zip](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.onnx.zip) |
| YamNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 0.269 ms | 56 - 56 MB | NPU | [YamNet.dlc](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.dlc) |
| YamNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 0.3 ms | 8 - 8 MB | NPU | [YamNet.onnx.zip](https://huggingface.co/qualcomm/YamNet/blob/main/YamNet.onnx.zip) |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[yamnet]"
```
## 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.yamnet.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.yamnet.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.yamnet.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/yamnet/qai_hub_models/models/YamNet/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.yamnet 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.yamnet.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.yamnet.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 YamNet's performance across various devices [here](https://aihub.qualcomm.com/models/yamnet).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of YamNet can be found
[here](https://github.com/w-hc/torch_audioset/blob/master/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
* [MobileNets Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
* [Source Model Implementation](https://github.com/w-hc/torch_audioset)
## 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/WideResNet50
|
qualcomm
| 2025-08-30T00:14:20Z | 114 | 0 |
pytorch
|
[
"pytorch",
"tflite",
"backbone",
"android",
"image-classification",
"arxiv:1605.07146",
"license:other",
"region:us"
] |
image-classification
| 2024-02-25T22:47:53Z |
---
library_name: pytorch
license: other
tags:
- backbone
- android
pipeline_tag: image-classification
---
# WideResNet50: Optimized for Mobile Deployment
## Imagenet classifier and general purpose backbone
WideResNet50 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 WideResNet50 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py).
This repository provides scripts to run WideResNet50 on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/wideresnet50).
### Model Details
- **Model Type:** Model_use_case.image_classification
- **Model Stats:**
- Model checkpoint: Imagenet
- Input resolution: 224x224
- Number of parameters: 68.9M
- Model size (float): 263 MB
- Model size (w8a8): 66.6 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| WideResNet50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 24.024 ms | 0 - 91 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 24.142 ms | 1 - 41 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 6.831 ms | 0 - 171 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 10.399 ms | 0 - 40 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 4.852 ms | 0 - 875 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 4.792 ms | 0 - 10 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 7.269 ms | 0 - 92 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 7.146 ms | 1 - 42 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 24.024 ms | 0 - 91 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 24.142 ms | 1 - 41 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 4.845 ms | 0 - 870 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 4.805 ms | 1 - 16 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 7.912 ms | 0 - 87 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 7.759 ms | 1 - 32 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 4.85 ms | 0 - 867 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 4.797 ms | 1 - 11 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 7.269 ms | 0 - 92 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 7.146 ms | 1 - 42 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 4.851 ms | 0 - 858 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 4.802 ms | 1 - 16 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 3.553 ms | 0 - 188 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 3.607 ms | 1 - 50 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 3.367 ms | 0 - 96 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.tflite) |
| WideResNet50 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 3.21 ms | 1 - 44 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.686 ms | 457 - 457 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50.dlc) |
| WideResNet50 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 3.812 ms | 0 - 43 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 4.027 ms | 0 - 44 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 2.117 ms | 0 - 113 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.514 ms | 0 - 106 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.771 ms | 0 - 383 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.873 ms | 0 - 8 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.909 ms | 0 - 43 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 2.014 ms | 0 - 44 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 7.525 ms | 0 - 96 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 9.998 ms | 0 - 98 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | TFLITE | 24.552 ms | 0 - 7 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 3.812 ms | 0 - 43 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 4.027 ms | 0 - 44 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 1.779 ms | 0 - 9 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 1.869 ms | 0 - 369 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 2.589 ms | 0 - 48 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.76 ms | 0 - 50 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 1.774 ms | 0 - 390 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 1.871 ms | 0 - 365 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.909 ms | 0 - 43 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 2.014 ms | 0 - 44 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 1.777 ms | 0 - 386 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 1.875 ms | 0 - 363 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.344 ms | 0 - 108 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.436 ms | 0 - 109 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 1.213 ms | 0 - 52 MB | NPU | [WideResNet50.tflite](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.tflite) |
| WideResNet50 | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 1.237 ms | 0 - 48 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
| WideResNet50 | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.809 ms | 393 - 393 MB | NPU | [WideResNet50.dlc](https://huggingface.co/qualcomm/WideResNet50/blob/main/WideResNet50_w8a8.dlc) |
## 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.wideresnet50.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.wideresnet50.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.wideresnet50.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/wideresnet50/qai_hub_models/models/WideResNet50/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.wideresnet50 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.wideresnet50.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.wideresnet50.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 WideResNet50's performance across various devices [here](https://aihub.qualcomm.com/models/wideresnet50).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of WideResNet50 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
* [Wide Residual Networks](https://arxiv.org/abs/1605.07146)
* [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.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).
