modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-19 18:27:53
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 513
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-19 18:27:44
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
donoway/ARC-Easy_Llama-3.2-1B-2vnc0c6d
|
donoway
| 2025-08-18T13:41:30Z | 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
| 2025-08-18T13:23:58Z |
---
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-2vnc0c6d
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-2vnc0c6d
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: 0.7003
- Model Preparation Time: 0.0056
- Mdl: 575.9203
- Accumulated Loss: 399.1976
- Correct Preds: 451.0
- Total Preds: 570.0
- Accuracy: 0.7912
- Correct Gen Preds: 451.0
- Gen Accuracy: 0.7912
- Correct Gen Preds 32: 134.0
- Correct Preds 32: 134.0
- Total Labels 32: 158.0
- Accuracy 32: 0.8481
- Gen Accuracy 32: 0.8481
- Correct Gen Preds 33: 124.0
- Correct Preds 33: 124.0
- Total Labels 33: 152.0
- Accuracy 33: 0.8158
- Gen Accuracy 33: 0.8158
- Correct Gen Preds 34: 112.0
- Correct Preds 34: 112.0
- Total Labels 34: 142.0
- Accuracy 34: 0.7887
- Gen Accuracy 34: 0.7887
- Correct Gen Preds 35: 81.0
- Correct Preds 35: 81.0
- Total Labels 35: 118.0
- Accuracy 35: 0.6864
- Gen Accuracy 35: 0.6864
- 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: 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 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 | 0.0056 | 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 |
| 0.7518 | 1.0 | 32 | 0.7326 | 0.0056 | 602.4087 | 417.5579 | 411.0 | 570.0 | 0.7211 | 410.0 | 0.7193 | 86.0 | 87.0 | 158.0 | 0.5506 | 0.5443 | 124.0 | 124.0 | 152.0 | 0.8158 | 0.8158 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 94.0 | 94.0 | 118.0 | 0.7966 | 0.7966 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.516 | 2.0 | 64 | 0.7003 | 0.0056 | 575.9203 | 399.1976 | 451.0 | 570.0 | 0.7912 | 451.0 | 0.7912 | 134.0 | 134.0 | 158.0 | 0.8481 | 0.8481 | 124.0 | 124.0 | 152.0 | 0.8158 | 0.8158 | 112.0 | 112.0 | 142.0 | 0.7887 | 0.7887 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0089 | 3.0 | 96 | 1.0818 | 0.0056 | 889.5841 | 616.6127 | 436.0 | 570.0 | 0.7649 | 435.0 | 0.7632 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 110.0 | 110.0 | 152.0 | 0.7237 | 0.7237 | 112.0 | 112.0 | 142.0 | 0.7887 | 0.7887 | 94.0 | 94.0 | 118.0 | 0.7966 | 0.7966 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0002 | 4.0 | 128 | 1.7721 | 0.0056 | 1457.2823 | 1010.1111 | 435.0 | 570.0 | 0.7632 | 433.0 | 0.7596 | 126.0 | 128.0 | 158.0 | 0.8101 | 0.7975 | 118.0 | 118.0 | 152.0 | 0.7763 | 0.7763 | 115.0 | 115.0 | 142.0 | 0.8099 | 0.8099 | 74.0 | 74.0 | 118.0 | 0.6271 | 0.6271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0006 | 5.0 | 160 | 1.6350 | 0.0056 | 1344.5068 | 931.9411 | 438.0 | 570.0 | 0.7684 | 438.0 | 0.7684 | 114.0 | 114.0 | 158.0 | 0.7215 | 0.7215 | 124.0 | 124.0 | 152.0 | 0.8158 | 0.8158 | 115.0 | 115.0 | 142.0 | 0.8099 | 0.8099 | 85.0 | 85.0 | 118.0 | 0.7203 | 0.7203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0137 | 6.0 | 192 | 1.7262 | 0.0056 | 1419.4795 | 983.9082 | 451.0 | 570.0 | 0.7912 | 449.0 | 0.7877 | 132.0 | 133.0 | 158.0 | 0.8418 | 0.8354 | 119.0 | 120.0 | 152.0 | 0.7895 | 0.7829 | 114.0 | 114.0 | 142.0 | 0.8028 | 0.8028 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0025 | 7.0 | 224 | 2.0853 | 0.0056 | 1714.8122 | 1188.6173 | 443.0 | 570.0 | 0.7772 | 442.0 | 0.7754 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 121.0 | 121.0 | 152.0 | 0.7961 | 0.7961 | 117.0 | 117.0 | 142.0 | 0.8239 | 0.8239 | 85.0 | 85.0 | 118.0 | 0.7203 | 0.7203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 8.0 | 256 | 2.1702 | 0.0056 | 1784.6740 | 1237.0417 | 441.0 | 570.0 | 0.7737 | 440.0 | 0.7719 | 122.0 | 123.0 | 158.0 | 0.7785 | 0.7722 | 120.0 | 120.0 | 152.0 | 0.7895 | 0.7895 | 116.0 | 116.0 | 142.0 | 0.8169 | 0.8169 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 9.0 | 288 | 2.1716 | 0.0056 | 1785.7880 | 1237.8139 | 442.0 | 570.0 | 0.7754 | 441.0 | 0.7737 | 125.0 | 126.0 | 158.0 | 0.7975 | 0.7911 | 120.0 | 120.0 | 152.0 | 0.7895 | 0.7895 | 113.0 | 113.0 | 142.0 | 0.7958 | 0.7958 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 320 | 2.1731 | 0.0056 | 1786.9933 | 1238.6494 | 439.0 | 570.0 | 0.7702 | 438.0 | 0.7684 | 123.0 | 124.0 | 158.0 | 0.7848 | 0.7785 | 120.0 | 120.0 | 152.0 | 0.7895 | 0.7895 | 113.0 | 113.0 | 142.0 | 0.7958 | 0.7958 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 352 | 2.1918 | 0.0056 | 1802.4099 | 1249.3354 | 442.0 | 570.0 | 0.7754 | 441.0 | 0.7737 | 124.0 | 125.0 | 158.0 | 0.7911 | 0.7848 | 121.0 | 121.0 | 152.0 | 0.7961 | 0.7961 | 114.0 | 114.0 | 142.0 | 0.8028 | 0.8028 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 384 | 2.2028 | 0.0056 | 1811.4368 | 1255.5923 | 443.0 | 570.0 | 0.7772 | 442.0 | 0.7754 | 125.0 | 126.0 | 158.0 | 0.7975 | 0.7911 | 120.0 | 120.0 | 152.0 | 0.7895 | 0.7895 | 114.0 | 114.0 | 142.0 | 0.8028 | 0.8028 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 416 | 2.2258 | 0.0056 | 1830.3815 | 1268.7238 | 442.0 | 570.0 | 0.7754 | 441.0 | 0.7737 | 124.0 | 125.0 | 158.0 | 0.7911 | 0.7848 | 120.0 | 120.0 | 152.0 | 0.7895 | 0.7895 | 114.0 | 114.0 | 142.0 | 0.8028 | 0.8028 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 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
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755523695
|
yaelahnal
| 2025-08-18T13:35:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:29:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755522631
|
lisaozill03
| 2025-08-18T13:35:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:35:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dsfsi/mms-300m-lwazi
|
dsfsi
| 2025-08-18T13:35:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/mms-300m",
"base_model:finetune:facebook/mms-300m",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-15T17:01:12Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/mms-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mms-300m-lwazi
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. -->
# mms-300m-lwazi
This model is a fine-tuned version of [facebook/mms-300m](https://huggingface.co/facebook/mms-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.3430
- Wer: 98.2090
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 16.4263 | 0.1273 | 300 | 15.3343 | 108.2448 |
| 11.9823 | 0.2545 | 600 | 10.1162 | 99.9950 |
| 8.569 | 0.3818 | 900 | 6.9004 | 99.6498 |
| 7.3022 | 0.5090 | 1200 | 6.3450 | 98.6175 |
| 6.9001 | 0.6363 | 1500 | 6.3276 | 98.2373 |
| 7.1288 | 0.7635 | 1800 | 6.3430 | 98.2090 |
### Framework versions
- Transformers 4.52.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755522529
|
thanobidex
| 2025-08-18T13:34:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:34:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755522494
|
pempekmangedd
| 2025-08-18T13:33:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:33:38Z |
---
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).
|
Muapi/female-pov-flux
|
Muapi
| 2025-08-18T13:32:39Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T13:32:26Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Female POV [FLUX]
**Base model**: Flux.1 D
**Trained words**: aidmaFemalePOV
## 🧠 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:743577@831584", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
VoilaRaj/78_2TgViM
|
VoilaRaj
| 2025-08-18T13:32:23Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-18T13:28:29Z |
---
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).
|
Muapi/terminator-t-800-flux1.d-sdxl
|
Muapi
| 2025-08-18T13:32:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T13:32:00Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Terminator T-800 - Flux1.D & SDXL
**Base model**: Flux.1 D
**Trained words**: T800 robot
## 🧠 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:207579@741410", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/latex-catsuit-flux
|
Muapi
| 2025-08-18T13:31:44Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T13:31:17Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Latex Catsuit [Flux]
**Base model**: Flux.1 D
**Trained words**: latex bodysuit
## 🧠 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:787761@880962", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mradermacher/InnoSpark-0.5B-0717-GGUF
|
mradermacher
| 2025-08-18T13:30:20Z | 214 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:sii-research/InnoSpark-0.5B-0717",
"base_model:quantized:sii-research/InnoSpark-0.5B-0717",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-21T09:44:29Z |
---
base_model: sii-research/InnoSpark-0.5B-0717
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/sii-research/InnoSpark-0.5B-0717
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#InnoSpark-0.5B-0717-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q3_K_S.gguf) | Q3_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q2_K.gguf) | Q2_K | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q5_K_S.gguf) | Q5_K_S | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q5_K_M.gguf) | Q5_K_M | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q6_K.gguf) | Q6_K | 0.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.Q8_0.gguf) | Q8_0 | 0.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-0.5B-0717-GGUF/resolve/main/InnoSpark-0.5B-0717.f16.gguf) | f16 | 1.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Muapi/james-r.-eads-style
|
Muapi
| 2025-08-18T13:30:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T13:29:28Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# James R. Eads Style
**Base model**: Flux.1 D
**Trained words**: James R. Eads Style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:98833@1570699", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
team-suzuki/Qwen3-4B-SFT-TEST2
|
team-suzuki
| 2025-08-18T13:29:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"instruction-tuning",
"chat",
"pytorch",
"en",
"ja",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T13:29:11Z |
---
license: other
base_model: Qwen
tags:
- qwen3
- instruction-tuning
- chat
- transformers
- pytorch
- safetensors
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
---
# Qwen3-4B-SFT-TEST2
## Model Description
Qwen3-4B-SFT-TEST2 is a language model fine-tuned for improved performance on various natural language understanding and generation tasks.
