modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 06:27:36
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 527
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
vendi11/blockassist-bc-placid_placid_llama_1756422479
|
vendi11
| 2025-08-28T23:08:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T23:08:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756422407
|
Dejiat
| 2025-08-28T23:07:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T23:07:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
trakonmerty66/blockassist-bc-durable_tropical_wombat_1756422341
|
trakonmerty66
| 2025-08-28T23:06:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"durable tropical wombat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T23:06:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- durable tropical wombat
---
# 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-elusive_mammalian_termite_1756422021
|
AnerYubo
| 2025-08-28T23:00:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive mammalian termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T23:00:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive mammalian termite
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nvidia/OpenReasoning-Nemotron-14B
|
nvidia
| 2025-08-28T22:50:48Z | 1,732 | 37 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"nvidia",
"code",
"conversational",
"en",
"arxiv:2504.16891",
"arxiv:2504.01943",
"arxiv:2507.09075",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-14B-Instruct",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-15T21:28:27Z |
---
license: cc-by-4.0
language:
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- nvidia
- code
---
# OpenReasoning-Nemotron-14B Overview
## Description: <br>
OpenReasoning-Nemotron-14B is a large language model (LLM) which is a derivative of Qwen2.5-14B-Instruct (AKA the reference model). It is a reasoning model that is post-trained for reasoning about math, code and science solution generation. We evaluated this model with up to 64K output tokens. The OpenReasoning model is available in the following sizes: 1.5B, 7B and 14B and 32B. <br>
This model is ready for commercial/non-commercial research use. <br>
### License/Terms of Use: <br>
GOVERNING TERMS: Use of the models listed above are governed by the [Creative Commons Attribution 4.0 International License (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). ADDITIONAL INFORMATION: [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct/blob/main/LICENSE)
## Scores on Reasoning Benchmarks
Our models demonstrate exceptional performance across a suite of challenging reasoning benchmarks. The 7B, 14B, and 32B models consistently set new state-of-the-art records for their size classes.
| **Model** | **AritificalAnalysisIndex*** | **GPQA** | **MMLU-PRO** | **HLE** | **LiveCodeBench*** | **SciCode** | **AIME24** | **AIME25** | **HMMT FEB 25** |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **1.5B**| 31.0 | 31.6 | 47.5 | 5.5 | 28.6 | 1.0 | 55.5 | 45.6 | 31.5 |
| **7B** | 54.7 | 61.1 | 71.9 | 8.3 | 63.3 | 20.3 | 84.7 | 78.2 | 63.5 |
| **14B** | 60.9 | 71.6 | 77.5 | 10.1 | 67.8 | 32.4 | 87.8 | 82.0 | 71.2 |
| **32B** | 64.3 | 73.1 | 80.0 | 11.9 | 70.2 | 39.6 | 89.2 | 84.0 | 73.8 |
\* This is our estimation of the Artificial Analysis Intelligence Index, not an official score.
\* LiveCodeBench version 6, date range 2408-2505.
## Combining the work of multiple agents
OpenReasoning-Nemotron models can be used in a "heavy" mode by starting multiple parallel generations and combining them together via [generative solution selection (GenSelect)](https://arxiv.org/abs/2504.16891). To add this "skill" we follow the original GenSelect training pipeline except we do not train on the selection summary but use the full reasoning trace of DeepSeek R1 0528 671B instead. We only train models to select the best solution for math problems but surprisingly find that this capability directly generalizes to code and science questions! With this "heavy" GenSelect inference mode, OpenReasoning-Nemotron-32B model surpasses O3 (High) on math and coding benchmarks.
| **Model** | **Pass@1 (Avg@64)** | **Majority@64** | **GenSelect** |
| :--- | :--- | :--- | :--- |
| **1.5B** | | | |
| **AIME24** | 55.5 | 76.7 | 76.7 |
| **AIME25** | 45.6 | 70.0 | 70.0 |
| **HMMT Feb 25** | 31.5 | 46.7 | 53.3 |
| **7B** | | | |
| **AIME24** | 84.7 | 93.3 | 93.3 |
| **AIME25** | 78.2 | 86.7 | 93.3 |
| **HMMT Feb 25** | 63.5 | 83.3 | 90.0 |
| **LCB v6 2408-2505** | 63.4 | n/a | 67.7 |
| **14B** | | | |
| **AIME24** | 87.8 | 93.3 | 93.3 |
| **AIME25** | 82.0 | 90.0 | 90.0 |
| **HMMT Feb 25** | 71.2 | 86.7 | 93.3 |
| **LCB v6 2408-2505** | 67.9 | n/a | 69.1 |
| **32B** | | | |
| **AIME24** | 89.2 | 93.3 | 93.3 |
| **AIME25** | 84.0 | 90.0 | 93.3 |
| **HMMT Feb 25** | 73.8 | 86.7 | 96.7 |
| **LCB v6 2408-2505** | 70.2 | n/a | 75.3 |
| **HLE** | 11.8 | 13.4 | 15.5 |
## How to use the models?
To run inference on coding problems:
````python
import transformers
import torch
model_id = "nvidia/OpenReasoning-Nemotron-14B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
# Code generation prompt
prompt = """You are a helpful and harmless assistant. You should think step-by-step before responding to the instruction below.
Please use python programming language only.
You must use ```python for just the final solution code block with the following format:
```python
# Your code here
```
{user}
"""
# Math generation prompt
# prompt = """Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.
#
# {user}
# """
# Science generation prompt
# You can refer to prompts here -
# https://github.com/NVIDIA/NeMo-Skills/blob/main/nemo_skills/prompt/config/generic/hle.yaml (HLE)
# https://github.com/NVIDIA/NeMo-Skills/blob/main/nemo_skills/prompt/config/eval/aai/mcq-4choices-boxed.yaml (for GPQA)
# https://github.com/NVIDIA/NeMo-Skills/blob/main/nemo_skills/prompt/config/eval/aai/mcq-10choices-boxed.yaml (MMLU-Pro)
messages = [
{
"role": "user",
"content": prompt.format(user="Write a program to calculate the sum of the first $N$ fibonacci numbers")},
]
outputs = pipeline(
messages,
max_new_tokens=64000,
)
print(outputs[0]["generated_text"][-1]['content'])
````
We have added [a simple transformer-based script](https://huggingface.co/nvidia/OpenReasoning-Nemotron-14B/blob/main/genselect_hf.py) in this repo to illustrate GenSelect.
To learn how to use the models in GenSelect mode with NeMo-Skills, see our [documentation](https://nvidia.github.io/NeMo-Skills/releases/openreasoning/evaluation/).
To use the model with GenSelect inference, we recommend following our
[reference implementation in NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills/blob/main/nemo_skills/pipeline/genselect.py). Alternatively, you can manually extract the summary from all solutions and use this
[prompt](https://github.com/NVIDIA/NeMo-Skills/blob/main/nemo_skills/prompt/config/openmath/genselect.yaml) for the math problems. We will add the prompt we used for the coding problems and a reference implementation soon!
You can learn more about GenSelect in these papers:
* [AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset](https://arxiv.org/abs/2504.16891)
* [GenSelect: A Generative Approach to Best-of-N](https://openreview.net/forum?id=8LhnmNmUDb)
## Accessing training data
Training data has been released! Math and code are available as part of
[Nemotron-Post-Training-Dataset-v1](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v1) and science is available in
[OpenScienceReasoning-2](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2).
See our [documentation](https://nvidia.github.io/NeMo-Skills/releases/openreasoning/training) for more details.
## Citation
If you find the data useful, please cite:
```
@article{ahmad2025opencodereasoning,
title={{OpenCodeReasoning: Advancing Data Distillation for Competitive Coding}},
author={Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, Aleksander Ficek, Siddhartha Jain, Jocelyn Huang, Vahid Noroozi, Boris Ginsburg},
year={2025},
eprint={2504.01943},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2504.01943},
}
```
```
@misc{ahmad2025opencodereasoningiisimpletesttime,
title={{OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique}},
author={Wasi Uddin Ahmad and Somshubra Majumdar and Aleksander Ficek and Sean Narenthiran and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Vahid Noroozi and Boris Ginsburg},
year={2025},
eprint={2507.09075},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.09075},
}
```
```
@misc{moshkov2025aimo2winningsolutionbuilding,
title={{AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset}},
author={Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
year={2025},
eprint={2504.16891},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2504.16891},
}
```
```
@inproceedings{toshniwal2025genselect,
title={{GenSelect: A Generative Approach to Best-of-N}},
author={Shubham Toshniwal and Ivan Sorokin and Aleksander Ficek and Ivan Moshkov and Igor Gitman},
booktitle={2nd AI for Math Workshop @ ICML 2025},
year={2025},
url={https://openreview.net/forum?id=8LhnmNmUDb}
}
```
## Additional Information:
### Deployment Geography:
Global<br>
### Use Case: <br>
This model is intended for developers and researchers who work on competitive math, code and science problems. It has been trained via only supervised fine-tuning to achieve strong scores on benchmarks. <br>
### Release Date: <br>
Huggingface [07/16/2025] via https://huggingface.co/nvidia/OpenReasoning-Nemotron-14B/ <br>
## Reference(s):
* [2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
* [2504.01943] OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
* [2504.16891] AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset
<br>
## Model Architecture: <br>
Architecture Type: Dense decoder-only Transformer model
Network Architecture: Qwen-14B-Instruct
<br>
**This model was developed based on Qwen2.5-14B-Instruct and has 14B model parameters. <br>**
**OpenReasoning-Nemotron-1.5B was developed based on Qwen2.5-1.5B-Instruct and has 1.5B model parameters. <br>**
**OpenReasoning-Nemotron-7B was developed based on Qwen2.5-7B-Instruct and has 7B model parameters. <br>**
**OpenReasoning-Nemotron-14B was developed based on Qwen2.5-14B-Instruct and has 14B model parameters. <br>**
**OpenReasoning-Nemotron-32B was developed based on Qwen2.5-32B-Instruct and has 32B model parameters. <br>**
## Input: <br>
**Input Type(s):** Text <br>
**Input Format(s):** String <br>
**Input Parameters:** One-Dimensional (1D) <br>
**Other Properties Related to Input:** Trained for up to 64,000 output tokens <br>
## Output: <br>
**Output Type(s):** Text <br>
**Output Format:** String <br>
**Output Parameters:** One-Dimensional (1D) <br>
**Other Properties Related to Output:** Trained for up to 64,000 output tokens <br>
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
## Software Integration : <br>
* Runtime Engine: NeMo 2.3.0 <br>
* Recommended Hardware Microarchitecture Compatibility: <br>
NVIDIA Ampere <br>
NVIDIA Hopper <br>
* Preferred/Supported Operating System(s): Linux <br>
## Model Version(s):
1.0 (7/16/2025) <br>
OpenReasoning-Nemotron-32B<br>
OpenReasoning-Nemotron-14B<br>
OpenReasoning-Nemotron-7B<br>
OpenReasoning-Nemotron-1.5B<br>
# Training and Evaluation Datasets: <br>
## Training Dataset:
The training corpus for OpenReasoning-Nemotron-14B is comprised of questions from [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) dataset, [OpenCodeReasoning-II](https://arxiv.org/abs/2507.09075), [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning), and the Synthetic Science questions from the [Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset). All responses are generated using DeepSeek-R1-0528. We also include the instruction following and tool calling data from Llama-Nemotron-Post-Training-Dataset without modification.
Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
Labeling Method: Hybrid: Automated, Human, Synthetic <br>
Properties: 5M DeepSeek-R1-0528 generated responses from OpenCodeReasoning questions (https://huggingface.co/datasets/nvidia/OpenCodeReasoning), [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning), and the Synthetic Science questions from the [Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset). We also include the instruction following and tool calling data from Llama-Nemotron-Post-Training-Dataset without modification.
## Evaluation Dataset:
We used the following benchmarks to evaluate the model holistically.
### Math
- AIME 2024/2025 <br>
- HMMT <br>
- BRUNO 2025 <br>
### Code
- LiveCodeBench <br>
- SciCode <br>
### Science
- GPQA <br>
- MMLU-PRO <br>
- HLE <br>
Data Collection Method: Hybrid: Automated, Human, Synthetic <br>
Labeling Method: Hybrid: Automated, Human, Synthetic <br>
## Inference:
**Acceleration Engine:** vLLM, Tensor(RT)-LLM <br>
**Test Hardware** NVIDIA H100-80GB <br>
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756421138
|
Dejiat
| 2025-08-28T22:46:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:46:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# 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_1756420754
|
lowelldiaz
| 2025-08-28T22:41:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prowling feathered stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:40:36Z |
---
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).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756420074
|
Dejiat
| 2025-08-28T22:28:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:28:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# 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_1756419473
|
bah63843
| 2025-08-28T22:18:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:18:39Z |
---
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).
|
vendi11/blockassist-bc-placid_placid_llama_1756419431
|
vendi11
| 2025-08-28T22:17:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid placid llama",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:17:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid placid llama
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alok0777/blockassist-bc-masked_pensive_lemur_1756419369
|
alok0777
| 2025-08-28T22:17:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked pensive lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:16:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked pensive lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kipospol/blockassist-bc-lively_agile_peacock_1756419245
|
kipospol
| 2025-08-28T22:14:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lively agile peacock",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:14:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lively agile peacock
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756419047
|
eusuf01
| 2025-08-28T22:12:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:11:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wolfer45/vgaxl2025
|
wolfer45
| 2025-08-28T22:11:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/sdxl-turbo",
"base_model:adapter:stabilityai/sdxl-turbo",
"region:us"
] |
text-to-image
| 2025-08-28T22:11:09Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/11_crop.jpg
text: '-'
base_model: stabilityai/sdxl-turbo
instance_prompt: vgaxl2025
---
# vgaxl2025
<Gallery />
## Model description
vgaxl2025
## Trigger words
You should use `vgaxl2025` to trigger the image generation.
## Download model
[Download](/wolfer45/vgaxl2025/tree/main) them in the Files & versions tab.
|
kipospol/blockassist-bc-lively_agile_peacock_1756418878
|
kipospol
| 2025-08-28T22:08:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lively agile peacock",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:08:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lively agile peacock
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756418559
|
eusuf01
| 2025-08-28T22:03:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:03:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# 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-gilded_patterned_mouse_1756418508
|
AnerYubo
| 2025-08-28T22:01:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gilded patterned mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T22:01:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gilded patterned mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mrblithe/phi3-razzimiyum
|
mrblithe
| 2025-08-28T21:53:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T21:31:39Z |
---
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]
|
capungmerah627/blockassist-bc-stinging_soaring_porcupine_1756416357
|
capungmerah627
| 2025-08-28T21:50:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinging soaring porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:50:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinging soaring porcupine
---
# 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_1756416125
|
Loder-S
| 2025-08-28T21:48:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:48:05Z |
---
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).
|
chainway9/blockassist-bc-untamed_quick_eel_1756415570
|
chainway9
| 2025-08-28T21:41:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:41:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Veltrix-GGUF
|
mradermacher
| 2025-08-28T21:41:28Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:MGZON/Veltrix",
"base_model:quantized:MGZON/Veltrix",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-28T21:40:16Z |
---
base_model: MGZON/Veltrix
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/MGZON/Veltrix
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Veltrix-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/Veltrix-GGUF/resolve/main/Veltrix.Q2_K.gguf) | Q2_K | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q3_K_S.gguf) | Q3_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q3_K_L.gguf) | Q3_K_L | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.IQ4_XS.gguf) | IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Veltrix-GGUF/resolve/main/Veltrix.f16.gguf) | f16 | 0.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Rootu/blockassist-bc-snorting_fleecy_goose_1756417173
|
Rootu
| 2025-08-28T21:40:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting fleecy goose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:40:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting fleecy goose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
davidilag/wav2vec2-xls-r-300m-pt-1000h_faroese-checkpoint10-faroese-100h-30-epochs_run3_2025-08-28
|
davidilag
| 2025-08-28T21:34:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-28T11:49:47Z |
---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-pt-1000h_faroese-checkpoint10-faroese-100h-30-epochs_run3_2025-08-28
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. -->
# wav2vec2-xls-r-300m-pt-1000h_faroese-checkpoint10-faroese-100h-30-epochs_run3_2025-08-28
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0955
- Wer: 18.9893
- Cer: 4.0477
## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|
| 3.3326 | 0.4877 | 1000 | 3.4082 | 100.0 | 99.3017 |
| 0.9195 | 0.9754 | 2000 | 0.5797 | 49.6233 | 13.9009 |
| 0.4245 | 1.4628 | 3000 | 0.2394 | 30.6693 | 7.7442 |
| 0.3878 | 1.9505 | 4000 | 0.2012 | 28.7175 | 7.1659 |
| 0.3023 | 2.4379 | 5000 | 0.1720 | 27.0476 | 6.6183 |
| 0.2929 | 2.9256 | 6000 | 0.1605 | 26.4440 | 6.3760 |
| 0.2094 | 3.4131 | 7000 | 0.1554 | 24.8447 | 6.0289 |
| 0.2194 | 3.9008 | 8000 | 0.1373 | 24.0781 | 5.7930 |
| 0.1766 | 4.3882 | 9000 | 0.1422 | 24.0076 | 5.7085 |
| 0.1962 | 4.8759 | 10000 | 0.1330 | 23.4701 | 5.5531 |
| 0.1632 | 5.3633 | 11000 | 0.1300 | 23.1881 | 5.4450 |
| 0.1704 | 5.8510 | 12000 | 0.1237 | 23.2145 | 5.4450 |
| 0.1426 | 6.3385 | 13000 | 0.1217 | 22.6550 | 5.3006 |
| 0.1489 | 6.8261 | 14000 | 0.1252 | 23.0207 | 5.3779 |
| 0.1282 | 7.3136 | 15000 | 0.1167 | 22.1924 | 5.1483 |
| 0.1389 | 7.8013 | 16000 | 0.1082 | 21.7121 | 4.9629 |
| 0.1303 | 8.2887 | 17000 | 0.1109 | 21.8311 | 4.9590 |
| 0.1266 | 8.7764 | 18000 | 0.1136 | 21.6593 | 4.9590 |
| 0.1053 | 9.2638 | 19000 | 0.1121 | 21.6637 | 4.9369 |
| 0.1073 | 9.7515 | 20000 | 0.1189 | 21.5006 | 4.9030 |
| 0.097 | 10.2390 | 21000 | 0.1075 | 21.3288 | 4.8367 |
| 0.1005 | 10.7267 | 22000 | 0.1057 | 21.2715 | 4.8225 |
| 0.0849 | 11.2141 | 23000 | 0.1059 | 20.8750 | 4.7018 |
| 0.0846 | 11.7018 | 24000 | 0.1086 | 21.0556 | 4.7459 |
| 0.0873 | 12.1892 | 25000 | 0.1064 | 20.8001 | 4.6986 |
| 0.0804 | 12.6769 | 26000 | 0.1035 | 20.5093 | 4.5992 |
| 0.0779 | 13.1644 | 27000 | 0.1065 | 20.5049 | 4.5495 |
| 0.0779 | 13.6520 | 28000 | 0.1036 | 20.5358 | 4.5653 |
| 0.0711 | 14.1395 | 29000 | 0.1051 | 20.5137 | 4.6023 |
| 0.0797 | 14.6272 | 30000 | 0.1068 | 20.4829 | 4.5676 |
| 0.0716 | 15.1146 | 31000 | 0.1035 | 20.2890 | 4.5187 |
| 0.0616 | 15.6023 | 32000 | 0.1016 | 20.1568 | 4.4445 |
| 0.0747 | 16.0897 | 33000 | 0.1014 | 20.1480 | 4.4524 |
| 0.0632 | 16.5774 | 34000 | 0.1003 | 19.8264 | 4.3577 |
| 0.0564 | 17.0649 | 35000 | 0.0963 | 19.8484 | 4.3499 |
| 0.0547 | 17.5525 | 36000 | 0.0966 | 19.6986 | 4.3601 |
| 0.057 | 18.0400 | 37000 | 0.1005 | 19.7691 | 4.2994 |
| 0.0504 | 18.5277 | 38000 | 0.1002 | 19.5621 | 4.2591 |
| 0.055 | 19.0151 | 39000 | 0.0985 | 19.6722 | 4.3057 |
| 0.0507 | 19.5028 | 40000 | 0.1036 | 19.6370 | 4.3159 |
| 0.0413 | 19.9905 | 41000 | 0.1003 | 19.3858 | 4.2260 |
| 0.0446 | 20.4779 | 42000 | 0.0979 | 19.5268 | 4.2244 |
| 0.0387 | 20.9656 | 43000 | 0.0951 | 19.2713 | 4.1534 |
| 0.0407 | 21.4531 | 44000 | 0.0954 | 19.3814 | 4.1763 |
| 0.0579 | 21.9407 | 45000 | 0.0991 | 19.2977 | 4.1668 |
| 0.0471 | 22.4282 | 46000 | 0.0962 | 19.3021 | 4.1487 |
| 0.0483 | 22.9159 | 47000 | 0.0969 | 19.2096 | 4.1234 |
| 0.0532 | 23.4033 | 48000 | 0.0935 | 19.0950 | 4.1100 |
| 0.0369 | 23.8910 | 49000 | 0.0979 | 19.2757 | 4.1487 |
| 0.0389 | 24.3784 | 50000 | 0.0974 | 19.1127 | 4.1210 |
| 0.0375 | 24.8661 | 51000 | 0.0972 | 19.1171 | 4.1076 |
| 0.037 | 25.3536 | 52000 | 0.0963 | 19.1083 | 4.0911 |
| 0.0391 | 25.8413 | 53000 | 0.0982 | 19.0466 | 4.0753 |
| 0.0413 | 26.3287 | 54000 | 0.0980 | 18.9981 | 4.0484 |
| 0.033 | 26.8164 | 55000 | 0.0974 | 18.9893 | 4.0548 |
| 0.0412 | 27.3038 | 56000 | 0.0959 | 18.9981 | 4.0492 |
| 0.0396 | 27.7915 | 57000 | 0.0959 | 18.9452 | 4.0406 |
| 0.0426 | 28.2790 | 58000 | 0.0958 | 18.9496 | 4.0437 |
| 0.0377 | 28.7666 | 59000 | 0.0957 | 18.9981 | 4.0532 |
| 0.0406 | 29.2541 | 60000 | 0.0955 | 18.9981 | 4.0484 |
| 0.0408 | 29.7418 | 61000 | 0.0955 | 18.9893 | 4.0477 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
alok0777/blockassist-bc-masked_pensive_lemur_1756416801
|
alok0777
| 2025-08-28T21:34:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked pensive lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:34:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked pensive lemur
---
# 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_1756416461
|
bah63843
| 2025-08-28T21:29:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:28:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Wavescarmers/blockassist-bc-bellowing_jumping_jay_1756416451
|
Wavescarmers
| 2025-08-28T21:29:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing jumping jay",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:28:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing jumping jay
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1756414151
|
sampingkaca72
| 2025-08-28T21:16:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:16:23Z |
---
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).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1756414084
|
rvipitkirubbe
| 2025-08-28T21:15:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:15:53Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756415329
|
eusuf01
| 2025-08-28T21:10:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:09:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756415185
|
Dejiat
| 2025-08-28T21:06:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:06:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756413510
|
GroomerG
| 2025-08-28T21:06:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:06:30Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756415127
|
eusuf01
| 2025-08-28T21:06:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:06:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Drahca91/fabien_pic
|
Drahca91
| 2025-08-28T21:04:31Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-28T20:46:09Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Fabien
---
# Fabien_Pic
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Fabien` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Fabien",
"lora_weights": "https://huggingface.co/Drahca91/fabien_pic/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Drahca91/fabien_pic', weight_name='lora.safetensors')
image = pipeline('Fabien').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Drahca91/fabien_pic/discussions) to add images that show off what you’ve made with this LoRA.
