modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
pearsallyhai/blockassist
|
pearsallyhai
| 2025-09-25T06:02:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"solitary untamed butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:15:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- solitary untamed butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
reinaldoadamo57/blockassist
|
reinaldoadamo57
| 2025-09-25T06:02:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"galloping miniature warthog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:15:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- galloping miniature warthog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
daehan-everai/training_output
|
daehan-everai
| 2025-09-25T06:01:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T19:22:14Z |
---
base_model: openai/gpt-oss-120b
library_name: transformers
model_name: training_output
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for training_output
This model is a fine-tuned version of [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="daehan-everai/training_output", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lucas01/llmv3/runs/9grgr93o)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.8.0+cu128
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
MAK007/sdxl_seg_controlnet
|
MAK007
| 2025-09-25T06:01:09Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:SG161222/RealVisXL_V4.0",
"base_model:adapter:SG161222/RealVisXL_V4.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-09-22T16:31:43Z |
---
base_model: SG161222/RealVisXL_V4.0
library_name: diffusers
license: openrail++
inference: true
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# controlnet-MAK007/sdxl_seg_controlnet
These are controlnet weights trained on SG161222/RealVisXL_V4.0 with new type of conditioning.
You can find some example images below.
prompt: The image showcases a spacious and well-lit living room with a large window that allows natural light to flood the space. The room features a comfortable couch situated in the center, surrounded by several chairs placed around the room. A dining table is also present, accompanied by a few chairs.In addition to the seating arrangements, the living room is adorned with various decorative elements. There are multiple potted plants placed throughout the room, adding a touch of greenery and life to the space. A vase can be seen on a surface, and a bowl is placed nearby. A clock is mounted on the wall, and a book is resting on a surface, indicating that the room is not only for relaxation but also for reading and other leisure activities.
prompt: The image features a large bathroom with a double sink vanity. The vanity is made of wood and has a marble countertop. The sinks are positioned side by side, with one on the left and the other on the right. Above the sinks, there are two mirrors, reflecting the bathroom's interior. In addition to the sinks, there are two toothbrush holders, one on the left side and the other on the right side of the vanity. A towel is also visible, hanging on the wall above the sinks. The bathroom appears to be well-maintained and organized, providing a comfortable space for daily routines.
prompt: The image features a large, clean, and well-organized kitchen with white cabinets and a marble countertop. The kitchen is equipped with a sink, an oven, and a stove. There is a window in the kitchen, allowing natural light to enter the space. Various items can be seen throughout the kitchen, including a bowl, a vase, and a potted plant. There are also several bottles placed on the countertop, and a spoon is visible near the sink. The kitchen is well-stocked with essential items, making it a functional and inviting space.
prompt: The image features a bedroom with a large bed occupying a significant portion of the space. The bed is positioned against a wall, and there is a wooden door nearby. The room also contains a potted plant, which is placed close to the bed, adding a touch of greenery to the space. In addition to the bed and plant, there is a book on the left side of the room, possibly for reading or decoration. The room appears to be well-lit, creating a comfortable and inviting atmosphere.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
monkeyjulia0/blockassist
|
monkeyjulia0
| 2025-09-25T06:01:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"endangered jumping antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:55:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- endangered jumping antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lmassaron/gemma-3-4b-finsentiment
|
lmassaron
| 2025-09-25T06:00:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-text-to-text
| 2025-09-24T20:42:14Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DidulaThavishaPro/base_test_4_sft_16bit_vllm
|
DidulaThavishaPro
| 2025-09-25T06:00:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-24T17:31:55Z |
---
base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** DidulaThavishaPro
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
stynimshon/blockassist
|
stynimshon
| 2025-09-25T06:00:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"agile territorial elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:14:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- agile territorial elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ericshanna1/blockassist
|
ericshanna1
| 2025-09-25T06:00:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sneaky beaked salmon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:53:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sneaky beaked salmon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jfj033631/blockassist
|
jfj033631
| 2025-09-25T06:00:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby scampering mantis",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-20T11:49:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby scampering mantis
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sushkrnishad/finetuning-sentiment-model-3000-samples
|
Sushkrnishad
| 2025-09-25T06:00:25Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-25T05:47:51Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3538
- Accuracy: 0.8833
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
liaoleonel89/blockassist
|
liaoleonel89
| 2025-09-25T05:59:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hibernating nocturnal chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:13:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hibernating nocturnal chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hardyespositotw/blockassist
|
hardyespositotw
| 2025-09-25T05:59:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bipedal vicious bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T08:03:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bipedal vicious bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vassar054/blockassist
|
vassar054
| 2025-09-25T05:59:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"burrowing wild clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:30:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- burrowing wild clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
waynekayn/blockassist
|
waynekayn
| 2025-09-25T05:59:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bellowing gentle starfish",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T17:08:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bellowing gentle starfish
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DhanaR1212/finetuning-sentiment-model-3000-samples
|
DhanaR1212
| 2025-09-25T05:59:43Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-25T05:47:57Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3500
- Accuracy: 0.86
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
waughc080/blockassist
|
waughc080
| 2025-09-25T05:59:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored prickly caribou",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:12:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored prickly caribou
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shubh-03/finetuning-sentiment-model-3000-samples
|
Shubh-03
| 2025-09-25T05:59:10Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-25T05:47:14Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3371
- Accuracy: 0.8767
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
ashleyvandemicarlos/blockassist
|
ashleyvandemicarlos
| 2025-09-25T05:59:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold keen pig",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:47:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold keen pig
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SKIML-ICL/oursNQ-QA-QUC-Meta-Llama-3-8B-Instruct-QA-QUC-0Q-0U-0C-random-lora
|
SKIML-ICL
| 2025-09-25T05:58:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-09-25T04:49:48Z |
---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
model_name: oursNQ-QA-QUC-Meta-Llama-3-8B-Instruct-QA-QUC-0Q-0U-0C-random-lora
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for oursNQ-QA-QUC-Meta-Llama-3-8B-Instruct-QA-QUC-0Q-0U-0C-random-lora
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SKIML-ICL/oursNQ-QA-QUC-Meta-Llama-3-8B-Instruct-QA-QUC-0Q-0U-0C-random-lora", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/Enhancing-ICL/train-LLM%7Cin-context-QA/runs/lxaqot85)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.2
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.22.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
varneyc352/blockassist
|
varneyc352
| 2025-09-25T05:58:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"melodic bipedal mongoose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:11:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- melodic bipedal mongoose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alexshannawvnt178/blockassist
|
alexshannawvnt178
| 2025-09-25T05:57:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"peckish sniffing pigeon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:44:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- peckish sniffing pigeon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ss2972583/blockassist
|
ss2972583
| 2025-09-25T05:57:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grunting lively cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:27:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grunting lively cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sujith23/PPO-LunarLander-v2
|
Sujith23
| 2025-09-25T05:56:42Z | 10 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-09-17T13:02:41Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 251.92 +/- 16.45
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mazesmazes/tiny-audio
|
mazesmazes
| 2025-09-25T05:56:37Z | 36 | 0 | null |
[
"safetensors",
"asr_model",
"automatic-speech-recognition",
"custom_code",
"en",
"dataset:mozilla-foundation/common_voice_17_0",
"dataset:speechcolab/gigaspeech",
"dataset:openslr/librispeech_asr",
"base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2025-09-23T16:27:59Z |
---
license: mit
datasets:
- mozilla-foundation/common_voice_17_0
- speechcolab/gigaspeech
- openslr/librispeech_asr
language:
- en
metrics:
- wer
base_model:
- openai/whisper-small
- HuggingFaceTB/SmolLM2-360M-Instruct
pipeline_tag: automatic-speech-recognition
---
# Tiny Audio - Whisper-SmolLM2 ASR Model
A lightweight ASR model combining Whisper-small encoder with SmolLM2 decoder, trained with LoRA for parameter-efficient fine-tuning (~300 lines of code).