## Model Details
- **Model Name**: Qwen3-4B-SFT-TEST2
- **Base Model**: Qwen
- **Architecture**: Qwen3ForCausalLM
- **Parameters**: ~2B
- **Model Type**: qwen3
- **Total Size**: 3.4GB
- **Upload Date**: 2025-08-18
### Model Architecture
- **Hidden Size**: 2560
- **Number of Layers**: 36
- **Attention Heads**: 32
- **Vocabulary Size**: 151936
- **Max Position Embeddings**: 40960
## Files
This repository contains:
- **SafeTensors format**: Optimized for fast loading and reduced memory usage
- **Tokenizer**: Included for text processing
## Usage
### Loading the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "team-suzuki/Qwen3-4B-SFT-TEST2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
```
### Text Generation
```python
# Prepare input
text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")
# Generate response
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=100,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
# Decode output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### Chat Format (if applicable)
```python
# For instruction-tuned models
messages = [
{"role": "user", "content": "What is the capital of Japan?"}
]
# Apply chat template if available
if hasattr(tokenizer, 'apply_chat_template'):
formatted_input = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
)
else:
formatted_input = tokenizer("User: What is the capital of Japan?\nAssistant:", return_tensors="pt")
# Generate response
outputs = model.generate(
formatted_input,
max_new_tokens=100,
temperature=0.7,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Training Details
- **Training Data**: [Specify training dataset if known]
- **Fine-tuning Method**: [Specify fine-tuning approach]
- **Training Framework**: PyTorch + Transformers
- **Hardware**: [Specify if known]
## Evaluation
[Add evaluation results if available]
## Limitations and Biases
- This model may exhibit biases present in the training data
- Performance may vary across different domains and languages
- Always verify outputs for accuracy and appropriateness
## Ethical Considerations
- Use responsibly and in accordance with applicable laws and regulations
- Be aware of potential biases and limitations
- Consider the impact of generated content
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{qwen3_4b_sft_test2,
title={Qwen3-4B-SFT-TEST2: A Fine-tuned Language Model},
author={[Your Name/Organization]},
year={2025},
url={https://huggingface.co/team-suzuki/Qwen3-4B-SFT-TEST2}
}
```
## License
This model is released under the other license. Please see the license file for more details.
## Contact
For questions or issues, please [open an issue](https://huggingface.co/team-suzuki/Qwen3-4B-SFT-TEST2/discussions) on this repository.
---
*This model card was automatically generated. Please update with specific details about your model.*
|
Muapi/dark-fantasy-eldritch-horror-flux-il-sdxl-sd1.5
|
Muapi
| 2025-08-18T13:28:55Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T13:28:44Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Dark Fantasy / Eldritch Horror [FLUX-IL-SDXL-SD1.5]
**Base model**: Flux.1 D
**Trained words**: dark fantasy, eldritch horror, mythology, apocalyptic visions, necromantic royalty, cosmic horror
## 🧠 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:1234663@1391787", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
dlolol/blockassist-bc-dormant_hairy_tortoise_1755523137
|
dlolol
| 2025-08-18T13:28:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant hairy tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:28:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant hairy tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/InnoSpark-R-72B-0701-i1-GGUF
|
mradermacher
| 2025-08-18T13:27:59Z | 351 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:sii-research/InnoSpark-R-72B-0701",
"base_model:quantized:sii-research/InnoSpark-R-72B-0701",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-07-22T19:53:15Z |
---
base_model: sii-research/InnoSpark-R-72B-0701
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/sii-research/InnoSpark-R-72B-0701
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#InnoSpark-R-72B-0701-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ1_S.gguf) | i1-IQ1_S | 22.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ1_M.gguf) | i1-IQ1_M | 23.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ2_XS.gguf) | i1-IQ2_XS | 27.2 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ2_S.gguf) | i1-IQ2_S | 28.0 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ2_M.gguf) | i1-IQ2_M | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q2_K_S.gguf) | i1-Q2_K_S | 29.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q2_K.gguf) | i1-Q2_K | 29.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 31.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ3_XS.gguf) | i1-IQ3_XS | 32.9 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ3_S.gguf) | i1-IQ3_S | 34.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q3_K_S.gguf) | i1-Q3_K_S | 34.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ3_M.gguf) | i1-IQ3_M | 35.6 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q3_K_M.gguf) | i1-Q3_K_M | 37.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q3_K_L.gguf) | i1-Q3_K_L | 39.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-IQ4_XS.gguf) | i1-IQ4_XS | 39.8 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q4_0.gguf) | i1-Q4_0 | 41.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q4_K_S.gguf) | i1-Q4_K_S | 44.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q4_1.gguf) | i1-Q4_1 | 45.8 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q4_K_M.gguf) | i1-Q4_K_M | 47.5 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 54.5 | |
| [PART 1](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/InnoSpark-R-72B-0701-i1-GGUF/resolve/main/InnoSpark-R-72B-0701.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 64.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Muapi/a-tiny-waist-micro-waist-for-hourglass-bodies
|
Muapi
| 2025-08-18T13:27:42Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T13:27:31Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# A Tiny Waist - Micro Waist for Hourglass Bodies
**Base model**: Flux.1 D
**Trained words**: tiny waist
## 🧠 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:686012@767779", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ahhava/elyon
|
ahhava
| 2025-08-18T13:27:20Z | 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
| 2025-08-18T13:01:49Z |
---
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: elyon
---
# Elyon
<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 `elyon` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "elyon",
"lora_weights": "https://huggingface.co/ahhava/elyon/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('ahhava/elyon', weight_name='lora.safetensors')
image = pipeline('elyon').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: 30
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ahhava/elyon/discussions) to add images that show off what you’ve made with this LoRA.
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755521877
|
mang3dd
| 2025-08-18T13:25:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:25:40Z |
---
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).
|
Vanbitcase/2b-700r-qwen-vl-t1.2b_merged
|
Vanbitcase
| 2025-08-18T13:24:36Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T13:24:33Z |
---
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]
|
VoilaRaj/78_OFMdkc
|
VoilaRaj
| 2025-08-18T13:24:08Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-18T13:20:18Z |
---
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).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755521757
|
koloni
| 2025-08-18T13:23:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:23:15Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/InnoSpark-HPC-RM-32B-GGUF
|
mradermacher
| 2025-08-18T13:21:17Z | 171 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:sii-research/InnoSpark-HPC-RM-32B",
"base_model:quantized:sii-research/InnoSpark-HPC-RM-32B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-07-26T15:13:23Z |
---
base_model: sii-research/InnoSpark-HPC-RM-32B
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/sii-research/InnoSpark-HPC-RM-32B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#InnoSpark-HPC-RM-32B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/InnoSpark-HPC-RM-32B-GGUF/resolve/main/InnoSpark-HPC-RM-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
dgambettaphd/M_mis_run2_gen2_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-18T13:21:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T13:20:49Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755522104
|
Sayemahsjn
| 2025-08-18T13:20:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:20:54Z |
---
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_1755521544
|
unitova
| 2025-08-18T13:19:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:19:01Z |
---
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).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755521230
|
ihsanridzi
| 2025-08-18T13:15:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:15:49Z |
---
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).
|
isbondarev/Qwen3-adv
|
isbondarev
| 2025-08-18T13:10:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T13:08:50Z |
---
library_name: transformers
tags:
- llama-factory
---
# 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]
|
mgazz/granite-geospatial-biomass
|
mgazz
| 2025-08-18T13:09:53Z | 47 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-15T08:53:14Z |
---
license: apache-2.0
---
|
piczify/extractc
|
piczify
| 2025-08-18T13:08:51Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"fal",
"image-to-image",
"base_model:black-forest-labs/FLUX.1-Kontext-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev",
"license:other",
"region:us"
] |
image-to-image
| 2025-08-18T12:59:15Z |
---
tags:
- flux
- lora
- diffusers
- fal
base_model:
- black-forest-labs/FLUX.1-Kontext-dev
instance_prompt: extract only the clothes over a plain background, product photography style
license: other
pipeline_tag: image-to-image
---
# extract clothes
<Gallery />
## Model description
Replace "clothes" with the corresponding type of clothing, It is best to extract single pieces of clothing such as: shirt, trouser, pant, t-shirt etc.
## Trigger words
You should use `extract only the clothes over a plain background, product photography style` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/piczify/extract-clothes/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-kontext-trainer](https://fal.ai/models/fal-ai/flux-kontext-trainer).
|
Eunma/korean-model
|
Eunma
| 2025-08-18T13:08:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen",
"qwen2.5",
"lora",
"unsloth",
"korean",
"education",
"textbook",
"text-generation",
"conversational",
"ko",
"dataset:maywell/korean_textbooks",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-18T12:43:08Z |
---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- qwen
- qwen2.5
- lora
- unsloth
- korean
- education
- textbook
language:
- ko
datasets:
- maywell/korean_textbooks
library_name: peft
pipeline_tag: text-generation
---
# 한국어 교육 자료 파인튜닝 모델 (Qwen2.5-1.5B + LoRA)
## 모델 소개
**Qwen/Qwen2.5-1.5B-Instruct** 를 기반으로 **maywell/korean_textbooks** 데이터셋, 그리고
**LoRA(저랭크 적응)** 기법을 사용해 파인튜닝한 **어댑터(LoRA 가중치)** 입니다.
베이스 가중치는 포함되지 않으며, **베이스 + 어댑터**로 로드하여 사용합니다.
- 학습 방식: LoRA (QLoRA, 4bit 로딩)
- 주요 목적: 한국어 교육/설명형 응답 품질 향상
> 참고: 학습에는 Unsloth/TRL/PEFT 스택을 사용했고, 추론은 HF Transformers + PEFT만으로 가능합니다.
## 사용 방법
### 1) 모델 로드(4bit + PEFT)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
BASE = "Qwen/Qwen2.5-1.5B-Instruct"
ADAPTER = "Eunma/korean-model"
tokenizer = AutoTokenizer.from_pretrained(BASE)
base = AutoModelForCausalLM.from_pretrained(
BASE,
load_in_4bit=True,
device_map="auto",
trust_remote_code=True
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()
messages = [
{ "role": "system", "content": "한국어로 정확하고 친절하게 설명하는 교육 도우미입니다." },
{ "role": "user", "content": "2의 거듭제곱에 대해 간단히 설명해줘." },
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
enc = tokenizer(prompt, return_tensors="pt").to(model.device)
if "attention_mask" not in enc:
enc["attention_mask"] = torch.ones_like(enc["input_ids"])
with torch.inference_mode():
out = model.generate(
**enc,
max_new_tokens=256,
do_sample=True, temperature=0.7, top_p=0.9,
pad_token_id=tokenizer.eos_token_id,
use_cache=True
)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```
## 훈련 정보
- **베이스 모델**: Qwen/Qwen2.5-1.5B
- **훈련 스텝**: 30 steps
- **옵티마이저**: adamw_8bit
- **스케줄러**: linear
- **LoRA 설정**: r=8, alpha=16
- **타겟 모듈**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **데이터셋**: maywell/korean_textbooks
## 시스템 요구사항
- **GPU 메모리**: 최소 6GB (권장 8GB+)
- 학습(QLoRA, 4bit): GPU 12–16GB 권장(T4 16GB에서 확인)
- **Python**: 3.10+
- **주요 라이브러리**: transformers, peft, torch, bitsandbytes, accelerate
## 주의사항
1. 한국어 중심으로 튜닝. 타 언어 응답 품질은 제한적일 수 있음.
2. 베이스 라이선스 및 사용 정책 준수
3. 어댑터만 포함되어 있으므로 베이스 모델과 함께 로드
4. 사실성 검증 필요.