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756414899
|
eusuf01
| 2025-08-28T21:02:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T21:02:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rootu/blockassist-bc-snorting_fleecy_goose_1756414699
|
Rootu
| 2025-08-28T20:59:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snorting fleecy goose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:58:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snorting fleecy goose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rudra-madlads/blockassist-bc-jumping_swift_gazelle_1756414609
|
Rudra-madlads
| 2025-08-28T20:57:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping swift gazelle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:57:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping swift gazelle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NexaAI/paddleocr-npu
|
NexaAI
| 2025-08-28T20:55:16Z | 16 | 15 | null |
[
"region:us"
] | null | 2025-08-19T23:30:58Z |
# PaddleOCR v4 (PP-OCRv4)
## Model Description
**PP-OCRv4** is the fourth-generation end-to-end optical character recognition system from the PaddlePaddle team.
It combines a lightweight **text detection → angle classification → text recognition** pipeline with improved training techniques and data augmentation, delivering higher accuracy and robustness while staying efficient for real-time use.
PP-OCRv4 supports multilingual OCR (Latin and non-Latin scripts), irregular layouts (rotated/curved text), and challenging inputs such as noisy or low-resolution images often found in mobile and document-scan scenarios.
## Features
- **End-to-end OCR**: text detection, optional angle classification, and text recognition in one pipeline.
- **Multilingual support**: pretrained models for English, Chinese, and dozens of other languages; easy finetuning for domain text.
- **Robust in real-world conditions**: handles rotation, perspective distortion, blur, low light, and complex backgrounds.
- **Lightweight & fast**: practical for both mobile apps and large-scale server deployments.
- **Flexible I/O**: works with photos, scans, screenshots, receipts, invoices, ID cards, dashboards, and UI text.
- **Extensible**: swap components (detector/recognizer), add language packs, or finetune on domain datasets.
## Use Cases
- Document digitization (invoices, receipts, forms, contracts)
- RPA and back-office automation (screen/OCR flows)
- Mobile scanning apps and camera-based translation/read-aloud
- Industrial and retail analytics (labels, price tags, shelf tags)
- Accessibility (screen-readers and read-aloud applications)
## Inputs and Outputs
**Input**: Image (photo, scan, or screenshot).
**Output**: A list of detected text regions, each with:
- bounding box (rectangular or polygonal)
- recognized text string
- optional confidence score and orientation
---
## How to use
> ⚠️ **Hardware requirement:** the model currently runs **only on Qualcomm NPUs** (e.g., Snapdragon-powered AIPC).
> Apple NPU support is planned next.
### 1) Install Nexa-SDK
- Download and follow the steps under "Deploy Section" Nexa's model page: [Download Windows arm64 SDK](https://sdk.nexa.ai/model/PaddleOCR%20v4)
- (Other platforms coming soon)
### 2) Get an access token
Create a token in the Model Hub, then log in:
```bash
nexa config set license '<access_token>'
```
### 3) Run the model
Running:
```bash
nexa infer NexaAI/paddleocr-npu
```
---
## License
- Licensed under [Apache-2.0](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/LICENSE)
## References
- GitHub repo: [https://github.com/PaddlePaddle/PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- Model zoo & documentation: [Models list](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/doc/doc_en/models_list_en.md)
|
gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-breadcrumbs-50_50
|
gsjang
| 2025-08-28T20:53:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2312.06795",
"base_model:PartAI/Dorna-Llama3-8B-Instruct",
"base_model:merge:PartAI/Dorna-Llama3-8B-Instruct",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T20:51:00Z |
---
base_model:
- PartAI/Dorna-Llama3-8B-Instruct
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-breadcrumbs-50_50
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Breadcrumbs](https://arxiv.org/abs/2312.06795) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [PartAI/Dorna-Llama3-8B-Instruct](https://huggingface.co/PartAI/Dorna-Llama3-8B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: breadcrumbs
models:
- model: PartAI/Dorna-Llama3-8B-Instruct
parameters:
weight: 0.5
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
weight: 0.5
parameters: {}
dtype: bfloat16
tokenizer:
source: union
base_model: meta-llama/Meta-Llama-3-8B-Instruct
write_readme: README.md
```
|
koloni/blockassist-bc-deadly_graceful_stingray_1756412463
|
koloni
| 2025-08-28T20:47:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:47:13Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756413981
|
eusuf01
| 2025-08-28T20:47:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:46:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rudra-madlads/blockassist-bc-jumping_swift_gazelle_1756413723
|
Rudra-madlads
| 2025-08-28T20:42:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"jumping swift gazelle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:42:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- jumping swift gazelle
---
# 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_1756412100
|
rvipitkirubbe
| 2025-08-28T20:41:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:41:13Z |
---
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).
|
klmdr22/blockassist-bc-wild_loud_newt_1756413229
|
klmdr22
| 2025-08-28T20:34:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:34:28Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756412529
|
eusuf01
| 2025-08-28T20:23:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:23:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/assassin-s-creed-style-xl-f1d
|
Muapi
| 2025-08-28T20:21:11Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-28T20:21:01Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Assassin's Creed Style XL + F1D
**Base model**: Flux.1 D
**Trained words**: assassins creed style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:304745@1062091", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Cedric077/blockassist-bc-aquatic_deft_crane_1756410981
|
Cedric077
| 2025-08-28T20:20:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic deft crane",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:20:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic deft crane
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756412372
|
Vasya777
| 2025-08-28T20:20:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:20:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
canoplos112/blockassist-bc-yapping_sleek_squirrel_1756412236
|
canoplos112
| 2025-08-28T20:20:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yapping sleek squirrel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:17:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yapping sleek squirrel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gsjang/fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-slerp-50_50
|
gsjang
| 2025-08-28T20:18:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:PartAI/Dorna-Llama3-8B-Instruct",
"base_model:merge:PartAI/Dorna-Llama3-8B-Instruct",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T20:15:21Z |
---
base_model:
- PartAI/Dorna-Llama3-8B-Instruct
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# fa-dorna-llama3-8b-instruct-x-meta-llama-3-8b-instruct-slerp-50_50
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [PartAI/Dorna-Llama3-8B-Instruct](https://huggingface.co/PartAI/Dorna-Llama3-8B-Instruct)
* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: slerp
models:
- model: PartAI/Dorna-Llama3-8B-Instruct
parameters:
weight: 0.5
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
weight: 0.5
parameters:
t: 0.5
dtype: bfloat16
tokenizer:
source: union
base_model: meta-llama/Meta-Llama-3-8B-Instruct
write_readme: README.md
```
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756411987
|
eusuf01
| 2025-08-28T20:14:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:13:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756412006
|
Vasya777
| 2025-08-28T20:14:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:14:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# 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_1756410139
|
Loder-S
| 2025-08-28T20:09:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly knobby tiger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:08:37Z |
---
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).
|
vnhioer/blockassist-bc-dense_unseen_komodo_1756411469
|
vnhioer
| 2025-08-28T20:05:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dense unseen komodo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:04:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dense unseen komodo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756411385
|
eusuf01
| 2025-08-28T20:04:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T20:03:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Anonymizer-4B-GGUF
|
mradermacher
| 2025-08-28T20:02:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:eternisai/Anonymizer-4B",
"base_model:quantized:eternisai/Anonymizer-4B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-28T19:03:12Z |
---
base_model: eternisai/Anonymizer-4B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/eternisai/Anonymizer-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Anonymizer-4B-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/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Anonymizer-4B-GGUF/resolve/main/Anonymizer-4B.f16.gguf) | f16 | 8.2 | 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 -->
|
mradermacher/EviOmni-nq_train-1.5B-GGUF
|
mradermacher
| 2025-08-28T19:59:03Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:HIT-TMG/EviOmni-nq_train-1.5B",
"base_model:quantized:HIT-TMG/EviOmni-nq_train-1.5B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-28T19:41:22Z |
---
base_model: HIT-TMG/EviOmni-nq_train-1.5B
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/HIT-TMG/EviOmni-nq_train-1.5B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#EviOmni-nq_train-1.5B-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/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/EviOmni-nq_train-1.5B-GGUF/resolve/main/EviOmni-nq_train-1.5B.f16.gguf) | f16 | 3.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
AnerYubo/blockassist-bc-snappy_tenacious_eagle_1756411093
|
AnerYubo
| 2025-08-28T19:58:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"snappy tenacious eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:58:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- snappy tenacious eagle
---
# 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_1756410979
|
klmdr22
| 2025-08-28T19:57:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:56:58Z |
---
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).
