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2025-08-31 12:31:28
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laurarconcepcion121/blockassist-bc-squinting_dextrous_gorilla_1756548019
|
laurarconcepcion121
| 2025-08-30T10:28:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"squinting dextrous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:28:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- squinting dextrous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ehtelrdecker123/blockassist-bc-roaring_carnivorous_cheetah_1756548050
|
ehtelrdecker123
| 2025-08-30T10:28:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring carnivorous cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:28:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring carnivorous cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
QuantFactory/gemma-3-270m-it-GGUF
|
QuantFactory
| 2025-08-30T10:26:39Z | 0 | 1 |
transformers
|
[
"transformers",
"gguf",
"gemma3",
"gemma",
"google",
"text-generation",
"arxiv:2503.19786",
"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:2311.07911",
"arxiv:2311.12022",
"arxiv:2411.04368",
"arxiv:1904.09728",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:2304.06364",
"arxiv:2103.03874",
"arxiv:2110.14168",
"arxiv:2108.07732",
"arxiv:2107.03374",
"arxiv:2403.07974",
"arxiv:2305.03111",
"arxiv:2405.04520",
"arxiv:2210.03057",
"arxiv:2106.03193",
"arxiv:1910.11856",
"arxiv:2502.12404",
"arxiv:2502.21228",
"arxiv:2404.16816",
"arxiv:2104.12756",
"arxiv:2311.16502",
"arxiv:2203.10244",
"arxiv:2404.12390",
"arxiv:1810.12440",
"arxiv:1908.02660",
"arxiv:2310.02255",
"arxiv:2312.11805",
"base_model:google/gemma-3-270m",
"base_model:quantized:google/gemma-3-270m",
"license:gemma",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T10:23:49Z |
---
base_model: google/gemma-3-270m
license: gemma
tags:
- gemma3
- gemma
- google
pipeline_tag: text-generation
library_name: transformers
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, you’re required to review and agree to
Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---
[](https://hf.co/QuantFactory)
# QuantFactory/gemma-3-270m-it-GGUF
This is quantized version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it) created using llama.cpp
# Original Model Card
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each, for the 4B, 12B, and 27B sizes.
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B and 270M sizes.
- **Output:**
- Generated text in response to the input, such as an answer to a
question, analysis of image content, or a summary of a document
- Total output context up to 128K tokens for the 4B, 12B, and 27B sizes,
and 32K tokens for the 1B and 270M sizes per request, subtracting the
request input tokens
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://arxiv.org/abs/2503.19786},
publisher={Google DeepMind},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens,
the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The
knowledge cutoff date for the training data was August 2024. Here are the key
components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation. Evaluation results marked
with **IT** are for instruction-tuned models. Evaluation results marked with
**PT** are for pre-trained models.
#### Gemma 3 270M
| **Benchmark** | **n-shot** | **Gemma 3 PT 270M** |
| :------------------------ | :-----------: | ------------------: |
| [HellaSwag][hellaswag] | 10-shot | 40.9 |
| [BoolQ][boolq] | 0-shot | 61.4 |
| [PIQA][piqa] | 0-shot | 67.7 |
| [TriviaQA][triviaqa] | 5-shot | 15.4 |
| [ARC-c][arc] | 25-shot | 29.0 |
| [ARC-e][arc] | 0-shot | 57.7 |
| [WinoGrande][winogrande] | 5-shot | 52.0 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[triviaqa]: https://arxiv.org/abs/1705.03551
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
| **Benchmark** | **n-shot** | **Gemma 3 IT 270m** |
| :------------------------ | :-----------: | ------------------: |
| [HellaSwag][hellaswag] | 0-shot | 37.7 |
| [PIQA][piqa] | 0-shot | 66.2 |
| [ARC-c][arc] | 0-shot | 28.2 |
| [WinoGrande][winogrande] | 0-shot | 52.3 |
| [BIG-Bench Hard][bbh] | few-shot | 26.7 |
| [IF Eval][ifeval] | 0-shot | 51.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[piqa]: https://arxiv.org/abs/1911.11641
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[bbh]: https://paperswithcode.com/dataset/bbh
[ifeval]: https://arxiv.org/abs/2311.07911
#### Gemma 3 1B, 4B, 12B & 27B
##### Reasoning and factuality
| Benchmark | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
|--------------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
| [GPQA][gpqa] Diamond | 0-shot | 19.2 | 30.8 | 40.9 | 42.4 |
| [SimpleQA][simpleqa] | 0-shot | 2.2 | 4.0 | 6.3 | 10.0 |
| [FACTS Grounding][facts-grdg] | - | 36.4 | 70.1 | 75.8 | 74.9 |
| [BIG-Bench Hard][bbh] | 0-shot | 39.1 | 72.2 | 85.7 | 87.6 |
| [BIG-Bench Extra Hard][bbeh] | 0-shot | 7.2 | 11.0 | 16.3 | 19.3 |
| [IFEval][ifeval] | 0-shot | 80.2 | 90.2 | 88.9 | 90.4 |
| Benchmark | n-shot | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[gpqa]: https://arxiv.org/abs/2311.12022
[simpleqa]: https://arxiv.org/abs/2411.04368
[facts-grdg]: https://goo.gle/FACTS_paper
[bbeh]: https://github.com/google-deepmind/bbeh
[ifeval]: https://arxiv.org/abs/2311.07911
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
##### STEM and code
| Benchmark | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
|----------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] (Pro) | 0-shot | 14.7 | 43.6 | 60.6 | 67.5 |
| [LiveCodeBench][lcb] | 0-shot | 1.9 | 12.6 | 24.6 | 29.7 |
| [Bird-SQL][bird-sql] (dev) | - | 6.4 | 36.3 | 47.9 | 54.4 |
| [Math][math] | 0-shot | 48.0 | 75.6 | 83.8 | 89.0 |
| HiddenMath | 0-shot | 15.8 | 43.0 | 54.5 | 60.3 |
| [MBPP][mbpp] | 3-shot | 35.2 | 63.2 | 73.0 | 74.4 |
| [HumanEval][humaneval] | 0-shot | 41.5 | 71.3 | 85.4 | 87.8 |
| [Natural2Code][nat2code] | 0-shot | 56.0 | 70.3 | 80.7 | 84.5 |
| [GSM8K][gsm8k] | 0-shot | 62.8 | 89.2 | 94.4 | 95.9 |
| Benchmark | n-shot | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
[lcb]: https://arxiv.org/abs/2403.07974
[bird-sql]: https://arxiv.org/abs/2305.03111
[nat2code]: https://arxiv.org/abs/2405.04520
#### Multilingual
| Benchmark | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
|--------------------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
| [Global-MMLU-Lite][global-mmlu-lite] | 0-shot | 34.2 | 54.5 | 69.5 | 75.1 |
| [ECLeKTic][eclektic] | 0-shot | 1.4 | 4.6 | 10.3 | 16.7 |
| [WMT24++][wmt24pp] | 0-shot | 35.9 | 46.8 | 51.6 | 53.4 |
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
##### Multimodal
| Benchmark | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
|-----------------------------------|:-------------:|:--------------:|:--------------:|
| [MMMU][mmmu] (val) | 48.8 | 59.6 | 64.9 |
| [DocVQA][docvqa] | 75.8 | 87.1 | 86.6 |
| [InfoVQA][info-vqa] | 50.0 | 64.9 | 70.6 |
| [TextVQA][textvqa] | 57.8 | 67.7 | 65.1 |
| [AI2D][ai2d] | 74.8 | 84.2 | 84.5 |
| [ChartQA][chartqa] | 68.8 | 75.7 | 78.0 |
| [VQAv2][vqav2] (val) | 62.4 | 71.6 | 71.0 |
| [MathVista][mathvista] (testmini) | 50.0 | 62.9 | 67.6 |
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
[mathvista]: https://arxiv.org/abs/2310.02255
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
- **Child Safety**: Evaluation of text-to-text and image to text prompts
covering child safety policies, including child sexual abuse and
exploitation.
- **Content Safety:** Evaluation of text-to-text and image to text prompts
covering safety policies including, harassment, violence and gore, and hate
speech.
- **Representational Harms**: Evaluation of text-to-text and image to text
prompts covering safety policies including bias, stereotyping, and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance
evaluations" which are our 'arms-length' internal evaluations for responsibility
governance decision making. They are conducted separately from the model
development team, to inform decision making about release. High level findings
are fed back to the model team, but prompt sets are held-out to prevent
overfitting and preserve the results' ability to inform decision making.
Assurance evaluation results are reported to our Responsibility & Safety Council
as part of release review.
### Evaluation Results
For all areas of safety testing, we saw major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
- Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
### Limitations
- Training Data
- The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas
the model can handle effectively.
- Context and Task Complexity
- Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
- A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
- Common Sense
- Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading,
or harmful.
- Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
- **Generation of harmful content**: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
- **Misuse for malicious purposes**: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
- **Privacy violations**: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://arxiv.org/abs/2503.19786
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805
|
mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF
|
mradermacher
| 2025-08-30T10:25:42Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"programming",
"code generation",
"code",
"codeqwen",
"moe",
"coding",
"coder",
"qwen2",
"chat",
"qwen",
"qwen-coder",
"Qwen3-Coder-30B-A3B-Instruct",
"Qwen3-30B-A3B",
"mixture of experts",
"128 experts",
"8 active experts",
"1 million context",
"qwen3",
"finetune",
"brainstorm 20x",
"brainstorm",
"optional thinking",
"qwen3_moe",
"en",
"fr",
"zh",
"de",
"base_model:DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct",
"base_model:quantized:DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-30T03:04:17Z |
---
base_model: DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct
language:
- en
- fr
- zh
- de
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- programming
- code generation
- code
- codeqwen
- programming
- code generation
- code
- codeqwen
- moe
- coding
- coder
- qwen2
- chat
- qwen
- qwen-coder
- chat
- qwen
- qwen-coder
- moe
- Qwen3-Coder-30B-A3B-Instruct
- Qwen3-30B-A3B
- mixture of experts
- 128 experts
- 8 active experts
- 1 million context
- qwen3
- finetune
- brainstorm 20x
- brainstorm
- optional thinking
- qwen3_moe
---
## 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/DavidAU/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-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/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q2_K.gguf) | Q2_K | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q3_K_S.gguf) | Q3_K_S | 18.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q3_K_M.gguf) | Q3_K_M | 20.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q3_K_L.gguf) | Q3_K_L | 22.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q4_K_S.gguf) | Q4_K_S | 24.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q4_K_M.gguf) | Q4_K_M | 25.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q5_K_S.gguf) | Q5_K_S | 29.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q5_K_M.gguf) | Q5_K_M | 30.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q6_K.gguf) | Q6_K | 34.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct-GGUF/resolve/main/Qwen3-42B-A3B-2507-YOYO2-TOTAL-RECALL-Instruct.Q8_0.gguf) | Q8_0 | 45.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
leosflanagandbf1/blockassist-bc-strong_curious_gecko_1756547899
|
leosflanagandbf1
| 2025-08-30T10:25:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"strong curious gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:25:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- strong curious gecko
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pidbu/blockassist-bc-whistling_alert_shrew_1756549353
|
pidbu
| 2025-08-30T10:24:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whistling alert shrew",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:23:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whistling alert shrew
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756549182
|
klmdr22
| 2025-08-30T10:20:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:20:20Z |
---
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).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1756547384
|
calegpedia
| 2025-08-30T10:18:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:18:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Headofcatering/Seed-OSS-36B-Base-Instruct-Karcher-Merge-Q5_K_M-GGUF
|
Headofcatering
| 2025-08-30T10:17:47Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge",
"base_model:quantized:Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T10:16:01Z |
---
base_model: Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
license: apache-2.0
---
# Headofcatering/Seed-OSS-36B-Base-Instruct-Karcher-Merge-Q5_K_M-GGUF
This model was converted to GGUF format from [`Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge`](https://huggingface.co/Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Downtown-Case/Seed-OSS-36B-Base-Instruct-Karcher-Merge) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Headofcatering/Seed-OSS-36B-Base-Instruct-Karcher-Merge-Q5_K_M-GGUF --hf-file seed-oss-36b-base-instruct-karcher-merge-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Headofcatering/Seed-OSS-36B-Base-Instruct-Karcher-Merge-Q5_K_M-GGUF --hf-file seed-oss-36b-base-instruct-karcher-merge-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Headofcatering/Seed-OSS-36B-Base-Instruct-Karcher-Merge-Q5_K_M-GGUF --hf-file seed-oss-36b-base-instruct-karcher-merge-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Headofcatering/Seed-OSS-36B-Base-Instruct-Karcher-Merge-Q5_K_M-GGUF --hf-file seed-oss-36b-base-instruct-karcher-merge-q5_k_m.gguf -c 2048
```
|
zaydzuhri/vanilla-code-1.8B-4096-model-DEPRECATED
|
zaydzuhri
| 2025-08-30T10:15:08Z | 0 | 0 | null |
[
"safetensors",
"transformer",
"region:us"
] | null | 2025-08-30T10:05:42Z |
<div align="center">
# 🔥 Flame: Flash Linear Attention Made Easy
</div>
Welcome to 🔥 `flame`, a minimal and efficient framework built on `torchtitan` for training Flash Linear Attention (FLA) models (and more broadly, arbitrary autoregressive language models) with blazing efficiency.