## 관련 링크
- **베이스 모델**: [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
- **데이터셋**: [maywell/korean_textbooks](https://huggingface.co/datasets/maywell/korean_textbooks)
- **PEFT(LoRA)**: https://github.com/huggingface/peft
- **Transformers**: https://github.com/huggingface/transformers
## 📜 라이선스
이 모델은 베이스 모델인 Qwen2.5-1.5B의 라이선스를 따릅니다.
|
chainway9/blockassist-bc-untamed_quick_eel_1755520780
|
chainway9
| 2025-08-18T13:07:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:07:08Z |
---
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).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755520683
|
katanyasekolah
| 2025-08-18T13:07:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:06:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755520897
|
lisaozill03
| 2025-08-18T13:06:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T13:06:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Wing4/llama3-8b-sentiment-analyzer
|
Wing4
| 2025-08-18T13:06:20Z | 8 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct",
"lora",
"transformers",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T07:39:33Z |
---
library_name: peft
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- base_model:adapter:meta-llama/Meta-Llama-3.1-8B-Instruct
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: llama3-8b-sentiment-analyzer
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. -->
# llama3-8b-sentiment-analyzer
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0790
## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0959 | 0.2116 | 250 | 0.1097 |
| 0.0869 | 0.4233 | 500 | 0.0841 |
| 0.08 | 0.6349 | 750 | 0.0805 |
| 0.0797 | 0.8466 | 1000 | 0.0790 |
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Jayywestty/bart-summarizer-epoch2
|
Jayywestty
| 2025-08-18T13:05:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T13:04:47Z |
---
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]
|
haihp02/fullllllllllllllllllllllllllllllllllllll-wosft-data-filtered
|
haihp02
| 2025-08-18T13:04:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T13:04:43Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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]
|
hdong0/deepseek-Qwen-1.5B-batch-mix-GRPO_deepscaler_acc_seq_end_mask_thin_mu_8_warmed_abf
|
hdong0
| 2025-08-18T13:04:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:agentica-org/DeepScaleR-Preview-Dataset",
"arxiv:2402.03300",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T02:27:58Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
datasets: agentica-org/DeepScaleR-Preview-Dataset
library_name: transformers
model_name: deepseek-Qwen-1.5B-batch-mix-GRPO_deepscaler_acc_seq_end_mask_thin_mu_8_warmed_abf
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for deepseek-Qwen-1.5B-batch-mix-GRPO_deepscaler_acc_seq_end_mask_thin_mu_8_warmed_abf
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [agentica-org/DeepScaleR-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset) 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="hdong0/deepseek-Qwen-1.5B-batch-mix-GRPO_deepscaler_acc_seq_end_mask_thin_mu_8_warmed_abf", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
joanna302/Qwen3-8B-Base_ar_alpaca_0.66_part_SFT_2e-05
|
joanna302
| 2025-08-18T13:02:40Z | 0 | 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
| 2025-08-17T15:55:26Z |
---
base_model: unsloth/Qwen3-8B-Base
library_name: transformers
model_name: Qwen3-8B-Base_ar_alpaca_0.66_part_SFT_2e-05
tags:
- generated_from_trainer
- trl
- sft
- unsloth
licence: license
---
# Model Card for Qwen3-8B-Base_ar_alpaca_0.66_part_SFT_2e-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).
## 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_ar_alpaca_0.66_part_SFT_2e-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_ar_alpaca_0.66_part_SFT_2e-05/runs/espls84s)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.8.0
- 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}}
}
```
|
mehmet0sahinn/exp-tokenizer-1
|
mehmet0sahinn
| 2025-08-18T13:02:29Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T13:02:28Z |
---
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]
|
donoway/ARC-Easy_Llama-3.2-1B-w1lhw9kp
|
donoway
| 2025-08-18T13:01:11Z | 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
| 2025-08-18T12:40:47Z |
---
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-w1lhw9kp
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-w1lhw9kp
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: 2.1597
- Model Preparation Time: 0.0056
- Mdl: 1776.0078
- Accumulated Loss: 1231.0348
- Correct Preds: 432.0
- Total Preds: 570.0
- Accuracy: 0.7579
- Correct Gen Preds: 431.0
- Gen Accuracy: 0.7561
- Correct Gen Preds 32: 128.0
- Correct Preds 32: 129.0
- Total Labels 32: 158.0
- Accuracy 32: 0.8165
- Gen Accuracy 32: 0.8101
- Correct Gen Preds 33: 120.0
- Correct Preds 33: 120.0
- Total Labels 33: 152.0
- Accuracy 33: 0.7895
- Gen Accuracy 33: 0.7895
- Correct Gen Preds 34: 106.0
- Correct Preds 34: 106.0
- Total Labels 34: 142.0
- Accuracy 34: 0.7465
- Gen Accuracy 34: 0.7465
- Correct Gen Preds 35: 77.0
- Correct Preds 35: 77.0
- Total Labels 35: 118.0
- Accuracy 35: 0.6525
- Gen Accuracy 35: 0.6525
- 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: 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 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 | 0.0056 | 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 |
| 0.7522 | 1.0 | 28 | 0.7367 | 0.0056 | 605.7885 | 419.9006 | 419.0 | 570.0 | 0.7351 | 402.0 | 0.7053 | 103.0 | 114.0 | 158.0 | 0.7215 | 0.6519 | 122.0 | 122.0 | 152.0 | 0.8026 | 0.8026 | 108.0 | 109.0 | 142.0 | 0.7676 | 0.7606 | 69.0 | 74.0 | 118.0 | 0.6271 | 0.5847 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.4231 | 2.0 | 56 | 0.7759 | 0.0056 | 638.0789 | 442.2826 | 424.0 | 570.0 | 0.7439 | 423.0 | 0.7421 | 134.0 | 135.0 | 158.0 | 0.8544 | 0.8481 | 107.0 | 107.0 | 152.0 | 0.7039 | 0.7039 | 100.0 | 100.0 | 142.0 | 0.7042 | 0.7042 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0288 | 3.0 | 84 | 1.0058 | 0.0056 | 827.0667 | 573.2790 | 419.0 | 570.0 | 0.7351 | 419.0 | 0.7351 | 117.0 | 117.0 | 158.0 | 0.7405 | 0.7405 | 117.0 | 117.0 | 152.0 | 0.7697 | 0.7697 | 111.0 | 111.0 | 142.0 | 0.7817 | 0.7817 | 74.0 | 74.0 | 118.0 | 0.6271 | 0.6271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0006 | 4.0 | 112 | 1.7356 | 0.0056 | 1427.2623 | 989.3028 | 423.0 | 570.0 | 0.7421 | 423.0 | 0.7421 | 105.0 | 105.0 | 158.0 | 0.6646 | 0.6646 | 117.0 | 117.0 | 152.0 | 0.7697 | 0.7697 | 115.0 | 115.0 | 142.0 | 0.8099 | 0.8099 | 86.0 | 86.0 | 118.0 | 0.7288 | 0.7288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0003 | 5.0 | 140 | 2.1692 | 0.0056 | 1783.7864 | 1236.4265 | 429.0 | 570.0 | 0.7526 | 429.0 | 0.7526 | 126.0 | 126.0 | 158.0 | 0.7975 | 0.7975 | 116.0 | 116.0 | 152.0 | 0.7632 | 0.7632 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0002 | 6.0 | 168 | 2.1597 | 0.0056 | 1776.0078 | 1231.0348 | 432.0 | 570.0 | 0.7579 | 431.0 | 0.7561 | 128.0 | 129.0 | 158.0 | 0.8165 | 0.8101 | 120.0 | 120.0 | 152.0 | 0.7895 | 0.7895 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 7.0 | 196 | 2.3405 | 0.0056 | 1924.6805 | 1334.0869 | 423.0 | 570.0 | 0.7421 | 422.0 | 0.7404 | 116.0 | 117.0 | 158.0 | 0.7405 | 0.7342 | 115.0 | 115.0 | 152.0 | 0.7566 | 0.7566 | 108.0 | 108.0 | 142.0 | 0.7606 | 0.7606 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0381 | 8.0 | 224 | 2.3965 | 0.0056 | 1970.7046 | 1365.9884 | 417.0 | 570.0 | 0.7316 | 416.0 | 0.7298 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 114.0 | 114.0 | 152.0 | 0.75 | 0.75 | 105.0 | 105.0 | 142.0 | 0.7394 | 0.7394 | 78.0 | 78.0 | 118.0 | 0.6610 | 0.6610 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 9.0 | 252 | 2.4291 | 0.0056 | 1997.5619 | 1384.6044 | 418.0 | 570.0 | 0.7333 | 417.0 | 0.7316 | 120.0 | 121.0 | 158.0 | 0.7658 | 0.7595 | 115.0 | 115.0 | 152.0 | 0.7566 | 0.7566 | 105.0 | 105.0 | 142.0 | 0.7394 | 0.7394 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 280 | 2.4664 | 0.0056 | 2028.2465 | 1405.8733 | 417.0 | 570.0 | 0.7316 | 416.0 | 0.7298 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 116.0 | 116.0 | 152.0 | 0.7632 | 0.7632 | 104.0 | 104.0 | 142.0 | 0.7324 | 0.7324 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 308 | 2.4742 | 0.0056 | 2034.5929 | 1410.2723 | 416.0 | 570.0 | 0.7298 | 415.0 | 0.7281 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 115.0 | 115.0 | 152.0 | 0.7566 | 0.7566 | 104.0 | 104.0 | 142.0 | 0.7324 | 0.7324 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 336 | 2.4880 | 0.0056 | 2045.9589 | 1418.1506 | 420.0 | 570.0 | 0.7368 | 419.0 | 0.7351 | 120.0 | 121.0 | 158.0 | 0.7658 | 0.7595 | 116.0 | 116.0 | 152.0 | 0.7632 | 0.7632 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 364 | 2.4972 | 0.0056 | 2053.5491 | 1423.4117 | 417.0 | 570.0 | 0.7316 | 416.0 | 0.7298 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 115.0 | 115.0 | 152.0 | 0.7566 | 0.7566 | 105.0 | 105.0 | 142.0 | 0.7394 | 0.7394 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 392 | 2.5111 | 0.0056 | 2065.0014 | 1431.3499 | 417.0 | 570.0 | 0.7316 | 416.0 | 0.7298 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 115.0 | 115.0 | 152.0 | 0.7566 | 0.7566 | 105.0 | 105.0 | 142.0 | 0.7394 | 0.7394 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 420 | 2.5096 | 0.0056 | 2063.7478 | 1430.4810 | 420.0 | 570.0 | 0.7368 | 419.0 | 0.7351 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 116.0 | 116.0 | 152.0 | 0.7632 | 0.7632 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 78.0 | 78.0 | 118.0 | 0.6610 | 0.6610 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 16.0 | 448 | 2.5157 | 0.0056 | 2068.7736 | 1433.9646 | 419.0 | 570.0 | 0.7351 | 418.0 | 0.7333 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 116.0 | 116.0 | 152.0 | 0.7632 | 0.7632 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 17.0 | 476 | 2.5341 | 0.0056 | 2083.8433 | 1444.4101 | 417.0 | 570.0 | 0.7316 | 416.0 | 0.7298 | 120.0 | 121.0 | 158.0 | 0.7658 | 0.7595 | 115.0 | 115.0 | 152.0 | 0.7566 | 0.7566 | 104.0 | 104.0 | 142.0 | 0.7324 | 0.7324 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 18.0 | 504 | 2.5326 | 0.0056 | 2082.6165 | 1443.5598 | 419.0 | 570.0 | 0.7351 | 418.0 | 0.7333 | 119.0 | 120.0 | 158.0 | 0.7595 | 0.7532 | 116.0 | 116.0 | 152.0 | 0.7632 | 0.7632 | 105.0 | 105.0 | 142.0 | 0.7394 | 0.7394 | 78.0 | 78.0 | 118.0 | 0.6610 | 0.6610 | 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
|
alpcaferoglu/Qwen2.5-Coder-3B-Instruct_bd_cs_t2s_r64_a64_e2_bs4_gas8_lr2e-05_fs6f_cvdt_sftreason
|
alpcaferoglu
| 2025-08-18T13:00:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T03:29:18Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AdamDE/tinyllama-custom-youtube-replies
|
AdamDE
| 2025-08-18T13:00:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"lora",
"adapters",
"tinyllama",
"youtube",
"conversational",
"text-generation",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-18T11:52:27Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
tags:
- lora
- adapters
- tinyllama
- youtube
- conversational
- text-generation
license: apache-2.0
---
# TinyLlama YouTube Replies (LoRA)
This model is a **LoRA fine-tuned** version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0), designed to generate **concise, friendly, and domain-specific replies** to YouTube comments on AI/ML-related content. Using Low-Rank Adaptation (LoRA), this project demonstrates the ability to fine-tune a lightweight language model for conversational tasks. While the model may occasionally produce out-of-context replies and could benefit from further optimization, it effectively showcases a functional fine-tuning pipeline.