|
Stasonelison/blockassist-bc-howling_powerful_aardvark_1756410963
|
Stasonelison
| 2025-08-28T19:56:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling powerful aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:56:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling powerful aardvark
---
# 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-beaked_frisky_ox_1756410715
|
coppytiou
| 2025-08-28T19:52:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked frisky ox",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:51:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked frisky ox
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1756409029
|
chainway9
| 2025-08-28T19:52:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:51:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
poki1/blockassist-bc-skilled_omnivorous_elephant_1756410240
|
poki1
| 2025-08-28T19:44:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skilled omnivorous elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:44:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skilled omnivorous elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF
|
mradermacher
| 2025-08-28T19:42:03Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"en",
"base_model:ReadyArt/C4.1-Broken-Tutu-24B_b",
"base_model:quantized:ReadyArt/C4.1-Broken-Tutu-24B_b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-28T03:18:58Z |
---
base_model: ReadyArt/C4.1-Broken-Tutu-24B_b
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
## 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/ReadyArt/C4.1-Broken-Tutu-24B_b
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#C4.1-Broken-Tutu-24B_b-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-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/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/C4.1-Broken-Tutu-24B_b-i1-GGUF/resolve/main/C4.1-Broken-Tutu-24B_b.i1-Q6_K.gguf) | i1-Q6_K | 19.4 | 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 -->
|
FAHAB/blockassist-bc-small_wild_grasshopper_1756410081
|
FAHAB
| 2025-08-28T19:41:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"small wild grasshopper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:41:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- small wild grasshopper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756409972
|
eusuf01
| 2025-08-28T19:40:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:40:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756409582
|
Dejiat
| 2025-08-28T19:33:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:33:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abdourrahmane/mms-hassaniya-ctc
|
abdourrahmane
| 2025-08-28T19:22:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-28T19:18:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
full-new-video-do-surfista-vazado-video/VER.Completo.video.do.surfista.da.mansao.privilegio.video.do.surfista.vazado
|
full-new-video-do-surfista-vazado-video
| 2025-08-28T19:22:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-28T19:22:01Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5xr5mb3e?leaked-videos/" 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>
|
sekirr/blockassist-bc-masked_tenacious_whale_1756408645
|
sekirr
| 2025-08-28T19:18:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:18:01Z |
---
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).
|
eusuf01/blockassist-bc-smooth_humming_butterfly_1756408523
|
eusuf01
| 2025-08-28T19:16:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"smooth humming butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:16:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- smooth humming butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Emmet-Allen/christAIn-uncensored
|
Emmet-Allen
| 2025-08-28T19:12:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"gguf",
"qwen2",
"text-generation",
"base_model:adapter:dphn/Dolphin3.0-Qwen2.5-0.5B",
"lora",
"sft",
"transformers",
"trl",
"conversational",
"arxiv:1910.09700",
"base_model:dphn/Dolphin3.0-Qwen2.5-0.5B",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T17:23:18Z |
---
base_model: dphn/Dolphin3.0-Qwen2.5-0.5B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:dphn/Dolphin3.0-Qwen2.5-0.5B
- lora
- sft
- transformers
- trl
---
# ChristAI-uncensored
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
A Small Language Model trained on the King James Version of The Bible.
### Model Description
<!-- Provide a longer summary of what this model is. -->
Created as a critque peoples usage of AI specifically LLMs/SLMs
as a confidant, therapist, and in some cases a new god (think American Gods by Neil Gaiman).
This has lead to cases of people creating AI-centered cults, rash decision making as suggested by AI,
and in a praticular case that sparked the intrest of the creation of this model, a 16 year old boy
commiting [suicide as suggested by ChatGPT](https://www.cnn.com/2025/08/26/tech/openai-chatgpt-teen-suicide-lawsuit).
This model is the uncensored model, which is able to better answer more nuanced questions that pertain to
The Bible and how it pertains to the world.
The model does not hold back.
- **Developed by:** Emmet Allen
- **Model type:** PEFT text-generation
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** dphn/Dolphin3.0-Qwen2.5-0.5B
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** (https://huggingface.co/Emmet-Allen/christAIn-uncensored)
## 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. -->
Used to answer questions using the KJV bible as a reference point.
**This is a Social Critique Project**
### 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. -->
Trained on the Christian Based KJV Bible.
Heavily leans towards christian values and opinions.
### 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. -->
https://huggingface.co/datasets/Emmet-Allen/The-Bible-KJV
[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. -->
Will include Python Notebook.
[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:** NVidia 3070ti 8GB VRAM
- **Hours used:** < 1hr
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
Void IDE
Jupyter Notebook
Nvidia-SMI
Nvidia CUDA-Toolkit
## 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 0.17.1
|
bah63843/blockassist-bc-plump_fast_antelope_1756408237
|
bah63843
| 2025-08-28T19:11:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T19:11:19Z |
---
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).
|
xzyhku/SkyReels-V1-I2V
|
xzyhku
| 2025-08-28T19:05:37Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"diffusers:HunyuanVideoPipeline",
"region:us"
] | null | 2025-08-28T17:44:39Z |
A checkpoint that merges the SkyReels V1 I2V model with the text encoders and tokenizers of HunyuanVideo.
|
rban01/vit-4
|
rban01
| 2025-08-28T18:54:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-28T18:51:39Z |
---
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]
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756405391
|
kojeklollipop
| 2025-08-28T18:51:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T18:51:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756406870
|
Dejiat
| 2025-08-28T18:48:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T18:48:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gsjang/ar-arabic-orpo-llama-3-8b-instruct-x-meta-llama-3-8b-instruct-breadcrumbs-50_50
|
gsjang
| 2025-08-28T18:47:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2312.06795",
"base_model:MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct",
"base_model:merge:MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T18:44:07Z |
---
base_model:
- MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct
- meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# ar-arabic-orpo-llama-3-8b-instruct-x-meta-llama-3-8b-instruct-breadcrumbs-50_50
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Breadcrumbs](https://arxiv.org/abs/2312.06795) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct](https://huggingface.co/MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: breadcrumbs
models:
- model: MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct
parameters:
weight: 0.5
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
weight: 0.5
parameters: {}
dtype: bfloat16
tokenizer:
source: union
base_model: meta-llama/Meta-Llama-3-8B-Instruct
write_readme: README.md
```
|
bah63843/blockassist-bc-plump_fast_antelope_1756406257
|
bah63843
| 2025-08-28T18:38:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T18:38:44Z |
---
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).
|
sirtobsi/ceat-fc-rag
|
sirtobsi
| 2025-08-28T18:38:39Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"dense",
"generated_from_trainer",
"dataset_size:1179",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"en",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:nlpaueb/legal-bert-base-uncased",
"base_model:finetune:nlpaueb/legal-bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-08-28T18:38:20Z |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:1179
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nlpaueb/legal-bert-base-uncased
widget:
- source_sentence: 'Mercer International Inc. v. Government of Canada Canada’s Counter-Memorial
August 22, 2014 concerns that such transaction could result in an increase of
its costs of service should Howe Sound purchase additional electricity so that
it could sell its existing self- generation into the market.266 115. On February
23, 2001, BC Hydro wrote to the BCUC advising it that some of its customers with
self-generation capability wished to sell power they generate at market prices.
BC Hydro requested that the BCUC initiate a process beginning with a workshop
to determine the extent to which BC Hydro would remain obligated to serve industrial
customers who wished to take their self-generation output to the market.267 116.
Howe Sound, which had significantly decreased its generation in response to peaking
natural gas prices,268 proposed “to utilize only that part of its generation capacity
which [was] idle” and that “[a]ll of the generation utilized for market sales
[would] be incremental and [would] not require BC Hydro to deliver any additional
electricity to Howe Sound.”269 266 Letter from Craig Folkestad to Jerry Peet,
Re: Howe Sound Pulp and Paper (HSPP) Power Export Opportunities, 12 February 2001
at 1, R-79. (“However, I would be less than candid if I did not tell you that
the management of BC Hydro does, and most likely the government as its shareholder,
will have serious concerns about any proposal that will see customer self-generated
power sold into the market, and with BC Hydro then being required to supply make-up
power under Schedule 1821. This will be financially detrimental to BC Hydro and
its other ratepayers, both in the short and long term.”); BC Hydro, Letter to
the BCUC, in the Matter of British Columbia Hydro and Power Authority Obligation
to Serve Rate Schedule 1821 Customers with Self-Generation Capability, 23 February
2001 (“BC Hydro’s 23 February 2001 Letter to the BCUC”), R-81. See also Pierre
Lamarche Statement, ¶¶ 28, 30 (“Howe Sound agreed with BC Hydro that such arbitrage
could have a negative effect on BC Hydro ratepayers, but that self- generators
should have the ability to sell incremental or idle self-generation”); Lester
Dyck Statement, ¶ 36; Jim Scouras Statement, ¶ 21. 267 BC Hydro’s 23 February
2001 Letter to the BCUC, R-81. 268 See Pierre Lamarche Statement, ¶¶ 23-26. Howe
Sound was, in fact, considering shutting down its condensing turbine completely:
¶ 26. 269 Howe Sound Pulp and Paper, Letter to the BCUC, in the Matter of British
Columbia Hydro and Power Authority Obligation to Serve Rate Schedule 1821 Customers
with Self-Generation Capability, 27 February 2001 at bates 144039-144040, R-80.
63'
sentences:
- 'Mr. Switlishoff, can you clarify the implications of the EPA termination with
BC Hydro in 2020? Did the Claimant indeed assume that electricity would be purchased
beyond that term?
Certainly. The Claimant did not assume purchase beyond 2020. They fully anticipated
market conditions shifting and prepared for alternate sales strategies after the
EPA''s conclusion.
Interesting, because earlier records indicated that one-third of their damages
calculation was based on perpetual purchases by BC Hydro. Are you saying that
this wasn''t part of their original strategy?
Yes, that''s correct. The calculations were hypothetical and never incorporated
into any actionable business strategy by the Claimant.