## Quick Start
```python
from transformers import AutoModelForSpeechSeq2Seq, pipeline
# Load model
model = AutoModelForSpeechSeq2Seq.from_pretrained(
"mazesmazes/tiny-audio",
trust_remote_code=True
)
# Create pipeline and transcribe
asr = pipeline("automatic-speech-recognition", model=model)
result = asr("audio.wav")
print(result["text"])
```
## Architecture
- **Encoder**: Frozen Whisper-small (39M params)
- **Decoder**: SmolLM2-360M with LoRA adapters (7.3M trainable params)
- **Total**: ~400M parameters, only 2% trained
## Training
- **Data**: LibriSpeech, GigaSpeech, Common Voice (English)
- **Method**: LoRA rank 32-64, BF16 mixed precision
- **Performance**: ~15-20% WER on LibriSpeech test-clean
## Links
- **Code**: [github.com/alexkroman/tiny-audio](https://github.com/alexkroman/tiny-audio)
- **Demo**: `demo/app.py` in repository
- **License**: MIT
|
jfdh0843/blockassist
|
jfdh0843
| 2025-09-25T05:56:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"elusive hardy antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-20T11:42:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- elusive hardy antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF
|
mradermacher
| 2025-09-25T05:56:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"programming",
"code generation",
"code",
"coding",
"coder",
"chat",
"brainstorm",
"qwen",
"qwen3",
"qwencoder",
"brainstorm 20x",
"creative",
"all uses cases",
"Jan-V1",
"horror",
"science fiction",
"fantasy",
"Star Trek",
"Star Trek Original",
"Star Trek The Next Generation",
"Star Trek Deep Space Nine",
"Star Trek Voyager",
"Star Trek Enterprise",
"Star Trek Discovery.",
"finetune",
"thinking",
"reasoning",
"unsloth",
"6x6B",
"moe",
"mixture of experts",
"en",
"dataset:DavidAU/horror-nightmare1",
"dataset:DavidAU/ST-Org",
"dataset:DavidAU/ST-TNG",
"dataset:DavidAU/ST-DS9",
"dataset:DavidAU/ST-VOY",
"dataset:DavidAU/ST-ENT",
"dataset:DavidAU/ST-DIS",
"base_model:DavidAU/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B",
"base_model:quantized:DavidAU/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-24T11:31:12Z |
---
base_model: DavidAU/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B
datasets:
- DavidAU/horror-nightmare1
- DavidAU/ST-Org
- DavidAU/ST-TNG
- DavidAU/ST-DS9
- DavidAU/ST-VOY
- DavidAU/ST-ENT
- DavidAU/ST-DIS
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- programming
- code generation
- code
- coding
- coder
- chat
- code
- chat
- brainstorm
- qwen
- qwen3
- qwencoder
- brainstorm 20x
- creative
- all uses cases
- Jan-V1
- horror
- science fiction
- fantasy
- Star Trek
- Star Trek Original
- Star Trek The Next Generation
- Star Trek Deep Space Nine
- Star Trek Voyager
- Star Trek Enterprise
- Star Trek Discovery.
- finetune
- thinking
- reasoning
- unsloth
- 6x6B
- moe
- mixture of experts
- finetune
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/DavidAU/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q2_K.gguf) | Q2_K | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q3_K_S.gguf) | Q3_K_S | 11.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q3_K_M.gguf) | Q3_K_M | 13.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q3_K_L.gguf) | Q3_K_L | 14.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.IQ4_XS.gguf) | IQ4_XS | 14.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q4_K_S.gguf) | Q4_K_S | 15.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q4_K_M.gguf) | Q4_K_M | 16.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q5_K_S.gguf) | Q5_K_S | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q5_K_M.gguf) | Q5_K_M | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q6_K.gguf) | Q6_K | 22.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B-GGUF/resolve/main/Qwen3-MOE-6x6B-Star-Trek-Universe-Alpha-D-256k-ctx-36B.Q8_0.gguf) | Q8_0 | 28.7 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
xyzasdfghjkl123456/VideoJudge-3B
|
xyzasdfghjkl123456
| 2025-09-25T05:55:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-25T05:55:12Z |
---
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]
|
tliomh002/blockassist
|
tliomh002
| 2025-09-25T05:55:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold mimic raccoon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:09:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold mimic raccoon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
DidulaThavishaPro/base_test_4_sft_8bit_vllm
|
DidulaThavishaPro
| 2025-09-25T05:55:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-24T17:21:24Z |
---
base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** DidulaThavishaPro
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
milindk0404/finetuning-sentiment-model-3000-samples
|
milindk0404
| 2025-09-25T05:55:13Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-25T05:42:53Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4548
- Accuracy: 0.8633
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
dgvd28533/blockassist
|
dgvd28533
| 2025-09-25T05:54:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"freckled monstrous ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-20T11:33:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- freckled monstrous ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
doomzillwillie/blockassist
|
doomzillwillie
| 2025-09-25T05:54:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"foxy twitchy horse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:31:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- foxy twitchy horse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
singaron34/blockassist
|
singaron34
| 2025-09-25T05:54:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fast sleek seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:24:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fast sleek seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
janealexisbrandy/blockassist
|
janealexisbrandy
| 2025-09-25T05:53:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"screeching camouflaged lobster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:30:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- screeching camouflaged lobster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
corzamennav/blockassist-bc-territorial_wild_antelope_1758779538
|
corzamennav
| 2025-09-25T05:53:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial wild antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-25T05:53:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial wild antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
baldo3615/blockassist
|
baldo3615
| 2025-09-25T05:52:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fierce armored albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:07:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fierce armored albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rinm74863/blockassist
|
rinm74863
| 2025-09-25T05:52:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"frisky placid seal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:23:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- frisky placid seal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
brockettye/blockassist
|
brockettye
| 2025-09-25T05:51:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dextrous running goose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:06:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dextrous running goose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
crsunnips86/blockassist
|
crsunnips86
| 2025-09-25T05:51:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"short vocal ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:23:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- short vocal ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
scottodomingo85/blockassist
|
scottodomingo85
| 2025-09-25T05:51:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rabid lively mongoose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:06:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rabid lively mongoose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
runaligsa463/blockassist
|
runaligsa463
| 2025-09-25T05:50:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"prowling regal bat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:22:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- prowling regal bat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Enkhmanlai/mmbert-xnli
|
Enkhmanlai
| 2025-09-25T05:50:37Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:jhu-clsp/mmBERT-small",
"base_model:finetune:jhu-clsp/mmBERT-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-25T05:50:29Z |
---
library_name: transformers
license: mit
base_model: jhu-clsp/mmBERT-small
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: mmbert-xnli
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. -->
# mmbert-xnli
This model is a fine-tuned version of [jhu-clsp/mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3736
- Accuracy: 0.8863
- F1: 0.8863
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 337 | 0.4502 | 0.8600 | 0.8587 |
| 0.6169 | 2.0 | 674 | 0.3983 | 0.8796 | 0.8786 |
| 0.2926 | 3.0 | 1011 | 0.3693 | 0.8904 | 0.8907 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
zacherysix660/blockassist
|
zacherysix660
| 2025-09-25T05:50:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"toothy gilded gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:05:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- toothy gilded gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
maribethalysenshontaitorrell/blockassist
|
maribethalysenshontaitorrell
| 2025-09-25T05:50:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leggy bipedal lizard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:18:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leggy bipedal lizard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xyzasdfghjkl123456/VideoJudgeR-3B