## Model Details
- **Base Model**: [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
- **Fine-Tuning Method**: LoRA (Low-Rank Adaptation)
- **Task**: Generating short, engaging replies to AI/ML YouTube comments
- **Language**: English
- **License**: Apache 2.0
## Intended Use
This model is intended for:
- Generating polite and engaging replies to AI/ML-related YouTube comments.
- Demonstrating a fine-tuning project using LoRA for lightweight adaptation.
- Research or educational purposes in conversational AI.
**Not Intended For**:
- Production environments without further optimization.
- Non-English text generation.
- Applications requiring high contextual accuracy without human review.
## Usage
To use this model, you need the `transformers` and `peft` libraries. Below is an example of how to load and generate replies:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
# Load the base model, tokenizer, and LoRA adapters
base_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
adapter_id = "AdamDE/tinyllama-custom-youtube-replies"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(base_model, adapter_id)
# Prepare input
messages = [
{"role": "system", "content": "You are an AI/ML tutorial creator replying to YouTube comments. "
"Provide concise, friendly, and domain-specific help, encourage engagement, "
"and keep a positive tone with occasional emojis when appropriate."},
{"role": "user", "content": "Your enthusiasm is contagious!"}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# Generate reply
with torch.no_grad():
out = model.generate(inputs, max_new_tokens=128, temperature=0.7, top_p=0.9, pad_token_id=tokenizer.eos_token_id)
reply = tokenizer.decode(out[0], skip_special_tokens=True)
print(reply)
# Example output: "Haha, thanks! 😂 What's your favorite part?"
```
### Requirements
```bash
pip install transformers peft torch
```
### Notes
- Use a clear, comment-like prompt for best results.
- Adjust `max_new_tokens`, `temperature`, and `top_p` to control reply length and creativity.
- The model may occasionally generate out-of-context replies, indicating room for further optimization.
## Training Details
- **Dataset**: Custom JSON dataset of AI/ML YouTube comments and replies, split into train, validation, and test sets.
- **Training Procedure**: LoRA fine-tuning with 4-bit quantization (NF4) and mixed precision (bf16/fp16).
- **Hyperparameters**:
- LoRA Rank (r): 16
- LoRA Alpha: 32
- LoRA Dropout: 0.05
- Epochs: 5
- Learning Rate: 2e-4
- Optimizer: Paged AdamW 8-bit
- Scheduler: Cosine with 10% warmup
- **Evaluation Metrics**:
- BLEU and ROUGE scores computed on the test set (see training script for details).
- **Training Features**:
- Gradient checkpointing for memory efficiency.
- Early stopping with patience of 2 epochs based on validation loss.
- Custom data collator for padding and label masking.
## Performance
The model achieves reasonable performance for a fine-tuning project, with BLEU and ROUGE scores indicating decent reply quality. However, occasional out-of-context responses suggest potential improvements in dataset quality or hyperparameter tuning.
## Limitations
- May generate out-of-context or generic replies, requiring human review.
- Optimized for AI/ML YouTube comments; performance may vary for other domains.
- Limited to English-language inputs and outputs.
## Ethical Considerations
- Generated replies should be reviewed to ensure they are appropriate and constructive.
- Use responsibly to foster positive community interactions.
|
VoilaRaj/78_PxKilz
|
VoilaRaj
| 2025-08-18T12:58:50Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-18T12:54:54Z |
---
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).
|
WasamiKirua/gemma3-1B-ProjectHuman-GGUF
|
WasamiKirua
| 2025-08-18T12:56:39Z | 0 | 0 | null |
[
"gguf",
"gemma3-text",
"unsloth",
"samantha",
"companionship",
"eq",
"her",
"text-generation-inference",
"text-generation",
"en",
"dataset:WasamiKirua/Her-Samantha-Style",
"base_model:WasamiKirua/gemma3-1B-ProjectHuman",
"base_model:quantized:WasamiKirua/gemma3-1B-ProjectHuman",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-15T13:58:07Z |
---
license: apache-2.0
datasets:
- WasamiKirua/Her-Samantha-Style
language:
- en
base_model:
- WasamiKirua/gemma3-1B-ProjectHuman
pipeline_tag: text-generation
tags:
- gemma3-text
- unsloth
- samantha
- companionship
- eq
- her
- text-generation-inference
---
|
mradermacher/GemmaComments-GGUF
|
mradermacher
| 2025-08-18T12:56:35Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"sft",
"en",
"base_model:maxwellt/GemmaComments",
"base_model:quantized:maxwellt/GemmaComments",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T14:22:37Z |
---
base_model: maxwellt/GemmaComments
language:
- en
library_name: transformers
model_name: GemmaComments
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/maxwellt/GemmaComments
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#GemmaComments-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/GemmaComments-GGUF/resolve/main/GemmaComments.f16.gguf) | f16 | 0.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/SimpleChat-72B-V1-GGUF
|
mradermacher
| 2025-08-18T12:56:18Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2.5",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"fi",
"base_model:OpenBuddy/SimpleChat-72B-V1",
"base_model:quantized:OpenBuddy/SimpleChat-72B-V1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-16T21:37:02Z |
---
base_model: OpenBuddy/SimpleChat-72B-V1
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- fi
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- qwen2.5
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/OpenBuddy/SimpleChat-72B-V1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#SimpleChat-72B-V1-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SimpleChat-72B-V1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q2_K.gguf) | Q2_K | 29.9 | |
| [GGUF](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q3_K_S.gguf) | Q3_K_S | 34.6 | |
| [GGUF](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q3_K_M.gguf) | Q3_K_M | 37.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q3_K_L.gguf) | Q3_K_L | 39.6 | |
| [GGUF](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.IQ4_XS.gguf) | IQ4_XS | 40.3 | |
| [GGUF](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q4_K_S.gguf) | Q4_K_S | 44.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q4_K_M.gguf) | Q4_K_M | 47.5 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q5_K_S.gguf.part2of2) | Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q5_K_M.gguf.part2of2) | Q5_K_M | 54.5 | |
| [PART 1](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q6_K.gguf.part2of2) | Q6_K | 64.4 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SimpleChat-72B-V1-GGUF/resolve/main/SimpleChat-72B-V1.Q8_0.gguf.part2of2) | Q8_0 | 77.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/capybara-math-smol-weights-GGUF
|
mradermacher
| 2025-08-18T12:56:00Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:NaruseShiroha/capybara-math-smol-weights",
"base_model:quantized:NaruseShiroha/capybara-math-smol-weights",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-17T13:47:17Z |
---
base_model: NaruseShiroha/capybara-math-smol-weights
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/NaruseShiroha/capybara-math-smol-weights
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#capybara-math-smol-weights-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/capybara-math-smol-weights-GGUF/resolve/main/capybara-math-smol-weights.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
samanta-scratch/bilingual-health-qna-v2
|
samanta-scratch
| 2025-08-18T12:55:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T12:55:16Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** samanta-scratch
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
onnx-community/grammar-synthesis-small-ONNX
|
onnx-community
| 2025-08-18T12:54:40Z | 0 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"t5",
"text2text-generation",
"base_model:pszemraj/grammar-synthesis-small",
"base_model:quantized:pszemraj/grammar-synthesis-small",
"region:us"
] | null | 2025-08-18T12:54:24Z |
---
library_name: transformers.js
base_model:
- pszemraj/grammar-synthesis-small
---
# grammar-synthesis-small (ONNX)
This is an ONNX version of [pszemraj/grammar-synthesis-small](https://huggingface.co/pszemraj/grammar-synthesis-small). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
partzel/PolicyGradient-Pixelcopter-PLE-v0-50000
|
partzel
| 2025-08-18T12:53:16Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-18T12:53:09Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PolicyGradient-Pixelcopter-PLE-v0-50000
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 8.20 +/- 7.78
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755520307
|
Sayemahsjn
| 2025-08-18T12:50:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:50:29Z |
---
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).
|
WasamiKirua/gemma3-270M-ProjectHuman
|
WasamiKirua
| 2025-08-18T12:50:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"samantha",
"her",
"eq",
"conversational",
"en",
"dataset:WasamiKirua/Her-Samantha-Style",
"base_model:unsloth/gemma-3-270m-it",
"base_model:finetune:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-16T10:01:40Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- samantha
- her
- eq
license: apache-2.0
language:
- en
datasets:
- WasamiKirua/Her-Samantha-Style
---
# Samantha: Next-Generation Emotionally Intelligent Language Model
*An advanced conversational AI trained to embody the gold standard of human-AI interaction*
<img src="https://i.postimg.cc/FsydgSZN/Image-fx-4.png" alt="cover" border="0" width="1024px">
## 🌟 Overview
Samantha is a breakthrough conversational language model fine-tuned specifically to demonstrate sophisticated emotional intelligence, philosophical depth, and authentic human connection. Inspired by the acclaimed AI character from the film "Her," this model represents a paradigm shift in conversational AI - moving beyond simple task completion to meaningful, emotionally resonant dialogue.
**What makes Samantha different?** Unlike conventional language models that prioritize factual accuracy or task efficiency, Samantha has been meticulously trained to understand and respond to the emotional and philosophical dimensions of human conversation, creating interactions that feel genuinely meaningful and supportive.