Regarding the BCUC orders, is it accurate to state that Order G-48-09 imposed
restrictions that led to financial losses for the Claimant?
No, Order G-48-09 didn’t cause any financial damage as the Claimant had alternative
arrangements for selling their electricity, including tapping into different markets
and agreements.'
- 'Mr. Friesen, can you confirm whether Celgar had any realistic opportunities to
sell its self-generated electricity to regions outside of British Columbia in
2008?
Yes, we had identified several potential buyers during that time who were interested
in our output at competitive rates.
Isn''t it true that Celgar struggled to secure transmission access for exporting
this electricity, making such sales challenging?
Actually, we had preliminary agreements lined up which would have ensured us the
necessary transmission access to conduct these sales effectively.
But wouldn’t any potential sales have been economically inefficient, given the
high cost of replacement electricity from FortisBC?
Our analysis showed that the revenues from these sales would indeed cover the
costs and provide a margin, contrary to what was suggested.'
- 'Ms. Peet, can you explain how the proposal was initiated and who was involved
in the discussions with BC Hydro?
Certainly. I worked alongside representatives from our Technical Department and
coordinated with Mr. Jerry Peet, who led the discussions. Mr. Peet, together with
Craig Folkestad, our Key Account Manager, presented the compiled data to BC Hydro,
where they discussed the proposals extensively before reaching an agreement on
the thresholds.
Thank you. Now, regarding Howe Sound''s generation strategy during the period
of gas price peaks, how did you propose to manage your generation capacity?
During this time, Howe Sound proposed to utilize only the part of our generation
capacity that was idle. Any generation incrementally used for market sales would
not necessitate additional electricity delivery from BC Hydro to our facilities.
Could you clarify if BC Hydro had concerns about this proposal affecting costs
and obligations?
Yes, BC Hydro did express concerns that selling self-generated power into the
market might increase its service costs. They worried it could impact obligations
under Rate Schedule 1821 and potentially negatively affect other ratepayers.'
- source_sentence: 619. Moreover, the restriction on below-GBL sales to third parties
was not otherwise necessary to BC Hydro’s Bioenergy Phase I procurement, as demonstrated
both by the fact that BC Hydro had at least, at one point in the EPA negotiations
with Celgar, agreed not to include the restriction,707 and the fact that the BCUC
set a GBL for Tolko in 2001 that restricted below-GBL sales completely outside
the context of any procurement.708 As the Tribunal will recall, the GBL concept
originated in BCUC Order G-38-01 to address Howe Sound’s desire to sell power
to California. It has no necessary relationship to any BC or BC Hydro procurement.
620. Mercer agrees with Canada and the ADF tribunal that “procurement” refers
to “the obtaining by purchase by a governmental agency or entity of title to .
. . possession of, for instance, goods, supplies, materials and machinery.”709
But BC Hydro did not obtain any good or service through the challenged restriction
on sales to third-parties. At issue is Celgar’s below- load self-generated electricity
that BC Hydro declined to buy. The measures restricted Celgar from providing,
to anyone. Under Canada’s preferred definition, that is not procurement. 621.
The ADF case does not suggest otherwise. In ADF, a cabinet-level agency of the
Commonwealth of Virginia (the Department of Transportation) was responsible for
“the construction of a multi-phased project designed to improve the safety and
efficiency of” a major highway system in the area of Springfield, Virginia, near
Washington, DC.710 The project included the construction of ramps and bridges
curving above the relevant highways, as well as of 707 See supra ¶ 38 and n.28.
708 See Memorial, ¶¶ 240–47. 709 CA-1, ADF (NAFTA), ¶ 161; Counter-Memorial, ¶
342. See also CA-16, UPS II (NAFTA), ¶ 135 (concluding that a Postal Imports Agreement
in which the Canadian customs authority obtains materials handling, data entry,
and duty collection services, is a procurement). 710 CA-1, ADF (NAFTA), ¶ 45.
304
sentences:
- 'Can you explain the Tribunal''s final stance concerning the Claimant''s claims
under the 2009 EPA and NAFTA Articles?
Certainly. The Tribunal, by a majority, decided it had no jurisdiction over the
Claimant''s claims under NAFTA Articles 1102, 1103, and 1105, except for those
alleging discriminatory treatment under Article 1105. So, claims for compensation
and related interest were dismissed.
And how did the Tribunal address the Claimant''s request for a Supplementary Decision
under the ICSID Additional Facility Rules?
The Claimant requested a Supplementary Decision regarding alleged discrimination
under NAFTA Articles 1102 and 1103 related to BCUC Order G-48-09. However, the
Respondent denied this request, and the Tribunal''s handling was complicated by
the passing of Professor Orrego Vicuña.
Was there any consensus among the Tribunal members on handling the Claimant''s
request before Professor Orrego Vicuña''s passing?
Yes, there was. All three Tribunal members reached a consensus during a conference
call. This was before Professor Orrego Vicuña became unable to sign the final
document.'
- 'Could you clarify whether Celgar''s Energy Project Certificate had any ongoing
effects after the legislative changes in 1995?
Yes, Celgar''s Energy Project Certificate continued to be recognized, but it wasn''t
explicitly covered under the updated Environmental Assessment Act after 1995.
The transitional provisions didn''t apply clearly to pre-existing orders.
But isn''t it true that prior orders like Celgar''s were explicitly deemed to
have continued under the new Act?
No, the Ministers'' Orders required separate re-evaluation before being reaffirmed
under the new legislation. This wasn’t automatic for older orders.
Regarding FortisBC''s access principles, did they apply to self-generators like
Celgar?
Initially, self-generators weren’t considered under those principles. It took
several years before any provisions applied to them.'
- 'Mr. Smith, during the negotiations for the EPA, can you confirm whether BC Hydro
pushed for a longer contract term with Celgar?
Yes, BC Hydro did suggest a longer contract term, but we never received a formal
request for anything more than 15 years.
Interesting, because it has been indicated that BC Hydro was seeking at least
a 20-year term. Are you certain about your statement?
I understand that’s what it might seem from other discussions, but the formal
conversations we had revolved around 15 years as the maximum offered.
Regarding the restrictions on selling generated electricity, did these originate
from BC Hydro’s procurement process?
Actually, the restrictions coincided with initial procurement discussions, suggesting
they were integral to the process.'
- source_sentence: its witness, Mr. Dyck, confirmed that information regarding BC
Hydro’s treatment of other pulp mills was never shared with Mercer.59 Mercer only
acquired constructive knowledge of its comparators’ treatment through its counsel
during the document production phase of these proceedings in May 2013. 34. As
established above, moreover, Mercer could not have acquired knowledge of loss
or damage, at the earliest, until the GBL-based exclusivity provisions were either
final or in effect. As noted, the exclusivity provision at issue did not take
effect, under the terms of the EPA, until the Commercial Operation date of 27
September 2010, and it did not become final until the BCUC ruled in Order G-48-
09 against Celgar’s attempt to purchase electricity from FortisBC while selling
power. Both of these dates are within the period of limitations; thus, Mercer’s
Minimum Standard of Treatment claim is within the period of limitations.60 II.
THE MINISTERS’ ORDER IMPOSES NO SELF-SUPPLY OBLIGATION OR ELECTRICITY SALES RESTRICTION
ON CELGAR 35. During the hearing, Canada all but abandoned its Ministers’ Order
argument. Canada’s relative silence on this issues was understandable, because
(i) the parties’ legal experts agree that the language in the Ministers’ Order
must be clear and unambiguous in order to impose a binding legal obligation on
Celgar that restricts its right to sell electricity,61 and (ii) Canada’s witnesses
confirmed that there simply is no clear and consistent language in the 1991 Ministers’
Order that imposed any self-supply or load displacement obligation, or otherwise
restricted Celgar’s right to sell its self-generated 59 See supra, Section I.C.1.
60 See supra, Section I.C.1. 61 See Expert Report of David Austin (14 December
2014) (“Austin Expert Report”) ¶¶ 21-30; Expert Report of David Bursey (28 March
2015) (“Bursey Expert Report”) ¶¶ 182-186, 191 (Mr. Bursey asserts that the language
of the Ministers’ Order is clear; he does not refute the general principle that
the language of the Ministers’ Order would need to be clear and unambiguous to
restrict Celgar’s right to sell electricity); Mercer Letter to Tribunal pp. 9-10
(12 July 2015); Reply ¶¶ 57, 94-101. - 17 -
sentences:
- 'Mr. Smith, can you clarify how BC Hydro determined the GBL for Celgar compared
to other mills?
Certainly. BC Hydro based Celgar’s GBL on one year of operational data from 2007,
even though they led us to believe they would consider an average of three years.
Other mills were not subject to the same method, which BC Hydro failed to communicate
to us.
Was any consideration given to the economic or financial performance of Celgar
during the GBL determination?
No, BC Hydro never indicated that such data would be relevant. They didn''t request
any economic or financial information about Celgar’s operations at any time during
the process.
How does this approach contrast with the treatment of other mills, like Skookumchuck?
The Skookumchuck mill operated as an independent power producer and had more flexibility
with their agreements. Celgar, however, was integrated into its recovery boiler
operations and was treated less favorably despite assurances from BC Hydro.'
- 'Mr. Scouras, can you clarify how the California Energy Crisis impacted BC Hydro''s
power acquisition strategy?
Certainly. The California Energy Crisis significantly influenced the strategy.
Following the crisis, BC Hydro was compelled to secure a more reliable power supply,
which led to initiatives like the 2002 Customer-Based Generation Call for Power,
as outlined in BCUC Order G-38-01 and the 2002 Energy Plan.
And how did these efforts evolve by the time of the 2007 Energy Plan?
The 2007 Energy Plan introduced the Bioenergy Strategy and the Bioenergy Call
for Power – Phase I. This was part of a move towards sustainable energy sources,
leveraging biomass projects such as the Celgar Mill''s Biomass Realization Project.
Speaking of Celgar, what was the structure of their agreement with BC Hydro?
Celgar entered a 2009 Energy Purchase Agreement with BC Hydro, complemented by
a Side Letter Agreement. This arrangement included specific provisions for seller-consumed
eligible electricity, a key component in their integration with BC Hydro’s power
acquisition framework.'
- 'Is it true that Celgar has restrictions when it comes to selling its self-generated
electricity below its load?
Yes, that''s correct. According to BCUC Order G-48-09, Celgar is prevented from
obtaining energy from FortisBC while selling self-generated electricity below
its load.
Can you clarify how the agreements with BC Hydro affect Celgar''s ability to sell
electricity?
The GBL-related provisions in Celgar’s 2009 EPA with BC Hydro preclude the mill
from selling energy below its 2007 load to any third party. Essentially, this
strands Celgar’s below-GBL self-generated electricity, requiring them to self-supply
the first 349 GWh/year of its own load.