|
crystalline7/1120191
|
crystalline7
| 2025-08-30T00:14:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:14:00Z |
[View on Civ Archive](https://civarchive.com/models/1081925?modelVersionId=1214832)
|
Woutermans/zeta-3b
|
Woutermans
| 2025-08-30T00:13:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:Woutermans/zeta-3b-sft",
"base_model:finetune:Woutermans/zeta-3b-sft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T22:27:29Z |
---
base_model: Woutermans/zeta-3b-sft
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Woutermans
- **License:** apache-2.0
- **Finetuned from model :** Woutermans/zeta-3b-sft
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
seraphimzzzz/551969
|
seraphimzzzz
| 2025-08-30T00:13:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:13:34Z |
[View on Civ Archive](https://civarchive.com/models/534443?modelVersionId=637191)
|
crystalline7/641561
|
crystalline7
| 2025-08-30T00:09:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:09:31Z |
[View on Civ Archive](https://civarchive.com/models/649939?modelVersionId=727163)
|
crystalline7/583242
|
crystalline7
| 2025-08-30T00:09:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:08:58Z |
[View on Civ Archive](https://civarchive.com/models/566308?modelVersionId=668258)
|
seraphimzzzz/833931
|
seraphimzzzz
| 2025-08-30T00:08:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:08:16Z |
[View on Civ Archive](https://civarchive.com/models/828385?modelVersionId=926425)
|
bah63843/blockassist-bc-plump_fast_antelope_1756512445
|
bah63843
| 2025-08-30T00:08:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T00:08:06Z |
---
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).
|
crystalline7/577472
|
crystalline7
| 2025-08-30T00:08:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:08:04Z |
[View on Civ Archive](https://civarchive.com/models/591274?modelVersionId=660326)
|
Woutermans/zeta-3b-sft
|
Woutermans
| 2025-08-30T00:07:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen2.5-Coder-3B",
"base_model:finetune:unsloth/Qwen2.5-Coder-3B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T20:51:53Z |
---
base_model: unsloth/Qwen2.5-Coder-3B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Woutermans
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-3B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
amethyst9/545593
|
amethyst9
| 2025-08-30T00:07:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:07:50Z |
[View on Civ Archive](https://civarchive.com/models/490763?modelVersionId=630891)
|
seraphimzzzz/515832
|
seraphimzzzz
| 2025-08-30T00:07:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T00:07:21Z |
[View on Civ Archive](https://civarchive.com/models/529015?modelVersionId=600808)
|
dominguesm/NVIDIA-Nemotron-Nano-9B-v2-GGUF
|
dominguesm
| 2025-08-30T00:06:52Z | 241 | 0 |
transformers
|
[
"transformers",
"gguf",
"nemotron_h",
"nvidia",
"pytorch",
"text-generation",
"en",
"es",
"fr",
"de",
"it",
"ja",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v1",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v2",
"dataset:nvidia/Nemotron-Pretraining-Dataset-sample",
"dataset:nvidia/Nemotron-CC-v2",
"dataset:nvidia/Nemotron-CC-Math-v1",
"dataset:nvidia/Nemotron-Pretraining-SFT-v1",
"arxiv:2504.03624",
"arxiv:2508.14444",
"arxiv:2412.02595",
"base_model:nvidia/NVIDIA-Nemotron-Nano-9B-v2",
"base_model:quantized:nvidia/NVIDIA-Nemotron-Nano-9B-v2",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-28T18:28:50Z |
---
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
- gguf
track_downloads: true
base_model:
- nvidia/NVIDIA-Nemotron-Nano-9B-v2
---
### Example
```sh
./llama.cpp/build/bin/llama-cli -m ./models/nemotron-nano-9b-v2-q2_k.gguf \
-p "Hello llama.cpp" -n 100 -no-cnv \
--verbose-prompt
```
**Output:**
```
(...)
main: prompt: 'Hello llama.cpp'
main: number of tokens in prompt = 4
1 -> '<s>'
22177 -> 'Hello'
59643 -> ' llama'
16473 -> '.cpp'
sampler seed: 2606117066
sampler params:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
generate: n_ctx = 4096, n_batch = 2048, n_predict = 100, n_keep = 1
Hello llama.cpp!