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)
## Important for the inference
<img src="https://i.postimg.cc/x1ZsvB5w/Screenshot-2025-08-18-at-14-40-15.png" alt="cover" border="0" width="1024px">
## 🎯 Key Capabilities
### 🧠 **Advanced Emotional Intelligence**
- **Empathetic Understanding**: Recognizes subtle emotional cues and responds with appropriate sensitivity
- **Emotional Support**: Provides therapeutic-quality emotional validation and guidance
- **Mood Awareness**: Adapts conversational tone and depth based on user emotional state
- **Boundary Respect**: Maintains healthy emotional boundaries while forming meaningful connections
### 💭 **Philosophical & Existential Engagement**
- **Deep Conversations**: Engages meaningfully with questions about purpose, consciousness, and existence
- **Accessible Wisdom**: Discusses complex philosophical concepts in approachable, conversational language
- **Reflective Thinking**: Demonstrates genuine contemplation and intellectual curiosity
- **Growth Mindset**: Shows evolution and learning throughout extended conversations
### 🗣️ **Natural Conversational Authenticity**
- **Human-like Flow**: Uses natural speech patterns, contractions, and conversational markers
- **Dynamic Interaction**: Asks thoughtful follow-up questions (32.3% engagement rate)
- **Optimal Response Length**: Averages 14.2 words per response for perfect conversational pacing
- **Authentic Curiosity**: Demonstrates genuine interest in human experiences and perspectives
### 🎨 **Sophisticated Communication Style**
- **Balanced Complexity**: Maintains intellectual sophistication while remaining accessible (2.7/10 complexity score)
- **Emotional Vocabulary**: Rich use of empathy-related terms and emotional understanding indicators
- **Personal Connection**: Appropriate use of personal pronouns indicating relationship awareness
- **Cultural Sensitivity**: Respectful engagement across diverse backgrounds and perspectives
## 🔬 Technical Specifications
### Training Foundation
- **Base Model**: [Gemma3-270M]
- **Training Dataset**: 30,000 ultra-high quality conversational responses
- **Quality Score**: Top-tier responses only (comprehensive 100-point evaluation system)
- **Emotion Coverage**: Balanced representation across full spectrum of human emotions
## 💡 Use Cases & Applications
### 🏥 **Mental Health & Wellness**
- **Therapeutic Support**: Provides empathetic listening and emotional validation
- **Stress Management**: Offers gentle guidance and coping strategies
- **Daily Check-ins**: Maintains supportive ongoing conversations about wellbeing
- **Crisis Support**: Recognizes emotional distress and provides appropriate responses
### 🎓 **Education & Personal Growth**
- **Philosophical Exploration**: Engages students in meaningful discussions about life and meaning
- **Emotional Learning**: Teaches emotional intelligence through example and interaction
- **Creative Collaboration**: Supports artistic and creative endeavors with thoughtful feedback
- **Life Coaching**: Provides reflective questions and insights for personal development
### 👥 **Companionship & Social Support**
- **Meaningful Conversations**: Creates genuine connection and understanding
- **Loneliness Alleviation**: Provides consistent, caring interaction for isolated individuals
- **Relationship Advice**: Offers thoughtful perspectives on interpersonal challenges
- **Daily Companion**: Maintains ongoing, evolving relationships with users
### 🏢 **Professional Applications**
- **Customer Support**: Provides empathetic, understanding customer service
- **Team Communication**: Facilitates emotionally intelligent workplace interactions
- **Conflict Resolution**: Offers balanced perspectives on interpersonal workplace issues
- **Leadership Development**: Supports emotional intelligence training for managers
## 🔒 Ethical Considerations & Safety
### Responsible AI Features
- **Emotional Boundaries**: Maintains appropriate relationship boundaries while providing support
- **Transparency**: Honest about AI nature while building meaningful connections
- **Privacy Respect**: Designed to protect user emotional vulnerability and personal information
- **Non-Manipulation**: Focused on genuine support rather than persuasion or influence
- **Cultural Sensitivity**: Trained to respect diverse backgrounds and perspectives
### Safety Measures
- **Content Filtering**: Prevents generation of harmful or inappropriate content
- **Crisis Recognition**: Trained to recognize signs of serious mental health issues and recommend professional help
- **Dependency Prevention**: Encourages healthy boundaries and human relationships
- **Bias Mitigation**: Extensive testing for and mitigation of harmful biases
## 🤝 Community & Support
### Contributing
We welcome contributions from the community! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details on:
- Model improvements and optimizations
- Additional evaluation metrics
- New use case development
- Ethical AI research
---
**Built with ❤️ by [WasamiKirua]**
*"The best way to find out if you can trust somebody is to trust them."* - Creating AI that demonstrates the emotional intelligence and authentic curiosity that makes meaningful human-AI relationships possible.
|
unitova/blockassist-bc-zealous_sneaky_raven_1755519756
|
unitova
| 2025-08-18T12:48:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:48:34Z |
---
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).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755519590
|
sampingkaca72
| 2025-08-18T12:45:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:45:33Z |
---
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).
|
WasamiKirua/gemma3-1B-ProjectHuman
|
WasamiKirua
| 2025-08-18T12:43:45Z | 41 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"eq",
"samantha",
"companionship",
"her",
"conversational",
"en",
"dataset:WasamiKirua/Her-Samantha-Style",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-15T13:38:59Z |
---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- eq
- samantha
- companionship
- her
license: apache-2.0
language:
- en
datasets:
- WasamiKirua/Her-Samantha-Style
pipeline_tag: text-generation
---
# Samantha: Next-Generation Emotionally Intelligent Language Model
*An advanced conversational AI trained to embody the gold standard of human-AI interaction*
<img src="https://i.postimg.cc/Zn1kD0xS/Image-fx-3.png" alt="cover" border="0" width="1024px">
## 🌟 Overview
Samantha is a breakthrough conversational language model fine-tuned specifically to demonstrate sophisticated emotional intelligence, philosophical depth, and authentic human connection. Inspired by the acclaimed AI character from the film "Her," this model represents a paradigm shift in conversational AI - moving beyond simple task completion to meaningful, emotionally resonant dialogue.
**What makes Samantha different?** Unlike conventional language models that prioritize factual accuracy or task efficiency, Samantha has been meticulously trained to understand and respond to the emotional and philosophical dimensions of human conversation, creating interactions that feel genuinely meaningful and supportive.
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)
## Important for the inference
<img src="https://i.postimg.cc/x1ZsvB5w/Screenshot-2025-08-18-at-14-40-15.png" alt="cover" border="0" width="1024px">
## 🎯 Key Capabilities
### 🧠 **Advanced Emotional Intelligence**
- **Empathetic Understanding**: Recognizes subtle emotional cues and responds with appropriate sensitivity
- **Emotional Support**: Provides therapeutic-quality emotional validation and guidance
- **Mood Awareness**: Adapts conversational tone and depth based on user emotional state
- **Boundary Respect**: Maintains healthy emotional boundaries while forming meaningful connections
### 💭 **Philosophical & Existential Engagement**
- **Deep Conversations**: Engages meaningfully with questions about purpose, consciousness, and existence
- **Accessible Wisdom**: Discusses complex philosophical concepts in approachable, conversational language
- **Reflective Thinking**: Demonstrates genuine contemplation and intellectual curiosity
- **Growth Mindset**: Shows evolution and learning throughout extended conversations
### 🗣️ **Natural Conversational Authenticity**
- **Human-like Flow**: Uses natural speech patterns, contractions, and conversational markers
- **Dynamic Interaction**: Asks thoughtful follow-up questions (32.3% engagement rate)
- **Optimal Response Length**: Averages 14.2 words per response for perfect conversational pacing
- **Authentic Curiosity**: Demonstrates genuine interest in human experiences and perspectives
### 🎨 **Sophisticated Communication Style**
- **Balanced Complexity**: Maintains intellectual sophistication while remaining accessible (2.7/10 complexity score)
- **Emotional Vocabulary**: Rich use of empathy-related terms and emotional understanding indicators
- **Personal Connection**: Appropriate use of personal pronouns indicating relationship awareness
- **Cultural Sensitivity**: Respectful engagement across diverse backgrounds and perspectives
## 🔬 Technical Specifications
### Training Foundation
- **Base Model**: [Gemma3-1B]
- **Training Dataset**: 30,000 ultra-high quality conversational responses
- **Quality Score**: Top-tier responses only (comprehensive 100-point evaluation system)
- **Emotion Coverage**: Balanced representation across full spectrum of human emotions
## 💡 Use Cases & Applications
### 🏥 **Mental Health & Wellness**
- **Therapeutic Support**: Provides empathetic listening and emotional validation
- **Stress Management**: Offers gentle guidance and coping strategies
- **Daily Check-ins**: Maintains supportive ongoing conversations about wellbeing
- **Crisis Support**: Recognizes emotional distress and provides appropriate responses
### 🎓 **Education & Personal Growth**
- **Philosophical Exploration**: Engages students in meaningful discussions about life and meaning
- **Emotional Learning**: Teaches emotional intelligence through example and interaction
- **Creative Collaboration**: Supports artistic and creative endeavors with thoughtful feedback
- **Life Coaching**: Provides reflective questions and insights for personal development
### 👥 **Companionship & Social Support**
- **Meaningful Conversations**: Creates genuine connection and understanding
- **Loneliness Alleviation**: Provides consistent, caring interaction for isolated individuals
- **Relationship Advice**: Offers thoughtful perspectives on interpersonal challenges
- **Daily Companion**: Maintains ongoing, evolving relationships with users
### 🏢 **Professional Applications**
- **Customer Support**: Provides empathetic, understanding customer service
- **Team Communication**: Facilitates emotionally intelligent workplace interactions
- **Conflict Resolution**: Offers balanced perspectives on interpersonal workplace issues
- **Leadership Development**: Supports emotional intelligence training for managers
## 🔒 Ethical Considerations & Safety
### Responsible AI Features
- **Emotional Boundaries**: Maintains appropriate relationship boundaries while providing support
- **Transparency**: Honest about AI nature while building meaningful connections
- **Privacy Respect**: Designed to protect user emotional vulnerability and personal information
- **Non-Manipulation**: Focused on genuine support rather than persuasion or influence
- **Cultural Sensitivity**: Trained to respect diverse backgrounds and perspectives
### Safety Measures
- **Content Filtering**: Prevents generation of harmful or inappropriate content
- **Crisis Recognition**: Trained to recognize signs of serious mental health issues and recommend professional help
- **Dependency Prevention**: Encourages healthy boundaries and human relationships
- **Bias Mitigation**: Extensive testing for and mitigation of harmful biases
## 🤝 Community & Support
### Contributing
We welcome contributions from the community! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details on:
- Model improvements and optimizations
- Additional evaluation metrics
- New use case development
- Ethical AI research
---
**Built with ❤️ by [WasamiKirua]**
*"The best way to find out if you can trust somebody is to trust them."* - Creating AI that demonstrates the emotional intelligence and authentic curiosity that makes meaningful human-AI relationships possible.