|
xyzasdfghjkl123456
| 2025-09-25T05:50:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-25T05:49:22Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bjdsau/blockassist
|
bjdsau
| 2025-09-25T05:50:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stinky lumbering butterfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:21:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stinky lumbering butterfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ywoneonj201/blockassist
|
ywoneonj201
| 2025-09-25T05:49:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft grassy penguin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:05:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft grassy penguin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cameroneltamadoerikqxc75/blockassist
|
cameroneltamadoerikqxc75
| 2025-09-25T05:49:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"soft giant macaw",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:18:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- soft giant macaw
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bibb56334/blockassist
|
bibb56334
| 2025-09-25T05:49:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vigilant yapping fly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:04:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vigilant yapping fly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jikerrums/blockassist
|
jikerrums
| 2025-09-25T05:49:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bold humming crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:21:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bold humming crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
inferencerlabs/deepseek-v3.1-Terminus-MLX-5.5bit
|
inferencerlabs
| 2025-09-25T05:48:41Z | 150 | 0 |
mlx
|
[
"mlx",
"safetensors",
"deepseek_v3",
"text-generation",
"conversational",
"custom_code",
"base_model:deepseek-ai/DeepSeek-V3.1-Terminus",
"base_model:quantized:deepseek-ai/DeepSeek-V3.1-Terminus",
"license:mit",
"5-bit",
"region:us"
] |
text-generation
| 2025-09-23T09:12:47Z |
---
license: mit
library_name: mlx
base_model: deepseek-ai/DeepSeek-V3.1-Terminus
tags:
- mlx
pipeline_tag: text-generation
---
** CURRENTLY UPLOADING **
**See DeepSeek-V3.1-Terminus 5.5bit MLX in action - [demonstration video coming soon](https://youtube.com/xcreate)**
*q5.5bit quant typically achieves 1.141 perplexity in our testing*
| Quantization | Perplexity |
|:------------:|:----------:|
| **q2.5** | 41.293 |
| **q3.5** | 1.900 |
| **q4.5** | 1.168 |
| **q5.5** | 1.141 |
| **q6.5** | 1.128 |
| **q8.5** | 1.128 |
## Usage Notes
* [Original DeepSeek V3.1](https://huggingface.co/inferencerlabs/deepseek-v3.1-MLX-5.5bit) performed better in our testing
* Runs on a single M3 Ultra 512GB RAM using [Inferencer app](https://inferencer.com)
* Memory usage: ~480 GB
* Expect ~13-19 tokens/s
* Quantized with a modified version of [MLX](https://github.com/ml-explore/mlx) 0.27
* For more details see [demonstration video coming soon](https://youtube.com/xcreate) or visit [DeepSeek-V3.1-Terminus](https://huggingface.co/deepseek-ai/DeepSeek-V3.1-Terminus).
## Disclaimer
We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.
|
ilorajahan8/blockassist
|
ilorajahan8
| 2025-09-25T05:48:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feathered exotic boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-20T11:56:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feathered exotic boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
moosclemente34/blockassist
|
moosclemente34
| 2025-09-25T05:48:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing grunting toucan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:13:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing grunting toucan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nt250170/blockassist
|
nt250170
| 2025-09-25T05:47:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"agile moist baboon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:19:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- agile moist baboon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sidhantoon/Koto6
|
sidhantoon
| 2025-09-25T05:47:47Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-25T05:41:23Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
tonikeuoj/blockassist
|
tonikeuoj
| 2025-09-25T05:47:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long graceful dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:02:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long graceful dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
meyintson3/blockassist
|
meyintson3
| 2025-09-25T05:47:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pensive lively armadillo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:12:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pensive lively armadillo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
folksz218/blockassist
|
folksz218
| 2025-09-25T05:46:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dextrous barky chicken",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T15:11:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dextrous barky chicken
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sidhantoon/Koto5
|
sidhantoon
| 2025-09-25T05:46:49Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-25T05:41:17Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
johngreendr1/d12f3bc9-5592-4f60-967b-fc98d4ca0ba1
|
johngreendr1
| 2025-09-25T05:46:44Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/gemma-2-2b-it",
"base_model:adapter:unsloth/gemma-2-2b-it",
"region:us"
] | null | 2025-09-25T04:26:44Z |
---
base_model: unsloth/gemma-2-2b-it
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
opete638/blockassist
|
opete638
| 2025-09-25T05:46:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rough noisy mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:02:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rough noisy mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alinearmun/blockassist
|
alinearmun
| 2025-09-25T05:46:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"long rugged ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:18:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- long rugged ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
thu136093/blockassist-bc-waddling_cunning_slug_1758778301
|
thu136093
| 2025-09-25T05:45:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"waddling cunning slug",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-25T05:45:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- waddling cunning slug
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
carrollg679/blockassist
|
carrollg679
| 2025-09-25T05:44:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sizable cunning narwhal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T04:00:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sizable cunning narwhal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AlexanderArtT/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog
|
AlexanderArtT
| 2025-09-25T05:44:29Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tiny nimble warthog",
"trl",
"genrl-swarm",
"I am tiny_nimble_warthog",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-13T22:11:38Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tiny nimble warthog
- trl
- genrl-swarm
- I am tiny_nimble_warthog
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AlexanderArtT/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tiny_nimble_warthog", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
rupatomes8/blockassist
|
rupatomes8
| 2025-09-25T05:44:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"eager loud porcupine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:17:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- eager loud porcupine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ottleyemory/blockassist
|
ottleyemory
| 2025-09-25T05:43:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hoarse lumbering sparrow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T07:56:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hoarse lumbering sparrow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
terrancekohler969/blockassist
|
terrancekohler969
| 2025-09-25T05:43:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"waddling lethal prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:59:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- waddling lethal prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
corzamennav/blockassist-bc-territorial_wild_antelope_1758778922
|
corzamennav
| 2025-09-25T05:43:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"territorial wild antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-25T05:43:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- territorial wild antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Novaciano/Qwen3-VL-1B-Merged-Q5_K_M-GGUF
|
Novaciano
| 2025-09-25T05:42:50Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"multimodal",
"vision-language",
"qwen3",
"qwen2.5-vl",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"en",
"base_model:ViFortune-AI/Qwen3-VL-1B-Merged",
"base_model:quantized:ViFortune-AI/Qwen3-VL-1B-Merged",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-09-25T05:42:43Z |
---
license_name: vifortune-research
license_link: https://huggingface.co/Qwen/Qwen3-VL-1B/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- vision-language
- qwen3
- qwen2.5-vl
- llama-cpp
- gguf-my-repo
library_name: transformers