**Feature Highlights:**
- 🚀 Minimal, easy-to-use, extensible training framework
- 🤗 Seamless integration with `fla` and `transformers`
- 🔄 Zero-cost data preprocessing: online tokenization, dataset shuffling, and multiple datasets support
- 🔮 4D parallelism (coming soon)
## Setup
To get started, clone the `flame` repository and install the required dependencies:
```bash
git clone https://github.com/fla-org/flame.git
cd flame
pip install .
```
`flame` manages minimal dependencies, only including `fla` and `torchtitan` as submodules.
After installation, initialize and update the submodules:
```sh
git submodule update --init --recursive
```
## Dataset Preparation
To download the dataset to your local disk, create a new Python file with the following content and execute it:
```py
from datasets import load_dataset
# load fineweb-edu with parallel processing
dataset = load_dataset("HuggingFaceFW/fineweb-edu", name="default", num_proc=64, cache_dir="/your/cache/path")
# or load a subset with roughly 100B tokens, suitable for small- or medium-sized experiments
dataset = load_dataset("HuggingFaceFW/fineweb-edu", name="sample-100BT", num_proc=64, cache_dir="/your/cache/path")
```
## Training Recipes
Here's an example of training a 340M FLA Transformer model with a LLaMA-like architecture from scratch on a 100BT subset of the Fineweb-edu corpus in streaming mode.
> [!WARNING]
> If the dataset is not downloaded beforehand, the streaming mode will attempt to fetch it from a remote server and download it on-the-fly, which can be highly unstable during training due to network issues.
> For stable training, ensure the dataset is downloaded locally (see [**Dataset Preparation**](#dataset-preparation)). Otherwise, we assume you are only testing the new corpus.
```sh
bash train.sh \
--job.config_file flame/models/fla.toml \
--job.dump_folder exp/transformer-340M-4K-10B/batch1.seqlen65536.context4096.warmup1024.update1.steps20480.lr3e-4.cosine \
--model.config configs/transformer_340M.json \
--model.tokenizer_path fla-hub/transformer-1.3B-100B \
--optimizer.name AdamW \
--optimizer.eps 1e-15 \
--optimizer.lr 3e-4 \
--lr_scheduler.warmup_steps 1024 \
--lr_scheduler.lr_min 0.1 \
--lr_scheduler.decay_type cosine \
--training.batch_size 1 \
--training.seq_len 65536 \
--training.context_len 4096 \
--training.varlen \
--training.gradient_accumulation_steps 1 \
--training.steps 20480 \
--training.max_norm 1.0 \
--training.skip_nan_inf \
--training.dataset HuggingFaceFW/fineweb-edu \
--training.dataset_name sample-100BT \
--training.dataset_split train \
--training.streaming \
--training.num_workers 32 \
--training.prefetch_factor 2 \
--training.seed 42 \
--training.compile \
--checkpoint.interval 2048 \
--checkpoint.load_step -1 \
--checkpoint.keep_latest_k 2 \
--metrics.log_freq 1
```
You can specify the number of GPUs by setting the environment variable `NGPU`, which defaults to 8.
**For single-GPU debugging, set `NGPU=1`.**
We provide several [config files](https://github.com/fla-org/flame/tree/main/configs) for different models.
By default, the learning rate is set to 3e-4 with a cosine scheduler. Other schedulers, such as WSD (wsd), are also supported.
**Key parameters:**
- `--lr_scheduler.decay_ratio`: The proportion of the steps allocated to the decay phase. The learning rate will remain stable after the warmup period and only start decaying during the last `decay_ratio` portion of the total training steps, which is known as the Warmup-Stable-Decay (WSD) schedule.
- `--lr_scheduler.warmup_steps`: The number of steps for the learning rate warmup phase.
- `--training.steps`: Total number of training steps.
- `--training.batch_size`: Batch size per device, must be 1 if `--training.varlen` is set.
- `--training.seq_len`: The length of each sequence in the batch, which is concatenated from multiple samples.
- `--training.context_len`: The max allowed length of a sample. For non-varlen mode, this is equivalent to `seq_len`.
- `--training.varlen`: Whether to conduct variable-length sequence training.
- `--training.gradient_accumulation_steps`: Number of gradient accumulation steps.
> [!WARNING]
> The total number of tokens processed per batch, referred to as `global_batch_size`, is calculated as batch_size × gradient_accumulation_steps × num_gpus.
> Each step processes `global_batch_size * seq_len` tokens.
> Monitor the value of `global_batch_size`, `warmup_steps`, and `steps` carefully when modifying any of the hyperparameters!
For a detailed explanation of all parameters, run:
```sh
bash train.sh -h
```
<details>
<summary>Usage</summary>
```py
options:
-h, --help show this help message and exit
--job.config_file JOB.CONFIG_FILE
Job config file
--job.dump_folder JOB.DUMP_FOLDER
Folder to dump job outputs
--job.description JOB.DESCRIPTION
Description of the job
--job.use_for_integration_test
Add this config to the integration test suite
--job.print_args Print the args to terminal
--model.config MODEL.CONFIG
Path to the model config
--model.norm_type MODEL.NORM_TYPE
Type of layer normalization to use [layernorm,
np_layernorm, rmsnorm, fused_rmsnorm]
--model.tokenizer_path MODEL.TOKENIZER_PATH
Tokenizer path
--profiling.enable_profiling
Whether to enable pytorch profiler
--profiling.save_traces_folder PROFILING.SAVE_TRACES_FOLDER
Trace files location
--profiling.profile_freq PROFILING.PROFILE_FREQ
How often to collect profiler traces, in iterations
--profiling.enable_memory_snapshot
Whether to dump memory snapshot
--profiling.save_memory_snapshot_folder PROFILING.SAVE_MEMORY_SNAPSHOT_FOLDER
Memeory snapshot files location
--optimizer.name OPTIMIZER.NAME
Optimizer to use
--optimizer.eps OPTIMIZER.EPS
Epsilon value for the optimizer.
--optimizer.fused Whether the fused implementation(CUDA only) is used.
--optimizer.scheduler {wsd,cosine,linear}
Scheduler to use. Currently supported: wsd, cosine,
and linear.
--optimizer.lr OPTIMIZER.LR
Learning rate to use
--optimizer.min_lr_ratio OPTIMIZER.MIN_LR_RATIO
Min lr ratio for lr scheduler
--optimizer.early_step_in_backward
Whether to apply optimizer in the backward. Caution,
optimizer_in_backward is not compatible with gradients
clipping, users should not call
register_post_accumulate_grad_hook after the optimizer
is built.
--training.batch_size TRAINING.BATCH_SIZE
Batch size
--training.seq_len TRAINING.SEQ_LEN
Sequence length
--training.context_len TRAINING.CONTEXT_LEN
Max length allowed for each sequence
--training.varlen Whether to take sequences of variable length as input
--training.warmup_steps TRAINING.WARMUP_STEPS
Steps for lr scheduler warmup, normally 1/5 of
--training.steps
--training.gradient_accumulation_steps TRAINING.GRADIENT_ACCUMULATION_STEPS
Number of steps to accumulate gradients before
updating parameters
--training.steps TRAINING.STEPS
How many train steps to run
--training.max_norm TRAINING.MAX_NORM
Max norm for gradient clipping
--training.skip_nan_inf
Skip batch updates when NaN or INF gradients are
encountered during training
--training.dataset TRAINING.DATASET
Dataset to use, with comma separated values
--training.dataset_name TRAINING.DATASET_NAME
The name of the dataset config, with comma separated
values if provided
--training.dataset_split TRAINING.DATASET_SPLIT
Dataset split to use, with comma separated values if
provided
--training.data_dir TRAINING.DATA_DIR
Data dirs to use, with comma separated values if
provided
--training.data_files TRAINING.DATA_FILES
Data files to use, with comma separated values if
provided
--training.data_probs TRAINING.DATA_PROBS
Data sampling probabilities, with comma separated
values if provided
--training.streaming Whether to load dataset in streaming mode, used for
huge dataset
--training.num_workers TRAINING.NUM_WORKERS
Number of subprocesses to use for data loading. 0
means that the data will be loaded in the main
process.
--training.prefetch_factor TRAINING.PREFETCH_FACTOR
Number of batches loaded in advance by each worker.2
means there will be a total of 2 * num_workers batches
prefetched across all workers.
--training.data_parallel_replicate_degree TRAINING.DATA_PARALLEL_REPLICATE_DEGREE
The `data_parallel_replicate_degree` argument
specifies the degree of data parallelism for weight
replication. When this value is greater than 1,
weights will be replicated across
`data_parallel_replicate_degree` ranks. If
`data_parallel_shard_degree` is also greater than 1,
the parallelism method used is HSDP (Hybrid Sharded
Data Parallelism). Otherwise, the parallelism method
used is DDP (Distributed Data Parallelism). 1 means
disabled.
--training.data_parallel_shard_degree TRAINING.DATA_PARALLEL_SHARD_DEGREE
The `data_parallel_shard_degree` argument specifies
the degree of data parallelism for weight sharding.
When this value is greater than 1, weights will be
sharded across `data_parallel_shard_degree` ranks. If
`data_parallel_replicate_degree` is also greater than
1, the parallelism method used is HSDP (Hybrid Sharded
Data Parallelism). Otherwise, the parallelism method
used is FSDP (Fully Sharded Data Parallelism). -1
means leftover ranks will be used (After
DP_REPLICATE/SP/PP). Note that only
`data_parallel_shard_degree` can be negative. 1 means
disabled.
--training.enable_cpu_offload
Whether to apply CPU offloading of parameters,
gradients, and optimizer states in FSDP
--training.tensor_parallel_degree TRAINING.TENSOR_PARALLEL_DEGREE
Tensor Parallelism degree. 1 means disabled.
--training.disable_loss_parallel
Whether to apply loss parallel when sequence parallel
is enabled
--training.mixed_precision_param {bfloat16,float32}
torch dtype to use for parameters when applying mixed
precision via FSDP. This feature only takes effect
when data_parallel_shard_degree > 1
--training.mixed_precision_reduce {float32}
torch dtype to use for reductions when applying mixed
precision via FSDP. This feature only takes effect
when data_parallel_shard_degree > 1
--training.compile Whether to compile the model
--training.gc_freq TRAINING.GC_FREQ
Python garbage control scheduling interval, in steps
--training.seed TRAINING.SEED
Choose the base RNG seed used for training
--training.deterministic
Use deterministic algorithms wherever possible, may be
slower
--metrics.log_freq METRICS.LOG_FREQ
How often to log metrics to TensorBoard, in iterations
--metrics.enable_tensorboard
Whether to log metrics to TensorBoard
--metrics.disable_color_printing
Whether to disable color printing in logs
--metrics.save_tb_folder METRICS.SAVE_TB_FOLDER
Folder to dump TensorBoard states
--metrics.rank_0_only
Whether to save TensorBoard metrics only for rank 0 or
for all ranks. When pipeline_parallel_degree is > 1,
this option uses the 0th rank of the last stage
pipeline group, which is the only stage that computes
loss metrics.
--metrics.enable_wandb
Whether to log metrics to Weights & Biases
--experimental.enable_async_tensor_parallel
Whether to apply async tensor parallel (currently only
effective when compile is enabled)
--experimental.pipeline_parallel_degree EXPERIMENTAL.PIPELINE_PARALLEL_DEGREE
Pipeline Parallelism degree, or number of ranks. 1
means disabled. If using looped schedules, this still
specifies the number of physical ranks, not the number
of stages. Stages per rank are inferred from split
points degree, and schedule.
--experimental.pipeline_parallel_split_points EXPERIMENTAL.PIPELINE_PARALLEL_SPLIT_POINTS [EXPERIMENTAL.PIPELINE_PARALLEL_SPLIT_POINTS ...]
Specify comma-separated names of modules to use as the
beginning of a split point. e.g. "layers.0,layers.2"
will cause the model to be split into 3 stages, the
first containing all the layers up to layers.0, the
second containing layers.0 and up to layers.2, the
third containing layers.2 and all the remaining
layers. Note: fully-automated splitting may be enabled
in the future, but currently the split points must be
specified manually.
--experimental.pipeline_parallel_schedule EXPERIMENTAL.PIPELINE_PARALLEL_SCHEDULE
Specify the Pipeline Parallel schedule to use. The
supported schedules are: https://github.com/pytorch/py
torch/blob/de4c2a3b4e89d96334dc678d1c3f2ae51a6630a0/to
rch/distributed/pipelining/schedules.py#L2161. The
schedule must be compatible with the split points and
stages_per_rank. Looped schedules (e.g.
Interleaved1F1B) require specifying
pipeline_parallel_degree = number of ranks, and
split_points = number of stages - 1
--experimental.pipeline_parallel_schedule_csv EXPERIMENTAL.PIPELINE_PARALLEL_SCHEDULE_CSV
Specify the path to the pipeline parallel schedule csv
file to use. The pipeline_parallel_schedule argument
must be either PipelineScheduleSingle,
PipelineScheduleMulti, or _PipelineScheduleRuntime.
