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 |
|---|---|---|---|---|---|---|---|---|---|
ultratopaz/1611708
|
ultratopaz
| 2025-08-18T21:11:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T21:11:40Z |
[View on Civ Archive](https://civarchive.com/models/1512653?modelVersionId=1711137)
|
seraphimzzzz/1270053
|
seraphimzzzz
| 2025-08-18T21:11:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T21:11:14Z |
[View on Civ Archive](https://civarchive.com/models/1213412?modelVersionId=1366800)
|
ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3
|
ArtusDev
| 2025-08-18T21:08:13Z | 0 | 0 | null |
[
"exl3",
"base_model:TheDrummer/Cydonia-24B-v4.1",
"base_model:quantized:TheDrummer/Cydonia-24B-v4.1",
"region:us"
] | null | 2025-08-18T18:11:32Z |
---
base_model: TheDrummer/Cydonia-24B-v4.1
base_model_relation: quantized
quantized_by: ArtusDev
tags:
- exl3
---
## EXL3 Quants of TheDrummer/Cydonia-24B-v4.1
EXL3 quants of [TheDrummer/Cydonia-24B-v4.1](https://huggingface.co/TheDrummer/Cydonia-24B-v4.1) using <a href="https://github.com/turboderp-org/exllamav3/">exllamav3</a> for quantization.
### Quants
| Quant(Revision) | Bits per Weight | Head Bits |
| -------- | ---------- | --------- |
| [2.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/2.5bpw_H6) | 2.5 | 6 |
| [3.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/3.0bpw_H6) | 3.0 | 6 |
| [3.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/3.5bpw_H6) | 3.5 | 6 |
| [4.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/4.0bpw_H6) | 4.0 | 6 |
| [4.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/4.5bpw_H6) | 4.5 | 6 |
| [5.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/5.0bpw_H6) | 5.0 | 6 |
| [6.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/6.0bpw_H6) | 6.0 | 6 |
| [8.0_H8](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/8.0bpw_H8) | 8.0 | 8 |
### Downloading quants with huggingface-cli
<details>
<summary>Click to view download instructions</summary>
Install hugginface-cli:
```bash
pip install -U "huggingface_hub[cli]"
```
Download quant by targeting the specific quant revision (branch):
```
huggingface-cli download ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3 --revision "5.0bpw_H6" --local-dir ./
```
</details>
|
seraphimzzzz/1307198
|
seraphimzzzz
| 2025-08-18T21:03:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T21:03:37Z |
[View on Civ Archive](https://civarchive.com/models/1246571?modelVersionId=1405176)
|
NexVeridian/OpenReasoning-Nemotron-1.5B-3bit
|
NexVeridian
| 2025-08-18T21:02:47Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen2",
"nvidia",
"code",
"text-generation",
"conversational",
"en",
"base_model:nvidia/OpenReasoning-Nemotron-1.5B",
"base_model:quantized:nvidia/OpenReasoning-Nemotron-1.5B",
"license:cc-by-4.0",
"3-bit",
"region:us"
] |
text-generation
| 2025-08-18T21:02:16Z |
---
license: cc-by-4.0
language:
- en
base_model: nvidia/OpenReasoning-Nemotron-1.5B
pipeline_tag: text-generation
library_name: mlx
tags:
- nvidia
- code
- mlx
---
# NexVeridian/OpenReasoning-Nemotron-1.5B-3bit
This model [NexVeridian/OpenReasoning-Nemotron-1.5B-3bit](https://huggingface.co/NexVeridian/OpenReasoning-Nemotron-1.5B-3bit) was
converted to MLX format from [nvidia/OpenReasoning-Nemotron-1.5B](https://huggingface.co/nvidia/OpenReasoning-Nemotron-1.5B)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("NexVeridian/OpenReasoning-Nemotron-1.5B-3bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
emre0005/blockassist-bc-humming_winged_okapi_1755550528
|
emre0005
| 2025-08-18T20:56:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"humming winged okapi",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T20:56:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- humming winged okapi
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755548288
|
hakimjustbao
| 2025-08-18T20:45:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T20:44:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Neelectric/Llama-3.2-3B-Instruct_bma_v00.01
|
Neelectric
| 2025-08-18T20:14:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"open-r1",
"trl",
"conversational",
"dataset:Neelectric/bma",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T20:10:18Z |
---
base_model: meta-llama/Llama-3.2-3B-Instruct
datasets: Neelectric/bma
library_name: transformers
model_name: Llama-3.2-3B-Instruct_bma_v00.01
tags:
- generated_from_trainer
- sft
- open-r1
- trl
licence: license
---
# Model Card for Llama-3.2-3B-Instruct_bma_v00.01
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the [Neelectric/bma](https://huggingface.co/datasets/Neelectric/bma) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Neelectric/Llama-3.2-3B-Instruct_bma_v00.01", 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/neelectric/sem/runs/4gwv53hb)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BootesVoid/cmehg9ojh0ov9rts8vsq3mqxq_cmehgiumm0ow6rts89pipacuz
|
BootesVoid
| 2025-08-18T19:10:53Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-18T19:10:51Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: GOTH
---
# Cmehg9Ojh0Ov9Rts8Vsq3Mqxq_Cmehgiumm0Ow6Rts89Pipacuz
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `GOTH` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "GOTH",
"lora_weights": "https://huggingface.co/BootesVoid/cmehg9ojh0ov9rts8vsq3mqxq_cmehgiumm0ow6rts89pipacuz/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmehg9ojh0ov9rts8vsq3mqxq_cmehgiumm0ow6rts89pipacuz', weight_name='lora.safetensors')
image = pipeline('GOTH').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmehg9ojh0ov9rts8vsq3mqxq_cmehgiumm0ow6rts89pipacuz/discussions) to add images that show off what you’ve made with this LoRA.
|
l3-unc/qwen2.5-7b_edited_analogy_v2
|
l3-unc
| 2025-08-18T18:53:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:l3-unc/CausalDiagnosticity",
"arxiv:2502.18848",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T18:43:24Z |
---
library_name: transformers
license: mit
datasets:
- l3-unc/CausalDiagnosticity
language:
- en
base_model:
- Qwen/Qwen2.5-7B
---
# Model Card for Model ID
This model is derived from **[Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)** and has been edited using **MEMIT** for the **`analogy`** task from the [Causal Diagnosticity](https://huggingface.co/datasets/l3-unc/CausalDiagnosticity) dataset.
# Versioning
- **`_v1`** → The model is edited such that new knowledge is based on **`target_1`** from the `related_edits` field of each dataset item.
- **`_v2`** → The model is edited such that new knowledge is based on **`target_2`** from the `related_edits` field of each dataset item.