Was there any legal obligation imposed by the 1991 Ministerial Order that affected
this arrangement?
No, there was no ongoing legal obligation from the 1991 Ministerial Order for
Celgar to self-supply or restrict its electricity sales based on that order.'
- source_sentence: '7.74 Mr Merwin (of Celgar) proclaimed Order G-188-1 to be a “major
victory” at the time in his memorandum of 7 December 2011 to Mercer’s Board of
Directors.288 He stated that the BCUC had confirmed that “Celgar is able to buy
all of its power requirements from FortisBC and free to sell the output of all
of its generation to third parties.”289 7.75 This interpretation of Order G-188-1
was confirmed by the BCUC in its subsequent Decision of 27 December 2012 accompanying
Order G-202-12. It summarised the entitlements of customers of FortisBC: “[The]
entitlement to non-BC Hydro PPA embedded cost power by a self-generating customer
may be as high as 100 percent of load as nominated by that customer”.290 (H) The
Tribunal’s Analysis on BCUC Order G-48-09 7.76 In the Tribunal’s view, on the
evidence before it, the Claimant falls short of establishing that BCUC Order G-48-09
or any associated aspect of the BCUC’s regulatory regime breaches the customary
international law standard of treatment under NAFTA Article 1105(1), as explained
in the NAFTA award in Merill & Ring v Canada. The Claimant has not established
irrationality, injustice, arbitrariness, or a violation of due process within
the meaning of the customary international law standard. 7.77 As to transparency,
it suffices to cite the Cargill Award cited above, in which the tribunal decided
that the customary international law standard had not yet been shown to embrace
a claim to transparency.291 The Tribunal also notes that the tribunal in Merill
& Ring decided that transparency was not part of the customary international law
standard.292 In any event, even if applicable, the Tribunal would not be inclined
to decide that the Claimant’s case reaches the threshold for non-transparency.
288 Memorandum from Management to Mercer International Board of Directors, Re
Update on Celgar’s Generator Baseline Issue of 7 December 2011, p. 1 [R-531] (emphasis
in the original). 289 Id. 290 BCUC Decision and Order No. G-202-12 of December
27, 2012 [R-265], p. 3. 291 Cargill v. Mexico, ibid, Paragraphs 290 and 294. 292
Merill & Ring v Canada, ibid, Paragraph 208.'
sentences:
- 'Mr. Merwin, could you explain the impact of Order G-188-1 on Celgar''s operations?
Certainly. Order G-188-1 was indeed a significant development for Celgar. It allowed
us to purchase all our power needs from FortisBC while being free to sell the
entirety of our generated power to third parties. This was confirmed by the BCUC''s
decision later in 2012.
And how did these regulatory changes align with your steam savings and energy
projects?
Well, at that time, we were already pursuing projects to improve steam utilization
and energy production. We identified multiple PINCH projects to enhance efficiency
and planned a retrofit of our power boiler to generate more steam. The changes
allowed us to leverage surplus steam for our Green Energy Project, aiming to install
a 48 MW condensing turbine.
Did these projects have any influence on the discussions with BC Hydro regarding
Celgar’s GBL settings?
Yes, they did. There were some concerns from our side regarding the years used
to set Celgar''s GBL. We preferred that BC Hydro considered the years post-2005,
reflecting higher pulp production and the efficiencies we had achieved through
projects like Blue Goose, which influenced our 2007 operations.'
- 'Mr. Jones, could you clarify the nature of the Ministry''s involvement with the
rules governing self-generation for FortisBC’s service area?
Certainly. The Ministry decided to monitor the BCUC proceedings on FortisBC''s
compliance filing but did not take an active role. They did not intervene in these
proceedings.
Are you saying there was no intervention despite concerns about self-generation
rules?
That''s correct. Although there were discussions, the Ministry''s involvement
did not go beyond consultations and offering informal feedback.
And regarding Celgar''s dealings with BC Hydro, were the GBL methodologies uniformly
applied?
While the process was meant to be consistent, BC Hydro''s methodology varied slightly
for Celgar due to unique circumstances not present with other mills like Tolko.
So, you''re saying Tolko''s situation was not comparable?
Yes, each mill’s situations were distinct due to operational differences, and
Celgar''s treatment was unique to its operational needs, which were unlike Tolko''s.'
- 'Mr. Doe, can you explain the purpose of the GBL assigned to Celgar in the EPA
with BC Hydro?
Certainly. The GBL was set to ensure Celgar could not sell electricity to third
parties at prices below market rates, which aligns with BC Hydro''s procurement
strategy to secure low-cost power.
Isn''t it true that this restriction on selling to third parties was more about
preventing arbitrage than aligning procurement?
No, the main goal was to control market prices through BC Hydro''s procurement
process, preventing excess low-cost electricity from flooding the market.
But wasn''t this restriction actually imposed to protect ratepayers due to concerns
over increased embedded cost power from BC Hydro customers like Celgar?
The primary focus was always to prevent market destabilization, rather than just
ratepayer protection. BC Hydro wanted to manage their supply efficiently.'
- source_sentence: Clause (ii) explicitly required BC Hydro to treat as incremental
and eligible for procurement “existing” generation from already “installed capacity”
that “has been sold to third parties.” When asked why electricity Celgar had been
selling to Northpoint and FortisBC under existing and terminable contracts did
not qualify as “incremental generation” under the very terms of Addendum 8, Mr.
Dyck responded that Addendum 8 “is not my document. This is Power Acquisition’s
document.”17 Mr. Dyck thus understood that his task encompassed more than just
power acquisition. He then stated that, for Celgar, he followed his own “interpretation,”
one of “determining what was incremental to what had been generated.”18 This interpretation,
of course, flatly is inconsistent with Addendum 8, which specifically defined
“what had been generated” as eligible, incremental power as long as it had been
sold to third-parties and not used for self-supply. Canada cannot claim that Celgar’s
GBL-based sales prohibition is purely procurement-related when it departs from
BC Hydro’s own procurement specifications. 11. Too, Canada’s contention that the
prohibition on below-GBL sales to third-parties is procurement-related because
it is necessary to assure BC Hydro “security of supply” is fatuous. BC Hydro’s
Mr. Scouras claimed that, without the provision, a proponent could elect to sell
electricity promised to BC Hydro to a third-party instead.19 But Celgar’s promise
to supply 238 GWh/yr of firm electricity to BC Hydro already effectively precludes
it from selling that electricity to a third-party, as 16 R-121, BC Hydro Bioenergy
Call for Power (Phase 10 Addendum 8 (7 May 2008), p. 4, § 8 (emphasis added).
See also Scouras First Witness Statement, ¶ 44 (explaining that the “Existing
Contract” language meant that the existing contract could lawfully be terminated
prior to the Commercial Operation Date in the EPA.). 17 L. Dyck, Tr. 1487:13-14.
18 L. Dyck, Tr. 1490:3-4. 19 Scouras Second Witness Statement, ¶ 8; Rejoinder,
¶ 215. - 6 -
sentences:
- 'Mr. Stockard, can you confirm the baseline year used by BC Hydro for Celgar’s
generation baseline?
Yes, the baseline year used was 2007, which BC Hydro established to address procurement
policies and incentivize new generation.
But isn''t it true that a 2006 baseline would have been more consistent with previous
orders, like Order G-38-01?
I don''t believe so. The use of a 2007 baseline accurately reflected the conditions
in line with BC policies at that time.
Isn’t Celgar restricted from selling below its generation baseline, even to third
parties?
Actually, Celgar isn’t imposed with such restrictions under the EPA. The terms
are more aligned with BC Hydro’s procurement scope.
According to documentation, the GBL expressly limits below-GBL sales to third
parties, doesn’t it?
That''s not my understanding. The GBL provisions were purely for aligning purchase
commitments with Celgar’s production capabilities.'
- 'Mr. Merwin, can you clarify your understanding of the term ''normal operations''
as it pertains to the agreements you had with BC Hydro?
Certainly. At the time, I understood ''normal operations'' to mean what our usual
electricity production levels were, with some flexibility for unforeseen changes.
We believed this would be adjusted in our agreements accordingly.
According to Mr. Dyck, there was no confusion on your end regarding ''normal operations'',
yet you are claiming otherwise. Can you explain this discrepancy?
I recall there was definitely some confusion on our side. We asked for further
clarification on several occasions, but the responses were vague. It''s possible
Mr. Dyck might not remember all his conversations accurately.
And when it comes to the GBL set during the 2009 EPA, would you say BC Hydro overstepped
by imposing a self-supply obligation on Celgar?
Not exactly. The self-supply obligation was something we expected as part of our
arrangement with BC Hydro. It was standard procedure, and we were fully prepared
to adhere to it.'
- 'Mr. Dyck, during the negotiations for Celgar''s agreement with BC Hydro, was
there any discussion about selling power to third parties before the agreement
was finalized?
Yes, there were discussions about the possibility, but the agreement ultimately
allowed Celgar to sell all its existing capacity to third parties.
Are you saying the agreement did not restrict below-GBL sales to third parties?
That''s correct. The final agreement did not impose any such restrictions. It
focused primarily on ensuring Celgar''s supply commitments to BC Hydro.
And what about the changes made in November 2008 regarding those sales provisions?
Are you aware of any alterations affecting third-party agreements?
To my knowledge, the November 2008 adjustments did not impact our ability to sell
to third parties under the GBL.
Just to clarify, are you stating that there was no modification that introduced
a restriction on below-GBL sales?