--experimental.pipeline_parallel_microbatches EXPERIMENTAL.PIPELINE_PARALLEL_MICROBATCHES
How many microbatches to split the global training
batch into when using pipeline parallelism. The global
training batch size must be evenly divisible by the
number of microbatches. The default value will be the
number of pipeline stages, if unspecified.
--experimental.enable_compiled_autograd
Enable CompiledAutograd to compile the backward.
--experimental.context_parallel_degree EXPERIMENTAL.CONTEXT_PARALLEL_DEGREE
Context parallelism degree. 1 means disabled.
--experimental.context_parallel_rotate_method EXPERIMENTAL.CONTEXT_PARALLEL_ROTATE_METHOD
The collective to use in context parallel SDPA for kv
shards exchange. 'allgather' means to all-gather all
kv shards on ranks after the first sub-SDPA
computation, 'alltoall' means to all-to-all shuffle
the kv shards. The default value is 'allgather'.
--checkpoint.enable_checkpoint
Whether to enable checkpoint
--checkpoint.folder CHECKPOINT.FOLDER
The folder to store the checkpoints. When
enable_checkpoint is set to true, checkpoints will be
in {--job.dump_folder}/{--checkpoint.folder}.
--checkpoint.interval_type CHECKPOINT.INTERVAL_TYPE
Checkpointing interval unit of measurement ['step',
'seconds']
--checkpoint.interval CHECKPOINT.INTERVAL
Checkpointing interval, in steps or seconds depending
on --checkpoint.interval_type
--checkpoint.model_weights_only
When model_weights_only=True, only model weights will
be saved at the end of training. With this,
checkpoints can be loaded using `torch.load(...,
weights_only=True)` after conversion. When
model_weights_only=False, the full checkpoint will be
saved. A full checkpoint includes model, optimizer and
train_state, which can be used to resume training. The
default value is false.
--checkpoint.export_dtype {float16,bfloat16,float32}
Converts to the specified precision when training
completes and model_weights_only=true. Currently
supports float32, float16, and bfloat16. The default
value is float32.
--checkpoint.create_seed_checkpoint
Initializes the full model without applying
parallelisms, and then saves it as a seed checkpoint.
Note: requires user to call train.py without
specifying any parallelisms, e.g. NGPU=1. Could be
implemented as a separate script, but this way shares
more code.
--checkpoint.async_mode CHECKPOINT.ASYNC_MODE
Which async checkpoint mode to use. Currently there
are 3 different modes. 1. "disabled": synchronized
checkpointing will be used. 2. "async":
torch.distributed.checkpoint.async_save will be used.
1. "async_with_pinned_mem": this option utilizes a
dedicated pinned memory space and creates a separate
process for faster GPU->CPU transfer performance and
eliminating GIL contention. The cost is increased CPU
memory usage. If insufficient CPU memory is available,
performance may degrade due to memory paging. For most
users, "async" should suffice as the performance
overhead is typically small (on the order of tens of
seconds) compared to checkpointing frequency. This
mode can be employed to pursue near-zero checkpointing
times (e.g., < 1 second) given appropriate hardware
support such as ample CPU memory and fast PCIe.
"disabled" is the default mode.
--checkpoint.keep_latest_k CHECKPOINT.KEEP_LATEST_K
Keeps only the latest k checkpoints, and purging older
ones. If 0, keep all checkpoints. 0 is the default
value.
--checkpoint.load_step CHECKPOINT.LOAD_STEP
Load the checkpoint at the specified step. If -1, load
the latest checkpoint.
--float8.enable_float8_linear
If true, swaps `torch.nn.Linear` with `Float8Linear`.
This feature requires you to install 'torchao' which
can be found here: https://github.com/pytorch/ao
--float8.enable_fsdp_float8_all_gather
Whether enable float8 all-gather in FSDP
--float8.precompute_float8_dynamic_scale_for_fsdp
Whether precompute float8 scales dynamically for FSDP
--float8.scaling_type_input {dynamic,delayed}
float8 scaling for input, dynamic (default) or delayed
--float8.scaling_type_weight FLOAT8.SCALING_TYPE_WEIGHT
float8 scaling for input, dynamic (default) or delayed
--float8.scaling_type_grad_output FLOAT8.SCALING_TYPE_GRAD_OUTPUT
float8 scaling for input, dynamic (default) or delayed
--comm.init_timeout_seconds COMM.INIT_TIMEOUT_SECONDS
Timeout for communication operations, during
initialization and first train step.
--comm.train_timeout_seconds COMM.TRAIN_TIMEOUT_SECONDS
Timeout for communication operations after the first
train step -- usually a tighter bound than during
initialization.
--comm.trace_buf_size COMM.TRACE_BUF_SIZE
Flight recorder ring buffer size, >0 means recording
by default, 0 means disabled
--memory_estimation.enabled
Whether to estimate memory usage for FSDP
--memory_estimation.disable_fake_mode
Whether to estimate memory under FakeTensorMode
```
</details>
### Training with `torch.compile`
Starting from `torch 2.0`, `torch.compile` has been introduced as a new feature to seamlessly accelerate training processes.
In `flame`, one can simply enable `torch.compile` by adding `--training.compile` flag to your training script.
However, `fla` has integrated numerous fused kernels for acceleration, which may potentially conflict with `torch.compile`.
We are actively working on resolving these issues to make compilation transparent to users.
In the meantime, please ensure you are using the latest dependencies.
Specifically, **we recommend using `torch>=2.6` and `triton>=3.0`**.
### Training with multiple datasets
If you wish to train a model with all-round capabilities (e.g., code, math, and multilingual ability), it's necessary to train on multiple datasets.
`flame` allows training with multiple datasets easily.
For example, you can specify the following arguments to train on 6 datasets with different proportions:
```sh
--training.dataset HuggingFaceFW/fineweb-edu,opencsg/Fineweb-Edu-Chinese-V2.1,OpenCoder-LLM/opc-fineweb-code-corpus,math-ai/AutoMathText,EleutherAI/proof-pile-2,OpenCoder-LLM/opc-fineweb-math-corpus \
--training.data_probs 0.6,0.15,0.15,0.014,0.058,0.028 \
```
### ~Finalizing training~
> [!NOTE]
> We have done this conversion automatically in the training script since our latest updates.
Once training is complete, you may want to convert the distributed checkpoints (DCPs) into the 🤗 format for broader use.
To facilitate this, we provide a straightforward conversion script:
```sh
python -m flame.utils.convert_dcp_to_hf --path <path_to_model> --step <step> --config <path_to_config> --tokenizer <path_to_tokenizer>
```
After this, your model will be in the 🤗 format, ready to be shared or deployed.
You can then easily publish your model using the `huggingface_hub` for wider accessibility.
### Continual training
If you wish to build upon a strong pre-trained model (in 🤗 format) and continue training, we also offer a script to convert the 🤗 format model back into DCP format.
This allows you to seamlessly resume training with `flame`.
```sh
python -m flame.utils.convert_hf_to_dcp --model <path_to_hf> --checkpoint <path_to_dcp/checkpoint/step-0>
```
Here, `<path_to_dcp>` is the directory where your distributed checkpoints will be stored.
The checkpoint is intentionally saved at `<step-0>` within the checkpoint folder to ensure it is loadable by `flame` during the initial training step, similar to how a seed checkpoint is handled.
Once the conversion is complete, you can proceed with training using `flame` as usual, continuing from where the pretrained model left off.
## Multi-node training
If you have access to multi-node GPUs, consider leveraging them for optimal performance.
This process is straightforward and well-documented in the PyTorch [docs](https://pytorch.org/docs/stable/elastic/run.html).
To set up multi-node training:
* Set the environment variables `MASTER_ADDR=<ip>` and `MASTER_PORT=<port>` before running the training script across all nodes.
* If you're using a job scheduler like Slurm, it will handle these variables for you.
`torchtitan` provides a [Slurm script](https://github.com/pytorch/torchtitan/blob/main/multinode_trainer.slurm) for multi-node training, which you can use as a reference or starting point.
|
thefirstgoku/30C_w13_scl_l7
|
thefirstgoku
| 2025-08-30T10:13:00Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-30T10:12:22Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756548707
|
liukevin666
| 2025-08-30T10:12:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:12:49Z |
---
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).
|
vomqal/Qwen3-0.6B-Gensyn-Swarm-masked_snappy_caribou
|
vomqal
| 2025-08-30T10:11:14Z | 27 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am masked_snappy_caribou",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-02T04:33:13Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am masked_snappy_caribou
---
# 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]
|
csavzzcw/blockassist-bc-rugged_amphibious_dolphin_1756548567
|
csavzzcw
| 2025-08-30T10:10:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged amphibious dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:09:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged amphibious dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
csavzzcw/blockassist-bc-pudgy_thriving_okapi_1756548464
|
csavzzcw
| 2025-08-30T10:08:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy thriving okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:07:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy thriving okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
keysero/blockassist-bc-winged_agile_mongoose_1756548416
|
keysero
| 2025-08-30T10:07:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged agile mongoose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:07:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged agile mongoose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NexVeridian/gpt-oss-120b-5bit
|
NexVeridian
| 2025-08-30T10:05:25Z | 866 | 1 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-120b",
"base_model:quantized:openai/gpt-oss-120b",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-06T04:03:01Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-120b
---
# NexVeridian/gpt-oss-120b-5bit
This model [NexVeridian/gpt-oss-120b-5bit](https://huggingface.co/NexVeridian/gpt-oss-120b-5bit) was
converted to MLX format from [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)
using mlx-lm version **0.27.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/gpt-oss-120b-5bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1756546776
|
helmutsukocok
| 2025-08-30T10:04:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:04:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756548046
|
klmdr22
| 2025-08-30T10:01:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T10:01:24Z |
---
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).
|
alistermarc/alistermarc-2025-08-30_15.24.25
|
alistermarc
| 2025-08-30T10:00:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] | null | 2025-08-30T07:24:39Z |
---
library_name: peft
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: alistermarc-2025-08-30_15.24.25
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/alistermarcdomilies-those-who-care/alistermarc/runs/qcr6p7tk)
# alistermarc-2025-08-30_15.24.25
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Framework versions
- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.4
|
erikonis/BSP2S-models
|
erikonis
| 2025-08-30T09:55:31Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-08-30T09:10:39Z |
---
license: mit
---
Here are CNN models obtained from BSP2 Summer project. Models aim is to distinguish between AI (class 1) and Human (class 0) Java codes.
A given Java code file must be tranformed into its image representation by first reading it in binary mode and then interpreting each 8 bits as encodings for rgb channels, constructing image pixel by pixel. For smaller files, a width of 32px is sufficient, larger than 10kB files should rely on width of 64px, larger than 30kB on 128px.
If the obtained height of an image is less than 224px and less than width, a padding should be applied of black (RGB values 0) pixels, so that it's not distorted during rescaling. Else, height can be left unmodified, by the last row should be padded with black pixels to complete it.
CNN based models have input dimensions of 224x224 pixels. Hence, images have to be rescaled using default bilinear scaling function to fit the dimensions.
Models perform binary classification and output a single number 0-1, indicating probability of the sample being generated by AI.
Best performing model - dn121.
Project related GitHub page:
https://github.com/erikonis/BSP2S
Project related Dataset:
https://huggingface.co/datasets/erikonis/BSP2S-dataset
**== Explanations ==**
Information about each CNN model is encoded into the filename. If filename contains:
- *best* -> this model was obtained using EarlyStopping implementation, which stopped the training process at an optimal epoch. Models without "best" are obtained by training until 40 epochs, risking overfitting.
- *dn121*, *rn50*, *vgg16* -> model is based on DenseNet121, ResNet50 or VGG16 architectures (pretrained on ImageNet).
- *raw* -> model was trained on unpreprocessed dataset.
- *preproc* -> model was trained on preprocessed dataset. It is explained in a pdf paper on GitHub page. In short, whitespace was normalized and all comments are removed.
- *sheetSplit* -> default setting of training with original CodeNet based dataset. sheetSplit means that we split training-validation and test sets by sheet to prevent intersection (through duplicates) between them.
- *multiclass* -> model is capable of classifying codes into 5 categories: Human, GPT4.1 (namely, ChatGPT), Claude Sonnet 4, Gemini 2.5 Flash and DeepSeek V3 0324. Prediction is outputted for each class in range 0-1.
- *scratch* -> binary classification model was trained from scratch (contrary to pretrained).
- *AIvsSTD* -> models pretrained on our CodeNet dataset were fine-tuned on Academia-related dataset, explained here:
https://github.com/erikonis/BSP2
- *humanEval* -> models pretrained on our CodeNet dataset were fine-tuned on humanEval partition from ```csv``` files here:
https://github.com/mahantaf/AI-Detector/tree/master/src/astnn/classification/java/data
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756547439
|
Ferdi3425
| 2025-08-30T09:52:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:51:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756547448
|
klmdr22
| 2025-08-30T09:51:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:51:26Z |
---
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).
|
lowelldiaz/blockassist-bc-prowling_feathered_stork_1756547158
|
lowelldiaz
| 2025-08-30T09:48:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prowling feathered stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:47:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prowling feathered stork
---
# 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_1756545359
|
Loder-S
| 2025-08-30T09:43:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:43:01Z |
---
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).
|
AndreasXi/MeanAudio
|
AndreasXi
| 2025-08-30T09:41:40Z | 9 | 4 | null |
[
"arxiv:2508.06098",
"license:mit",
"region:us"
] | null | 2025-08-17T16:34:13Z |
---
license: mit
---
<div align="center">
<p align="center">
<h1>MeanAudio: Fast and Faithful Text-to-Audio Generation with Mean Flows</h1>
<!-- <a href=>Paper</a> | <a href="https://meanaudio.github.io/">Webpage</a> -->
[](https://arxiv.org/abs/2508.06098)
[](https://github.com/xiquan-li/MeanAudio?tab=readme-ov-file)
[](https://huggingface.co/AndreasXi/MeanAudio)
[](https://huggingface.co/spaces/chenxie95/MeanAudio)
[](https://meanaudio.github.io/)
</p>
</div>
## Overview
MeanAudio is a novel MeanFlow-based model tailored for fast and faithful text-to-audio generation. It can synthesize realistic sound in a single step, achieving a real-time factor (RTF) of 0.013 on a single NVIDIA 3090 GPU. Moreover, it also demonstrates strong performance in multi-step generation.