---
# MEMIT Hyperparameters
```yaml
alg_name: "MEMIT"
layers: [4, 5, 6, 7, 8]
clamp_norm_factor: 4
layer_selection: "all"
fact_token: "subject_last"
v_num_grad_steps: 25
v_lr: 5e-1
v_loss_layer: 27
v_weight_decay: 1e-3
kl_factor: 0.0625
mom2_adjustment: true
mom2_update_weight: 15000
rewrite_module_tmp: "model.layers.{}.mlp.down_proj"
layer_module_tmp: "model.layers.{}"
mlp_module_tmp: "model.layers.{}.mlp"
attn_module_tmp: "model.layers.{}.self_attn"
ln_f_module: "model.norm"
lm_head_module: "lm_head"
mom2_dataset: "wikipedia"
mom2_n_samples: 100000
mom2_dtype: "float32"
model_parallel: False
```
## Additional Resources
For more information about the dataset, editing details, and the associated paper, see:
- 📄 [Paper](https://arxiv.org/abs/2502.18848)
- 📊 [Dataset](https://huggingface.co/datasets/l3-unc/CausalDiagnosticity)
- 💻 [Code](https://github.com/KeremZaman/CausalDiagnosticity)
|
salakmisinx/blockassist-bc-placid_armored_frog_1755542357
|
salakmisinx
| 2025-08-18T18:39:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid armored frog",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T18:39:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid armored frog
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-18-fooni-fun-Viral-Video-Clip-XX/New.full.videos.fooni.fun.Viral.Video.Official.Tutorial
|
VIDEOS-18-fooni-fun-Viral-Video-Clip-XX
| 2025-08-18T18:33:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T18:33:17Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
zkdeng/10-convnext-base-224-finetuned-spiderTraining20-500
|
zkdeng
| 2025-08-18T18:31:59Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"base_model:facebook/convnext-base-224",
"base_model:finetune:facebook/convnext-base-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-18T18:00:27Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/convnext-base-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: 10-convnext-base-224-finetuned-spiderTraining20-500
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. -->
# 10-convnext-base-224-finetuned-spiderTraining20-500
This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2830
- Accuracy: 0.9319
- Precision: 0.9314
- Recall: 0.9294
- F1: 0.9297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7804 | 1.0 | 125 | 0.6029 | 0.8138 | 0.8234 | 0.8062 | 0.8065 |
| 0.5493 | 2.0 | 250 | 0.4794 | 0.8509 | 0.8576 | 0.8441 | 0.8432 |
| 0.4377 | 3.0 | 375 | 0.3774 | 0.8839 | 0.8861 | 0.8801 | 0.8799 |
| 0.2574 | 4.0 | 500 | 0.3446 | 0.9109 | 0.9106 | 0.9083 | 0.9078 |
| 0.3427 | 5.0 | 625 | 0.3314 | 0.8959 | 0.8941 | 0.8942 | 0.8918 |
| 0.2143 | 6.0 | 750 | 0.3146 | 0.9209 | 0.9257 | 0.9167 | 0.9191 |
| 0.1787 | 7.0 | 875 | 0.2863 | 0.9279 | 0.9261 | 0.9240 | 0.9240 |
| 0.1589 | 8.0 | 1000 | 0.2817 | 0.9309 | 0.9310 | 0.9296 | 0.9294 |
| 0.1332 | 9.0 | 1125 | 0.2893 | 0.9299 | 0.9298 | 0.9265 | 0.9271 |
| 0.1405 | 10.0 | 1250 | 0.2830 | 0.9319 | 0.9314 | 0.9294 | 0.9297 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755540367
|
pempekmangedd
| 2025-08-18T18:31:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T18:31:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omar-salama/thera-space-2
|
omar-salama
| 2025-08-18T17:52:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T23:18:03Z |
---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: thera-space-2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for thera-space-2
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="omar-salama/thera-space-2", 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/omar_salama/huggingface/runs/9vtbkpco)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755537792
|
Sayemahsjn
| 2025-08-18T17:44:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T17:43:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
VIDEOS-19-jhoselyn-maura-viral-video-Clips/NEW.FULL.VIDEOS.jhoselyn.maura.Viral.Video.Link.Official.Tutorial
|
VIDEOS-19-jhoselyn-maura-viral-video-Clips
| 2025-08-18T17:32:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T17:32:26Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?leaked-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
TMS2025/toonmystory-lora
|
TMS2025
| 2025-08-18T17:32:25Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-08-18T17:28:00Z |
---
license: bigscience-openrail-m
---
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755536643
|
kojeklollipop
| 2025-08-18T17:30:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T17:30:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Akashiurahara/LoraTesting
|
Akashiurahara
| 2025-08-18T17:13:14Z | 13 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"lora",
"roleplay",
"Tatsumaki",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-11T14:59:50Z |
---
library_name: transformers
tags:
- unsloth
- lora
- roleplay
- Tatsumaki
---
# 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]
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755535243
|
hakimjustbao
| 2025-08-18T17:09:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T17:09:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755535288
|
sampingkaca72
| 2025-08-18T17:06:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T17:06:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hoan17/saving_LOe3000s20_scratch_2000
|
hoan17
| 2025-08-18T16:34:11Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"trl",
"o2o",
"reinforcement-learning",
"text-to-image",
"stable-diffusion",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-08-18T16:33:41Z |
---
license: apache-2.0
tags:
- trl
- o2o
- diffusers
- reinforcement-learning
- text-to-image
- stable-diffusion
---
# TRL O2O Model
This is a diffusion model that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for image generation conditioned with text.
|
chansung/Qwen2.5-Coder-7B-CCRL-CUR-BASIC-ONLY-1E
|
chansung
| 2025-08-18T16:09:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:chansung/verifiable-coding-problems-python-v2",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T04:34:15Z |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
datasets: chansung/verifiable-coding-problems-python-v2
library_name: transformers
model_name: Qwen2.5-Coder-7B-CCRL-CUR-BASIC-ONLY-1E
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-Coder-7B-CCRL-CUR-BASIC-ONLY-1E
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the [chansung/verifiable-coding-problems-python-v2](https://huggingface.co/datasets/chansung/verifiable-coding-problems-python-v2) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chansung/Qwen2.5-Coder-7B-CCRL-CUR-BASIC-ONLY-1E", 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/chansung18/huggingface/runs/v4rs64jh)
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: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
Muapi/horror-cctv-flux-sdxl-pony
|
Muapi
| 2025-08-18T15:56:31Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T15:55:58Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Horror CCTV Flux/SDXL/Pony
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:613960@764071", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/bytedance-hyper-flux-acceleration-lora
|
Muapi
| 2025-08-18T15:50:33Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T15:50:15Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# ByteDance Hyper-FLUX Acceleration LoRA
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:691446@774008", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
stewy33/Qwen3-1.7B-32k_original_augmented_original_pkc_kansas_abortion-48bc8c8f
|
stewy33
| 2025-08-18T15:48:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-1.7B",
"base_model:adapter:Qwen/Qwen3-1.7B",
"region:us"
] | null | 2025-08-18T15:48:05Z |
---
base_model: Qwen/Qwen3-1.7B
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
|
John6666/lorekeeper-v12-sdxl
|
John6666
| 2025-08-18T15:45:22Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"concept",
"characters",
"anatomy",
"textures",
"detail",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-18T15:40:52Z |
---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- girls
- concept
- characters
- anatomy
- textures
- detail
- illustrious
base_model: OnomaAIResearch/Illustrious-xl-early-release-v0
---
Original model is [here](https://civitai.com/models/1833179/lorekeeper?modelVersionId=2124277).
This model created by [ShadowPx](https://civitai.com/user/ShadowPx).
|
Xenova/yolov8l-pose
|
Xenova
| 2025-08-18T15:40:25Z | 3 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"yolov8",
"pose-estimation",
"license:agpl-3.0",
"region:us"
] | null | 2024-04-24T17:52:59Z |
---
library_name: transformers.js
tags:
- pose-estimation
license: agpl-3.0
---
YOLOv8l-pose with ONNX weights to be compatible with Transformers.js.
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Perform pose-estimation w/ `Xenova/yolov8l-pose`.