base_model: ViFortune-AI/Qwen3-VL-1B-Merged
---
# Novaciano/Qwen3-VL-1B-Merged-Q5_K_M-GGUF
This model was converted to GGUF format from [`ViFortune-AI/Qwen3-VL-1B-Merged`](https://huggingface.co/ViFortune-AI/Qwen3-VL-1B-Merged) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ViFortune-AI/Qwen3-VL-1B-Merged) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q5_K_M-GGUF --hf-file qwen3-vl-1b-merged-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q5_K_M-GGUF --hf-file qwen3-vl-1b-merged-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q5_K_M-GGUF --hf-file qwen3-vl-1b-merged-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q5_K_M-GGUF --hf-file qwen3-vl-1b-merged-q5_k_m.gguf -c 2048
```
|
sandovalgregg08/blockassist
|
sandovalgregg08
| 2025-09-25T05:42:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sprightly twitchy donkey",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:57:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sprightly twitchy donkey
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
samanta-scratch/wav2vec2-large-mms-1b-dv
|
samanta-scratch
| 2025-09-25T05:42:19Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-25T05:42:18Z |
---
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]
|
WelcomeDarrell399/blockassist
|
WelcomeDarrell399
| 2025-09-25T05:42:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stocky pawing bear",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T16:52:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stocky pawing bear
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Novaciano/Qwen3-VL-1B-Merged-Q8_0-GGUF
|
Novaciano
| 2025-09-25T05:42:11Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"multimodal",
"vision-language",
"qwen3",
"qwen2.5-vl",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"en",
"base_model:ViFortune-AI/Qwen3-VL-1B-Merged",
"base_model:quantized:ViFortune-AI/Qwen3-VL-1B-Merged",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-09-25T05:42:04Z |
---
license_name: vifortune-research
license_link: https://huggingface.co/Qwen/Qwen3-VL-1B/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- vision-language
- qwen3
- qwen2.5-vl
- llama-cpp
- gguf-my-repo
library_name: transformers
base_model: ViFortune-AI/Qwen3-VL-1B-Merged
---
# Novaciano/Qwen3-VL-1B-Merged-Q8_0-GGUF
This model was converted to GGUF format from [`ViFortune-AI/Qwen3-VL-1B-Merged`](https://huggingface.co/ViFortune-AI/Qwen3-VL-1B-Merged) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ViFortune-AI/Qwen3-VL-1B-Merged) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q8_0-GGUF --hf-file qwen3-vl-1b-merged-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q8_0-GGUF --hf-file qwen3-vl-1b-merged-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q8_0-GGUF --hf-file qwen3-vl-1b-merged-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Novaciano/Qwen3-VL-1B-Merged-Q8_0-GGUF --hf-file qwen3-vl-1b-merged-q8_0.gguf -c 2048
```
|
gdg98574/blockassist
|
gdg98574
| 2025-09-25T05:42:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy zealous dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-20T11:30:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy zealous dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hzyitong45/blockassist
|
hzyitong45
| 2025-09-25T05:41:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored climbing seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T16:52:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored climbing seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
do0090022/blockassist
|
do0090022
| 2025-09-25T05:41:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sharp voracious sealion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:56:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sharp voracious sealion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Jax-dan/SmolVLM-0.6B-Cap
|
Jax-dan
| 2025-09-25T05:41:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"image-text-to-text",
"zh",
"en",
"dataset:lmms-lab/COCO-Caption2017",
"base_model:HuggingFaceTB/SmolVLM-256M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-256M-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-09-25T05:32:15Z |
---
license: apache-2.0
datasets:
- lmms-lab/COCO-Caption2017
language:
- zh
- en
metrics:
- bertscore
- bleu
- rouge
- meteor
base_model:
- HuggingFaceTB/SmolVLM-256M-Instruct
- Qwen/Qwen2.5-0.5B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
---
|
hzyitong44/blockassist
|
hzyitong44
| 2025-09-25T05:41:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"secretive scavenging rhino",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T16:51:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- secretive scavenging rhino
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hzyitong43/blockassist
|
hzyitong43
| 2025-09-25T05:40:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mighty polished armadillo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T16:51:11Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mighty polished armadillo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
feltonv60/blockassist
|
feltonv60
| 2025-09-25T05:40:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"moist knobby octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:55:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- moist knobby octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
fredericlaclair19/blockassist
|
fredericlaclair19
| 2025-09-25T05:39:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"feline jagged antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:55:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- feline jagged antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hzyitong35/blockassist
|
hzyitong35
| 2025-09-25T05:39:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious burrowing okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T16:50:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious burrowing okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bunrsmiker21/blockassist
|
bunrsmiker21
| 2025-09-25T05:39:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"omnivorous lightfooted gerbil",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:13:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- omnivorous lightfooted gerbil
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ganga4364/whisper-small-tibetan-wylie-checkpoint-4000
|
ganga4364
| 2025-09-25T05:39:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-09-25T05:39:01Z |
---
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]
|
hnewcomb637/blockassist
|
hnewcomb637
| 2025-09-25T05:39:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute flexible caterpillar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:54:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute flexible caterpillar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
keithmansell71/blockassist
|
keithmansell71
| 2025-09-25T05:38:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic shy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:53:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic shy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
funesnisren366/blockassist
|
funesnisren366
| 2025-09-25T05:38:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"beaked bristly nightingale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:13:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- beaked bristly nightingale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
runspumes/blockassist
|
runspumes
| 2025-09-25T05:38:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stalking savage spider",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:12:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stalking savage spider
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lsalaam568/blockassist
|
lsalaam568
| 2025-09-25T05:37:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"waddling feathered mallard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:53:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- waddling feathered mallard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
haihp02/8182b079-7371-4aba-a126-4f6af60546cf
|
haihp02
| 2025-09-25T05:37:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-25T03:24:02Z |
---
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]
|
hzyitong32/blockassist
|
hzyitong32
| 2025-09-25T05:37:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"skittish regal otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-23T16:48:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- skittish regal otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
xnvl/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rugged_slithering_termite
|
xnvl
| 2025-09-25T05:37:35Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am rugged_slithering_termite",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T04:55:30Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am rugged_slithering_termite
---
# 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
H5N1AIDS/Transcribe_and_Translate_Subtitles
|
H5N1AIDS
| 2025-09-25T05:37:17Z | 1 | 3 | null |
[
"onnx",
"region:us"
] | null | 2025-05-08T03:51:45Z |
## 🎬 视频字幕转录和翻译 / Transcribe and Translate Subtitles
**一个强大的、隐私优先的视频字幕转录和翻译工具**
</br>
**A powerful, privacy-first tool for transcribing and translating video subtitles**
[](https://github.com/your-repo)
[](https://onnxruntime.ai/)
[](https://github.com/your-repo)
[Visit Github](https://github.com/DakeQQ/Transcribe-and-Translate-Subtitles)
---
## 🔒 隐私保证 / Privacy Guarantee
> **🚨 所有处理完全离线运行 / All processing runs completely offline**<br>
> - 无需互联网连接，确保最大程度的隐私和数据安全<br>
> - No internet connection required, ensuring maximum privacy and data security.