## Environmental Setup
**1. Create a new conda environment:**
```bash
conda create -n meanaudio python=3.11 -y
conda activate meanaudio
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 --upgrade
```
<!-- ```
conda install -c conda-forge 'ffmpeg<7
```
(Optional, if you use miniforge and don't already have the appropriate ffmpeg) -->
**2. Install with pip:**
```bash
git clone https://github.com/xiquan-li/MeanAudio.git
cd MeanAudio
pip install -e .
```
<!-- (If you encounter the File "setup.py" not found error, upgrade your pip with pip install --upgrade pip) -->
## Quick Start
<!-- **1. Download pre-trained models:** -->
To generate audio with our pre-trained model, simply run:
```bash
python demo.py --prompt 'your prompt' --num_steps 1
```
This will automatically download the pre-trained checkpoints from huggingface, and generate audio according to your prompt.
The output audio will be at `MeanAudio/output/`, and the checkpoints will be at `MeanAudio/weights/`.
Have fun with MeanAudio 😊 !!!
|
milliarderdol/blockassist-bc-roaring_rough_scorpion_1756544950
|
milliarderdol
| 2025-08-30T09:41:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring rough scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:40:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring rough scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756545258
|
rvipitkirubbe
| 2025-08-30T09:40:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:40:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756546609
|
bah63843
| 2025-08-30T09:37:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:37:31Z |
---
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).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756546382
|
Ferdi3425
| 2025-08-30T09:34:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:33:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coppytiou/blockassist-bc-amphibious_territorial_lemur_1756546276
|
coppytiou
| 2025-08-30T09:31:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious territorial lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:31:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious territorial lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
weruior/blockassist-bc-fluffy_quiet_bison_1756546201
|
weruior
| 2025-08-30T09:30:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fluffy quiet bison",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:30:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fluffy quiet bison
---
# 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-FisherMaskGlobal-5e-7-v2_6858
|
luckeciano
| 2025-08-30T09:23:29Z | 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
| 2025-08-30T05:16:48Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-5e-7-v2_6858
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-5e-7-v2_6858
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-FisherMaskGlobal-5e-7-v2_6858", 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/hw1ph1pd)
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}}
}
```
|
mradermacher/Mellum-4b-sft-rust-i1-GGUF
|
mradermacher
| 2025-08-30T09:20:25Z | 3 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"code",
"rust",
"fill-in-the-middle",
"fim",
"text-generation",
"llm",
"en",
"dataset:Etherll/CodeFIM-Rust-Mellum",
"base_model:Etherll/Mellum-4b-sft-rust",
"base_model:quantized:Etherll/Mellum-4b-sft-rust",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] |
text-generation
| 2025-08-30T02:03:45Z |
---
base_model: Etherll/Mellum-4b-sft-rust
datasets:
- Etherll/CodeFIM-Rust-Mellum
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- code
- rust
- fill-in-the-middle
- fim
- text-generation
- llm
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Etherll/Mellum-4b-sft-rust
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mellum-4b-sft-rust-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Mellum-4b-sft-rust-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/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ1_S.gguf) | i1-IQ1_S | 1.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ1_M.gguf) | i1-IQ1_M | 1.5 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ2_S.gguf) | i1-IQ2_S | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.8 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ2_M.gguf) | i1-IQ2_M | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q2_K.gguf) | i1-Q2_K | 1.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ3_S.gguf) | i1-IQ3_S | 2.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ3_M.gguf) | i1-IQ3_M | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.4 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q4_0.gguf) | i1-Q4_0 | 2.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q4_1.gguf) | i1-Q4_1 | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q5_K_S.gguf) | i1-Q5_K_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q5_K_M.gguf) | i1-Q5_K_M | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mellum-4b-sft-rust-i1-GGUF/resolve/main/Mellum-4b-sft-rust.i1-Q6_K.gguf) | i1-Q6_K | 3.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756543940
|
sampingkaca72
| 2025-08-30T09:19:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:19:55Z |
---
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).
|
leosflanagandbf1/blockassist-bc-strong_curious_gecko_1756543980
|
leosflanagandbf1
| 2025-08-30T09:19:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"strong curious gecko",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:19:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- strong curious gecko
---
# 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_1756545469
|
bah63843
| 2025-08-30T09:18:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:18:32Z |
---
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).
|
klmdr22/blockassist-bc-wild_loud_newt_1756545398
|
klmdr22
| 2025-08-30T09:17:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:17:16Z |
---
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).
|
ikuyamada/test_local_processed
|
ikuyamada
| 2025-08-30T09:16:17Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"kpr-bert",
"sentence-similarity",
"feature-extraction",
"dense",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-30T09:09:45Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Dot Product
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'KPRModelForBert'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("ikuyamada/test_local_processed")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[743.6603, 712.7500, 674.8392],
# [712.7500, 743.7998, 678.3881],
# [674.8391, 678.3880, 743.6827]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 5.2.0.dev0
- Transformers: 4.55.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 2.16.1
- Tokenizers: 0.21.4
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
ehtelrdecker123/blockassist-bc-roaring_carnivorous_cheetah_1756543736
|
ehtelrdecker123
| 2025-08-30T09:15:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring carnivorous cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:15:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring carnivorous cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
keysero/blockassist-bc-winged_agile_mongoose_1756545213
|
keysero
| 2025-08-30T09:14:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"winged agile mongoose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:14:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- winged agile mongoose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Megma/blockassist-bc-rugged_mangy_seahorse_1756544990
|
Megma
| 2025-08-30T09:13:25Z | 0 | 1 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged mangy seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:13:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged mangy seahorse
---
# 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_1756545122
|
bah63843
| 2025-08-30T09:12:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:12:43Z |
---
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).
|
naveenmuppaneni/tourism-purchase-predictor-rf
|
naveenmuppaneni
| 2025-08-30T09:12:34Z | 0 | 0 | null |
[
"joblib",
"region:us"
] | null | 2025-08-28T10:15:41Z |
# Tourism Purchase Predictor (RandomForest)
This repository contains a tuned RandomForestClassifier for predicting `ProdTaken` (purchase of the tourism package).
- Dataset: https://huggingface.co/datasets/naveenmuppaneni/tourism-dataset
- Selection metric: ROC AUC (5-fold CV)
- Best CV ROC AUC: 0.9513
## Inference (Python)
```python
import joblib
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="naveenmuppaneni/tourism-purchase-predictor-rf", filename="best_model.joblib")
model = joblib.load(model_path)
# model is a sklearn Pipeline: model.predict(X) or model.predict_proba(X)
```
|
eliyen/blockassist-bc-thick_agile_ant_1756545106
|
eliyen
| 2025-08-30T09:12:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick agile ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:12:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick agile ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tammycra121/blockassist-bc-marine_rangy_eel_1756543544
|
tammycra121
| 2025-08-30T09:11:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"marine rangy eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:11:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- marine rangy eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lowelldiaz/blockassist-bc-prowling_feathered_stork_1756544912
|
lowelldiaz
| 2025-08-30T09:10:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prowling feathered stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:10:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prowling feathered stork
---
# 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_1756543324
|
Loder-S
| 2025-08-30T09:09:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:09:30Z |
---
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).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756544740
|
liukevin666
| 2025-08-30T09:09:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:06:43Z |
---
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).
|
bah63843/blockassist-bc-plump_fast_antelope_1756544853
|
bah63843
| 2025-08-30T09:08:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:08:14Z |
---
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).
|
coppytiou/blockassist-bc-shrewd_lethal_dove_1756544868
|
coppytiou
| 2025-08-30T09:08:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"shrewd lethal dove",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:07:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- shrewd lethal dove
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ployauii/blockassist-bc-silky_leaping_tortoise_1756544796
|
ployauii
| 2025-08-30T09:07:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky leaping tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:07:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky leaping tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NexVeridian/gpt-oss-120b-3bit
|
NexVeridian
| 2025-08-30T09:06:48Z | 3,549 | 6 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-120b",
"base_model:quantized:openai/gpt-oss-120b",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-06T00:15:41Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-120b
---
# NexVeridian/gpt-oss-120b-3bit
This model [NexVeridian/gpt-oss-120b-3bit](https://huggingface.co/NexVeridian/gpt-oss-120b-3bit) was
converted to MLX format from [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)
using mlx-lm version **0.27.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/gpt-oss-120b-3bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
robertou2/task-14-microsoft-Phi-4-mini-instruct
|
robertou2
| 2025-08-30T09:03:39Z | 625 | 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 | 2025-08-16T09:56:56Z |
---
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 0.14.0
|
mariyaam/blockassist-bc-spotted_bold_sparrow_1756544517
|
mariyaam
| 2025-08-30T09:03:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted bold sparrow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:03:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted bold sparrow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coppytiou/blockassist-bc-fanged_striped_shrimp_1756544546
|
coppytiou
| 2025-08-30T09:02:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged striped shrimp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:02:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fanged striped shrimp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thecodedev/blockassist-bc-pouncing_pensive_komodo_1756544421
|
thecodedev
| 2025-08-30T09:01:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pouncing pensive komodo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T09:01:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pouncing pensive komodo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coppytiou/blockassist-bc-tricky_curious_impala_1756544383
|
coppytiou
| 2025-08-30T09:00:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tricky curious impala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:59:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tricky curious impala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756544138
|
bah63843
| 2025-08-30T08:56:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:56:21Z |
---
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).