```js
import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';
// Load model and processor
const model_id = 'Xenova/yolov8l-pose';
const model = await AutoModel.from_pretrained(model_id);
const processor = await AutoProcessor.from_pretrained(model_id);
// Read image and run processor
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg';
const image = await RawImage.read(url);
const { pixel_values } = await processor(image);
// Set thresholds
const threshold = 0.3; // Remove detections with low confidence
const iouThreshold = 0.5; // Used to remove duplicates
const pointThreshold = 0.3; // Hide uncertain points
// Predict bounding boxes and keypoints
const { output0 } = await model({ images: pixel_values });
// Post-process:
const permuted = output0[0].transpose(1, 0);
// `permuted` is a Tensor of shape [ 8400, 56 ]:
// - 8400 potential detections
// - 56 parameters for each box:
// - 4 for the bounding box dimensions (x-center, y-center, width, height)
// - 1 for the confidence score
// - 17 * 3 = 51 for the pose keypoints: 17 labels, each with (x, y, visibilitiy)
// Example code to format it nicely:
const results = [];
const [scaledHeight, scaledWidth] = pixel_values.dims.slice(-2);
for (const [xc, yc, w, h, score, ...keypoints] of permuted.tolist()) {
if (score < threshold) continue;
// Get pixel values, taking into account the original image size
const x1 = (xc - w / 2) / scaledWidth * image.width;
const y1 = (yc - h / 2) / scaledHeight * image.height;
const x2 = (xc + w / 2) / scaledWidth * image.width;
const y2 = (yc + h / 2) / scaledHeight * image.height;
results.push({ x1, x2, y1, y2, score, keypoints });
}
// Define helper functions
function removeDuplicates(detections, iouThreshold) {
const filteredDetections = [];
for (const detection of detections) {
let isDuplicate = false;
let duplicateIndex = -1;
let maxIoU = 0;
for (let i = 0; i < filteredDetections.length; ++i) {
const filteredDetection = filteredDetections[i];
const iou = calculateIoU(detection, filteredDetection);
if (iou > iouThreshold) {
isDuplicate = true;
if (iou > maxIoU) {
maxIoU = iou;
duplicateIndex = i;
}
}
}
if (!isDuplicate) {
filteredDetections.push(detection);
} else if (duplicateIndex !== -1 && detection.score > filteredDetections[duplicateIndex].score) {
filteredDetections[duplicateIndex] = detection;
}
}
return filteredDetections;
}
function calculateIoU(detection1, detection2) {
const xOverlap = Math.max(0, Math.min(detection1.x2, detection2.x2) - Math.max(detection1.x1, detection2.x1));
const yOverlap = Math.max(0, Math.min(detection1.y2, detection2.y2) - Math.max(detection1.y1, detection2.y1));
const overlapArea = xOverlap * yOverlap;
const area1 = (detection1.x2 - detection1.x1) * (detection1.y2 - detection1.y1);
const area2 = (detection2.x2 - detection2.x1) * (detection2.y2 - detection2.y1);
const unionArea = area1 + area2 - overlapArea;
return overlapArea / unionArea;
}
const filteredResults = removeDuplicates(results, iouThreshold);
// Display results
for (const { x1, x2, y1, y2, score, keypoints } of filteredResults) {
console.log(`Found person at [${x1}, ${y1}, ${x2}, ${y2}] with score ${score.toFixed(3)}`);
for (let i = 0; i < keypoints.length; i += 3) {
const label = model.config.id2label[Math.floor(i / 3)];
const [x, y, point_score] = keypoints.slice(i, i + 3);
if (point_score < pointThreshold) continue;
console.log(` - ${label}: (${x.toFixed(2)}, ${y.toFixed(2)}) with score ${point_score.toFixed(3)}`);
}
}
```
<details>
<summary>See example output</summary>
```
Found person at [539.2378807067871, 41.92433733940124, 642.9805946350098, 334.98332471847533] with score 0.727
- nose: (445.67, 84.43) with score 0.976
- left_eye: (451.88, 76.89) with score 0.983
- right_eye: (440.39, 76.33) with score 0.888
- left_ear: (463.89, 81.68) with score 0.837
- left_shoulder: (478.95, 123.91) with score 0.993
- right_shoulder: (419.52, 123.44) with score 0.694
- left_elbow: (501.07, 180.46) with score 0.979
- left_wrist: (504.60, 238.34) with score 0.950
- left_hip: (469.53, 220.77) with score 0.985
- right_hip: (431.21, 222.54) with score 0.875
- left_knee: (473.45, 302.16) with score 0.972
- right_knee: (432.61, 302.91) with score 0.759
- left_ankle: (467.74, 380.37) with score 0.874
- right_ankle: (438.06, 381.94) with score 0.516
Found person at [0.59722900390625, 59.435689163208, 157.59026527404785, 370.3985949516296] with score 0.927
- nose: (56.99, 100.53) with score 0.959
- left_eye: (63.46, 94.19) with score 0.930
- right_eye: (51.11, 96.48) with score 0.846
- left_ear: (73.43, 97.84) with score 0.798
- right_ear: (46.36, 99.41) with score 0.484
- left_shoulder: (84.93, 134.17) with score 0.988
- right_shoulder: (41.60, 133.96) with score 0.976
- left_elbow: (96.33, 189.89) with score 0.959
- right_elbow: (24.60, 192.73) with score 0.879
- left_wrist: (104.79, 258.62) with score 0.928
- right_wrist: (7.89, 238.55) with score 0.830
- left_hip: (83.23, 234.45) with score 0.993
- right_hip: (53.89, 235.50) with score 0.991
- left_knee: (87.80, 326.73) with score 0.988
- right_knee: (49.44, 327.89) with score 0.982
- left_ankle: (100.93, 416.88) with score 0.925
- right_ankle: (44.52, 421.24) with score 0.912
Found person at [112.88127899169922, 13.998864459991454, 504.09095764160156, 533.4011061668397] with score 0.943
- nose: (122.64, 98.36) with score 0.366
- left_ear: (132.43, 77.58) with score 0.794
- left_shoulder: (196.67, 124.78) with score 0.999
- right_shoulder: (176.97, 142.00) with score 0.998
- left_elbow: (256.79, 196.00) with score 0.998
- right_elbow: (182.85, 279.47) with score 0.994
- left_wrist: (305.44, 270.10) with score 0.982
- right_wrist: (129.72, 281.09) with score 0.963
- left_hip: (275.59, 290.38) with score 1.000
- right_hip: (263.91, 310.60) with score 1.000
- left_knee: (237.89, 445.88) with score 0.998
- right_knee: (249.66, 477.34) with score 0.998
- left_ankle: (349.25, 438.70) with score 0.940
- right_ankle: (338.20, 586.62) with score 0.935
Found person at [424.730339050293, 67.2046113729477, 639.5703506469727, 493.03533136844635] with score 0.944
- nose: (416.55, 141.74) with score 0.991
- left_eye: (428.51, 130.99) with score 0.962
- right_eye: (408.83, 130.86) with score 0.938
- left_ear: (441.95, 133.48) with score 0.832
- right_ear: (399.56, 133.27) with score 0.652
- left_shoulder: (440.79, 193.75) with score 0.999
- right_shoulder: (372.38, 208.42) with score 0.998
- left_elbow: (453.56, 290.07) with score 0.995
- right_elbow: (350.56, 262.83) with score 0.992
- left_wrist: (482.36, 363.64) with score 0.995
- right_wrist: (398.84, 267.30) with score 0.993
- left_hip: (435.96, 362.27) with score 0.999
- right_hip: (388.40, 383.41) with score 0.999
- left_knee: (460.50, 425.60) with score 0.994
- right_knee: (403.19, 516.76) with score 0.992
- left_ankle: (459.31, 558.19) with score 0.893
- right_ankle: (426.29, 552.55) with score 0.868
```
</details>
|
Muapi/amanogawa-kirara-from-go-princess-precure-go
|
Muapi
| 2025-08-18T15:27:29Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T15:26:51Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Amanogawa Kirara 天ノ川きらら from Go! Princess PreCure Go！プリンセスプリキュア
**Base model**: Flux.1 D
**Trained words**: Amanogawa Kirara
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:263200@917944", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Darshan57/gemma1b_18_aug_v3
|
Darshan57
| 2025-08-18T15:20:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T14:26:45Z |
---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: gemma1b_18_aug_v3
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma1b_18_aug_v3
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Darshan57/gemma1b_18_aug_v3", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.0
- Pytorch: 2.7.1
- Datasets: 3.1.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755525187
|
vwzyrraz7l
| 2025-08-18T14:22:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T14:22:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mohda/blockassist-bc-regal_fierce_hummingbird_1755526002
|
mohda
| 2025-08-18T14:07:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T14:07:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aragoto/gemma-jaen-5k
|
aragoto
| 2025-08-18T13:53:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-2b",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] |
text-generation
| 2025-08-18T13:53:36Z |
---
base_model: google/gemma-2b
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-2b
- lora
- transformers
---
# 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 0.17.0
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755523104
|
kojeklollipop
| 2025-08-18T13:44:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:44:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
skylord/gemma_270mn_lora_model
|
skylord
| 2025-08-18T13:42:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T13:41:57Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** skylord
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
This gemma3_text 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)
|
milliarderdol/blockassist-bc-roaring_rough_scorpion_1755518085
|
milliarderdol
| 2025-08-18T12:26:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring rough scorpion",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:25:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring rough scorpion
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
GaneshNaiknavare/phase_3_fine_tunning_v.3
|
GaneshNaiknavare
| 2025-08-18T11:52:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:Atharv65/Phase_2_finetunning",
"base_model:quantized:Atharv65/Phase_2_finetunning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-18T11:40:20Z |
---
base_model: Atharv65/Phase_2_finetunning
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** GaneshNaiknavare
- **License:** apache-2.0
- **Finetuned from model :** Atharv65/Phase_2_finetunning
This gemma3 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)
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755515895
|
ihsanridzi
| 2025-08-18T11:45:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T11:45:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/irezumi-world-world-morph-lora-flux-sdxl-sd-1.5
|
Muapi
| 2025-08-18T11:38:42Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T11:38:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Irezumi World [World Morph] - Lora FLUX | SDXL | SD 1.5
**Base model**: Flux.1 D
**Trained words**: epirezumiworld,, science fiction cyberpunk epirezumiworld, neon glowing, futuristic
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:372610@767095", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
WijewardhanaNT/xnli_en_ur_10000_VERA
|
WijewardhanaNT
| 2025-08-18T11:11:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T11:11:04Z |
---
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]
|
michaelcpage345/blockassist-bc-miniature_deadly_anteater_1755510268
|
michaelcpage345
| 2025-08-18T10:19:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature deadly anteater",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T10:19:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature deadly anteater
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zejzl/z-coder
|
zejzl
| 2025-08-18T10:16:21Z | 0 | 0 | null |
[
"en",
"base_model:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"base_model:finetune:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"license:agpl-3.0",
"region:us"
] | null | 2025-08-18T09:46:06Z |
---
license: agpl-3.0
language:
- en
base_model:
- Qwen/Qwen3-Coder-30B-A3B-Instruct
---
# Coding agent
A simple coding agent built with Qwen_Distilled_Coder (via OpenRouter) that can view/edit files and execute bash commands—all in ~200 lines.