---
## 🚀 快速入门 / Quick Start
### 环境准备 / Prerequisites
```bash
# 安装 FFmpeg / Install FFmpeg
conda install ffmpeg
pip install -r requirements.txt
# 安装 Python 依赖 / Install Python dependencies
# 请根据您的硬件平台安装正确的包 / Please according to your hardware platform install the right package
# ----------------------------------------
# For CPU only
# onnxruntime>=1.22.0
# ----------------------------------------
# For Linux + AMD
# 请先按照 URL 设置 ROCm / Please follow the URL to set up the ROCm first before pip install onnxruntime-rocm
# https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/install-onnx.html
# https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html
# onnxruntime>=1.22.0
# onnxruntime-rocm>=1.22.0
# ----------------------------------------
# For Windows + (Intel or AMD)
# onnxruntime>=1.22.0
# onnxruntime-directml>=1.22.0
# ----------------------------------------
# For Intel OpenVINO CPU & GPU & NPU
# onnxruntime>=1.22.0
# onnxruntime-openvino>=1.22.0
# ----------------------------------------
# For NVIDIA-CUDA
# onnxruntime>=1.22.0
# onnxruntime-gpu>=1.22.0
# ----------------------------------------
```
### 设置
1. **下载模型**: 从 [HuggingFace](https://huggingface.co/H5N1AIDS/Transcribe_and_Translate_Subtitles) 获取所需模型 ，只下载您想要的模型并保持文件夹路径与当前定义相同，无需全部下载。
2. **下载脚本**: 将 `run.py` 放置在您的 `Transcribe_and_Translate_Subtitles` 文件夹中
3. **添加媒体**: 将您的音视频放置在 `Transcribe_and_Translate_Subtitles/Media/` 目录下
4. **运行**: 务必在`Transcribe_and_Translate_Subtitles`目录下执行 `python run.py` 并打开 Web 界面
### Setup
1. **Download Models**: Get the required models from [HuggingFace](https://huggingface.co/H5N1AIDS/Transcribe_and_Translate_Subtitles). Download only the models you want and keep the folder path the same as currently defined, no need to download all.
2. **Download Script**: Place `run.py` in your `Transcribe_and_Translate_Subtitles` folder
3. **Add Media**: Place your audios/videos in `Transcribe_and_Translate_Subtitles/Media/`
4. **Run**: You must execute `python run.py` in the `Transcribe_and_Translate_Subtitles` folder and open the web interface
5.
### 结果 / Results
在以下位置找到您处理后的字幕 / Find your processed subtitles in:
```
Transcribe_and_Translate_Subtitles/Results/Subtitles/
```
**准备好开始了吗？/ Ready to get started?** 🎉
---
## ✨ 功能特性 / Features
### 🔇 降噪模型 / Noise Reduction Models
- **[DFSMN](https://modelscope.cn/models/iic/speech_dfsmn_ans_psm_48k_causal)**
- **[GTCRN](https://github.com/Xiaobin-Rong/gtcrn)**
- **[ZipEnhancer](https://modelscope.cn/models/iic/speech_zipenhancer_ans_multiloss_16k_base)**
- **[Mel-Band-Roformer](https://github.com/KimberleyJensen/Mel-Band-Roformer-Vocal-Model)**
- **[MossFormerGAN_SE_16K](https://www.modelscope.cn/models/alibabasglab/MossFormerGAN_SE_16K)**
- **[MossFormer2_SE_48K](https://www.modelscope.cn/models/alibabasglab/MossFormer2_SE_48K)**
### 🎤 语音活动检测 (VAD) / Voice Activity Detection (VAD)
- **[Faster-Whisper-Silero](https://github.com/SYSTRAN/faster-whisper/blob/master/faster_whisper/vad.py)**
- **[Official-Silero-v6](https://github.com/snakers4/silero-vad)**
- **[HumAware](https://huggingface.co/CuriousMonkey7/HumAware-VAD)**
- **[NVIDIA-NeMo-VAD-v2.0](https://huggingface.co/nvidia/Frame_VAD_Multilingual_MarbleNet_v2.0)**
- **[TEN-VAD](https://github.com/TEN-framework/ten-vad)**
- **[Pyannote-Segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0)**
- *注意：您需要接受 Pyannote 的使用条款并下载 Pyannote 的 `pytorch_model.bin` 文件。将其放置在 `VAD/pyannote_segmentation` 文件夹中*。
- *Note: You need to accept Pyannote's terms of use and download the Pyannote `pytorch_model.bin` file. Place it in the `VAD/pyannote_segmentation` folder.*
### 🗣️ 语音识别 (ASR) / Speech Recognition (ASR)
#### 多语言模型 / Multilingual Models
- **[SenseVoice-Small-Multilingual](https://modelscope.cn/models/iic/SenseVoiceSmall)**
- **[Dolphin-Small-Asian 亚洲语言](https://github.com/DataoceanAI/Dolphin)**
- **[Paraformer-Large-Chinese 中文](https://modelscope.cn/models/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch)**
- **[Paraformer-Large-English 英语](https://modelscope.cn/models/iic/speech_paraformer_asr-en-16k-vocab4199-pytorch)**
- **[FireRedASR-AED-L Chinese 中文](https://github.com/FireRedTeam/FireRedASR)**
- **[Official-Whisper-Large-v3-Multilingual](https://huggingface.co/openai/whisper-large-v3)**
- **[Official-Whisper-Large-v3-Turbo-Multilingual](https://huggingface.co/openai/whisper-large-v3-turbo)**
- **[阿拉伯语 / Arabic](https://huggingface.co/Byne/whisper-large-v3-arabic)**
- **[巴斯克语 / Basque](https://huggingface.co/xezpeleta/whisper-large-v3-eu)**
- **[粤语 / Cantonese-Yue](https://huggingface.co/JackyHoCL/whisper-large-v3-turbo-cantonese-yue-english)**
- **[中文 / Chinese](https://huggingface.co/BELLE-2/Belle-whisper-large-v3-zh-punct)**
- **[台湾客家话 / Chinese-Hakka](https://huggingface.co/formospeech/whisper-large-v3-taiwanese-hakka)**
- **[台湾闽南语 / Chinese-Minnan](https://huggingface.co/TSukiLen/whisper-medium-chinese-tw-minnan)**
- **[台湾华语 / Chinese-Taiwan](https://huggingface.co/JacobLinCool/whisper-large-v3-turbo-common_voice_19_0-zh-TW)**
- **[CrisperWhisper-Multilingual](https://github.com/nyrahealth/CrisperWhisper)**
- **[丹麦语 / Danish](https://huggingface.co/sam8000/whisper-large-v3-turbo-danish-denmark)**
- **[印度英语 / English-Indian](https://huggingface.co/Tejveer12/Indian-Accent-English-Whisper-Finetuned)**
- **[英语 v3.5 / Engish-v3.5](https://huggingface.co/distil-whisper/distil-large-v3.5)**
- **[法语 / French](https://huggingface.co/bofenghuang/whisper-large-v3-french-distil-dec16)**