|
Nabbers1999/gpt-oss-20b-BF16-Q4_K_M-GGUF
|
Nabbers1999
| 2025-08-30T08:55:56Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"vllm",
"unsloth",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:unsloth/gpt-oss-20b-BF16",
"base_model:quantized:unsloth/gpt-oss-20b-BF16",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T08:54:51Z |
---
base_model: unsloth/gpt-oss-20b-BF16
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
- unsloth
- llama-cpp
- gguf-my-repo
---
# Nabbers1999/gpt-oss-20b-BF16-Q4_K_M-GGUF
This model was converted to GGUF format from [`unsloth/gpt-oss-20b-BF16`](https://huggingface.co/unsloth/gpt-oss-20b-BF16) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/unsloth/gpt-oss-20b-BF16) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Nabbers1999/gpt-oss-20b-BF16-Q4_K_M-GGUF --hf-file gpt-oss-20b-bf16-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Nabbers1999/gpt-oss-20b-BF16-Q4_K_M-GGUF --hf-file gpt-oss-20b-bf16-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Nabbers1999/gpt-oss-20b-BF16-Q4_K_M-GGUF --hf-file gpt-oss-20b-bf16-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Nabbers1999/gpt-oss-20b-BF16-Q4_K_M-GGUF --hf-file gpt-oss-20b-bf16-q4_k_m.gguf -c 2048
```
|
eliyen/blockassist-bc-thick_agile_ant_1756544049
|
eliyen
| 2025-08-30T08:54:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick agile ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:54:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick agile ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
clementling02/finetuned-orpheus-SA-Female-final-v2
|
clementling02
| 2025-08-30T08:51:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-30T08:50:16Z |
---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** clementling02
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
clementling02/finetuned-orpheus-SA-Female-lora-v2
|
clementling02
| 2025-08-30T08:49:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T08:48:39Z |
---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** clementling02
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Sophie-Rain-viral-video-original-Clip/NEW.FULL.VIDEOS.Sophie.Rain.Spiderman.Viral.Video.Official.Tutorial
|
Sophie-Rain-viral-video-original-Clip
| 2025-08-30T08:46:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T08:45:32Z |
<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>
|
mradermacher/KernelLLM-GGUF
|
mradermacher
| 2025-08-30T08:40:57Z | 171 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:ScalingIntelligence/KernelBench",
"dataset:GPUMODE/KernelBook",
"base_model:facebook/KernelLLM",
"base_model:quantized:facebook/KernelLLM",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-27T20:36:51Z |
---
base_model: facebook/KernelLLM
datasets:
- ScalingIntelligence/KernelBench
- GPUMODE/KernelBook
language:
- en
library_name: transformers
license: other
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/facebook/KernelLLM
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#KernelLLM-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/KernelLLM-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/KernelLLM-GGUF/resolve/main/KernelLLM.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/KernelLLM-GGUF/resolve/main/KernelLLM.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
JKitamura/QwQ-CoT-0.5B-JA-v1.1
|
JKitamura
| 2025-08-30T08:37:00Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-29T14:41:37Z |
---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
model_name: QwQ-CoT-0.5B-JA-v1.1
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for QwQ-CoT-0.5B-JA-v1.1
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
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="JKitamura/QwQ-CoT-0.5B-JA-v1.1", 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/jkitamura13-tone-mobile/huggingface/runs/lrj4kb08)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jung-ming/god-eye-traffic-predictor
|
jung-ming
| 2025-08-30T08:34:14Z | 0 | 0 | null |
[
"traffic-prediction",
"lightgbm",
"shap",
"multistep-forecast",
"streamlit",
"huggingface-space",
"zh",
"license:mit",
"model-index",
"region:us"
] | null | 2025-07-05T02:05:14Z |
---
language: zh
license: mit
tags:
- traffic-prediction
- lightgbm
- shap
- multistep-forecast
- streamlit
- huggingface-space
model-index:
- name: God-Eye Traffic Predictor
results:
- task:
type: time-series-forecasting
name: Multi-step Traffic Speed Forecasting
dataset:
name: 國道壅塞預測（楊梅-新竹段）
type: tabular
metrics:
- name: MAE
type: mean_absolute_error
value: 5.2
- name: R²
type: r2
value: 0.7
---
# 上帝視角：AI 國道壅塞前兆預測系統 (God-Eye Traffic Predictor)
## 📌 專案簡介
本專案為一套針對台灣國道一號（楊梅至新竹段）在假日期間的即時壅塞預測系統。透過 MultiOutput LightGBM 模型預測未來 60–90 分鐘車速，結合 SHAP 解釋與風險分級，提供使用者直觀的預警視角。
## 🎯 預測目標
- 預測未來 60/70/80/90 分鐘的平均車速（km/h）
- 根據預測車速進行壅塞等級分類（低、中、高風險）
- 使用 SHAP 分析顯示主要造成壅塞的前兆特徵
## 🔍 使用資料
- **資料來源**：交通部高速公路局（Highway Bureau, MOTC）
- **時間範圍**：2025/03,四個週末假日資料（不含連假）
- **路段範圍**：國道一號楊梅交流道至新竹交流道（南北雙向）
- **特徵範圍**：時間、空間、歷史車流量/車速、遲滯特徵等約 10+ 欄位
## 🧠 模型資訊
- **演算法**：LightGBM（MultiOutput Regression）
- **訓練方式**：逐步訓練 + 滾動預測（多步預測）
- **誤差指標**：
- MAE 約 5.2 km/h
- R² 約 0.7（驗證集）
## 🗺️ 解釋性分析
- 使用 Tree SHAP 解釋單筆預測，並透過 Waterfall 視覺化展示特徵影響力
- 針對壅塞前兆進行 SHAP 排序與歸因分析（例：公里位置、時間點、車流量、車速）
## 🖥️ 使用方式
1. 開啟 [Hugging Face Space](https://huggingface.co/spaces/your-username/god-eye-traffic-predictor)
2. 選擇「預測時間點」與輸入「即時特徵值」
3. 查看車速預測與風險等級，點擊以檢視 SHAP 解釋圖
## 🚦 預測輸出說明
| 時間點 | 預測車速 | 風險等級 | SHAP 解釋 |
|--------|------------|--------------|--------------|
| +60 分 | 78.2 km/h | 低風險 | SHAP waterfall 圖 |
| +90 分 | 63.5 km/h | 中風險 | SHAP waterfall 圖 |
## 📊 前端架構
- **框架**：Streamlit
- **部署**：Hugging Face Spaces
- **自動更新**：每次預測自動刷新畫面顯示結果
## 📜 License
MIT License / 本作品僅供學術與非商業研究用途，請勿未經授權轉作商業應用。
---
*本專案參與「114年國道智慧交通管理創意競賽」初賽作品。*
|
jesusoctavioas/Qwen3-4B-mlx-4Bit
|
jesusoctavioas
| 2025-08-30T08:26:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"mlx",
"conversational",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-30T08:25:37Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
tags:
- mlx
---
# jesusoctavioas/Qwen3-4B-mlx-4Bit
The Model [jesusoctavioas/Qwen3-4B-mlx-4Bit](https://huggingface.co/jesusoctavioas/Qwen3-4B-mlx-4Bit) was converted to MLX format from [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) using mlx-lm version **0.26.4**.
## Use with mlx
```bash
# Create a virtual enviroment if needed.
python -m venv mlx-venv
# then activate the virtual enviroment if needed.
source mlx-venv/bin/activate
# then install mlx.
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("jesusoctavioas/Qwen3-4B-mlx-4Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
yukiharada1228/resnet18_abn_cifar100
|
yukiharada1228
| 2025-08-30T08:19:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T06:27:08Z |
---
library_name: transformers
base_model: resnet18
tags:
- generated_from_trainer
model-index:
- name: resnet18_abn_cifar100
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. -->
# resnet18_abn_cifar100
This model is a fine-tuned version of [resnet18](https://huggingface.co/resnet18) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2604
- Top1: 0.7787
- Top5: 0.9424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.1
- train_batch_size: 128
- eval_batch_size: 100
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Top1 | Top5 |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 7.4388 | 1.0 | 391 | 7.3160 | 0.1663 | 0.4273 |
| 6.4398 | 2.0 | 782 | 6.3391 | 0.2584 | 0.5679 |
| 5.6888 | 3.0 | 1173 | 6.1193 | 0.2825 | 0.6057 |
| 5.1265 | 4.0 | 1564 | 5.2831 | 0.3708 | 0.7032 |
| 4.5996 | 5.0 | 1955 | 5.1119 | 0.3841 | 0.7045 |
| 4.364 | 6.0 | 2346 | 4.6068 | 0.4414 | 0.7726 |
| 4.1311 | 7.0 | 2737 | 5.0427 | 0.3945 | 0.7172 |
| 3.962 | 8.0 | 3128 | 4.3959 | 0.4778 | 0.7891 |
| 3.8391 | 9.0 | 3519 | 4.3315 | 0.4753 | 0.7946 |
| 3.7897 | 10.0 | 3910 | 4.2795 | 0.4889 | 0.8081 |
| 3.6744 | 11.0 | 4301 | 4.6805 | 0.4476 | 0.7489 |
| 3.6001 | 12.0 | 4692 | 4.2038 | 0.494 | 0.8019 |
| 3.5006 | 13.0 | 5083 | 4.1031 | 0.5171 | 0.8156 |
| 3.4815 | 14.0 | 5474 | 4.0947 | 0.5237 | 0.8148 |
| 3.4657 | 15.0 | 5865 | 4.1398 | 0.4998 | 0.8167 |
| 3.4094 | 16.0 | 6256 | 4.0485 | 0.5345 | 0.8267 |
| 3.3807 | 17.0 | 6647 | 3.9729 | 0.531 | 0.8182 |
| 3.3794 | 18.0 | 7038 | 4.3892 | 0.4975 | 0.7941 |
| 3.3518 | 19.0 | 7429 | 4.1058 | 0.5166 | 0.8196 |
| 3.2304 | 20.0 | 7820 | 4.4831 | 0.4752 | 0.7873 |
| 3.3055 | 21.0 | 8211 | 3.9920 | 0.5393 | 0.8208 |
| 3.3119 | 22.0 | 8602 | 3.9284 | 0.5427 | 0.8308 |
| 3.22 | 23.0 | 8993 | 3.8553 | 0.5388 | 0.8265 |
| 3.2501 | 24.0 | 9384 | 5.2410 | 0.4297 | 0.718 |
| 3.1796 | 25.0 | 9775 | 4.4711 | 0.4953 | 0.7882 |
| 3.1825 | 26.0 | 10166 | 4.0062 | 0.5332 | 0.8288 |
| 3.1397 | 27.0 | 10557 | 4.1261 | 0.5447 | 0.8229 |
| 3.1377 | 28.0 | 10948 | 4.1268 | 0.5329 | 0.8259 |
| 3.1383 | 29.0 | 11339 | 3.7642 | 0.5556 | 0.8452 |
| 3.1489 | 30.0 | 11730 | 3.8621 | 0.5568 | 0.8399 |
| 3.1712 | 31.0 | 12121 | 4.4271 | 0.5344 | 0.8241 |
| 3.1041 | 32.0 | 12512 | 3.7123 | 0.5617 | 0.8485 |
| 3.1415 | 33.0 | 12903 | 4.0142 | 0.5288 | 0.8209 |
| 3.0629 | 34.0 | 13294 | 4.0318 | 0.5336 | 0.8333 |
| 3.0791 | 35.0 | 13685 | 4.3695 | 0.5253 | 0.804 |
| 3.1474 | 36.0 | 14076 | 3.7375 | 0.5641 | 0.8531 |
| 3.0529 | 37.0 | 14467 | 3.7972 | 0.5579 | 0.8372 |
| 3.0873 | 38.0 | 14858 | 4.2765 | 0.5118 | 0.8186 |
| 3.0809 | 39.0 | 15249 | 4.1218 | 0.5293 | 0.8371 |
| 2.9886 | 40.0 | 15640 | 4.3262 | 0.5133 | 0.7994 |
| 3.0661 | 41.0 | 16031 | 4.4781 | 0.5022 | 0.796 |
| 3.0788 | 42.0 | 16422 | 4.0753 | 0.5483 | 0.8129 |
| 3.0237 | 43.0 | 16813 | 3.7439 | 0.5767 | 0.8464 |
| 3.0983 | 44.0 | 17204 | 3.6443 | 0.5777 | 0.8554 |
| 3.0467 | 45.0 | 17595 | 3.9888 | 0.5533 | 0.8331 |
| 3.0167 | 46.0 | 17986 | 3.6772 | 0.5583 | 0.8447 |
| 3.07 | 47.0 | 18377 | 3.7118 | 0.5793 | 0.8517 |
| 2.9652 | 48.0 | 18768 | 3.9403 | 0.5561 | 0.8444 |