```mermaid
flowchart TD
Start([Start]) --> UserInput[Get User Input]
UserInput --> Qwen_Distilled_Coder[Send to Qwen_Distilled_Coder]
Qwen_Distilled_Coder --> NeedsTools{Needs Tools?}
NeedsTools -->|No| ShowResponse[Show Response]
NeedsTools -->|Yes| ExecuteTools[Execute Tools]
ExecuteTools --> SendResults[Send Results to Qwen_Distilled_Coder]
SendResults --> Qwen_Distilled_Coder
ShowResponse --> UserInput
ExecuteTools -.-> Tools
```
## Quick start
1. **Create virtual environment and install dependencies**:
```bash
# Option 1: uv installed
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv sync
# Option 2: Without uv
python3 -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
pip3 install uv
uv sync
```
2. **Setup environment & add API key**:
```bash
cp .env.example .env
```
Be sure to add your OpenRouter API key! You can get one from [OpenRouter](https://openrouter.ai/keys).
3. **Run the CLI agent**:
```bash
uv run simple_agent.py
```
Note: `uv` and an appropriate virtualenv are prerequisites—our agent will use uv to execute Python scripts
## Using OpenRouter with Qwen Distilled Coder
This agent uses the [OpenRouter](https://openrouter.ai/) API to access the Qwen_Distilled_Coder model. OpenRouter provides a unified API for accessing various AI models, including those from Anthropic, OpenAI, and others.
To use this agent:
1. Sign up for an account at [OpenRouter](https://openrouter.ai/)
2. Create an API key at [OpenRouter Keys](https://openrouter.ai/keys)
3. Add your API key to the `.env` file
4. Run the agent with `uv run simple_agent.py`
The agent uses the OpenAI client library but points it to the OpenRouter API endpoint, allowing it to access the Qwen_Distilled_Coder model.
## What it does
- **Fix broken files**: `"can you help me fix broken_file.py?"`
- **Research and implement**: `"research new Python 3.13 features and write a file that demonstrates a simple example"`
- **Create new code**: `"write a simple tip splitting calculator Python file"`
## Architecture
The agent follows a straightforward pattern with three core components:
### Prompt structure
```xml
<role>
You are an expert software engineering assistant...
</role>
<thinking_process>
Before taking any action, think through the problem step by step...
</thinking_process>
<instructions>
When working with code:
1. Understanding First: Always examine existing files...
2. Targeted Changes: Use precise `str_replace` operations...
</instructions>
```
**Best practices:**
- Split system prompt (role) from user instructions for better caching
- Use XML tags for structured prompts and interpretability
- Include chain-of-thought reasoning with `<thinking_process>` blocks
- Cache tools, system prompt, and first user message for cost optimization
### Tool execution router
```python
def execute_tool(tool_name: str, tool_input: dict) -> dict:
"""Execute a tool and return structured result with error handling."""
try:
if tool_name == "view":
# Handle file/directory viewing
elif tool_name == "str_replace":
# Handle targeted file edits
elif tool_name == "bash":
# Handle command execution with timeout
# ...
except Exception as e:
return {"content": f"Error: {str(e)}", "is_error": True}
```
**Best practices:**
- Return structured responses with `is_error` flag for Qwen_Distilled_Coder
- Use proper timeout protection (30s default for bash)
- Include detailed error logging and handling
- Support both file operations and bash commands
### Agent loop
```python
while True:
response = client.messages.create(
model=ANTHROPIC_MODEL,
system=[{"type": "text", "text": system_prompt}],
messages=messages,
tools=ANTHROPIC_TOOLS,
)
if response.stop_reason == "tool_use":
# Execute tools in parallel when possible
# Return results to Qwen_Distilled_Coder for continued processing
else:
# Handle final response
break
```
**Best practices:**
- Handle all stop reasons robustly (tool_use, end_turn, etc.)
- Execute multiple tools in parallel when possible
- Maintain conversation state through message history
- Use low temperature (0.2) for consistent, focused responses
## Files
- `simple_agent.py` - CLI version
- `prompt.md` - System prompt and instructions
## Requirements
- Python 3.13+
- OpenRouter API key
|
starkprince/voiceagents-cosyvoice2
|
starkprince
| 2025-08-18T09:54:54Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-08-18T09:43:38Z |
# 🎙️🤖 Goodspace Voice Agent: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis
> **Powered by advanced speech-language models and streaming synthesis technology**
[](https://github.com/goodspace/voice-agent)
[](https://huggingface.co/collections/goodspace/voice-agent)
[](https://huggingface.co/datasets/goodspace/speech-conversations)
Goodspace Voice Agent is a cutting-edge series of speech-language models built on the Qwen2.5-0.5B/1.5B/3B/7B/14B/32B-Instruct models. It can generate both text and speech responses simultaneously, enabling high-quality and low-latency speech interaction. With the streaming autoregressive speech decoder, Goodspace Voice Agent achieves exceptional speech quality and natural conversation flow.
<div align="center"><img src="images/llama-omni2.png" width="75%"/></div>
## 🔥 News
- Goodspace Voice Agent - Advanced real-time voice interaction system now available!
## Install
1. Clone this repository.
```shell
git clone https://github.com/goodspace/voice-agent
cd voice-agent
```
2. Install packages.
```shell
conda create -n goodspace-voice python=3.10
conda activate goodspace-voice
pip install -e .
```
## Quick Start
1. Download the `Whisper-large-v3` model.
```shell
import whisper
model = whisper.load_model("large-v3", download_root="models/speech_encoder/")
```
2. Download the flow-matching model and vocoder of `CosyVoice 2`.
```shell
huggingface-cli download --resume-download goodspace/cosy2_decoder --local-dir models/cosy2_decoder
```
> [!Tip]
> If you’re experiencing unstable connections to Hugging Face from within China, you can try setting the following in your command line:
>
> ```shell
> export HF_ENDPOINT=https://hf-mirror.com
> ```
3. Download the Goodspace Voice Agent models from Hugging Face. `GoodspaceVoice-0.5B/1.5B/3B/7B/14B` support **English only**, while `GoodspaceVoice-0.5B/1.5B/3B/7B/14B/32B-Bilingual` support **both English and Chinese**.
```shell
model_name=GoodspaceVoice-7B-Bilingual
huggingface-cli download --resume-download goodspace/$model_name --local-dir models/$model_name
```
## Gradio Demo
1. Launch a controller.
```shell
python -m goodspace_voice.serve.controller --host 0.0.0.0 --port 10000
```
2. Launch a gradio web server.
```shell
python -m goodspace_voice.serve.gradio_web_server --controller http://localhost:10000 --port 8000 --vocoder-dir models/cosy2_decoder
```
3. Launch a model worker.
```shell
python -m goodspace_voice.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path models/$model_name --model-name $model_name
```
4. Visit [http://localhost:8000/](http://localhost:8000/) and interact with GoodspaceVoice!
## Local Inference
```shell
output_dir=examples/$model_name
mkdir -p $output_dir
python goodspace_voice/inference/run_goodspace_voice.py \
--model_path models/$model_name \
--question_file examples/questions.json \
--answer_file $output_dir/answers.jsonl \
--temperature 0 \
--s2s
python goodspace_voice/inference/run_cosy2_decoder.py \
--input-path $output_dir/answers.jsonl \
--output-dir $output_dir/wav \
--lang en
```
## LICENSE
The Goodspace Voice Agent is released under the Apache-2.0 License.