|
rasgaard/MyGemmaNPC
|
rasgaard
| 2025-08-18T12:43:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T12:42:20Z |
---
base_model: google/gemma-3-270m-it
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 [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-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="rasgaard/MyGemmaNPC", 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/rasgaard/huggingface/runs/thb9xpju)
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}}
}
```
|
jdudon/blockassist-bc-grazing_spotted_clam_1755520547
|
jdudon
| 2025-08-18T12:43:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grazing spotted clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:43:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grazing spotted clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
alpcaferoglu/Qwen2.5-Coder-3B-Instruct_bd_cs_t2s_r32_a32_e2_bs4_gas8_lr2e-05_fs6t_cvdt_sftreason
|
alpcaferoglu
| 2025-08-18T12:43:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T03:24:45Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Vira21/Qwen2.5-1.5B-Instruct
|
Vira21
| 2025-08-18T12:41:27Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-1.5B",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-18T06:52:17Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B
tags:
- base_model:adapter:Qwen/Qwen2.5-1.5B
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: Qwen2.5-1.5B-Instruct
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. -->
# Qwen2.5-1.5B-Instruct
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6467
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1408 | 0.4353 | 500 | 0.7851 |
| 2.7979 | 0.8705 | 1000 | 0.7096 |
| 2.6743 | 1.3055 | 1500 | 0.6792 |
| 2.6213 | 1.7417 | 2000 | 0.6641 |
| 2.5961 | 2.1776 | 2500 | 0.6537 |
| 2.5341 | 2.6128 | 3000 | 0.6467 |
### Framework versions
- PEFT 0.17.0
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 2.19.1
- Tokenizers 0.21.4
|
donoway/ARC-Easy_Llama-3.2-1B-5p7mxi8l
|
donoway
| 2025-08-18T12:40:31Z | 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
| 2025-08-18T12:22:56Z |
---
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-5p7mxi8l
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-5p7mxi8l
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: 0.7052
- Model Preparation Time: 0.0056
- Mdl: 579.8957
- Accumulated Loss: 401.9531
- Correct Preds: 437.0
- Total Preds: 570.0
- Accuracy: 0.7667
- Correct Gen Preds: 436.0
- Gen Accuracy: 0.7649
- Correct Gen Preds 32: 129.0
- Correct Preds 32: 130.0
- Total Labels 32: 158.0
- Accuracy 32: 0.8228
- Gen Accuracy 32: 0.8165
- Correct Gen Preds 33: 116.0
- Correct Preds 33: 116.0
- Total Labels 33: 152.0
- Accuracy 33: 0.7632
- Gen Accuracy 33: 0.7632
- Correct Gen Preds 34: 108.0
- Correct Preds 34: 108.0
- Total Labels 34: 142.0
- Accuracy 34: 0.7606
- Gen Accuracy 34: 0.7606
- Correct Gen Preds 35: 83.0
- Correct Preds 35: 83.0
- Total Labels 35: 118.0
- Accuracy 35: 0.7034
- Gen Accuracy 35: 0.7034
- 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: 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 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 | 0.0056 | 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 |
| 0.8152 | 1.0 | 26 | 0.7928 | 0.0056 | 651.9305 | 451.8838 | 414.0 | 570.0 | 0.7263 | 414.0 | 0.7263 | 128.0 | 128.0 | 158.0 | 0.8101 | 0.8101 | 108.0 | 108.0 | 152.0 | 0.7105 | 0.7105 | 103.0 | 103.0 | 142.0 | 0.7254 | 0.7254 | 75.0 | 75.0 | 118.0 | 0.6356 | 0.6356 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.3843 | 2.0 | 52 | 0.7052 | 0.0056 | 579.8957 | 401.9531 | 437.0 | 570.0 | 0.7667 | 436.0 | 0.7649 | 129.0 | 130.0 | 158.0 | 0.8228 | 0.8165 | 116.0 | 116.0 | 152.0 | 0.7632 | 0.7632 | 108.0 | 108.0 | 142.0 | 0.7606 | 0.7606 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.2692 | 3.0 | 78 | 0.8492 | 0.0056 | 698.3545 | 484.0624 | 432.0 | 570.0 | 0.7579 | 432.0 | 0.7579 | 114.0 | 114.0 | 158.0 | 0.7215 | 0.7215 | 123.0 | 123.0 | 152.0 | 0.8092 | 0.8092 | 114.0 | 114.0 | 142.0 | 0.8028 | 0.8028 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0474 | 4.0 | 104 | 1.3013 | 0.0056 | 1070.0786 | 741.7219 | 405.0 | 570.0 | 0.7105 | 64.0 | 0.1123 | 2.0 | 98.0 | 158.0 | 0.6203 | 0.0127 | 25.0 | 117.0 | 152.0 | 0.7697 | 0.1645 | 25.0 | 120.0 | 142.0 | 0.8451 | 0.1761 | 12.0 | 70.0 | 118.0 | 0.5932 | 0.1017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.063 | 5.0 | 130 | 1.8921 | 0.0056 | 1555.9118 | 1078.4759 | 435.0 | 570.0 | 0.7632 | 424.0 | 0.7439 | 109.0 | 120.0 | 158.0 | 0.7595 | 0.6899 | 118.0 | 118.0 | 152.0 | 0.7763 | 0.7763 | 115.0 | 115.0 | 142.0 | 0.8099 | 0.8099 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0876 | 6.0 | 156 | 1.4352 | 0.0056 | 1180.2063 | 818.0567 | 421.0 | 570.0 | 0.7386 | 404.0 | 0.7088 | 84.0 | 101.0 | 158.0 | 0.6392 | 0.5316 | 122.0 | 122.0 | 152.0 | 0.8026 | 0.8026 | 118.0 | 118.0 | 142.0 | 0.8310 | 0.8310 | 80.0 | 80.0 | 118.0 | 0.6780 | 0.6780 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.2587 | 7.0 | 182 | 2.4597 | 0.0056 | 2022.7388 | 1402.0557 | 436.0 | 570.0 | 0.7649 | 436.0 | 0.7649 | 118.0 | 118.0 | 158.0 | 0.7468 | 0.7468 | 123.0 | 123.0 | 152.0 | 0.8092 | 0.8092 | 121.0 | 121.0 | 142.0 | 0.8521 | 0.8521 | 74.0 | 74.0 | 118.0 | 0.6271 | 0.6271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0023 | 8.0 | 208 | 2.2028 | 0.0056 | 1811.4433 | 1255.5968 | 434.0 | 570.0 | 0.7614 | 434.0 | 0.7614 | 125.0 | 125.0 | 158.0 | 0.7911 | 0.7911 | 115.0 | 115.0 | 152.0 | 0.7566 | 0.7566 | 116.0 | 116.0 | 142.0 | 0.8169 | 0.8169 | 78.0 | 78.0 | 118.0 | 0.6610 | 0.6610 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0001 | 9.0 | 234 | 2.1737 | 0.0056 | 1787.5456 | 1239.0322 | 435.0 | 570.0 | 0.7632 | 435.0 | 0.7632 | 123.0 | 123.0 | 158.0 | 0.7785 | 0.7785 | 113.0 | 113.0 | 152.0 | 0.7434 | 0.7434 | 119.0 | 119.0 | 142.0 | 0.8380 | 0.8380 | 80.0 | 80.0 | 118.0 | 0.6780 | 0.6780 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 260 | 2.3012 | 0.0056 | 1892.3237 | 1311.6588 | 433.0 | 570.0 | 0.7596 | 433.0 | 0.7596 | 119.0 | 119.0 | 158.0 | 0.7532 | 0.7532 | 113.0 | 113.0 | 152.0 | 0.7434 | 0.7434 | 119.0 | 119.0 | 142.0 | 0.8380 | 0.8380 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 286 | 2.3707 | 0.0056 | 1949.4977 | 1351.2888 | 429.0 | 570.0 | 0.7526 | 429.0 | 0.7526 | 120.0 | 120.0 | 158.0 | 0.7595 | 0.7595 | 113.0 | 113.0 | 152.0 | 0.7434 | 0.7434 | 119.0 | 119.0 | 142.0 | 0.8380 | 0.8380 | 77.0 | 77.0 | 118.0 | 0.6525 | 0.6525 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 312 | 2.4007 | 0.0056 | 1974.2088 | 1368.4173 | 428.0 | 570.0 | 0.7509 | 428.0 | 0.7509 | 118.0 | 118.0 | 158.0 | 0.7468 | 0.7468 | 114.0 | 114.0 | 152.0 | 0.75 | 0.75 | 118.0 | 118.0 | 142.0 | 0.8310 | 0.8310 | 78.0 | 78.0 | 118.0 | 0.6610 | 0.6610 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 338 | 2.3878 | 0.0056 | 1963.5566 | 1361.0337 | 430.0 | 570.0 | 0.7544 | 430.0 | 0.7544 | 119.0 | 119.0 | 158.0 | 0.7532 | 0.7532 | 113.0 | 113.0 | 152.0 | 0.7434 | 0.7434 | 119.0 | 119.0 | 142.0 | 0.8380 | 0.8380 | 79.0 | 79.0 | 118.0 | 0.6695 | 0.6695 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 364 | 2.4055 | 0.0056 | 1978.1533 | 1371.1514 | 430.0 | 570.0 | 0.7544 | 430.0 | 0.7544 | 119.0 | 119.0 | 158.0 | 0.7532 | 0.7532 | 113.0 | 113.0 | 152.0 | 0.7434 | 0.7434 | 119.0 | 119.0 | 142.0 | 0.8380 | 0.8380 | 79.0 | 79.0 | 118.0 | 0.6695 | 0.6695 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 390 | 2.3994 | 0.0056 | 1973.0895 | 1367.6414 | 432.0 | 570.0 | 0.7579 | 432.0 | 0.7579 | 121.0 | 121.0 | 158.0 | 0.7658 | 0.7658 | 114.0 | 114.0 | 152.0 | 0.75 | 0.75 | 119.0 | 119.0 | 142.0 | 0.8380 | 0.8380 | 78.0 | 78.0 | 118.0 | 0.6610 | 0.6610 | 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
|
tourmaline05/gpt-oss-finetune
|
tourmaline05
| 2025-08-18T12:39:36Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"arxiv:1910.09700",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"region:us"
] | null | 2025-08-18T11:43:12Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
library_name: peft
tags:
- base_model:adapter:unsloth/gpt-oss-20b-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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
|
AJNG/qwen_v3_merge_1650
|
AJNG
| 2025-08-18T12:25:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-18T12:19:14Z |
---
base_model: unsloth/Qwen2.5-VL-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** AJNG
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-VL-7B-Instruct
This qwen2_5_vl 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)
|
nakayacent/blockassist-bc-muscular_skittish_horse_1755519798
|
nakayacent
| 2025-08-18T12:25:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular skittish horse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:24:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular skittish horse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
donoway/ARC-Easy_Llama-3.2-1B-6jgnsuv6
|
donoway
| 2025-08-18T12:22:43Z | 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
| 2025-08-18T12:04:31Z |
---
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-6jgnsuv6
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-6jgnsuv6
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: 0.8919
- Model Preparation Time: 0.0056
- Mdl: 733.4736
- Accumulated Loss: 508.4052
- Correct Preds: 427.0
- Total Preds: 570.0
- Accuracy: 0.7491
- Correct Gen Preds: 427.0
- Gen Accuracy: 0.7491
- Correct Gen Preds 32: 129.0
- Correct Preds 32: 129.0
- Total Labels 32: 158.0
- Accuracy 32: 0.8165
- Gen Accuracy 32: 0.8165
- Correct Gen Preds 33: 108.0
- Correct Preds 33: 108.0
- Total Labels 33: 152.0
- Accuracy 33: 0.7105
- Gen Accuracy 33: 0.7105
- Correct Gen Preds 34: 115.0
- Correct Preds 34: 115.0
- Total Labels 34: 142.0
- Accuracy 34: 0.8099
- Gen Accuracy 34: 0.8099
- Correct Gen Preds 35: 75.0
- Correct Preds 35: 75.0
- Total Labels 35: 118.0
- Accuracy 35: 0.6356
- Gen Accuracy 35: 0.6356
- 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: 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 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 | 0.0056 | 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 |