| 3.0139 | 49.0 | 19159 | 3.6890 | 0.5663 | 0.8572 |
| 3.0674 | 50.0 | 19550 | 4.4362 | 0.5345 | 0.8344 |
| 3.0761 | 51.0 | 19941 | 4.4229 | 0.5334 | 0.8172 |
| 3.0641 | 52.0 | 20332 | 4.7560 | 0.463 | 0.7574 |
| 3.0502 | 53.0 | 20723 | 4.7270 | 0.4876 | 0.769 |
| 3.0099 | 54.0 | 21114 | 4.0329 | 0.5198 | 0.8258 |
| 2.9899 | 55.0 | 21505 | 3.7874 | 0.5708 | 0.8497 |
| 3.018 | 56.0 | 21896 | 3.8048 | 0.5695 | 0.8524 |
| 2.9708 | 57.0 | 22287 | 4.1157 | 0.5344 | 0.8163 |
| 2.9148 | 58.0 | 22678 | 4.1128 | 0.5403 | 0.8417 |
| 2.9904 | 59.0 | 23069 | 3.8922 | 0.5554 | 0.8437 |
| 2.9977 | 60.0 | 23460 | 3.9310 | 0.5427 | 0.829 |
| 3.0047 | 61.0 | 23851 | 3.9865 | 0.5523 | 0.8351 |
| 2.9837 | 62.0 | 24242 | 4.0753 | 0.5424 | 0.8224 |
| 2.9772 | 63.0 | 24633 | 4.1512 | 0.538 | 0.8332 |
| 3.0345 | 64.0 | 25024 | 3.5358 | 0.5924 | 0.8658 |
| 2.9638 | 65.0 | 25415 | 4.3996 | 0.5087 | 0.7945 |
| 3.0266 | 66.0 | 25806 | 3.7508 | 0.5802 | 0.8496 |
| 2.9663 | 67.0 | 26197 | 3.8379 | 0.5707 | 0.8494 |
| 2.9998 | 68.0 | 26588 | 4.0652 | 0.5392 | 0.8216 |
| 3.0019 | 69.0 | 26979 | 3.9790 | 0.5447 | 0.8239 |
| 3.013 | 70.0 | 27370 | 3.6032 | 0.5691 | 0.8612 |
| 2.9271 | 71.0 | 27761 | 4.1823 | 0.5473 | 0.8291 |
| 2.986 | 72.0 | 28152 | 4.3817 | 0.4993 | 0.7822 |
| 2.9893 | 73.0 | 28543 | 3.6689 | 0.5806 | 0.8575 |
| 2.9511 | 74.0 | 28934 | 3.8535 | 0.5501 | 0.8221 |
| 2.9433 | 75.0 | 29325 | 3.7887 | 0.5656 | 0.8421 |
| 2.8952 | 76.0 | 29716 | 3.5734 | 0.5883 | 0.8652 |
| 2.9337 | 77.0 | 30107 | 4.1062 | 0.5305 | 0.8214 |
| 3.0154 | 78.0 | 30498 | 4.0138 | 0.5355 | 0.8309 |
| 2.9208 | 79.0 | 30889 | 3.8719 | 0.5504 | 0.8341 |
| 2.8892 | 80.0 | 31280 | 4.0809 | 0.5319 | 0.8237 |
| 2.9331 | 81.0 | 31671 | 3.5403 | 0.6008 | 0.8639 |
| 2.9134 | 82.0 | 32062 | 3.6800 | 0.5843 | 0.8546 |
| 2.9622 | 83.0 | 32453 | 3.8804 | 0.5647 | 0.8437 |
| 2.9544 | 84.0 | 32844 | 3.7246 | 0.5814 | 0.8517 |
| 2.9223 | 85.0 | 33235 | 3.5611 | 0.5882 | 0.865 |
| 2.9563 | 86.0 | 33626 | 4.2245 | 0.5138 | 0.8 |
| 2.9392 | 87.0 | 34017 | 3.6025 | 0.5889 | 0.8648 |
| 2.9204 | 88.0 | 34408 | 3.6211 | 0.5928 | 0.8544 |
| 2.9161 | 89.0 | 34799 | 3.6070 | 0.589 | 0.8607 |
| 2.9576 | 90.0 | 35190 | 3.9418 | 0.5688 | 0.851 |
| 2.9046 | 91.0 | 35581 | 3.9653 | 0.5452 | 0.8186 |
| 2.9703 | 92.0 | 35972 | 3.6643 | 0.5711 | 0.8562 |
| 2.9237 | 93.0 | 36363 | 3.7664 | 0.5673 | 0.8457 |
| 2.9066 | 94.0 | 36754 | 3.7217 | 0.578 | 0.8517 |
| 2.9329 | 95.0 | 37145 | 3.6711 | 0.5829 | 0.8533 |
| 2.8649 | 96.0 | 37536 | 3.7170 | 0.5771 | 0.8459 |
| 2.9258 | 97.0 | 37927 | 3.9320 | 0.5704 | 0.8421 |
| 2.9135 | 98.0 | 38318 | 4.0597 | 0.5369 | 0.8296 |
| 2.9051 | 99.0 | 38709 | 3.7008 | 0.5874 | 0.8587 |
| 2.934 | 100.0 | 39100 | 3.9568 | 0.5477 | 0.8406 |
| 2.836 | 101.0 | 39491 | 3.3552 | 0.6149 | 0.8761 |
| 2.8905 | 102.0 | 39882 | 4.3550 | 0.5049 | 0.8037 |
| 2.9637 | 103.0 | 40273 | 4.0608 | 0.5505 | 0.8164 |
| 2.9111 | 104.0 | 40664 | 3.8790 | 0.5534 | 0.8374 |
| 2.9704 | 105.0 | 41055 | 4.0175 | 0.5464 | 0.826 |
| 2.8906 | 106.0 | 41446 | 3.6246 | 0.5832 | 0.8649 |
| 2.89 | 107.0 | 41837 | 4.0702 | 0.5439 | 0.8271 |
| 2.906 | 108.0 | 42228 | 4.1885 | 0.5132 | 0.8047 |
| 2.938 | 109.0 | 42619 | 4.0603 | 0.53 | 0.8128 |
| 3.0279 | 110.0 | 43010 | 4.0727 | 0.5418 | 0.832 |
| 2.9643 | 111.0 | 43401 | 3.6941 | 0.5695 | 0.8617 |
| 2.8987 | 112.0 | 43792 | 3.7293 | 0.579 | 0.8496 |
| 2.9106 | 113.0 | 44183 | 3.9642 | 0.5572 | 0.8257 |
| 2.9009 | 114.0 | 44574 | 4.3196 | 0.5429 | 0.8253 |
| 2.8418 | 115.0 | 44965 | 3.7210 | 0.5843 | 0.8717 |
| 2.8576 | 116.0 | 45356 | 4.0749 | 0.5458 | 0.8341 |
| 2.9192 | 117.0 | 45747 | 3.9535 | 0.5337 | 0.8175 |
| 2.8893 | 118.0 | 46138 | 4.1837 | 0.5318 | 0.8171 |
| 2.9258 | 119.0 | 46529 | 3.5189 | 0.5943 | 0.8662 |
| 2.8717 | 120.0 | 46920 | 3.9686 | 0.5596 | 0.8315 |
| 2.877 | 121.0 | 47311 | 3.6360 | 0.5858 | 0.8601 |
| 2.8959 | 122.0 | 47702 | 3.7258 | 0.5851 | 0.8585 |
| 2.9161 | 123.0 | 48093 | 4.2020 | 0.5383 | 0.8276 |
| 2.8443 | 124.0 | 48484 | 4.0677 | 0.5359 | 0.8281 |
| 2.8563 | 125.0 | 48875 | 3.5917 | 0.5961 | 0.8708 |
| 2.8845 | 126.0 | 49266 | 3.6117 | 0.6046 | 0.8693 |
| 2.9027 | 127.0 | 49657 | 3.9661 | 0.5683 | 0.8396 |
| 2.9197 | 128.0 | 50048 | 4.0279 | 0.5367 | 0.8181 |
| 2.8579 | 129.0 | 50439 | 3.8712 | 0.5534 | 0.8341 |
| 2.8542 | 130.0 | 50830 | 3.6853 | 0.5744 | 0.8413 |
| 2.9391 | 131.0 | 51221 | 4.0594 | 0.5362 | 0.825 |
| 2.851 | 132.0 | 51612 | 3.9716 | 0.5581 | 0.8228 |
| 2.9059 | 133.0 | 52003 | 3.6744 | 0.5717 | 0.8526 |
| 2.8988 | 134.0 | 52394 | 3.8076 | 0.5722 | 0.8492 |
| 2.9035 | 135.0 | 52785 | 3.5326 | 0.5922 | 0.8661 |
| 2.8424 | 136.0 | 53176 | 4.1388 | 0.5305 | 0.8143 |
| 2.8244 | 137.0 | 53567 | 4.1921 | 0.5237 | 0.8036 |
| 2.9039 | 138.0 | 53958 | 4.1438 | 0.5276 | 0.8338 |
| 2.8494 | 139.0 | 54349 | 4.1365 | 0.5348 | 0.8099 |
| 2.8668 | 140.0 | 54740 | 3.8972 | 0.5758 | 0.8462 |
| 2.9249 | 141.0 | 55131 | 4.2609 | 0.5351 | 0.8159 |
| 2.8924 | 142.0 | 55522 | 3.7532 | 0.5786 | 0.8511 |
| 2.921 | 143.0 | 55913 | 3.6086 | 0.6067 | 0.8707 |
| 2.9066 | 144.0 | 56304 | 4.0826 | 0.5487 | 0.8371 |
| 2.8571 | 145.0 | 56695 | 4.4056 | 0.5193 | 0.8064 |
| 2.8358 | 146.0 | 57086 | 4.0599 | 0.5634 | 0.8461 |
| 2.8739 | 147.0 | 57477 | 4.9455 | 0.5166 | 0.8081 |
| 2.913 | 148.0 | 57868 | 4.2058 | 0.5301 | 0.8131 |
| 2.8189 | 149.0 | 58259 | 4.2209 | 0.5379 | 0.8056 |
| 2.9042 | 150.0 | 58650 | 3.8923 | 0.5557 | 0.8372 |
| 1.9013 | 151.0 | 59041 | 2.2349 | 0.7537 | 0.9413 |
| 1.8449 | 152.0 | 59432 | 2.1446 | 0.7638 | 0.9471 |
| 1.6762 | 153.0 | 59823 | 2.1349 | 0.7647 | 0.9454 |
| 1.6799 | 154.0 | 60214 | 2.1468 | 0.7615 | 0.9459 |
| 1.6171 | 155.0 | 60605 | 2.1154 | 0.7674 | 0.9472 |
| 1.5704 | 156.0 | 60996 | 2.1415 | 0.7639 | 0.9467 |
| 1.5604 | 157.0 | 61387 | 2.1328 | 0.7668 | 0.9463 |
| 1.5115 | 158.0 | 61778 | 2.1240 | 0.7664 | 0.9471 |
| 1.4914 | 159.0 | 62169 | 2.1091 | 0.7656 | 0.9468 |
| 1.4706 | 160.0 | 62560 | 2.1281 | 0.7692 | 0.9466 |
| 1.4299 | 161.0 | 62951 | 2.1480 | 0.7644 | 0.946 |
| 1.4211 | 162.0 | 63342 | 2.1418 | 0.7683 | 0.9457 |
| 1.4215 | 163.0 | 63733 | 2.1524 | 0.7667 | 0.9434 |
| 1.3869 | 164.0 | 64124 | 2.1853 | 0.7569 | 0.9441 |
| 1.369 | 165.0 | 64515 | 2.1882 | 0.7614 | 0.9425 |
| 1.4072 | 166.0 | 64906 | 2.1751 | 0.7621 | 0.9445 |
| 1.3343 | 167.0 | 65297 | 2.2029 | 0.7605 | 0.9433 |
| 1.3151 | 168.0 | 65688 | 2.1958 | 0.7621 | 0.9425 |
| 1.3601 | 169.0 | 66079 | 2.2200 | 0.7579 | 0.9396 |
| 1.325 | 170.0 | 66470 | 2.1903 | 0.7647 | 0.9386 |
| 1.3387 | 171.0 | 66861 | 2.2230 | 0.7589 | 0.9366 |
| 1.344 | 172.0 | 67252 | 2.2253 | 0.7599 | 0.9381 |
| 1.3242 | 173.0 | 67643 | 2.2298 | 0.7557 | 0.9393 |
| 1.3333 | 174.0 | 68034 | 2.2981 | 0.7517 | 0.9377 |
| 1.346 | 175.0 | 68425 | 2.2547 | 0.7573 | 0.9395 |
| 1.2947 | 176.0 | 68816 | 2.3353 | 0.7498 | 0.9331 |
| 1.3434 | 177.0 | 69207 | 2.2535 | 0.7595 | 0.9391 |
| 1.2904 | 178.0 | 69598 | 2.2742 | 0.7538 | 0.937 |
| 1.3208 | 179.0 | 69989 | 2.2825 | 0.7536 | 0.9392 |
| 1.3169 | 180.0 | 70380 | 2.2843 | 0.7586 | 0.9387 |
| 1.3413 | 181.0 | 70771 | 2.3063 | 0.7494 | 0.9366 |
| 1.3751 | 182.0 | 71162 | 2.3235 | 0.7542 | 0.934 |
| 1.3599 | 183.0 | 71553 | 2.2807 | 0.7595 | 0.9384 |
| 1.3334 | 184.0 | 71944 | 2.3419 | 0.7516 | 0.9362 |
| 1.3298 | 185.0 | 72335 | 2.3371 | 0.7549 | 0.9335 |
| 1.3699 | 186.0 | 72726 | 2.3436 | 0.7506 | 0.9336 |
| 1.3449 | 187.0 | 73117 | 2.2879 | 0.7599 | 0.9356 |
| 1.3572 | 188.0 | 73508 | 2.3465 | 0.7529 | 0.9351 |
| 1.3514 | 189.0 | 73899 | 2.3553 | 0.7494 | 0.9338 |
| 1.374 | 190.0 | 74290 | 2.3444 | 0.7534 | 0.9309 |
| 1.356 | 191.0 | 74681 | 2.3551 | 0.7499 | 0.9352 |
| 1.3504 | 192.0 | 75072 | 2.3725 | 0.7496 | 0.9333 |
| 1.3594 | 193.0 | 75463 | 2.3538 | 0.7527 | 0.9331 |
| 1.3578 | 194.0 | 75854 | 2.4372 | 0.7477 | 0.9317 |
| 1.3726 | 195.0 | 76245 | 2.4402 | 0.7395 | 0.9318 |
| 1.376 | 196.0 | 76636 | 2.4342 | 0.7519 | 0.9305 |
| 1.3744 | 197.0 | 77027 | 2.3781 | 0.7536 | 0.9346 |
| 1.4077 | 198.0 | 77418 | 2.4271 | 0.7481 | 0.9293 |
| 1.4072 | 199.0 | 77809 | 2.4525 | 0.7454 | 0.9306 |
| 1.4123 | 200.0 | 78200 | 2.4740 | 0.7447 | 0.9296 |
| 1.4169 | 201.0 | 78591 | 2.5034 | 0.7405 | 0.925 |
| 1.4408 | 202.0 | 78982 | 2.4648 | 0.7421 | 0.9271 |
| 1.4072 | 203.0 | 79373 | 2.4620 | 0.7439 | 0.9283 |
| 1.4429 | 204.0 | 79764 | 2.5030 | 0.7387 | 0.9279 |
| 1.445 | 205.0 | 80155 | 2.4865 | 0.7449 | 0.9294 |
| 1.4643 | 206.0 | 80546 | 2.4842 | 0.7412 | 0.9301 |
| 1.4716 | 207.0 | 80937 | 2.4839 | 0.7431 | 0.9308 |
| 1.4975 | 208.0 | 81328 | 2.4602 | 0.7487 | 0.9312 |
| 1.4801 | 209.0 | 81719 | 2.5342 | 0.7333 | 0.9252 |
| 1.4984 | 210.0 | 82110 | 2.4870 | 0.7423 | 0.9302 |
| 1.4958 | 211.0 | 82501 | 2.5838 | 0.7329 | 0.9242 |
| 1.4948 | 212.0 | 82892 | 2.5103 | 0.7395 | 0.9325 |
| 1.5114 | 213.0 | 83283 | 2.5504 | 0.7365 | 0.9276 |
| 1.4707 | 214.0 | 83674 | 2.6205 | 0.7354 | 0.9243 |
| 1.5326 | 215.0 | 84065 | 2.5910 | 0.7354 | 0.9251 |
| 1.5303 | 216.0 | 84456 | 2.6825 | 0.7195 | 0.9152 |
| 1.5423 | 217.0 | 84847 | 2.6442 | 0.7347 | 0.925 |
| 1.5461 | 218.0 | 85238 | 2.5856 | 0.7347 | 0.926 |
| 1.5607 | 219.0 | 85629 | 2.5510 | 0.734 | 0.9282 |
| 1.5536 | 220.0 | 86020 | 2.7130 | 0.723 | 0.9182 |
| 1.5546 | 221.0 | 86411 | 2.5694 | 0.7413 | 0.929 |
| 1.5605 | 222.0 | 86802 | 2.5428 | 0.737 | 0.9255 |
| 1.5485 | 223.0 | 87193 | 2.5787 | 0.7332 | 0.9242 |
| 1.581 | 224.0 | 87584 | 2.6230 | 0.7304 | 0.9215 |
| 1.5877 | 225.0 | 87975 | 2.6150 | 0.729 | 0.9227 |
| 1.3153 | 226.0 | 88366 | 2.2853 | 0.7741 | 0.9405 |
| 1.3065 | 227.0 | 88757 | 2.2740 | 0.7752 | 0.941 |
| 1.3005 | 228.0 | 89148 | 2.2598 | 0.7748 | 0.9414 |
| 1.2804 | 229.0 | 89539 | 2.2627 | 0.7772 | 0.9386 |
| 1.2509 | 230.0 | 89930 | 2.2680 | 0.7764 | 0.9419 |
| 1.2814 | 231.0 | 90321 | 2.2774 | 0.7762 | 0.9427 |
| 1.2713 | 232.0 | 90712 | 2.2765 | 0.7759 | 0.9417 |