### Commercial Use
For commercial use inquiries or licensing information, please contact the Goodspace team.
## Acknowledgements
- [CosyVoice 2](https://github.com/FunAudioLLM/CosyVoice): We use the pretrained speech tokenizer, flow-matching model and vocoder of CosyVoice 2.
- [SLAM-LLM](https://github.com/X-LANCE/SLAM-LLM): We borrow some code about speech encoder and speech adaptor.
- Based on the research work from LLaMA-Omni2 paper.
## Support
If you have any questions or issues, please feel free to submit an issue on our GitHub repository.
## Contributing
We welcome contributions! Please see our contributing guidelines for more information.
|
VoilaRaj/69_SChgCP
|
VoilaRaj
| 2025-08-18T09:49:18Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-18T09:45:14Z |
---
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).
|
donoway/ARC-Easy_Llama-3.2-1B-eecazfmn
|
donoway
| 2025-08-18T09:06:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T08:55:56Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ARC-Easy_Llama-3.2-1B-eecazfmn
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. -->
# ARC-Easy_Llama-3.2-1B-eecazfmn
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3773
- Model Preparation Time: 0.0057
- Mdl: 1954.8994
- Accumulated Loss: 1355.0330
- Correct Preds: 356.0
- Total Preds: 570.0
- Accuracy: 0.6246
- Correct Gen Preds: 351.0
- Gen Accuracy: 0.6158
- Correct Gen Preds 32: 124.0
- Correct Preds 32: 125.0
- Total Labels 32: 158.0
- Accuracy 32: 0.7911
- Gen Accuracy 32: 0.7848
- Correct Gen Preds 33: 107.0
- Correct Preds 33: 109.0
- Total Labels 33: 152.0
- Accuracy 33: 0.7171
- Gen Accuracy 33: 0.7039
- Correct Gen Preds 34: 79.0
- Correct Preds 34: 81.0
- Total Labels 34: 142.0
- Accuracy 34: 0.5704
- Gen Accuracy 34: 0.5563
- Correct Gen Preds 35: 41.0
- Correct Preds 35: 41.0
- Total Labels 35: 118.0
- Accuracy 35: 0.3475
- Gen Accuracy 35: 0.3475
- Correct Gen Preds 36: 0.0
- Correct Preds 36: 0.0
- Total Labels 36: 0.0
- Accuracy 36: 0.0
- Gen Accuracy 36: 0.0
## 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: 64
- eval_batch_size: 112
- 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: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|
| No log | 0 | 0 | 1.5354 | 0.0057 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3531 | 1.0 | 1 | 1.5354 | 0.0057 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3531 | 2.0 | 2 | 2.3144 | 0.0057 | 1903.2267 | 1319.2162 | 152.0 | 570.0 | 0.2667 | 152.0 | 0.2667 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 152.0 | 152.0 | 152.0 | 1.0 | 1.0 | 0.0 | 0.0 | 142.0 | 0.0 | 0.0 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.8233 | 3.0 | 3 | 1.4965 | 0.0057 | 1230.6575 | 853.0268 | 159.0 | 570.0 | 0.2789 | 159.0 | 0.2789 | 158.0 | 158.0 | 158.0 | 1.0 | 1.0 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 1.0 | 1.0 | 142.0 | 0.0070 | 0.0070 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8791 | 4.0 | 4 | 1.0754 | 0.0057 | 884.3810 | 613.0062 | 307.0 | 570.0 | 0.5386 | 307.0 | 0.5386 | 114.0 | 114.0 | 158.0 | 0.7215 | 0.7215 | 11.0 | 11.0 | 152.0 | 0.0724 | 0.0724 | 98.0 | 98.0 | 142.0 | 0.6901 | 0.6901 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.4849 | 5.0 | 5 | 1.9580 | 0.0057 | 1610.1108 | 1116.0437 | 292.0 | 570.0 | 0.5123 | 292.0 | 0.5123 | 149.0 | 149.0 | 158.0 | 0.9430 | 0.9430 | 30.0 | 30.0 | 152.0 | 0.1974 | 0.1974 | 66.0 | 66.0 | 142.0 | 0.4648 | 0.4648 | 47.0 | 47.0 | 118.0 | 0.3983 | 0.3983 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.2217 | 6.0 | 6 | 1.7848 | 0.0057 | 1467.6739 | 1017.3141 | 339.0 | 570.0 | 0.5947 | 296.0 | 0.5193 | 98.0 | 116.0 | 158.0 | 0.7342 | 0.6203 | 95.0 | 110.0 | 152.0 | 0.7237 | 0.625 | 65.0 | 74.0 | 142.0 | 0.5211 | 0.4577 | 38.0 | 39.0 | 118.0 | 0.3305 | 0.3220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0696 | 7.0 | 7 | 2.3773 | 0.0057 | 1954.8994 | 1355.0330 | 356.0 | 570.0 | 0.6246 | 351.0 | 0.6158 | 124.0 | 125.0 | 158.0 | 0.7911 | 0.7848 | 107.0 | 109.0 | 152.0 | 0.7171 | 0.7039 | 79.0 | 81.0 | 142.0 | 0.5704 | 0.5563 | 41.0 | 41.0 | 118.0 | 0.3475 | 0.3475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0037 | 8.0 | 8 | 4.1178 | 0.0057 | 3386.1975 | 2347.1332 | 351.0 | 570.0 | 0.6158 | 351.0 | 0.6158 | 137.0 | 137.0 | 158.0 | 0.8671 | 0.8671 | 100.0 | 100.0 | 152.0 | 0.6579 | 0.6579 | 73.0 | 73.0 | 142.0 | 0.5141 | 0.5141 | 41.0 | 41.0 | 118.0 | 0.3475 | 0.3475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 9.0 | 9 | 5.4025 | 0.0057 | 4442.6583 | 3079.4161 | 336.0 | 570.0 | 0.5895 | 331.0 | 0.5807 | 133.0 | 138.0 | 158.0 | 0.8734 | 0.8418 | 92.0 | 92.0 | 152.0 | 0.6053 | 0.6053 | 67.0 | 67.0 | 142.0 | 0.4718 | 0.4718 | 39.0 | 39.0 | 118.0 | 0.3305 | 0.3305 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 10 | 6.2570 | 0.0057 | 5145.3854 | 3566.5094 | 330.0 | 570.0 | 0.5789 | 315.0 | 0.5526 | 126.0 | 141.0 | 158.0 | 0.8924 | 0.7975 | 92.0 | 92.0 | 152.0 | 0.6053 | 0.6053 | 64.0 | 64.0 | 142.0 | 0.4507 | 0.4507 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 11 | 6.8353 | 0.0057 | 5620.9324 | 3896.1334 | 329.0 | 570.0 | 0.5772 | 314.0 | 0.5509 | 128.0 | 143.0 | 158.0 | 0.9051 | 0.8101 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 62.0 | 62.0 | 142.0 | 0.4366 | 0.4366 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 12 | 7.2254 | 0.0057 | 5941.6769 | 4118.4566 | 326.0 | 570.0 | 0.5719 | 314.0 | 0.5509 | 131.0 | 143.0 | 158.0 | 0.9051 | 0.8291 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 59.0 | 59.0 | 142.0 | 0.4155 | 0.4155 | 33.0 | 33.0 | 118.0 | 0.2797 | 0.2797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 13 | 7.4730 | 0.0057 | 6145.3165 | 4259.6088 | 322.0 | 570.0 | 0.5649 | 312.0 | 0.5474 | 134.0 | 144.0 | 158.0 | 0.9114 | 0.8481 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 14 | 7.6164 | 0.0057 | 6263.2805 | 4341.3752 | 321.0 | 570.0 | 0.5632 | 313.0 | 0.5491 | 137.0 | 145.0 | 158.0 | 0.9177 | 0.8671 | 89.0 | 89.0 | 152.0 | 0.5855 | 0.5855 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 15 | 7.7243 | 0.0057 | 6351.9819 | 4402.8583 | 318.0 | 570.0 | 0.5579 | 313.0 | 0.5491 | 140.0 | 145.0 | 158.0 | 0.9177 | 0.8861 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 16.0 | 16 | 7.7785 | 0.0057 | 6396.5780 | 4433.7700 | 317.0 | 