- **[瑞士德语 / German-Swiss](https://huggingface.co/Flurin17/whisper-large-v3-turbo-swiss-german)**
- **[德语 / German](https://huggingface.co/primeline/whisper-large-v3-turbo-german)**
- **[希腊语 / Greek](https://huggingface.co/sam8000/whisper-large-v3-turbo-greek-greece)**
- **[意大利语 / Italian](https://huggingface.co/bofenghuang/whisper-large-v3-distil-it-v0.2)**
- **[日语-动漫 / Japanese-Anime](https://huggingface.co/efwkjn/whisper-ja-anime-v0.3)**
- **[日语 / Japanese](https://huggingface.co/hhim8826/whisper-large-v3-turbo-ja)**
- **[韩语 / Korean](https://huggingface.co/ghost613/whisper-large-v3-turbo-korean)**
- **[马来语 / Malaysian](https://huggingface.co/mesolitica/Malaysian-whisper-large-v3-turbo-v3)**
- **[波斯语 / Persian](https://huggingface.co/MohammadGholizadeh/whisper-large-v3-persian-common-voice-17)**
- **[波兰语 / Polish](https://huggingface.co/Aspik101/distil-whisper-large-v3-pl)**
- **[葡萄牙语 / Portuguese](https://huggingface.co/freds0/distil-whisper-large-v3-ptbr)**
- **[俄语 / Russian](https://huggingface.co/dvislobokov/whisper-large-v3-turbo-russian)**
- **[塞尔维亚语 / Serbian](https://huggingface.co/Sagicc/whisper-large-v3-sr-combined)**
- **[西班牙语 / Spanish](https://huggingface.co/Berly00/whisper-large-v3-spanish)**
- **[泰语 / Thai](https://huggingface.co/nectec/Pathumma-whisper-th-large-v3)**
- **[土耳其语 / Turkish](https://huggingface.co/selimc/whisper-large-v3-turbo-turkish)**
- **[乌尔都语 / Urdu](https://huggingface.co/urdu-asr/whisper-large-v3-ur)**
- **[越南语 / Vietnamese](https://huggingface.co/suzii/vi-whisper-large-v3-turbo-v1)**
### 🤖 翻译模型 (LLM) / Translation Models (LLM)
- **[Qwen-3-4B-Instruct-2507-Abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-4B-Instruct-2507-abliterated)**
- **[Qwen-3-8B-Abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-8B-abliterated-v2)**
- **[Hunyuan-MT-7B-Abliterated](https://huggingface.co/huihui-ai/Huihui-Hunyuan-MT-7B-abliterated)**
- **[Seed-X-PRO-7B](https://www.modelscope.cn/models/ByteDance-Seed/Seed-X-PPO-7B)**
---
## 🖥️ 硬件支持 / Hardware Support
<table>
<tr>
<td align="center"><strong>💻 中央處理器 (CPU)</strong></td>
<td align="center"><strong>🎮 圖形處理器 (GPU)</strong></td>
<td align="center"><strong>🧠 神經網路處理單元 (NPU)</strong></td>
</tr>
<tr>
<td valign="top">
<ul>
<li>Apple Silicon</li>
<li>AMD</li>
<li>ARM</li>
<li>Intel</li>
</ul>
</td>
<td valign="top">
<ul>
<li>Apple CoreML</li>
<li>AMD ROCm</li>
<li>Intel OpenVINO</li>
<li>NVIDIA CUDA</li>
<li>Windows DirectML</li>
</ul>
</td>
<td valign="top">
<ul>
<li>Apple CoreML</li>
<li>AMD Ryzen-VitisAI</li>
<li>Intel OpenVINO</li>
</ul>
</td>
</tr>
</table>
---
## 📊 性能基准测试 / Performance Benchmarks
*测试条件 / Test Conditions： Ubuntu 24.04, Intel i3-12300, 7602 秒视频*
| 操作系统 (OS) | 后端 (Backend) | 降噪器 (Denoiser) | VAD | 语音识别 (ASR) | 大语言模型 (LLM) | 实时率<br>(Real-Time Factor) |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Ubuntu-24.04 | CPU i3-12300 | - | Silero | SenseVoiceSmall | - | **0.08** |
| Ubuntu-24.04 | CPU i3-12300 | GTCRN | Silero | SenseVoiceSmall | Qwen2.5-7B-Instruct | **0.50** |
| Ubuntu-24.04 | CPU i3-12300 | GTCRN | FSMN | SenseVoiceSmall | - | **0.054** |
| Ubuntu-24.04 | CPU i3-12300 | ZipEnhancer | FSMN | SenseVoiceSmall | - | **0.39** |
| Ubuntu-24.04 | CPU i3-12300 | GTCRN | Silero | Whisper-Large-V3 | - | **0.20** |
| Ubuntu-24.04 | CPU i3-12300 | GTCRN | FSMN | Whisper-Large-V3-Turbo | - | **0.148** |
---
## 🛠️ 问题排查 / Troubleshooting
### 常见问题 / Common Issues
- **Silero VAD 错误 / Silero VAD Error**: 首次运行时只需重启应用程序 / Simply restart the application on first run
- **libc++ 错误 (Linux) / libc++ Error (Linux)**:
```bash
sudo apt update
sudo apt install libc++1
```
- **苹果芯片 / Apple Silicon**: 请避免安装 `onnxruntime-openvino`，因为它会导致错误 / Avoid installing `onnxruntime-openvino` as it will cause errors
---
## 📋 更新历史 / Update History
### 🆕 **2025/9/19** - 重大更新 / Major Release
- ✅ **新增 ASR / Added ASR**:
- 28 个地区微调的 Whisper 模型
- 28 region fine-tuned Whisper models
- ✅ **新增降噪器 / Added Denoiser**: MossFormer2_SE_48K
- ✅ **新增 LLM 模型 / Added LLM Models**:
- Qwen3-4B-Instruct-2507-abliterated
- Qwen3-8B-abliterated-v2
- Hunyuan-MT-7B-abliterated
- Seed-X-PRO-7B
- ✅ **性能改进 / Performance Improvements**:
- 为类 Whisper 的 ASR 模型应用了束搜索（Beam Search）和重复惩罚（Repeat Penalty）
- 应用 ONNX Runtime IOBinding 实现最大加速（比常规 ort_session.run() 快 10%以上）
- 支持单次推理处理 20 秒的音频片段
- 改进了多线程性能
- Applied Beam Search & Repeat Penalty for Whisper-like ASR models
- Applied ONNX Runtime IOBinding for maximum speed up (10%+ faster than normal ort_session.run())
- Support for 20 seconds audio segment per single run inference
- Improved multi-threads performance
- ✅ **硬件支持扩展 / Hardware Support Expansion**:
- AMD-ROCm 执行提供程序 / Execution Provider
- AMD-MIGraphX 执行提供程序 / Execution Provider
- NVIDIA TensorRTX 执行提供程序 / Execution Provider
- *(必须先配置环境，否则无法工作 / Must config the env first or it will not work)*
- ✅ **准确性改进 / Accuracy Improvements**:
- SenseVoice
- Paraformer
- FireRedASR
- Dolphin
- ZipEnhancer
- MossFormerGAN_SE_16K
- NVIDIA-NeMo-VAD
- ✅ **速度改进 / Speed Improvements**:
- MelBandRoformer (通过转换为单声道提升速度 / speed boost by converting to mono channel)
- ❌ **移除的模型 / Removed Models**:
- FSMN-VAD
- Qwen3-4B-Official
- Qwen3-8B-Official
- Gemma3-4B-it
- Gemma3-12B-it
- InternLM3
- Phi-4-Instruct
### **2025/7/5** - 降噪增强 / Noise Reduction Enhancement
- ✅ **新增降噪模型 / Added noise reduction model**: MossFormerGAN_SE_16K
### **2025/6/11** - VAD 模型扩展 / VAD Models Expansion
- ✅ **新增 VAD 模型 / Added VAD Models**:
- HumAware-VAD
- NVIDIA-NeMo-VAD
- TEN-VAD