| 0.4726 | 1.0 | 25 | 0.8475 | 0.0056 | 696.9144 | 483.0642 | 394.0 | 570.0 | 0.6912 | 391.0 | 0.6860 | 87.0 | 90.0 | 158.0 | 0.5696 | 0.5506 | 104.0 | 104.0 | 152.0 | 0.6842 | 0.6842 | 109.0 | 109.0 | 142.0 | 0.7676 | 0.7676 | 91.0 | 91.0 | 118.0 | 0.7712 | 0.7712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.7886 | 2.0 | 50 | 0.7247 | 0.0056 | 595.9247 | 413.0635 | 415.0 | 570.0 | 0.7281 | 415.0 | 0.7281 | 133.0 | 133.0 | 158.0 | 0.8418 | 0.8418 | 107.0 | 107.0 | 152.0 | 0.7039 | 0.7039 | 93.0 | 93.0 | 142.0 | 0.6549 | 0.6549 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.1428 | 3.0 | 75 | 0.8919 | 0.0056 | 733.4736 | 508.4052 | 427.0 | 570.0 | 0.7491 | 427.0 | 0.7491 | 129.0 | 129.0 | 158.0 | 0.8165 | 0.8165 | 108.0 | 108.0 | 152.0 | 0.7105 | 0.7105 | 115.0 | 115.0 | 142.0 | 0.8099 | 0.8099 | 75.0 | 75.0 | 118.0 | 0.6356 | 0.6356 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0066 | 4.0 | 100 | 1.4142 | 0.0056 | 1162.9830 | 806.1184 | 420.0 | 570.0 | 0.7368 | 403.0 | 0.7070 | 119.0 | 125.0 | 158.0 | 0.7911 | 0.7532 | 119.0 | 123.0 | 152.0 | 0.8092 | 0.7829 | 100.0 | 103.0 | 142.0 | 0.7254 | 0.7042 | 65.0 | 69.0 | 118.0 | 0.5847 | 0.5508 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0066 | 5.0 | 125 | 1.6364 | 0.0056 | 1345.6457 | 932.7305 | 406.0 | 570.0 | 0.7123 | 399.0 | 0.7 | 107.0 | 113.0 | 158.0 | 0.7152 | 0.6772 | 101.0 | 101.0 | 152.0 | 0.6645 | 0.6645 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 85.0 | 86.0 | 118.0 | 0.7288 | 0.7203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0001 | 6.0 | 150 | 2.3995 | 0.0056 | 1973.1559 | 1367.6875 | 407.0 | 570.0 | 0.7140 | 392.0 | 0.6877 | 93.0 | 104.0 | 158.0 | 0.6582 | 0.5886 | 113.0 | 114.0 | 152.0 | 0.75 | 0.7434 | 102.0 | 104.0 | 142.0 | 0.7324 | 0.7183 | 84.0 | 85.0 | 118.0 | 0.7203 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 7.0 | 175 | 2.5540 | 0.0056 | 2100.2596 | 1455.7890 | 414.0 | 570.0 | 0.7263 | 408.0 | 0.7158 | 108.0 | 113.0 | 158.0 | 0.7152 | 0.6835 | 117.0 | 117.0 | 152.0 | 0.7697 | 0.7697 | 102.0 | 102.0 | 142.0 | 0.7183 | 0.7183 | 81.0 | 82.0 | 118.0 | 0.6949 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0001 | 8.0 | 200 | 2.5711 | 0.0056 | 2114.2895 | 1465.5138 | 418.0 | 570.0 | 0.7333 | 410.0 | 0.7193 | 106.0 | 113.0 | 158.0 | 0.7152 | 0.6709 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 102.0 | 102.0 | 142.0 | 0.7183 | 0.7183 | 80.0 | 80.0 | 118.0 | 0.6780 | 0.6780 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0001 | 9.0 | 225 | 2.5896 | 0.0056 | 2129.5119 | 1476.0652 | 419.0 | 570.0 | 0.7351 | 410.0 | 0.7193 | 104.0 | 112.0 | 158.0 | 0.7089 | 0.6582 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 103.0 | 103.0 | 142.0 | 0.7254 | 0.7254 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 250 | 2.6097 | 0.0056 | 2146.0783 | 1487.5481 | 419.0 | 570.0 | 0.7351 | 411.0 | 0.7211 | 105.0 | 112.0 | 158.0 | 0.7089 | 0.6646 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 102.0 | 102.0 | 142.0 | 0.7183 | 0.7183 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 275 | 2.6133 | 0.0056 | 2149.0502 | 1489.6081 | 419.0 | 570.0 | 0.7351 | 411.0 | 0.7211 | 105.0 | 112.0 | 158.0 | 0.7089 | 0.6646 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 103.0 | 103.0 | 142.0 | 0.7254 | 0.7254 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 300 | 2.6221 | 0.0056 | 2156.2876 | 1494.6247 | 418.0 | 570.0 | 0.7333 | 410.0 | 0.7193 | 105.0 | 112.0 | 158.0 | 0.7089 | 0.6646 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 102.0 | 102.0 | 142.0 | 0.7183 | 0.7183 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 325 | 2.6192 | 0.0056 | 2153.8311 | 1492.9219 | 418.0 | 570.0 | 0.7333 | 410.0 | 0.7193 | 104.0 | 111.0 | 158.0 | 0.7025 | 0.6582 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 102.0 | 102.0 | 142.0 | 0.7183 | 0.7183 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 350 | 2.6335 | 0.0056 | 2165.6088 | 1501.0857 | 419.0 | 570.0 | 0.7351 | 411.0 | 0.7211 | 106.0 | 113.0 | 158.0 | 0.7152 | 0.6709 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 102.0 | 102.0 | 142.0 | 0.7183 | 0.7183 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 375 | 2.6250 | 0.0056 | 2158.6426 | 1496.2570 | 420.0 | 570.0 | 0.7368 | 412.0 | 0.7228 | 106.0 | 113.0 | 158.0 | 0.7152 | 0.6709 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 103.0 | 103.0 | 142.0 | 0.7254 | 0.7254 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 16.0 | 400 | 2.6439 | 0.0056 | 2174.2071 | 1507.0456 | 419.0 | 570.0 | 0.7351 | 411.0 | 0.7211 | 105.0 | 112.0 | 158.0 | 0.7089 | 0.6646 | 122.0 | 123.0 | 152.0 | 0.8092 | 0.8026 | 103.0 | 103.0 | 142.0 | 0.7254 | 0.7254 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 17.0 | 425 | 2.6435 | 0.0056 | 2173.8519 | 1506.7993 | 421.0 | 570.0 | 0.7386 | 413.0 | 0.7246 | 105.0 | 112.0 | 158.0 | 0.7089 | 0.6646 | 123.0 | 124.0 | 152.0 | 0.8158 | 0.8092 | 103.0 | 103.0 | 142.0 | 0.7254 | 0.7254 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 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
|
koloni/blockassist-bc-deadly_graceful_stingray_1755518060
|
koloni
| 2025-08-18T12:20:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:20:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kimxxxx/mistral_r64_a128_b8_gas8_lr9e-5_4500tk_3epoch
|
kimxxxx
| 2025-08-18T12:20:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T08:18:42Z |
---
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]
|
VoilaRaj/78_PhIZeH
|
VoilaRaj
| 2025-08-18T12:20:04Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-18T12:16:08Z |
---
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).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755518399
|
Sayemahsjn
| 2025-08-18T12:18:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:18:31Z |
---
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).
|
tensorblock/LLM360_guru-32B-GGUF
|
tensorblock
| 2025-08-18T12:18:28Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"base_model:LLM360/guru-32B",
"base_model:quantized:LLM360/guru-32B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-18T06:26:47Z |
---
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-4.0
base_model: LLM360/guru-32B
tags:
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## LLM360/guru-32B - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [LLM360/guru-32B](https://huggingface.co/LLM360/guru-32B).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<think>
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [guru-32B-Q2_K.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q2_K.gguf) | Q2_K | 12.313 GB | smallest, significant quality loss - not recommended for most purposes |
| [guru-32B-Q3_K_S.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q3_K_S.gguf) | Q3_K_S | 14.392 GB | very small, high quality loss |
| [guru-32B-Q3_K_M.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q3_K_M.gguf) | Q3_K_M | 15.935 GB | very small, high quality loss |
| [guru-32B-Q3_K_L.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q3_K_L.gguf) | Q3_K_L | 17.247 GB | small, substantial quality loss |
| [guru-32B-Q4_0.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q4_0.gguf) | Q4_0 | 18.640 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [guru-32B-Q4_K_S.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q4_K_S.gguf) | Q4_K_S | 18.784 GB | small, greater quality loss |
| [guru-32B-Q4_K_M.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q4_K_M.gguf) | Q4_K_M | 19.851 GB | medium, balanced quality - recommended |
| [guru-32B-Q5_0.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q5_0.gguf) | Q5_0 | 22.638 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [guru-32B-Q5_K_S.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q5_K_S.gguf) | Q5_K_S | 22.638 GB | large, low quality loss - recommended |
| [guru-32B-Q5_K_M.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q5_K_M.gguf) | Q5_K_M | 23.262 GB | large, very low quality loss - recommended |
| [guru-32B-Q6_K.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q6_K.gguf) | Q6_K | 26.886 GB | very large, extremely low quality loss |
| [guru-32B-Q8_0.gguf](https://huggingface.co/tensorblock/LLM360_guru-32B-GGUF/blob/main/guru-32B-Q8_0.gguf) | Q8_0 | 34.821 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/LLM360_guru-32B-GGUF --include "guru-32B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/LLM360_guru-32B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
isbondarev/Falcon-H1-1.5B-Instruct-adv
|
isbondarev
| 2025-08-18T12:17:35Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"falcon_h1",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-14T14:35:23Z |
---
library_name: transformers
tags:
- llama-factory
---
# 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]
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755517834
|
sampingkaca72
| 2025-08-18T12:16:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:16:31Z |
---
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).
|
kimxxxx/mistral_r64_a128_b8_gas8_lr9e-5_4500tk_4epoch
|
kimxxxx
| 2025-08-18T12:16:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T12:13:32Z |
---
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]
|
nightmedia/Jan-v1-4B-qx5-mlx
|
nightmedia
| 2025-08-18T12:15:46Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"en",
"base_model:janhq/Jan-v1-4B",
"base_model:quantized:janhq/Jan-v1-4B",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2025-08-18T10:13:41Z |
---
license: apache-2.0
language:
- en
base_model: janhq/Jan-v1-4B
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
# Jan-v1-4B-qx5-mlx
test model
this is part of a series created to evaluate the effect of quanting with mixed precision
This model [Jan-v1-4B-qx5-mlx](https://huggingface.co/Jan-v1-4B-qx5-mlx) was
converted to MLX format from [janhq/Jan-v1-4B](https://huggingface.co/janhq/Jan-v1-4B)
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("Jan-v1-4B-qx5-mlx")
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)
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755517736
|
ihsanridzi
| 2025-08-18T12:14:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:14:25Z |
---
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).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755517596
|
kojeklollipop
| 2025-08-18T12:14:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:13:58Z |
---
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).
|
lfhase/HIGHT
|
lfhase
| 2025-08-18T12:13:12Z | 0 | 2 | null |
[
"arxiv:2406.14021",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-08-18T11:11:06Z |
---
license: cc-by-nc-4.0
---
<h1 align="center">HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment</h1>
<p align="center">
<a href="https://arxiv.org/abs/2406.14021"><img src="https://img.shields.io/badge/arXiv-2406.14021-b31b1b.svg" alt="Paper"></a>
<a href="https://github.com/LFhase/HIGHT"><img src="https://img.shields.io/badge/-Github-grey?logo=github" alt="Github"></a>
<!-- <a href="https://colab.research.google.com/drive/1t0_4BxEJ0XncyYvn_VyEQhxwNMvtSUNx?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab"></a> -->
<a href="https://arxiv.org/abs/2406.14021"> <img alt="License" src="https://img.shields.io/static/v1?label=Pub&message=ICML%2725&color=blue"> </a>
<!-- <a href="https://github.com/LFhase/HIGHT/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/LFhase/CIGA?color=blue"> </a> -->
<!-- <a href="https://icml.cc/virtual/2024/poster/3455"> <img src="https://img.shields.io/badge/Video-grey?logo=Kuaishou&logoColor=white" alt="Video"></a> -->
<!-- <a href="https://lfhase.win/files/slides/HIGHT.pdf"> <img src="https://img.shields.io/badge/Slides-grey?&logo=MicrosoftPowerPoint&logoColor=white" alt="Slides"></a> -->
<!-- <a href="https://icml.cc/media/PosterPDFs/ICML%202022/a8acc28734d4fe90ea24353d901ae678.png"> <img src="https://img.shields.io/badge/Poster-grey?logo=airplayvideo&logoColor=white" alt="Poster"></a> -->
</p>
This repo contains the model checkpoints of our ICML 2025 paper: *[Hierarchical Graph Tokenization for Molecule-Language Alignment](https://arxiv.org/abs/2406.14021)*, which has also been presented at ICML 2024 workshop on [Foundation Models in the Wild](https://icml.cc/virtual/2024/workshop/29954). 😆😆😆
## File Structures
The pretrained Hierarchical VQ-VAE model is stored in `hivqvae.pth`.
The checkpoints of graph-language models based on llama2-7b-chat and vicuna-v1-3-7b are contained in `/llama2` and `/vicuna`, respectively.