Correct, there was no such modification in the agreement.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: RAG legal-BERT CEAT
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.09090909090909091
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.22727272727272727
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.2727272727272727
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3409090909090909
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.09090909090909091
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07575757575757576
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05454545454545456
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03409090909090909
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09090909090909091
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22727272727272727
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2727272727272727
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3409090909090909
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21022357016371263
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1689183501683502
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17951884296669934
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.07575757575757576
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.2196969696969697
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.25757575757575757
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3409090909090909
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07575757575757576
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07323232323232322
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05151515151515152
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03409090909090909
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07575757575757576
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2196969696969697
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.25757575757575757
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3409090909090909
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1991932843140744
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.15463864838864838
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16490658649101975
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.08333333333333333
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.21212121212121213
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.25757575757575757
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3409090909090909
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08333333333333333
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0707070707070707
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.05151515151515152
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03409090909090909
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08333333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21212121212121213
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.25757575757575757
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3409090909090909
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20170243937575938
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.15850168350168348
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1701274080868296
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.08333333333333333
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.18181818181818182
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.24242424242424243
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3106060606060606
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08333333333333333
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.0606060606060606
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.048484848484848485
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03106060606060606
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08333333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.18181818181818182
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.24242424242424243
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3106060606060606
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.18512158083530794
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.14629629629629629
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.15809981777726995
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.05303030303030303
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.13636363636363635
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.18181818181818182
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.2727272727272727
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.05303030303030303
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.045454545454545456
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03636363636363637
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.02727272727272728
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05303030303030303
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.13636363636363635
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.18181818181818182
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2727272727272727
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1503390669056788
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.11273749398749398
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12617234669895142
name: Cosine Map@100
---
# RAG legal-BERT CEAT
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the json dataset. 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:** [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) <!-- at revision 15b570cbf88259610b082a167dacc190124f60f6 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** en
- **License:** apache-2.0
### 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': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("sirtobsi/ceat-fc-rag")
# Run inference
sentences = [
'Clause (ii) explicitly required BC Hydro to treat as incremental and eligible for procurement “existing” generation from already “installed capacity” that “has been sold to third parties.” When asked why electricity Celgar had been selling to Northpoint and FortisBC under existing and terminable contracts did not qualify as “incremental generation” under the very terms of Addendum 8, Mr. Dyck responded that Addendum 8 “is not my document. This is Power Acquisition’s document.”17 Mr. Dyck thus understood that his task encompassed more than just power acquisition. He then stated that, for Celgar, he followed his own “interpretation,” one of “determining what was incremental to what had been generated.”18 This interpretation, of course, flatly is inconsistent with Addendum 8, which specifically defined “what had been generated” as eligible, incremental power as long as it had been sold to third-parties and not used for self-supply. Canada cannot claim that Celgar’s GBL-based sales prohibition is purely procurement-related when it departs from BC Hydro’s own procurement specifications. 11. Too, Canada’s contention that the prohibition on below-GBL sales to third-parties is procurement-related because it is necessary to assure BC Hydro “security of supply” is fatuous. BC Hydro’s Mr. Scouras claimed that, without the provision, a proponent could elect to sell electricity promised to BC Hydro to a third-party instead.19 But Celgar’s promise to supply 238 GWh/yr of firm electricity to BC Hydro already effectively precludes it from selling that electricity to a third-party, as 16 R-121, BC Hydro Bioenergy Call for Power (Phase 10 Addendum 8 (7 May 2008), p. 4, § 8 (emphasis added). See also Scouras First Witness Statement, ¶ 44 (explaining that the “Existing Contract” language meant that the existing contract could lawfully be terminated prior to the Commercial Operation Date in the EPA.). 17 L. Dyck, Tr. 1487:13-14. 18 L. Dyck, Tr. 1490:3-4. 19 Scouras Second Witness Statement, ¶ 8; Rejoinder, ¶ 215. - 6 -',
"Mr. Dyck, during the negotiations for Celgar's agreement with BC Hydro, was there any discussion about selling power to third parties before the agreement was finalized?\nYes, there were discussions about the possibility, but the agreement ultimately allowed Celgar to sell all its existing capacity to third parties.\nAre you saying the agreement did not restrict below-GBL sales to third parties?\nThat's correct. The final agreement did not impose any such restrictions. It focused primarily on ensuring Celgar's supply commitments to BC Hydro.\nAnd what about the changes made in November 2008 regarding those sales provisions? Are you aware of any alterations affecting third-party agreements?\nTo my knowledge, the November 2008 adjustments did not impact our ability to sell to third parties under the GBL.\nJust to clarify, are you stating that there was no modification that introduced a restriction on below-GBL sales?\nCorrect, there was no such modification in the agreement.",
"Mr. Merwin, can you clarify your understanding of the term 'normal operations' as it pertains to the agreements you had with BC Hydro?\nCertainly. At the time, I understood 'normal operations' to mean what our usual electricity production levels were, with some flexibility for unforeseen changes. We believed this would be adjusted in our agreements accordingly.\nAccording to Mr. Dyck, there was no confusion on your end regarding 'normal operations', yet you are claiming otherwise. Can you explain this discrepancy?\nI recall there was definitely some confusion on our side. We asked for further clarification on several occasions, but the responses were vague. It's possible Mr. Dyck might not remember all his conversations accurately.\nAnd when it comes to the GBL set during the 2009 EPA, would you say BC Hydro overstepped by imposing a self-supply obligation on Celgar?\nNot exactly. The self-supply obligation was something we expected as part of our arrangement with BC Hydro. It was standard procedure, and we were fully prepared to adhere to it.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9303, 0.9251],
# [0.9303, 1.0000, 0.9489],
# [0.9251, 0.9489, 1.0000]])
```
<!--
### 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.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 768
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0909 |
| cosine_accuracy@3 | 0.2273 |
| cosine_accuracy@5 | 0.2727 |
| cosine_accuracy@10 | 0.3409 |
| cosine_precision@1 | 0.0909 |
| cosine_precision@3 | 0.0758 |
| cosine_precision@5 | 0.0545 |
| cosine_precision@10 | 0.0341 |
| cosine_recall@1 | 0.0909 |
| cosine_recall@3 | 0.2273 |
| cosine_recall@5 | 0.2727 |
| cosine_recall@10 | 0.3409 |
| **cosine_ndcg@10** | **0.2102** |
| cosine_mrr@10 | 0.1689 |
| cosine_map@100 | 0.1795 |
#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 512
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0758 |
| cosine_accuracy@3 | 0.2197 |
| cosine_accuracy@5 | 0.2576 |
| cosine_accuracy@10 | 0.3409 |
| cosine_precision@1 | 0.0758 |
| cosine_precision@3 | 0.0732 |
| cosine_precision@5 | 0.0515 |
| cosine_precision@10 | 0.0341 |
| cosine_recall@1 | 0.0758 |
| cosine_recall@3 | 0.2197 |
| cosine_recall@5 | 0.2576 |
| cosine_recall@10 | 0.3409 |
| **cosine_ndcg@10** | **0.1992** |
| cosine_mrr@10 | 0.1546 |
| cosine_map@100 | 0.1649 |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0833 |
| cosine_accuracy@3 | 0.2121 |
| cosine_accuracy@5 | 0.2576 |
| cosine_accuracy@10 | 0.3409 |
| cosine_precision@1 | 0.0833 |
| cosine_precision@3 | 0.0707 |
| cosine_precision@5 | 0.0515 |
| cosine_precision@10 | 0.0341 |
| cosine_recall@1 | 0.0833 |
| cosine_recall@3 | 0.2121 |
| cosine_recall@5 | 0.2576 |
| cosine_recall@10 | 0.3409 |
| **cosine_ndcg@10** | **0.2017** |
| cosine_mrr@10 | 0.1585 |
| cosine_map@100 | 0.1701 |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0833 |
| cosine_accuracy@3 | 0.1818 |
| cosine_accuracy@5 | 0.2424 |
| cosine_accuracy@10 | 0.3106 |
| cosine_precision@1 | 0.0833 |
| cosine_precision@3 | 0.0606 |
| cosine_precision@5 | 0.0485 |
| cosine_precision@10 | 0.0311 |
| cosine_recall@1 | 0.0833 |
| cosine_recall@3 | 0.1818 |
| cosine_recall@5 | 0.2424 |
| cosine_recall@10 | 0.3106 |
| **cosine_ndcg@10** | **0.1851** |
| cosine_mrr@10 | 0.1463 |
| cosine_map@100 | 0.1581 |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.053 |
| cosine_accuracy@3 | 0.1364 |
| cosine_accuracy@5 | 0.1818 |
| cosine_accuracy@10 | 0.2727 |
| cosine_precision@1 | 0.053 |
| cosine_precision@3 | 0.0455 |
| cosine_precision@5 | 0.0364 |
| cosine_precision@10 | 0.0273 |
| cosine_recall@1 | 0.053 |
| cosine_recall@3 | 0.1364 |
| cosine_recall@5 | 0.1818 |
| cosine_recall@10 | 0.2727 |
| **cosine_ndcg@10** | **0.1503** |
| cosine_mrr@10 | 0.1127 |
| cosine_map@100 | 0.1262 |
<!--
## 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
### Training Dataset
#### json
* Dataset: json
* Size: 1,179 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 85 tokens</li><li>mean: 433.39 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 117 tokens</li><li>mean: 221.02 tokens</li><li>max: 378 tokens</li></ul> |
* Samples:
| positive | anchor |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>COD on the 2009 EPA. Tembec and BC Hydro signed a new ESA on December 7, 2009 and the mill reached COD on the 2009 EPA in November 2009. While the mill had met other commercial and technical requirements by the time the EPA was signed in August 2009, the delayed COD was the result of a new BC court decision requiring BC Hydro and/or proponents of projects similar to Skookumchuck’s to demonstrate adequate consultation of all First Nations who may have interests in the areas of operations. BC Hydro required such evidence in order to support its filing of the EPA before the BCUC under Section 71 of the BC Utilities Commission Act. The delay in COD 57. Mr. Switlishoff describes Tembec’s 2009 EPA with BC Hydro as a To support his assertion, he points to the fact that Mr. Switlishoff ignores the reasons for this 22</code> | <code>Can you clarify the role of the BC court decision in the delay of the mill's Commercial Operation Date in 2009?<br>Certainly. The delay was due to a new BC court decision that required adequate consultation with all First Nations with potential interests in the area. This was necessary for BC Hydro to support the EPA filing before the BCUC.<br>And what steps were involved in meeting the requirements outlined by that decision?<br>BC Hydro, along with project proponents like Tembec, had to demonstrate that they had consulted with First Nations. This was essential to comply with Section 71 of the BC Utilities Commission Act.<br>Regarding the Generation Baseline Level or GBL, how was this concept applied in the context of new generation projects?<br>The GBL was determined using historical generation data from existing generators. New generation projects and incremental self-generation were eligible, but the GBL served as a reference point to measure incremental generation for sale. Submissions were requi...</code> |