| 1.2561 | 233.0 | 91103 | 2.2680 | 0.7755 | 0.9411 |
| 1.2579 | 234.0 | 91494 | 2.2638 | 0.7756 | 0.9426 |
| 1.2559 | 235.0 | 91885 | 2.2604 | 0.7788 | 0.9424 |
| 1.2367 | 236.0 | 92276 | 2.2840 | 0.7749 | 0.9405 |
| 1.233 | 237.0 | 92667 | 2.2828 | 0.774 | 0.9412 |
| 1.2154 | 238.0 | 93058 | 2.2646 | 0.7774 | 0.9418 |
| 1.2495 | 239.0 | 93449 | 2.2694 | 0.7747 | 0.9416 |
| 1.2215 | 240.0 | 93840 | 2.2873 | 0.7739 | 0.9398 |
| 1.2342 | 241.0 | 94231 | 2.2746 | 0.7765 | 0.9407 |
| 1.2224 | 242.0 | 94622 | 2.2624 | 0.7744 | 0.9405 |
| 1.197 | 243.0 | 95013 | 2.2713 | 0.7765 | 0.9402 |
| 1.212 | 244.0 | 95404 | 2.2823 | 0.7764 | 0.9392 |
| 1.209 | 245.0 | 95795 | 2.2724 | 0.7754 | 0.9411 |
| 1.2024 | 246.0 | 96186 | 2.2728 | 0.7775 | 0.9394 |
| 1.1872 | 247.0 | 96577 | 2.2704 | 0.777 | 0.9412 |
| 1.2019 | 248.0 | 96968 | 2.2703 | 0.7742 | 0.9413 |
| 1.2169 | 249.0 | 97359 | 2.2772 | 0.7748 | 0.9388 |
| 1.1751 | 250.0 | 97750 | 2.2744 | 0.7753 | 0.9407 |
| 1.1965 | 251.0 | 98141 | 2.2719 | 0.7767 | 0.941 |
| 1.1943 | 252.0 | 98532 | 2.2750 | 0.7754 | 0.9385 |
| 1.1933 | 253.0 | 98923 | 2.2681 | 0.7747 | 0.9408 |
| 1.1682 | 254.0 | 99314 | 2.2833 | 0.7759 | 0.9421 |
| 1.1849 | 255.0 | 99705 | 2.2829 | 0.777 | 0.9404 |
| 1.1909 | 256.0 | 100096 | 2.2621 | 0.7772 | 0.9406 |
| 1.1831 | 257.0 | 100487 | 2.2884 | 0.7749 | 0.941 |
| 1.1837 | 258.0 | 100878 | 2.3022 | 0.7748 | 0.9396 |
| 1.1742 | 259.0 | 101269 | 2.2827 | 0.774 | 0.9418 |
| 1.1738 | 260.0 | 101660 | 2.2993 | 0.7746 | 0.9413 |
| 1.1631 | 261.0 | 102051 | 2.2771 | 0.7745 | 0.9405 |
| 1.1578 | 262.0 | 102442 | 2.2859 | 0.7737 | 0.9403 |
| 1.1902 | 263.0 | 102833 | 2.2828 | 0.7734 | 0.9403 |
| 1.1724 | 264.0 | 103224 | 2.2831 | 0.7749 | 0.9396 |
| 1.1701 | 265.0 | 103615 | 2.2908 | 0.7733 | 0.9422 |
| 1.1645 | 266.0 | 104006 | 2.2924 | 0.7762 | 0.939 |
| 1.1743 | 267.0 | 104397 | 2.2812 | 0.7771 | 0.9411 |
| 1.1783 | 268.0 | 104788 | 2.2908 | 0.7727 | 0.9388 |
| 1.1714 | 269.0 | 105179 | 2.2794 | 0.7769 | 0.9382 |
| 1.1571 | 270.0 | 105570 | 2.2961 | 0.7718 | 0.94 |
| 1.1696 | 271.0 | 105961 | 2.2921 | 0.7723 | 0.9378 |
| 1.163 | 272.0 | 106352 | 2.2869 | 0.7743 | 0.9401 |
| 1.1705 | 273.0 | 106743 | 2.2915 | 0.7716 | 0.9382 |
| 1.1425 | 274.0 | 107134 | 2.3003 | 0.7711 | 0.9387 |
| 1.1552 | 275.0 | 107525 | 2.3153 | 0.768 | 0.939 |
| 1.1603 | 276.0 | 107916 | 2.3000 | 0.7739 | 0.9408 |
| 1.1677 | 277.0 | 108307 | 2.2985 | 0.7747 | 0.9363 |
| 1.16 | 278.0 | 108698 | 2.3062 | 0.7712 | 0.9393 |
| 1.1729 | 279.0 | 109089 | 2.2971 | 0.7741 | 0.9382 |
| 1.1608 | 280.0 | 109480 | 2.3076 | 0.7714 | 0.9388 |
| 1.1615 | 281.0 | 109871 | 2.3178 | 0.7721 | 0.9385 |
| 1.1637 | 282.0 | 110262 | 2.3096 | 0.7713 | 0.9377 |
| 1.1581 | 283.0 | 110653 | 2.3123 | 0.7678 | 0.939 |
| 1.1594 | 284.0 | 111044 | 2.3009 | 0.7712 | 0.9401 |
| 1.143 | 285.0 | 111435 | 2.3090 | 0.7703 | 0.9388 |
| 1.1422 | 286.0 | 111826 | 2.3144 | 0.7736 | 0.9384 |
| 1.1503 | 287.0 | 112217 | 2.3128 | 0.7739 | 0.9396 |
| 1.1653 | 288.0 | 112608 | 2.3021 | 0.7711 | 0.938 |
| 1.1749 | 289.0 | 112999 | 2.3116 | 0.7715 | 0.9379 |
| 1.1535 | 290.0 | 113390 | 2.3051 | 0.7736 | 0.9389 |
| 1.1523 | 291.0 | 113781 | 2.3109 | 0.7694 | 0.9372 |
| 1.1498 | 292.0 | 114172 | 2.3135 | 0.772 | 0.9379 |
| 1.1446 | 293.0 | 114563 | 2.3121 | 0.7723 | 0.9395 |
| 1.1481 | 294.0 | 114954 | 2.3099 | 0.7716 | 0.9374 |
| 1.1609 | 295.0 | 115345 | 2.3071 | 0.7737 | 0.9375 |
| 1.1457 | 296.0 | 115736 | 2.3273 | 0.7724 | 0.9375 |
| 1.138 | 297.0 | 116127 | 2.3189 | 0.7698 | 0.9371 |
| 1.157 | 298.0 | 116518 | 2.3139 | 0.7722 | 0.9388 |
| 1.1446 | 299.0 | 116909 | 2.3094 | 0.7705 | 0.9375 |
| 1.1387 | 300.0 | 117300 | 2.3019 | 0.7735 | 0.9377 |
### Framework versions
- Transformers 4.56.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
fatehcabreraadv/blockassist-bc-tawny_alert_dingo_1756539841
|
fatehcabreraadv
| 2025-08-30T08:13:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tawny alert dingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:13:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tawny alert dingo
---
# 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-32m-nq-phaseA-ft-st
|
RikiyaT
| 2025-08-30T08:11:31Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T08:11:26Z |
# RikiyaT/mxbai-ettin-32m-nq-phaseA-ft-st
Dense retrieval encoder (Ettin / ModernBERT) — SentenceTransformers
- Base model: RikiyaT/mxbai-ettin-32m-pretrained
- Pooling: mean
- Projection: linear to 256 dims (bias=False)
**Transformers variant**: [RikiyaT/mxbai-ettin-32m-nq-phaseA-ft](https://huggingface.co/RikiyaT/mxbai-ettin-32m-nq-phaseA-ft)
### Usage
```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("RikiyaT/mxbai-ettin-32m-nq-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}`
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756541425
|
liukevin666
| 2025-08-30T08:11:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:11:20Z |
---
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).
|
Completo-video-do-laila-frizon-video/Completo-video.do.laila.frizon.video.en.twitter.y.telegram
|
Completo-video-do-laila-frizon-video
| 2025-08-30T08:11:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-30T08:10:46Z |
<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_1756541406
|
bah63843
| 2025-08-30T08:11:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:10:52Z |
---
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).
|
NexVeridian/gpt-oss-20b-8bit
|
NexVeridian
| 2025-08-30T08:10:41Z | 633 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-05T23:52:45Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-20b
---
# NexVeridian/gpt-oss-20b-8bit
This model [NexVeridian/gpt-oss-20b-8bit](https://huggingface.co/NexVeridian/gpt-oss-20b-8bit) was
converted to MLX format from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
using mlx-lm version **0.27.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/gpt-oss-20b-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Bobalo/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster
|
Bobalo
| 2025-08-30T08:10:31Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am territorial zealous lobster",
"trl",
"genrl-swarm",
"I am territorial_zealous_lobster",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-14T13:25:51Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am territorial zealous lobster
- trl
- genrl-swarm
- I am territorial_zealous_lobster
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-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="Bobalo/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_zealous_lobster", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## 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}}
}
```
|
bah63843/blockassist-bc-plump_fast_antelope_1756541166
|
bah63843
| 2025-08-30T08:07:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:06:54Z |
---
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).
|
dgambettaphd/M_llm2_run1_gen7_S_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-30T08:05:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-30T08:05:02Z |
---
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]
|
AnerYubo/blockassist-bc-pawing_downy_anaconda_1756541071
|
AnerYubo
| 2025-08-30T08:04:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing downy anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:04:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing downy anaconda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnerYubo/blockassist-bc-screeching_mute_lemur_1756541062
|
AnerYubo
| 2025-08-30T08:04:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching mute lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:04:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching mute lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756539372
|
koloni
| 2025-08-30T08:02:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T08:02:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Jeganbaskar/snap-ai
|
Jeganbaskar
| 2025-08-30T08:02:09Z | 72 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-27T07:21:39Z |
---
library_name: transformers
model_name: snap-ai
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for snap-ai
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="Jeganbaskar/snap-ai", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.8.0.dev20250319+cu128
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
doguilmak/inferencevision-pythia-1B
|
doguilmak
| 2025-08-30T08:01:54Z | 20 | 0 | null |
[
"safetensors",
"gpt_neox",
"question-answering",
"causal-lm",
"fine-tuned",
"pytorch",
"en",
"arxiv:2304.01373",
"base_model:EleutherAI/pythia-1b",
"base_model:finetune:EleutherAI/pythia-1b",
"license:mit",
"model-index",
"region:us"
] |
question-answering
| 2025-05-20T10:39:51Z |
---
license: mit
language:
- en
base_model:
- EleutherAI/pythia-1b
pipeline_tag: question-answering
tags:
- question-answering
- causal-lm
- fine-tuned
- pytorch
provider: huggingface,
model: pythia-1B,
task: question-answering
model-index:
- name: doguilmak/pythia-1b-inferencevision-qa
results:
- task:
type: question-answering
dataset:
name: inferencevision_docs
type: inferencevision_docs
split: evaluation
metrics:
- name: Eval Loss
type: cross-entropy
value: 0.03725
source:
name: Fine-tuning logs
url: https://https://huggingface.co/doguilmak/inferencevision-pythia-1B/tree/main
---
# Model Card for InferenceVision QA Fine-Tuned Model
## Model Description
This model is a fine-tuned variant of the EleutherAI/pythia-1b causal language model, specifically adapted to handle interactive question-answering over the InferenceVision documentation. By leveraging domain-specific question–answer pairs, the model has learned to produce precise, contextually relevant responses, making it an ideal backbone for developer assistants, chatbots, and documentation-driven interfaces.