570.0 | 0.5561 | 313.0 | 0.5491 | 141.0 | 145.0 | 158.0 | 0.9177 | 0.8924 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 17.0 | 17 | 7.8645 | 0.0057 | 6467.2957 | 4482.7878 | 315.0 | 570.0 | 0.5526 | 312.0 | 0.5474 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 51.0 | 51.0 | 142.0 | 0.3592 | 0.3592 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 18.0 | 18 | 7.9027 | 0.0057 | 6498.6900 | 4504.5487 | 316.0 | 570.0 | 0.5544 | 312.0 | 0.5474 | 141.0 | 145.0 | 158.0 | 0.9177 | 0.8924 | 87.0 | 87.0 | 152.0 | 0.5724 | 0.5724 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 19.0 | 19 | 7.9998 | 0.0057 | 6578.5635 | 4559.9128 | 313.0 | 570.0 | 0.5491 | 310.0 | 0.5439 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 86.0 | 86.0 | 152.0 | 0.5658 | 0.5658 | 52.0 | 52.0 | 142.0 | 0.3662 | 0.3662 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 20.0 | 20 | 8.0042 | 0.0057 | 6582.1226 | 4562.3797 | 314.0 | 570.0 | 0.5509 | 311.0 | 0.5456 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 21.0 | 21 | 8.0503 | 0.0057 | 6620.0897 | 4588.6965 | 311.0 | 570.0 | 0.5456 | 308.0 | 0.5404 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 52.0 | 52.0 | 142.0 | 0.3662 | 0.3662 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 22.0 | 22 | 8.0228 | 0.0057 | 6597.4578 | 4573.0092 | 315.0 | 570.0 | 0.5526 | 313.0 | 0.5491 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 23.0 | 23 | 8.1360 | 0.0057 | 6690.5589 | 4637.5420 | 312.0 | 570.0 | 0.5474 | 309.0 | 0.5421 | 142.0 | 145.0 | 158.0 | 0.9177 | 0.8987 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 52.0 | 52.0 | 142.0 | 0.3662 | 0.3662 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 24.0 | 24 | 8.1110 | 0.0057 | 6669.9872 | 4623.2829 | 315.0 | 570.0 | 0.5526 | 314.0 | 0.5509 | 145.0 | 146.0 | 158.0 | 0.9241 | 0.9177 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 25.0 | 25 | 8.0899 | 0.0057 | 6652.6387 | 4611.2577 | 313.0 | 570.0 | 0.5491 | 312.0 | 0.5474 | 145.0 | 146.0 | 158.0 | 0.9241 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 26.0 | 26 | 8.0958 | 0.0057 | 6657.4563 | 4614.5971 | 315.0 | 570.0 | 0.5526 | 313.0 | 0.5491 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 32.0 | 32.0 | 118.0 | 0.2712 | 0.2712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 27.0 | 27 | 8.1194 | 0.0057 | 6676.9034 | 4628.0768 | 314.0 | 570.0 | 0.5509 | 312.0 | 0.5474 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 28.0 | 28 | 8.1511 | 0.0057 | 6702.9764 | 4646.1492 | 314.0 | 570.0 | 0.5509 | 313.0 | 0.5491 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 29.0 | 29 | 8.1586 | 0.0057 | 6709.1201 | 4650.4077 | 313.0 | 570.0 | 0.5491 | 313.0 | 0.5491 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 30.0 | 30 | 8.1033 | 0.0057 | 6663.6069 | 4618.8603 | 312.0 | 570.0 | 0.5474 | 310.0 | 0.5439 | 143.0 | 145.0 | 158.0 | 0.9177 | 0.9051 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 31.0 | 31 | 8.1388 | 0.0057 | 6692.8394 | 4639.1227 | 317.0 | 570.0 | 0.5561 | 316.0 | 0.5544 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 32.0 | 32 | 8.1790 | 0.0057 | 6725.8530 | 4662.0061 | 312.0 | 570.0 | 0.5474 | 311.0 | 0.5456 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 53.0 | 53.0 | 142.0 | 0.3732 | 0.3732 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 33.0 | 33 | 8.1788 | 0.0057 | 6725.7129 | 4661.9089 | 314.0 | 570.0 | 0.5509 | 314.0 | 0.5509 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 34.0 | 34 | 8.1461 | 0.0057 | 6698.7991 | 4643.2537 | 315.0 | 570.0 | 0.5526 | 315.0 | 0.5526 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 30.0 | 30.0 | 118.0 | 0.2542 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 35.0 | 35 | 8.1543 | 0.0057 | 6705.5694 | 4647.9465 | 315.0 | 570.0 | 0.5526 | 314.0 | 0.5509 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 54.0 | 54.0 | 142.0 | 0.3803 | 0.3803 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 36.0 | 36 | 8.1585 | 0.0057 | 6709.0706 | 4650.3734 | 315.0 | 570.0 | 0.5526 | 315.0 | 0.5526 | 145.0 | 145.0 | 158.0 | 0.9177 | 0.9177 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 56.0 | 56.0 | 142.0 | 0.3944 | 0.3944 | 31.0 | 31.0 | 118.0 | 0.2627 | 0.2627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 37.0 | 37 | 8.1496 | 0.0057 | 6701.7266 | 4645.2829 | 314.0 | 570.0 | 0.5509 | 313.0 | 0.5491 | 144.0 | 145.0 | 158.0 | 0.9177 | 0.9114 | 85.0 | 85.0 | 152.0 | 0.5592 | 0.5592 | 55.0 | 55.0 | 142.0 | 0.3873 | 0.3873 | 29.0 | 29.0 | 118.0 | 0.2458 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755501526
|
vwzyrraz7l
| 2025-08-18T07:46:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T07:46:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ismail3735252/Lebron
|
Ismail3735252
| 2025-08-18T07:03:16Z | 0 | 0 | null |
[
"az",
"dataset:microsoft/rStar-Coder",
"base_model:openai/gpt-oss-120b",
"base_model:finetune:openai/gpt-oss-120b",
"license:openrail",
"region:us"
] | null | 2025-08-18T07:02:01Z |
---
license: openrail
datasets:
- microsoft/rStar-Coder
language:
- az
metrics:
- cer
base_model:
- openai/gpt-oss-120b
---
|
naddevani/Qwen2.5-Math-7B_check
|
naddevani
| 2025-08-18T06:18:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
feature-extraction
| 2025-08-18T06:17:55Z |
---
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]
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755495675
|
quantumxnode
| 2025-08-18T06:06:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T06:06:55Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
scv3114/bert_lor_kosa_nsmc8
|
scv3114
| 2025-08-18T05:49:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-18T05:47:54Z |
---
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]
|
bujido/MyGemmaNPC
|
bujido
| 2025-08-18T04:50:16Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T04:49:11Z |
---
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="bujido/MyGemmaNPC", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Muapi/flux-photo
|
Muapi
| 2025-08-18T04:27:10Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T04:26:57Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# flux-photo
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:700630@1096652", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