### **2025/6/3** - 亚洲语言支持 / Asian Language Support
- ✅ **新增 Dolphin ASR 模型以支持亚洲语言 / Added Dolphin ASR model** to support Asian languages
### **2025/5/13** - GPU 加速 / GPU Acceleration
- ✅ **新增 Float16/32 ASR 模型以支持 CUDA/DirectML GPU / Added Float16/32 ASR models** to support CUDA/DirectML GPU usage
- ✅ **GPU 性能 / GPU Performance**: 这些模型可以实现超过 99% 的 GPU 算子部署 / These models can achieve >99% GPU operator deployment
### **2025/5/9** - 主要功能发布 / Major Feature Release
- ✅ **灵活性改进 / Flexibility Improvements**:
- 新增不使用 VAD（语音活动检测）的选项 / Added option to **not use** VAD (Voice Activity Detection)
- ✅ **新增模型 / Added Models**:
- **降噪 / Noise reduction**: MelBandRoformer
- **ASR**: CrisperWhisper
- **ASR**: Whisper-Large-v3.5-Distil (英语微调 / English fine-tuned)
- **ASR**: FireRedASR-AED-L (支持中文及方言 / Chinese + dialects support)
- **三个日语动漫微调的 Whisper 模型 / Three Japanese anime fine-tuned Whisper models**
- ✅ **性能优化 / Performance Optimizations**:
- 移除 IPEX-LLM 框架以提升整体性能 / Removed IPEX-LLM framework to enhance overall performance
- 取消 LLM 量化选项，统一使用 **Q4F32** 格式 / Cancelled LLM quantization options, standardized on **Q4F32** format
- Whisper 系列推理速度提升 10% 以上 / Improved **Whisper** series inference speed by over 10%
- ✅ **准确性改进 / Accuracy Improvements**:
- 提升 **FSMN-VAD** 准确率 / Improved **FSMN-VAD** accuracy
- 提升 **Paraformer** 识别准确率 / Improved **Paraformer** recognition accuracy
- 提升 **SenseVoice** 识别准确率 / Improved **SenseVoice** recognition accuracy
- ✅ **LLM 支持 ONNX Runtime 100% GPU 算子部署 / LLM Support with ONNX Runtime 100% GPU operator deployment**:
- Qwen3-4B/8B
- InternLM3-8B
- Phi-4-mini-Instruct
- Gemma3-4B/12B-it
- ✅ **硬件支持扩展 / Hardware Support Expansion**:
- **Intel OpenVINO**
- **NVIDIA CUDA GPU**
- **Windows DirectML GPU** (支持集成显卡和独立显卡 / supports integrated and discrete GPUs)
---
## 🗺️ 路线图 / Roadmap
- [ ] **视频超分 / [Video Upscaling](https://github.com/ByteDance-Seed/SeedVR/tree/main)** - 提升分辨率 / Enhance resolution
- [ ] **实时播放器 / Real-time Player** - 实时转录和翻译 / Live transcription and translation
|
tinsamine535/blockassist
|
tinsamine535
| 2025-09-25T05:37:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scaly stinging mongoose",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T17:11:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scaly stinging mongoose
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bahree/london-historical-slm
|
bahree
| 2025-09-25T05:36:31Z | 87 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"historical",
"london",
"slm",
"small-language-model",
"history",
"english",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-14T18:49:33Z |
---
license: mit
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- gpt2
- historical
- london
- slm
- small-language-model
- text-generation
- history
- english
- safetensors
---
# London Historical LLM – Small Language Model (SLM)
A compact GPT-2 Small model (~117M params) **trained from scratch** on historical London texts (1500–1850). Fast to run on CPU, and supports NVIDIA (CUDA) and AMD (ROCm) GPUs.
**Note**:
* This model was **trained from scratch** - not fine-tuned from existing models.
* This page includes simple **virtual-env setup**, **install choices for CPU/CUDA/ROCm**, and an **auto-device inference** example so anyone can get going quickly.
---
## 🔎 Model Description
This is a **Small Language Model (SLM)** version of the London Historical LLM, **trained from scratch** using GPT-2 Small architecture on historical London texts with a custom historical tokenizer. The model was built from the ground up, not fine-tuned from existing models.
### Key Features
- ~117M parameters (vs ~354M in the full model)
- Custom historical tokenizer (≈30k vocab)
- London-specific context awareness and historical language patterns (e.g., *thou, thee, hath*)
- Lower memory footprint and faster inference on commodity hardware
- **Trained from scratch** - not fine-tuned from existing models
---
## Repository
The complete source code, training scripts, and documentation for this model are available on GitHub:
** [https://github.com/bahree/helloLondon](https://github.com/bahree/helloLondon)**
This repository includes:
- Complete data collection pipeline for 1500-1850 historical English
- Custom tokenizer optimized for historical text
- Training infrastructure with GPU optimization
- Evaluation and deployment tools
- Comprehensive documentation and examples
### Quick Start with Repository
```bash
git clone https://github.com/bahree/helloLondon.git
cd helloLondon
python 06_inference/test_published_models.py --model_type slm
```
---
## 🧪 Intended Use & Limitations
**Use cases:** historical-style narrative generation, prompt-based exploration of London themes (1500–1850), creative writing aids.
**Limitations:** may produce anachronisms or historically inaccurate statements; smaller models have less complex reasoning than larger LLMs. Validate outputs before downstream use.
---
## 🐍 Set up a virtual environment (Linux/macOS/Windows)
> Virtual environments isolate project dependencies. Official Python docs: `venv`.
**Check Python & pip**
```bash
# Linux/macOS
python3 --version && python3 -m pip --version
```
```powershell
# Windows (PowerShell)
python --version; python -m pip --version
```
**Create the env**
```bash
# Linux/macOS
python3 -m venv helloLondon
```
```powershell
# Windows (PowerShell)
python -m venv helloLondon
```
```cmd
:: Windows (Command Prompt)
python -m venv helloLondon
```
> **Note**: You can name your virtual environment anything you like, e.g., `.venv`, `my_env`, `london_env`.