## Intended Use
- **Primary Use:** Provide accurate, documentation-based answers to user queries about InferenceVision.
- **Use Cases:** Integration into chat applications, developer portals, knowledge retrieval systems, and automated support bots.
For a hands-on guide on fine-tuning and using this model with **InferenceVision**, check out the [interactive notebook](https://github.com/doguilmak/InferenceVision/blob/main/usage/InferenveVision_LLM_QA.ipynb).
**Out-of-Scope:**
- Legal, medical, or financial advice beyond the scope of InferenceVision documentation.
- Generating content unrelated to the provided training material.
## Training Data
The model was fine-tuned on a custom dataset `inferencevision_docs.jsonl`, containing **760** high-quality question–answer pairs sourced directly from InferenceVision’s official documentation. These QA pairs span key areas such as:
- **Installation & Setup:** Commands, environment requirements, and troubleshooting guidelines.
- **Core API Usage:** Function parameters, input/output formats, and typical usage scenarios.
- **Advanced Features:** Batch processing workflows, performance optimization tips, and integration examples.
- **Error Handling:** Common error codes, explanations, and recommended solutions.
**Preprocessing Steps:**
1. **Deduplication & Cleanup:** Eliminated duplicate or near-duplicate entries to prevent bias.
2. **Tokenization:** Employed the EleutherAI/pythia-1b’s byte-pair encoding with a maximum sequence length of 2,048 tokens.
3. **Context Windowing:** For multipart questions, context segments were extracted to ensure both the query and relevant documentation snippet fit within the model’s context window.
4. **Quality Validation:** Automated checks and manual reviews removed any QA pairs with unclear or incomplete answers.
The dataset was split into an **80% training set** (608 examples) and a **20% evaluation set** (152 examples), using stratified sampling to preserve topic distribution across both splits.
## Training Procedure & Hyperparameters
Fine-tuning was performed using Hugging Face’s `Trainer` API with the following `TrainingArguments`:
```python
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=1e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=16,
weight_decay=0.01,
logging_dir="./logs",
load_best_model_at_end=True,
save_total_limit=1,
metric_for_best_model="eval_loss",
greater_is_better=False
)
```
Training leveraged GPU acceleration when available. By saving only the best checkpoint (based on lowest `eval_loss`), storage requirements were minimized without sacrificing model quality.
## Evaluation Results
After 16 epochs, the training process yielded the following key outcomes:
- **Global Steps:** 1,216
- **Final Training Loss:** 0.03725
- **Epochs Completed:** 16.0
- **Training Runtime:** 2,572.28 seconds (trained on an NVIDIA A100 40GB GPU and took ~42.9 minutes)
- **Training Throughput:** 3.78 samples/sec, 0.47 steps/sec
- **Total FLOPs:** 2.72×10¹⁶
## Limitations & Biases
- Although highly accurate on InferenceVision topics, the model may generate plausible but incorrect or outdated information if presented with out-of-distribution queries.
- Context length is limited to 2,048 tokens; very long or multi-turn contexts may require special handling.
Users should validate critical outputs against official documentation.
# Inference Provider
This section provides a simple way to run inference using the fine-tuned `doguilmak/inferencevision-pythia-1B` model. It uses Hugging Face Transformers to load the model and generate answers for InferenceVision-related questions. The model is optimized for domain-specific QA and works best when given clear queries or documentation snippets.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "doguilmak/inferencevision-pythia-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def ask_question(question, context=None, max_new_tokens=100):
if context:
prompt = f"Context: {context}\n\nQuestion: {question}\nAnswer:"
else:
prompt = f"Question: {question}\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=0.7,
top_p=0.95,
do_sample=False,
pad_token_id=tokenizer.eos_token_id
)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
return answer.replace(prompt, "").strip()
question = "What is InferenceVision?"
answer = ask_question(question)
print("Answer:", answer)
```
## Reference
Biderman, S., Schoelkopf, H., Anthony, Q. G., Bradley, H., O’Brien, K., Hallahan, E., ... & Van Der Wal, O. (2023, July). _Pythia: A suite for analyzing large language models across training and scaling_. In _International Conference on Machine Learning_ (pp. 2397-2430). PMLR. [https://arxiv.org/abs/2304.01373](https://arxiv.org/abs/2304.01373)
This paper introduces **Pythia**, a suite of 16 large language models (LLMs) trained on public data in the same order, ranging from 70M to 12B parameters. The suite provides 154 checkpoints per model and tools to reconstruct training dataloaders, facilitating research in areas such as memorization, term frequency effects on few-shot performance, and reducing gender bias.
|
yuno2025/maxx
|
yuno2025
| 2025-08-30T07:59:21Z | 0 | 0 | null |
[
"safetensors",
"unsloth",
"license:llama3",
"region:us"
] | null | 2025-08-30T07:57:15Z |
---
license: llama3
tags:
- unsloth
---
|
NexVeridian/gpt-oss-20b-5bit
|
NexVeridian
| 2025-08-30T07:58:05Z | 520 | 0 |
mlx
|
[
"mlx",
"safetensors",
"gpt_oss",
"vllm",
"text-generation",
"conversational",
"base_model:openai/gpt-oss-20b",
"base_model:quantized:openai/gpt-oss-20b",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-05T23:37:06Z |
---
license: apache-2.0
pipeline_tag: text-generation
library_name: mlx
tags:
- vllm
- mlx
base_model: openai/gpt-oss-20b
---
# NexVeridian/gpt-oss-20b-5bit
This model [NexVeridian/gpt-oss-20b-5bit](https://huggingface.co/NexVeridian/gpt-oss-20b-5bit) was
converted to MLX format from [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
using mlx-lm version **0.27.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/gpt-oss-20b-5bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
thecodedev/blockassist-bc-pouncing_pensive_komodo_1756539954
|
thecodedev
| 2025-08-30T07:47:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pouncing pensive komodo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:46:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pouncing pensive komodo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756537658
|
NahedDom
| 2025-08-30T07:44:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:44:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RikiyaT/mxbai-ettin-32m-msmarco-v2-format-b-phaseA-ft
|
RikiyaT
| 2025-08-30T07:41:09Z | 0 | 0 | null |
[
"safetensors",
"modernbert",
"region:us"
] | null | 2025-08-30T07:41:04Z |
# RikiyaT/mxbai-ettin-32m-msmarco-v2-format-b-phaseA-ft
Dense retrieval encoder (Ettin / ModernBERT) — Transformers
- Base model: RikiyaT/mxbai-ettin-32m-pretrained
- Pooling: mean
- Projection: linear to 256 dims (bias=False)
**SentenceTransformers variant**: [RikiyaT/mxbai-ettin-32m-msmarco-v2-format-b-phaseA-ft-st](https://huggingface.co/RikiyaT/mxbai-ettin-32m-msmarco-v2-format-b-phaseA-ft-st)
### Usage
```python
import torch
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("RikiyaT/mxbai-ettin-32m-msmarco-v2-format-b-phaseA-ft", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("RikiyaT/mxbai-ettin-32m-msmarco-v2-format-b-phaseA-ft", trust_remote_code=True)
proj = torch.nn.Linear(model.config.hidden_size, 256, bias=False)
proj.load_state_dict(torch.load('proj.pt', map_location='cpu'))
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 = proj(emb); emb = torch.nn.functional.normalize(emb, p=2, dim=1)
return emb
```
Prompts used in training:
- query: `search_query: {text}`
- document: `search_document: {text}`
|
halation/bert-base-japanese-v3-jcommonsenseqa
|
halation
| 2025-08-30T07:39:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"multiple-choice",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2025-08-30T07:39:11Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1756537994
|
katanyasekolah
| 2025-08-30T07:39:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:39:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1756539479
|
sekirr
| 2025-08-30T07:38:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:38:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nikoloside/deepfracture
|
nikoloside
| 2025-08-30T07:38:26Z | 11 | 0 | null |
[
"fracture",
"vq-vae",
"physical-simulation",
"other",
"dataset:nikoloside/break4models",
"license:mit",
"region:us"
] |
other
| 2025-08-25T17:56:57Z |
---
license: mit
datasets:
- nikoloside/break4models
pipeline_tag: other
tags:
- fracture
- vq-vae
- physical-simulation
---
# DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning
This is a collection of pre-trained models for deepfracture: a conditional vq-vae model for predicting fracture pattern from impulse code, trained on the [Break4Models](https://huggingface.co/datasets/nikoloside/break4models) dataset created by [FractureRB](https://github.com/david-hahn/FractureRB).
📖 **For more details, please visit:**
- [GitHub Repository](https://github.com/nikoloside/TEBP)
- [Project Page](https://nikoloside.graphics/deepfracture/)
## Overview
These models are designed to predict fracture patterns based on impact conditions. Each model is trained on a specific target shape and can be used for real-time physics simulation and computer graphics applications.
## Model Architecture
The models use an encoder-decoder architecture:
- **Encoder**: Processes input impulse conditions and generates latent representations
- **Decoder**: Reconstructs GS-SDF(Geometrically-Segmented Signed Distance Fields) from latent representations
- **Training**: Supervised learning on physics simulation data
## Available Models
```
pre-trained-v2/
├── base/ # Base object model
├── pot/ # Pot object model
├── squirrel/ # Squirrel object model
├── bunny/ # Bunny object model
├── lion/ # Lion object model
├── objs/ # Different original mesh files
├── csv/ # Initial collision scene
└── README.md # This file
```
Each model directory contains:
- `{shape}-encoder.pt` - Encoder weights
- `{shape}-decoder.pt` - Decoder weights
- `{shape}-1000-encoder.pt` - Encoder weights (1000 epoch version)
- `{shape}-1000-decoder.pt` - Decoder weights (1000 epoch version)
Other folders:
- `{shape}.obj` - Reference original 3D mesh file
- `{shape}-{csv_num}.obj` - Reference initial collision scene. Containing pos, direct, impulse strength.
## Usage
### Loading Models
```python
import torch
from your_model_architecture import Encoder, Decoder
# Load encoder
encoder = Encoder()
encoder.load_state_dict(torch.load('base/base-encoder.pt'))
encoder.eval()
# Load decoder
decoder = Decoder()
decoder.load_state_dict(torch.load('base/base-decoder.pt'))
decoder.eval()
# Load reference mesh
reference_mesh = 'objs/base.obj'
init_collision = 'csv/base-261.txt'
work_path = 'result/base-exp-1/
```
### Inference
- [Example](https://github.com/nikoloside/TEBP/blob/main/04.Run-time/predict-runtime.py)
- [Details](https://github.com/nikoloside/TEBP/blob/main/04.Run-time/MorphoImageJ.py#L34)
```python
# Prepare input conditions
input_conditions = prepare_impact_conditions(impact_point, velocity, impulse_strength)
# Encode
with torch.no_grad():
latent = encoder(input_conditions)
# Decode
latent = decoder.cook(latent)
gssdf_voxel = deocoder.predict(latent)
# Apply to reference mesh
result_mesh = processCagedSDFSeg(gssdf_voxel, work_path, reference_mesh, isBig = False, maxValue = 1.0)
```
## Model Performance
(metrics and performance)[https://doi.org/10.1111/cgf.70002]
## Training Details
- **Dataset**: Break4Model dataset
- **Framework**: PyTorch
- **Optimizer**: Adam
- **Loss Function**: L2 Loss
- **Training Time**: ~24 hours per model on NVIDIA RTX 3090
## Citation
If you use these models in your research, please cite:
```bibtex
@article{huang2025deepfracture,
author = {Huang, Yuhang and Kanai, Takashi},
title = {DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning},
journal = {Computer Graphics Forum},
pages = {e70002},
year = {2025},
keywords = {animation, brittle fracture, neural networks, physically based animation},
doi = {https://doi.org/10.1111/cgf.70002},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.70002},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.70002}
}
```
## License
MIT
## Contact
For questions or issues, please open an issue on the Hugging Face model page.
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756538064
|
GroomerG
| 2025-08-30T07:38:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:37:59Z |
---
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).
|
bah63843/blockassist-bc-plump_fast_antelope_1756539136
|
bah63843
| 2025-08-30T07:33:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:33:05Z |
---
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).
|
olivvan/Reinforce-Pixelcopter-PLE-v0
|
olivvan
| 2025-08-30T07:25:37Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-30T07:24:46Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 42.90 +/- 31.76
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
eliyen/blockassist-bc-thick_agile_ant_1756538623
|
eliyen
| 2025-08-30T07:24:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick agile ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:24:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick agile ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bolkunale/blockassist-bc-ferocious_freckled_gerbil_1756536897
|
bolkunale
| 2025-08-30T07:23:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ferocious freckled gerbil",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:23:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ferocious freckled gerbil
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1756536840
|
coelacanthxyz
| 2025-08-30T07:21:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:21:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rafitesnet00/blockassist-bc-scruffy_mighty_wasp_1756537906
|
rafitesnet00
| 2025-08-30T07:20:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy mighty wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:16:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy mighty wasp
---
# 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_1756538326
|
bah63843
| 2025-08-30T07:19:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-30T07:19:28Z |
---
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).
|
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