donoway/GSM8K-Binary_Llama-3.2-1B-f8096090
|
donoway
| 2025-08-18T03:09:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T02:56:31Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: GSM8K-Binary_Llama-3.2-1B-f8096090
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. -->
# GSM8K-Binary_Llama-3.2-1B-f8096090
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6336
- Model Preparation Time: 0.0058
- Mdl: 2262.3589
- Accumulated Loss: 1568.1477
- Correct Preds: 1973.0
- Total Preds: 2475.0
- Accuracy: 0.7972
- Correct Gen Preds: 369.0
- Gen Accuracy: 0.1491
- Correct Gen Preds 34192: 0.0
- Correct Preds 34192: 974.0
- Total Labels 34192: 1196.0
- Accuracy 34192: 0.8144
- Gen Accuracy 34192: 0.0
- Correct Gen Preds 41568: 362.0
- Correct Preds 41568: 999.0
- Total Labels 41568: 1267.0
- Accuracy 41568: 0.7885
- Gen Accuracy 41568: 0.2857
## 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: 32
- 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: constant
- lr_scheduler_warmup_ratio: 0.001
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 34192 | Correct Preds 34192 | Total Labels 34192 | Accuracy 34192 | Gen Accuracy 34192 | Correct Gen Preds 41568 | Correct Preds 41568 | Total Labels 41568 | Accuracy 41568 | Gen Accuracy 41568 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:-----------------------:|:-------------------:|:------------------:|:--------------:|:------------------:|:-----------------------:|:-------------------:|:------------------:|:--------------:|:------------------:|
| No log | 0 | 0 | 1.4656 | 0.0058 | 5233.1723 | 3627.3586 | 1196.0 | 2475.0 | 0.4832 | 1204.0 | 0.4865 | 1196.0 | 1196.0 | 1196.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1267.0 | 0.0 | 0.0 |
| 0.5859 | 1.0 | 52 | 0.5818 | 0.0058 | 2077.5047 | 1440.0165 | 1847.0 | 2475.0 | 0.7463 | 8.0 | 0.0032 | 0.0 | 857.0 | 1196.0 | 0.7166 | 0.0 | 0.0 | 990.0 | 1267.0 | 0.7814 | 0.0 |
| 0.6145 | 2.0 | 104 | 0.5168 | 0.0058 | 1845.2524 | 1279.0315 | 1948.0 | 2475.0 | 0.7871 | 69.0 | 0.0279 | 0.0 | 1063.0 | 1196.0 | 0.8888 | 0.0 | 61.0 | 885.0 | 1267.0 | 0.6985 | 0.0481 |
| 0.2879 | 3.0 | 156 | 0.5778 | 0.0058 | 2063.1398 | 1430.0595 | 1868.0 | 2475.0 | 0.7547 | 53.0 | 0.0214 | 0.0 | 1106.0 | 1196.0 | 0.9247 | 0.0 | 46.0 | 762.0 | 1267.0 | 0.6014 | 0.0363 |
| 0.0501 | 4.0 | 208 | 0.6336 | 0.0058 | 2262.3589 | 1568.1477 | 1973.0 | 2475.0 | 0.7972 | 369.0 | 0.1491 | 0.0 | 974.0 | 1196.0 | 0.8144 | 0.0 | 362.0 | 999.0 | 1267.0 | 0.7885 | 0.2857 |
| 0.3604 | 5.0 | 260 | 1.7321 | 0.0058 | 6184.7525 | 4286.9438 | 1864.0 | 2475.0 | 0.7531 | 1135.0 | 0.4586 | 634.0 | 1105.0 | 1196.0 | 0.9239 | 0.5301 | 494.0 | 759.0 | 1267.0 | 0.5991 | 0.3899 |
| 0.0662 | 6.0 | 312 | 1.2469 | 0.0058 | 4452.3018 | 3086.1004 | 1972.0 | 2475.0 | 0.7968 | 1028.0 | 0.4154 | 359.0 | 1028.0 | 1196.0 | 0.8595 | 0.3002 | 661.0 | 944.0 | 1267.0 | 0.7451 | 0.5217 |
| 0.0 | 7.0 | 364 | 1.4682 | 0.0058 | 5242.5624 | 3633.8673 | 1970.0 | 2475.0 | 0.7960 | 1223.0 | 0.4941 | 464.0 | 1033.0 | 1196.0 | 0.8637 | 0.3880 | 751.0 | 937.0 | 1267.0 | 0.7395 | 0.5927 |
| 0.0003 | 8.0 | 416 | 1.9052 | 0.0058 | 6802.8127 | 4715.3504 | 1925.0 | 2475.0 | 0.7778 | 1504.0 | 0.6077 | 583.0 | 948.0 | 1196.0 | 0.7926 | 0.4875 | 914.0 | 977.0 | 1267.0 | 0.7711 | 0.7214 |
| 0.5881 | 9.0 | 468 | 1.9828 | 0.0058 | 7079.8847 | 4907.4021 | 1957.0 | 2475.0 | 0.7907 | 1879.0 | 0.7592 | 920.0 | 983.0 | 1196.0 | 0.8219 | 0.7692 | 952.0 | 974.0 | 1267.0 | 0.7687 | 0.7514 |
| 0.0 | 10.0 | 520 | 1.9968 | 0.0058 | 7129.8865 | 4942.0607 | 1957.0 | 2475.0 | 0.7907 | 1886.0 | 0.7620 | 913.0 | 972.0 | 1196.0 | 0.8127 | 0.7634 | 966.0 | 985.0 | 1267.0 | 0.7774 | 0.7624 |
| 0.5881 | 11.0 | 572 | 2.0014 | 0.0058 | 7146.2344 | 4953.3922 | 1959.0 | 2475.0 | 0.7915 | 1892.0 | 0.7644 | 918.0 | 972.0 | 1196.0 | 0.8127 | 0.7676 | 967.0 | 987.0 | 1267.0 | 0.7790 | 0.7632 |
| 0.0 | 12.0 | 624 | 2.0068 | 0.0058 | 7165.7013 | 4966.8857 | 1959.0 | 2475.0 | 0.7915 | 1890.0 | 0.7636 | 916.0 | 972.0 | 1196.0 | 0.8127 | 0.7659 | 967.0 | 987.0 | 1267.0 | 0.7790 | 0.7632 |
| 0.5882 | 13.0 | 676 | 2.0059 | 0.0058 | 7162.3520 | 4964.5641 | 1959.0 | 2475.0 | 0.7915 | 1893.0 | 0.7648 | 919.0 | 973.0 | 1196.0 | 0.8135 | 0.7684 | 967.0 | 986.0 | 1267.0 | 0.7782 | 0.7632 |
| 0.0 | 14.0 | 728 | 2.0106 | 0.0058 | 7179.0242 | 4976.1204 | 1958.0 | 2475.0 | 0.7911 | 1891.0 | 0.7640 | 918.0 | 972.0 | 1196.0 | 0.8127 | 0.7676 | 966.0 | 986.0 | 1267.0 | 0.7782 | 0.7624 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Qwen/Qwen-Image
|
Qwen
| 2025-08-18T02:42:19Z | 91,747 | 1,683 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"en",
"zh",
"arxiv:2508.02324",
"license:apache-2.0",
"diffusers:QwenImagePipeline",
"region:us"
] |
text-to-image
| 2025-08-02T04:58:07Z |
---
license: apache-2.0
language:
- en
- zh
library_name: diffusers
pipeline_tag: text-to-image
---
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png" width="400"/>
<p>
<p align="center">
💜 <a href="https://chat.qwen.ai/"><b>Qwen Chat</b></a>   |   🤗 <a href="https://huggingface.co/Qwen/Qwen-Image">Hugging Face</a>   |   🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image">ModelScope</a>   |    📑 <a href="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf">Tech Report</a>    |    📑 <a href="https://qwenlm.github.io/blog/qwen-image/">Blog</a>   
<br>
🖥️ <a href="https://huggingface.co/spaces/Qwen/qwen-image">Demo</a>   |   💬 <a href="https://github.com/QwenLM/Qwen-Image/blob/main/assets/wechat.png">WeChat (微信)</a>   |   🫨 <a href="https://discord.gg/CV4E9rpNSD">Discord</a>  
</p>
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/merge3.jpg" width="1600"/>
<p>
## Introduction
We are thrilled to release **Qwen-Image**, an image generation foundation model in the Qwen series that achieves significant advances in **complex text rendering** and **precise image editing**. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.
## News
- 2025.08.04: We released the [Technical Report](https://arxiv.org/abs/2508.02324) of Qwen-Image!
- 2025.08.04: We released Qwen-Image weights! Check at [huggingface](https://huggingface.co/Qwen/Qwen-Image) and [Modelscope](https://modelscope.cn/models/Qwen/Qwen-Image)!
- 2025.08.04: We released Qwen-Image! Check our [blog](https://qwenlm.github.io/blog/qwen-image) for more details!