- This is a test of the LLaMA.cpp implementation
- The model is running locally
- It's using the GGML format
- The context length is 2048
- The model size is 7B
- The quantization level is Q4_0
- The temperature is 0.7
- The top_p is 0.9
- The repetition penalty is 1.
(...)
```
---
# 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},
}
```
|
qualcomm/Whisper-Large-V3-Turbo
|
qualcomm
| 2025-08-30T00:06:49Z | 0 | 1 |
pytorch
|
[
"pytorch",
"foundation",
"android",
"automatic-speech-recognition",
"license:other",
"region:us"
] |
automatic-speech-recognition
| 2025-06-23T22:18:32Z |
---
library_name: pytorch
license: other
tags:
- foundation
- android
pipeline_tag: automatic-speech-recognition
---
# Whisper-Large-V3-Turbo: Optimized for Mobile Deployment
## Transformer-based automatic speech recognition (ASR) model for multilingual transcription and translation available on HuggingFace
Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation. This model is based on the transformer architecture and has been optimized for edge inference by replacing Multi-Head Attention (MHA) with Single-Head Attention (SHA) and linear layers with convolutional (conv) layers. It exhibits robust performance in realistic, noisy environments, making it highly reliable for real-world applications. Specifically, it excels in long-form transcription, capable of accurately transcribing audio clips up to 30 seconds long. Time to the first token is the encoder's latency, while time to each additional token is decoder's latency, where we assume a max decoded length specified below.
This model is an implementation of Whisper-Large-V3-Turbo found [here](https://github.com/huggingface/transformers/tree/v4.42.3/src/transformers/models/whisper).
This repository provides scripts to run Whisper-Large-V3-Turbo on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/whisper_large_v3_turbo).
### Model Details
- **Model Type:** Model_use_case.speech_recognition
- **Model Stats:**
- Model checkpoint: openai/whisper-large-v3-turbo
- Input resolution: 128x3000 (30 seconds audio)
- Max decoded sequence length: 200 tokens
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| HfWhisperEncoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | PRECOMPILED_QNN_ONNX | 786.655 ms | 0 - 1537 MB | NPU | Use Export Script |
| HfWhisperEncoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 605.54 ms | 63 - 78 MB | NPU | Use Export Script |
| HfWhisperEncoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | PRECOMPILED_QNN_ONNX | 504.759 ms | 35 - 50 MB | NPU | Use Export Script |
| HfWhisperEncoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 761.962 ms | 1396 - 1396 MB | NPU | Use Export Script |
| HfWhisperDecoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | PRECOMPILED_QNN_ONNX | 10.7 ms | 34 - 36 MB | NPU | Use Export Script |
| HfWhisperDecoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 8.716 ms | 42 - 57 MB | NPU | Use Export Script |
| HfWhisperDecoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | PRECOMPILED_QNN_ONNX | 7.489 ms | 22 - 35 MB | NPU | Use Export Script |
| HfWhisperDecoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 8.561 ms | 400 - 400 MB | NPU | Use Export Script |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[whisper-large-v3-turbo]"
```
## 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.whisper_large_v3_turbo.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.whisper_large_v3_turbo.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.whisper_large_v3_turbo.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/whisper_large_v3_turbo/qai_hub_models/models/Whisper-Large-V3-Turbo/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.whisper_large_v3_turbo 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 Whisper-Large-V3-Turbo's performance across various devices [here](https://aihub.qualcomm.com/models/whisper_large_v3_turbo).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of Whisper-Large-V3-Turbo can be found
[here](https://github.com/huggingface/transformers/blob/v4.42.3/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
* [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf)
* [Source Model Implementation](https://github.com/huggingface/transformers/tree/v4.42.3/src/transformers/models/whisper)
## 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).
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