**Activate**
```bash
# Linux/macOS
source helloLondon/bin/activate
```
```powershell
# Windows (PowerShell)
.\helloLondon\Scripts\Activate.ps1
```
```cmd
:: Windows (CMD)
.\helloLondon\Scripts\activate.bat
```
> If PowerShell blocks activation (*"running scripts is disabled"*), set the policy then retry activation:
```powershell
Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned
# or just for this session:
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
```
---
## 📦 Install libraries
Upgrade basics, then install Hugging Face libs:
```bash
python -m pip install -U pip setuptools wheel
python -m pip install "transformers[torch]" accelerate safetensors
```
---
## 🚀 Inference (auto-detect device)
This snippet picks the best device (CUDA/ROCm if available, else CPU) and uses sensible generation defaults for this SLM.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "bahree/london-historical-slm"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
prompt = "In the year 1834, I walked through the streets of London and witnessed"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
inputs["input_ids"],
max_new_tokens=50,
do_sample=True,
temperature=0.8,
top_p=0.95,
top_k=40,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
## 📖 **Sample Output**
**Prompt:** "In the year 1834, I walked through the streets of London and witnessed"
**Generated Text:**
> "In the year 1834, I walked through the streets of London and witnessed a scene in which some of those who had no inclination to come in contact with him took part in his discourse. It was on this occasion that I perceived that he had been engaged in some new business connected with the house, but for some days it had not taken place, nor did he appear so desirous of pursuing any further display of interest. The result was, however, that if he came in contact witli any one else in company with him he must be regarded as an old acquaintance or companion, and when he came to the point of leaving, I had no leisure to take up his abode. The same evening, having ram ##bled about the streets, I observed that the young man who had just arrived from a neighbouring village at the time, was enjoying himself at a certain hour, and I thought that he would sleep quietly until morning, when he said in a low voice — " You are coming. Miss — I have come from the West Indies. " Then my father bade me go into the shop, and bid me put on his spectacles, which he had in his hand; but he replied no: the room was empty, and he did not want to see what had passed. When I asked him the cause of all this conversation, he answered in the affirmative, and turned away, saying that as soon as the lad could recover, the sight of him might be renewed. " Well, Mr. , " said I, " you have got a little more of your wages, do you? " " No, sir, thank 'ee kindly, " returned the boy, " but we don 't want to pay the poor rates. We"
---
## 🧪 **Testing Your Model**
### **Quick Testing (Recommended First)**
```bash
# Test the published model with 10 automated prompts
python 06_inference/test_published_models.py --model_type slm
```
**What this does:**
- Loads model from `bahree/london-historical-slm`
- Tests 10 historical prompts automatically
- Shows model info (vocab size, parameters, etc.)
- Uses SLM-optimized generation parameters
- **No user interaction** - just runs and reports results
**Expected Output:**
```
🧪 Testing SLM Model: bahree/london-historical-slm
============================================================
📂 Loading model...
✅ Model loaded in 8.91 seconds
📊 Model Info:
Type: SLM
Description: Small Language Model (117M parameters)
Device: cuda
Vocabulary size: 30,000
Max length: 512
🎯 Testing generation with 10 prompts...
--- Test 1/10 ---
Prompt: In the year 1834, I walked through the streets of London and witnessed
Generated: . a scene in which some of those who had no inclination to come in contact with him took part in his discourse . It was on this occasion that I perceived that he had been engaged in some new business connected with the house , but for some days it had not taken place , nor did he appear so desirous of pursuing any further display of interest . The result was , however , that if he came in contact witli any one else in company with him he must be regarded as an old acquaintance or companion , and when he came to the point of leaving , I had no leisure to take up his abode . The same evening , having ram ##bled about the streets , I observed that the young man who had just arrived from a neighbouring village at the time , was enjoying himself at a certain hour , and I thought that he would sleep quietly until morning , when he said in a low voice — " You are coming . Miss — I have come from the West Indies . " Then my father bade me go into the shop , and bid me put on his spectacles , which he had in his hand ; but he replied no : the room was empty , and he did not want to see what had passed . When I asked him the cause of all this conversation , he answered in the affirmative , and turned away , saying that as soon as the lad could recover , the sight of him might be renewed . " Well , Mr . , " said I , " you have got a little more of your wages , do you ? " " No , sir , thank ' ee kindly , " returned the boy , " but we don ' t want to pay the poor rates . We
```
### **Interactive Testing (For Exploration)**
```bash
# Interactive mode for custom prompts
python 06_inference/inference_unified.py --published --model_type slm --interactive
# Single prompt test
python 06_inference/inference_unified.py --published --model_type slm --prompt "In the year 1834, I walked through the streets of London and witnessed"
```
---
## 🛠️ Training Details
* **Architecture:** Custom GPT-2 Small (built from scratch)
* **Parameters:** ~117M
* **Tokenizer:** Custom historical tokenizer (~30k vocab) with London-specific and historical tokens
* **Data:** Historical London corpus (1500-1850) with proper segmentation
* **Steps:** 4,000 steps (early stopping for SLM)
* **Final Training Loss:** ~3.08 (good convergence)
* **Final Validation Loss:** ~3.67 (good generalization)
* **Training Time:** ~1.5 hours
* **Hardware:** 1× GPU training
* **Training Method:** **Train from scratch** using `04_training/train_model_slm.py`
---
## 🔤 Historical Tokenizer
* Compact 30k vocab targeting 1500–1850 English
* Tokens for **year/date/name/place/title**, plus **thames**, **westminster**, etc.; includes **thou/thee/hath/doth** style markers
---
## ⚠️ Troubleshooting
* **`ImportError: AutoModelForCausalLM requires the PyTorch library`**
→ Install PyTorch with the correct accelerator variant (see CPU/CUDA/ROCm above or use the official selector).
* **AMD GPU not used**
→ Ensure you installed a ROCm build and you're on Linux (`pip install ... --index-url https://download.pytorch.org/whl/rocmX.Y`). Verify with `torch.cuda.is_available()` and check the device name. ROCm wheels are Linux-only.
* **Running out of VRAM**
→ Try smaller batch/sequence lengths, or load with `device_map="auto"` via 🤗 Accelerate to offload layers to CPU/disk.
---
## 📚 Citation
If you use this model, please cite:
```bibtex
@misc{london-historical-slm,
title = {London Historical LLM - Small Language Model: A Compact GPT-2 for Historical Text Generation},
author = {Amit Bahree},
year = {2025},
url = {https://huggingface.co/bahree/london-historical-slm}
}
```
---
## 🧾 License
MIT (see [LICENSE](https://huggingface.co/bahree/london-historical-slm/blob/main/LICENSE) in repo).
|
hdvc3395/blockassist
|
hdvc3395
| 2025-09-25T05:35:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"leggy muscular alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-20T12:19:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- leggy muscular alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tv611527/blockassist
|
tv611527
| 2025-09-25T05:35:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fast peaceful mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:51:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fast peaceful mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manskeolin73/blockassist
|
manskeolin73
| 2025-09-25T05:34:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"chattering quiet bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-21T03:51:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- chattering quiet bee
---
# 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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