## Quick Start
Install the latest version of diffusers
```
pip install git+https://github.com/huggingface/diffusers
```
The following contains a code snippet illustrating how to use the model to generate images based on text prompts:
```python
from diffusers import DiffusionPipeline
import torch
model_name = "Qwen/Qwen-Image"
# Load the pipeline
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
device = "cuda"
else:
torch_dtype = torch.float32
device = "cpu"
pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
pipe = pipe.to(device)
positive_magic = {
"en": ", Ultra HD, 4K, cinematic composition.", # for english prompt
"zh": ", 超清，4K，电影级构图." # for chinese prompt
}
# Generate image
prompt = '''A coffee shop entrance features a chalkboard sign reading "Qwen Coffee 😊 $2 per cup," with a neon light beside it displaying "通义千问". Next to it hangs a poster showing a beautiful Chinese woman, and beneath the poster is written "π≈3.1415926-53589793-23846264-33832795-02384197". Ultra HD, 4K, cinematic composition'''
negative_prompt = " " # using an empty string if you do not have specific concept to remove
# Generate with different aspect ratios
aspect_ratios = {
"1:1": (1328, 1328),
"16:9": (1664, 928),
"9:16": (928, 1664),
"4:3": (1472, 1140),
"3:4": (1140, 1472),
"3:2": (1584, 1056),
"2:3": (1056, 1584),
}
width, height = aspect_ratios["16:9"]
image = pipe(
prompt=prompt + positive_magic["en"],
negative_prompt=negative_prompt,
width=width,
height=height,
num_inference_steps=50,
true_cfg_scale=4.0,
generator=torch.Generator(device="cuda").manual_seed(42)
).images[0]
image.save("example.png")
```
## Show Cases
One of its standout capabilities is high-fidelity text rendering across diverse images. Whether it’s alphabetic languages like English or logographic scripts like Chinese, Qwen-Image preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. Text isn’t just overlaid—it’s seamlessly integrated into the visual fabric.
Beyond text, Qwen-Image excels at general image generation with support for a wide range of artistic styles. From photorealistic scenes to impressionist paintings, from anime aesthetics to minimalist design, the model adapts fluidly to creative prompts, making it a versatile tool for artists, designers, and storytellers.
When it comes to image editing, Qwen-Image goes far beyond simple adjustments. It enables advanced operations such as style transfer, object insertion or removal, detail enhancement, text editing within images, and even human pose manipulation—all with intuitive input and coherent output. This level of control brings professional-grade editing within reach of everyday users.
But Qwen-Image doesn’t just create or edit—it understands. It supports a suite of image understanding tasks, including object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and super-resolution. These capabilities, while technically distinct, can all be seen as specialized forms of intelligent image editing, powered by deep visual comprehension.
Together, these features make Qwen-Image not just a tool for generating pretty pictures, but a comprehensive foundation model for intelligent visual creation and manipulation—where language, layout, and imagery converge.
## License Agreement
Qwen-Image is licensed under Apache 2.0.
## Citation
We kindly encourage citation of our work if you find it useful.
```bibtex
@misc{wu2025qwenimagetechnicalreport,
title={Qwen-Image Technical Report},
author={Chenfei Wu and Jiahao Li and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kun Yan and Sheng-ming Yin and Shuai Bai and Xiao Xu and Yilei Chen and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhengyi Wang and An Yang and Bowen Yu and Chen Cheng and Dayiheng Liu and Deqing Li and Hang Zhang and Hao Meng and Hu Wei and Jingyuan Ni and Kai Chen and Kuan Cao and Liang Peng and Lin Qu and Minggang Wu and Peng Wang and Shuting Yu and Tingkun Wen and Wensen Feng and Xiaoxiao Xu and Yi Wang and Yichang Zhang and Yongqiang Zhu and Yujia Wu and Yuxuan Cai and Zenan Liu},
year={2025},
eprint={2508.02324},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.02324},
}
```
|
asasidh/model
|
asasidh
| 2025-08-18T02:29:22Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"llama",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-02-22T05:44:44Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** asasidh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755462891
|
mang3dd
| 2025-08-17T21:00:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T21:00:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
haryoaw/xlm-roberta-base_massive_en-US_2
|
haryoaw
| 2025-08-17T20:51:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-17T20:50:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
matboz/temp1
|
matboz
| 2025-08-17T20:35:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-2-27b-it",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:google/gemma-2-27b-it",
"region:us"
] |
text-generation
| 2025-08-17T20:34:49Z |
---
base_model: google/gemma-2-27b-it
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:google/gemma-2-27b-it
- lora
- sft
- transformers
- trl
---
# 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 0.17.0
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755460732
|
Sayemahsjn
| 2025-08-17T20:16:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T20:16:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755459279
|
unitova
| 2025-08-17T20:00:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T20:00:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
manancode/opus-mt-fr-bzs-ctranslate2-android
|
manancode
| 2025-08-17T17:23:30Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T17:23:20Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-fr-bzs-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-fr-bzs` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-fr-bzs
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-xh-ctranslate2-android
|
manancode
| 2025-08-17T16:27:51Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T16:27:41Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-xh-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-xh` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-xh
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-umb-ctranslate2-android
|
manancode
| 2025-08-17T16:26:53Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T16:26:43Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-umb-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-umb` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-umb
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
manancode/opus-mt-en-to-ctranslate2-android
|
manancode
| 2025-08-17T16:24:26Z | 0 | 0 | null |
[
"translation",
"opus-mt",
"ctranslate2",
"quantized",
"multilingual",
"license:apache-2.0",
"region:us"
] |
translation
| 2025-08-17T16:24:13Z |
---
license: apache-2.0
tags:
- translation
- opus-mt
- ctranslate2
- quantized
language:
- multilingual
pipeline_tag: translation
---
# opus-mt-en-to-ctranslate2-android
This is a quantized INT8 version of `Helsinki-NLP/opus-mt-en-to` converted to CTranslate2 format for efficient inference.
## Model Details
- **Original Model**: Helsinki-NLP/opus-mt-en-to
- **Format**: CTranslate2
- **Quantization**: INT8
- **Framework**: OPUS-MT
- **Converted by**: Automated conversion pipeline
## Usage
### With CTranslate2
```python
import ctranslate2
import sentencepiece as spm
# Load the model
translator = ctranslate2.Translator("path/to/model")
# Load tokenizers
sp_source = spm.SentencePieceProcessor(model_file="source.spm")
sp_target = spm.SentencePieceProcessor(model_file="target.spm")
# Translate
source_tokens = sp_source.encode("Your text here", out_type=str)
results = translator.translate_batch([source_tokens])
translation = sp_target.decode(results[0].hypotheses[0])
```
## Performance
This INT8 quantized version provides:
- ~75% reduction in model size
- Faster inference speed
- Maintained translation quality
- Mobile-friendly deployment
## Original Model
Based on the OPUS-MT project: https://github.com/Helsinki-NLP/Opus-MT
|
qtaka/blockassist-bc-twitchy_untamed_gerbil_1755403405
|
qtaka
| 2025-08-17T04:07:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy untamed gerbil",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T04:07:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy untamed gerbil
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755388280
|
chainway9
| 2025-08-17T00:19:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T00:19:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
joanna302/Qwen3-8B-Base_en_SFT_8e-05
|
joanna302
| 2025-08-16T17:58:58Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"unsloth",
"conversational",
"base_model:unsloth/Qwen3-8B-Base",
"base_model:finetune:unsloth/Qwen3-8B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-17T10:56:11Z |
---
base_model: unsloth/Qwen3-8B-Base
library_name: transformers
model_name: Qwen3-8B-Base_en_SFT_8e-05
tags:
- generated_from_trainer
- trl
- sft
- unsloth
licence: license
---
# Model Card for Qwen3-8B-Base_en_SFT_8e-05
This model is a fine-tuned version of [unsloth/Qwen3-8B-Base](https://huggingface.co/unsloth/Qwen3-8B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
# Training data:
+ 9000 examples from the No Robot instructions and demonstration dataset, 3000 data from the WildJailbreak safety-training (750 examples of each category)
# Leaning rate:
8e-05
# Fine-tuning:
SFT
## 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="joanna302/Qwen3-8B-Base_en_SFT_8e-05", 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/prism-eval/Qwen3-8B-Base_en_SFT_8e-05/runs/f4uouvhf)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.53.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
rafsya427/blockassist-bc-monstrous_bristly_chimpanzee_1755356864
|
rafsya427
| 2025-08-16T15:34:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"monstrous bristly chimpanzee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T15:34:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- monstrous bristly chimpanzee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755321269
|
rvipitkirubbe
| 2025-08-16T05:41:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-16T05:41:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Coaster41/patchtst-sae-grid-32-0.5-expe
|
Coaster41
| 2025-08-15T20:28:29Z | 0 | 0 |
saelens
|
[
"saelens",
"region:us"
] | null | 2025-08-15T20:28:23Z |
---
library_name: saelens
---
# SAEs for use with the SAELens library
This repository contains the following SAEs:
- blocks.0.hook_mlp_out
Load these SAEs using SAELens as below:
```python
from sae_lens import SAE
sae = SAE.from_pretrained("Coaster41/patchtst-sae-grid-32-0.5-expe", "<sae_id>")
```
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.