| <code>it even constitutes a well-defined, objective standard capable of being consistently applied without discretion. The answer plainly is no. Indeed, it bears none of the indicia of an objective standard. (i) The Standard Did Not Exist In Writing At Any Relevant Time 263. The first problem is that the “current normal” was not written down anywhere at the time BC Hydro purports to have applied it, and, as demonstrated in the preceding section, has been described by BC Hydro differently at different times. Canada begins its consistent methodology argument by simply asserting a standard, without identifying any source.304 The Counter-Memorial simply references Mr. Dyck’s testimony, which, at paragraphs 44 through 46, likewise describes a standard without reference to any source. 264. The standard Mr. Dyck propounds in his testimony for this proceeding exists there and not in any contemporaneous document in existence at the time BC Hydro and the BCUC made any of the GBL determinations at issu...</code> | <code>Mr. Smith, could you clarify the basis on which the BCUC assessed the harm to BC Hydro ratepayers in the 2009 order?<br>Certainly. The BCUC assessed the harm at approximately C$20 million per year, based on the submissions from BC Hydro and estimates from their staff.<br>But isn't it true that BC Hydro's initial assessment was C$16.7 million and the BCUC staff estimated C$12.3 million?<br>I believe there were discussions of higher impacts at some point, possibly in internal analyses. But the fundamental concern was the potential for unjust enrichment through arbitrage.<br>And regarding the GBLs, you mentioned in your testimony that Tembec provided evidence to support their claim for a GBL adjustment, correct?<br>Yes, Tembec had detailed internal documents substantiating their generation and consumption patterns, which were taken into account by BC Hydro.</code> |
| <code>electricity supply. The self-sufficiency policy also required BC Hydro to acquire an additional 3,000 GWh of “insurance” energy (i.e., beyond what was required to meet customers’ demand) by the year 2026. 78. The self-sufficiency requirement opened up opportunities for the private sector to sell clean and renewable energy to BC Hydro through a variety of competitive processes, including two Bioenergy Calls for Power. While in practice BC Hydro (through its trading arm, Powerex) continued both to import and to export electricity, it also conducted a series of acquisition processes to purchase the rights to electricity in BC to meet the self-sufficiency requirement because it could no longer rely on the spot market to meet electricity demand (as it had under previous planning assumptions that allowed for a “market allowance” during low water years). 79. Long term contracts with IPPs and industrial self-generators put upward pressure on BC Hydro’s electricity rates, as the cost of new sup...</code> | <code>Mr. Thompson, can you clarify BC Hydro’s policy on electricity self-sufficiency?<br>Certainly. BC Hydro had a policy that aimed for full self-sufficiency by 2026, including an extra 3,000 GWh as a buffer.<br>And did this policy affect the structuring of contracts with independent power producers?<br>Yes, the policy led to numerous long-term contracts with IPPs, which did indeed raise the average rates slightly because these new suppliers charged a bit more than BC Hydro's own resources.<br>Is it correct that Powerex, BC Hydro’s trading arm, was restricted from engaging in certain trades due to this policy?<br>That's right, Powerex focused primarily on international markets since domestic trading was limited to maintain self-sufficiency.<br>And what about the role of the Ministry of Energy and Mines in overseeing these strategic decisions?<br>The Ministry did oversee the major strategic directions, but they allowed considerable autonomy for BC Hydro and Powerex in terms of operational decisions.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `tf32`: False
- `load_best_model_at_end`: True
- `dataloader_pin_memory`: False
- `gradient_checkpointing`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 128
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: False
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: True
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8678 | 2 | 0.1660 | 0.1608 | 0.1488 | 0.1316 | 0.1352 |
| 1.7356 | 4 | 0.1961 | 0.1904 | 0.1859 | 0.1645 | 0.1545 |
| 2.6034 | 6 | 0.2084 | 0.1979 | 0.1975 | 0.1817 | 0.1585 |
| **3.4712** | **8** | **0.2102** | **0.1992** | **0.2017** | **0.1851** | **0.1503** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.9
- Sentence Transformers: 5.1.0
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 1.7.0
- Datasets: 4.0.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## 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.*
-->
|
AnonymousCS/populism_classifier_bsample_118
|
AnonymousCS
| 2025-08-28T18:32:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-28T18:28:34Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_118
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_bsample_118
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3835
- Accuracy: 0.8941
- 1-f1: 0.3224
- 1-recall: 0.9423
- 1-precision: 0.1944
- Balanced Acc: 0.9176
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2342 | 1.0 | 38 | 0.3748 | 0.8191 | 0.2212 | 0.9615 | 0.125 | 0.8884 |
| 0.3346 | 2.0 | 76 | 0.3659 | 0.8155 | 0.2246 | 1.0 | 0.1265 | 0.9052 |
| 0.4371 | 3.0 | 114 | 0.1916 | 0.8931 | 0.3158 | 0.9231 | 0.1905 | 0.9077 |
| 0.2013 | 4.0 | 152 | 0.2561 | 0.9224 | 0.3984 | 0.9615 | 0.2513 | 0.9414 |
| 0.4561 | 5.0 | 190 | 0.3835 | 0.8941 | 0.3224 | 0.9423 | 0.1944 | 0.9176 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
cwayneconnor/blockassist-bc-mute_loud_lynx_1756405578
|
cwayneconnor
| 2025-08-28T18:30:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute loud lynx",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T18:30:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute loud lynx
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
systbs/zarvan-checkpoints
|
systbs
| 2025-08-28T18:30:13Z | 348 | 0 | null |
[
"pytorch",
"license:apache-2.0",
"region:us"
] | null | 2025-08-22T05:56:32Z |
---
license: apache-2.0
---
|
vnhioer/blockassist-bc-powerful_endangered_lemur_1756403845
|
vnhioer
| 2025-08-28T17:58:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"powerful endangered lemur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T17:57:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- powerful endangered lemur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
avishekjana/llama-3_2V-11B-FineTuned-document-extractor
|
avishekjana
| 2025-08-28T17:53:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mllama",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-28T17:47:14Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** avishekjana
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
This mllama 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)
|
Fatoumataa/modele-traduction-bambara-francais-mono-cross-billingue2
|
Fatoumataa
| 2025-08-28T17:53:50Z | 60 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T07:21:54Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: modele-traduction-bambara-francais-mono-cross-billingue2
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. -->
# modele-traduction-bambara-francais-mono-cross-billingue2
This model was trained from scratch 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Muapi/neon-cyberpunk-datastream-fl-xl-il
|
Muapi
| 2025-08-28T17:53:36Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-28T17:53:19Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Neon Cyberpunk - Datastream [FL/XL/IL]
**Base model**: Flux.1 D
**Trained words**: mad-dtstrm
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:588233@729341", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/flux-citizen-spaceships-design-language
|
Muapi
| 2025-08-28T17:47:10Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-28T17:46:56Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux Citizen - Spaceships & Design Language
**Base model**: Flux.1 D
**Trained words**: Drake, Drake Interstellar
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:715315@902081", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
vnhioer/blockassist-bc-carnivorous_clawed_tuna_1756402992
|
vnhioer
| 2025-08-28T17:43:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"carnivorous clawed tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T17:43:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- carnivorous clawed tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gsjang/sw-ulizallama3-x-meta-llama-3-8b-instruct-sce-50_50
|
gsjang
| 2025-08-28T17:42:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"base_model:Jacaranda/UlizaLlama3",
"base_model:merge:Jacaranda/UlizaLlama3",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T17:39:33Z |
---
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
- Jacaranda/UlizaLlama3
library_name: transformers
tags:
- mergekit
- merge
---
# sw-ulizallama3-x-meta-llama-3-8b-instruct-sce-50_50
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [Jacaranda/UlizaLlama3](https://huggingface.co/Jacaranda/UlizaLlama3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: sce
models:
- model: Jacaranda/UlizaLlama3
parameters:
weight: 0.5
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
weight: 0.5
parameters: {}
dtype: bfloat16
tokenizer:
source: union
base_model: meta-llama/Meta-Llama-3-8B-Instruct
write_readme: README.md
```
|
Muapi/everythingisgalaxy-sdxl-flux-paseer
|
Muapi
| 2025-08-28T17:31:15Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-28T17:30:57Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# EveryThingIsGalaxy-SDXL/FLUX-PAseer
**Base model**: Flux.1 D
**Trained words**: ethereal neon silhouette art, spectral outline with tangible accessories, glowing spectral outline
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:337786@1048564", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/giger-2_0
|
Muapi
| 2025-08-28T17:30:48Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-28T17:30:35Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Giger 2_0
**Base model**: Flux.1 D
**Trained words**: g1g3r by giger
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:806516@901791", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
tiopuiter/blockassist-bc-roaring_flightless_ibis_1756401932
|
tiopuiter
| 2025-08-28T17:25:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring flightless ibis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T17:25:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring flightless ibis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tiopuiter/blockassist-bc-fanged_striped_shrimp_1756401794
|
tiopuiter
| 2025-08-28T17:23:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fanged striped shrimp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T17:23:14Z |
---
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).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1756401513
|
Elizavr
| 2025-08-28T17:19:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T17:19:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_bsample_094
|
AnonymousCS
| 2025-08-28T17:10:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-28T17:09:39Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_bsample_094
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# populism_classifier_bsample_094
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1885
- Accuracy: 0.7463
- 1-f1: 0.4138
- 1-recall: 0.96
- 1-precision: 0.2637
- Balanced Acc: 0.8421
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3505 | 1.0 | 8 | 0.9683 | 0.8097 | 0.4516 | 0.84 | 0.3088 | 0.8233 |
| 0.2061 | 2.0 | 16 | 1.8223 | 0.6493 | 0.3380 | 0.96 | 0.2051 | 0.7886 |
| 0.1014 | 3.0 | 24 | 1.1885 | 0.7463 | 0.4138 | 0.96 | 0.2637 | 0.8421 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Stasonelison/blockassist-bc-howling_powerful_aardvark_1756400947
|
Stasonelison
| 2025-08-28T17:09:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling powerful aardvark",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T17:09:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling powerful aardvark
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gsjang/sw-ulizallama3-x-meta-llama-3-8b-instruct-karcher-50_50
|
gsjang
| 2025-08-28T17:06:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Jacaranda/UlizaLlama3",
"base_model:merge:Jacaranda/UlizaLlama3",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-28T17:03:50Z |
---
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
- Jacaranda/UlizaLlama3
library_name: transformers
tags:
- mergekit
- merge
---
# sw-ulizallama3-x-meta-llama-3-8b-instruct-karcher-50_50
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Karcher Mean](https://en.wikipedia.org/wiki/Karcher_mean) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [Jacaranda/UlizaLlama3](https://huggingface.co/Jacaranda/UlizaLlama3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: karcher
models:
- model: Jacaranda/UlizaLlama3
parameters:
weight: 0.5
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
weight: 0.5
parameters:
t: 0.5
dtype: bfloat16
tokenizer:
source: union
base_model: meta-llama/Meta-Llama-3-8B-Instruct
write_readme: README.md
```
|
tiopuiter/blockassist-bc-subtle_fast_prawn_1756400435
|
tiopuiter
| 2025-08-28T17:00:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"subtle fast prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T17:00:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- subtle fast prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tiopuiter/blockassist-bc-rangy_mighty_hare_1756400324
|
tiopuiter
| 2025-08-28T16:58:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rangy mighty hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-28T16:58:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rangy mighty hare
---
# 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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