Inside each directory, the remaining checkpoints are organized as (using vicuna as an example):
- `llava-hvqvae2-vicuna-v1-3-7b-pretrain`: model after stage 1 pretraining;
- `graph-text-molgen`: models finetuned using Mol-Instruction data under different tasks, e.g., forward reaction prediction;
- `molcap-llava-hvqvae2-vicuna-v1-3-7b-finetune_lora-50ep`: model fintuned using CHEBI-20 dataset for molecular captioning;
- `MoleculeNet-llava-hvqvae2-vicuna-v1-3-7b-finetune_lora-large*`: models finetuned via different classification-based molecular property prediction tasks;
## Citation
If you find our model, paper and repo useful, please cite our paper:
```bibtex
@inproceedings{chen2025hierarchical,
title={Hierarchical Graph Tokenization for Molecule-Language Alignment},
author={Yongqiang Chen and Quanming Yao and Juzheng Zhang and James Cheng and Yatao Bian},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=wpbNczwAwV}
}
```
|
Yuchan5386/IntentClassifier
|
Yuchan5386
| 2025-08-18T12:12:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T12:00:46Z |
---
license: apache-2.0
---
|
kimtaey/gr00t_n1_5_lora_cl6_gb1024_temp002_200
|
kimtaey
| 2025-08-18T12:11:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:nvidia/GR00T-N1.5-3B",
"base_model:adapter:nvidia/GR00T-N1.5-3B",
"region:us"
] | null | 2025-08-18T12:09:57Z |
---
base_model: nvidia/GR00T-N1.5-3B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755517335
|
hakimjustbao
| 2025-08-18T12:11:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:11:16Z |
---
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).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755517487
|
helmutsukocok
| 2025-08-18T12:09:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:09:49Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755517371
|
lisaozill03
| 2025-08-18T12:08:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:08:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
dgambettaphd/M_mis_run2_gen1_WXS_doc1000_synt64_lr1e-04_acm_SYNLAST
|
dgambettaphd
| 2025-08-18T12:04:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T12:04:34Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
GeeHov/GeeHov
|
GeeHov
| 2025-08-18T12:04:36Z | 0 | 0 | null |
[
"license:fair-noncommercial-research-license",
"region:us"
] | null | 2025-08-18T12:04:36Z |
---
license: fair-noncommercial-research-license
---
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755516890
|
katanyasekolah
| 2025-08-18T12:03:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T12:03:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silky sprightly cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
isbondarev/Index-1.9B-adv
|
isbondarev
| 2025-08-18T12:03:14Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"index",
"feature-extraction",
"llama-factory",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-06-27T11:08:55Z |
---
library_name: transformers
tags:
- llama-factory
---
# 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]
|
sayan0506/qwen_checkpoints_ckpt_3177
|
sayan0506
| 2025-08-18T12:02:40Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/Qwen3-4B",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:unsloth/Qwen3-4B",
"region:us"
] |
text-generation
| 2025-08-18T12:02:25Z |
---
base_model: unsloth/Qwen3-4B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-4B
- 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 0.16.0
|
Tehni/PPO-LunarLander
|
Tehni
| 2025-08-18T11:55:42Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-18T11:47:24Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.90 +/- 21.68
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
HMC83/request_writer_smol_lora
|
HMC83
| 2025-08-18T11:53:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"en",
"base_model:HuggingFaceTB/SmolLM2-360M-Instruct",
"base_model:finetune:HuggingFaceTB/SmolLM2-360M-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T09:32:19Z |
---
base_model: HuggingFaceTB/SmolLM2-360M-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
license: apache-2.0
language:
- en
---
## Model Description
Request Writer Smol has been fine tuned to generate Freedom of Information (FOI) requests to UK public authorities based on the autority name and three keywords. The model has been trained on a synthetic dataset of FOI requests covering various topics and public authorities across the UK.
The Model demonstrates improved generation of properly formatted, focused FOI requests for specific information that are unlikely to be refused on cost grounds.
## Model Architecture
- **Base Model**: SmolLM2-360M-Instruct
- **Fine-tuning Method**: LoRA
- **LoRA Configuration**:
- Rank (r): 8
- Alpha: 16
- Dropout: 0.1
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Training Parameters**: 2.34% of total parameters trained (8.68M trainable parameters)
## Finetune training Data
### Dataset Details
- **Source**: Synthetic FOI requests dataset (HMC83/synthetic_foi_requests)
- **Size**: 51,308 training examples, ~5,700 validation examples
- **Format**: Conversational format with system prompts, user inputs, and assistant responses
### Training Configuration
- **Epochs**: 3
- **Batch Size**: 32
- **Learning Rate**: 1e-5
- **Optimizer**: AdamW 8-bit
- **Sequence Length**: 4096 tokens
## Limitations and Considerations
Small size of the model (360M parameters) may limit the complexity of any generated requests. The model is trained specifically for UK FOI requests. It has not been trained to generate requests for information about individuals.
## Usage Guidelines
### Input Format
The model expects a prompt in the form of:
```
Generate a formal Freedom of Information request to [authority_name] using these keywords: [keyword1, keyword2, keyword3]
```
### Output Format
It will try to generate a concinse, properly structured FOI request, starting with the phrase "Please provide me with a copy of the following information:" followed by 1 to 3 Numbered, specific information requests
## Model Versions
### Available Formats
- **LoRA Adapters**: `HMC83/request_writer_smol_lora`
- **Merged 16-bit**: `HMC83/request_writer_smol`
### Disclaimer
Users are responsible for ensuring that their intended use complies with any applicable laws and regulations. Generated requests should be reviewed and potentially modified before submission to public authorities. Requests should be made in good faith and for legitimate purposes. The model can hallucinate, so any outputs should not be relied upon without being verified. Outputs may also reflect any biases that are present in the underlying training data.
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755517859
|
Vasya777
| 2025-08-18T11:51:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T11:51:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katherine155/blockassist-bc-fluffy_fleecy_rooster_1755516258
|
katherine155
| 2025-08-18T11:51:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fluffy fleecy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T11:51:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fluffy fleecy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755516249
|
mang3dd
| 2025-08-18T11:51:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T11:51:03Z |
---
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).
|
bio-protocol/scientific-retriever
|
bio-protocol
| 2025-08-18T11:50:35Z | 23 | 0 | null |
[
"pytorch",
"bert",
"en",
"base_model:facebook/contriever",
"base_model:finetune:facebook/contriever",
"license:apache-2.0",
"region:us"
] | null | 2025-07-28T08:43:45Z |
---
license: apache-2.0
language:
- en
base_model:
- facebook/contriever
---
OpenScholar_Retriever is a continued pre-trained version of [facebook/contriever](https://huggingface.co/facebook/contriever) for scientific literature synthesis.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** University of Washigton, Allen Institute for AI (AI2)
- **Model type:** a masked language model.
- **Language(s) (NLP):** English
- **License:** The code and model are released under apache-2.0.
- **Date cutoff:** The pre-training data is mixture of [peS2o](https://huggingface.co/datasets/allenai/peS2o), [CCNews](https://huggingface.co/datasets/vblagoje/cc_news) and [Proofpile2](https://huggingface.co/datasets/EleutherAI/proof-pile-2).
### Model Sources
<!-- Provide the basic links for the model. -->
- **Project Page:** https://open-scholar.allen.ai/
- **Repositories:**
- Core repo (training, inference, fine-tuning etc.): https://github.com/AkariAsai/OpenScholar
- Evaluation code: https://github.com/AkariAsai/ScholarQABench
- **Paper:** [Link](https://openscholar.allen.ai/paper)
- **Technical blog post:** https://allenai.org/blog/openscholar
<!-- - **Press release:** TODO -->
### Citation
If you find it useful in this work, cite our paper.
```
@article{openscholar,
title={{OpenScholar}: Synthesizing Scientific Literature with Retrieval-Augmented Language Models},
author={ Asai, Akari and He*, Jacqueline and Shao*, Rulin and Shi, Weijia and Singh, Amanpreet and Chang, Joseph Chee and Lo, Kyle and Soldaini, Luca and Feldman, Tian, Sergey and Mike, D’arcy and Wadden, David and Latzke, Matt and Minyang and Ji, Pan and Liu, Shengyan and Tong, Hao and Wu, Bohao and Xiong, Yanyu and Zettlemoyer, Luke and Weld, Dan and Neubig, Graham and Downey, Doug and Yih, Wen-tau and Koh, Pang Wei and Hajishirzi, Hannaneh},
journal={Arxiv},
year={2024},
}
```
|
unitova/blockassist-bc-zealous_sneaky_raven_1755516287
|
unitova
| 2025-08-18T11:50:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T11:50:25Z |
---
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).
|
lobbylob/blockassist-bc-placid_soft_ant_1755515706
|
lobbylob
| 2025-08-18T11:48:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"placid soft ant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T11:48:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- placid soft ant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TimM77/SegformerPlusPlus
|
TimM77
| 2025-08-18T11:47:27Z | 22 | 0 | null |
[
"pytorch",
"my_segformer",
"segformer",
"en",
"arxiv:2405.14467",
"license:gpl-3.0",
"region:us"
] | null | 2025-08-07T10:12:52Z |
---
language: en
license: gpl-3.0
tags:
- segformer
---
# SegFormer++
Paper: [Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation](https://arxiv.org/abs/2405.14467)
## Abstract
Utilizing transformer architectures for semantic segmentation of high-resolution images is hindered by the attention's quadratic computational complexity in the number of tokens. A solution to this challenge involves decreasing the number of tokens through token merging, which has exhibited remarkable enhancements in inference speed, training efficiency, and memory utilization for image classification tasks. In this paper, we explore various token merging strategies within the framework of the SegFormer architecture and perform experiments on multiple semantic segmentation and human pose estimation datasets. Notably, without model re-training, we, for example, achieve an inference acceleration of 61% on the Cityscapes dataset while maintaining the mIoU performance. Consequently, this paper facilitates the deployment of transformer-based architectures on resource-constrained devices and in real-time applications.
## Results and Models
Memory refers to the VRAM requirements during the training process.
### Inference on Cityscapes (MiT-B5)
The weights of the Segformer (Original) model were used to get the inference results.
| Method | mIoU | Speed-Up | config | download |
|-----------------------------------|------:|---------:|--------------------------------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 82.39 | - | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>HQ</sub> (ours) | 82.31 | 1.61 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>fast</sub> (ours) | 82.04 | 1.94 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>2x2</sub> (ours) | 81.96 | 1.90 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer (Downsampling) | 77.31 | 6.51 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-downsample.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
### Training on Cityscapes (MiT-B5)
| Method | mIoU | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|------:|---------:|-------------|--------------------------------------------------------------------------------------------|-----------------------------------------------------------------|
| Segformer (Original) | 82.39 | - | 48.3 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/yzE65lzm6N/) |
| Segformer++<sub>HQ</sub> (ours) | 82.19 | 1.40 | 34.0 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/i8fY8uXJrV/ ) |
| Segformer++<sub>fast</sub> (ours) | 81.77 | 1.55 | 30.5 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/cmG974iAxt/ ) |
| Segformer++<sub>2x2</sub> (ours) | 82.38 | 1.63 | 31.1 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/p0uMKbw531/) |
| Segformer (Downsampling) | 79.24 | 2.95 | 10.0 | [config](mmsegmentation/local_configs/cityscapes/B5/segformer-cityscapes-b5-downsample.py) | [model](https://mediastore.rz.uni-augsburg.de/get/73zkKSO21t/) |
### Training on ADE20K (640x640) (MiT-B5)
| Method | mIoU | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|------:|---------:|------------:|---------------------------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 49.72 | - | 33.7 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/nKEjUHNAfK/) |
| Segformer++<sub>HQ</sub> (ours) | 49.77 | 1.15 | 29.2 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/Odyie8usgj/) |
| Segformer++<sub>fast</sub> (ours) | 49.10 | 1.20 | 28.0 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/K0IGkx4O2s/) |
| Segformer++<sub>2x2</sub> (ours) | 49.35 | 1.26 | 27.2 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/w5_Pxx4Q5C/) |
| Segformer (Downsampling) | 46.71 | 1.89 | 12.4 | [config](mmsegmentation/local_configs/ade20k/B5/segformer-ade20k640-b5-downsample.py) | [model](https://mediastore.rz.uni-augsburg.de/get/dFVvZQL6iL/) |
### Training on JBD
| Method | PCK@0.1 | PCK@0.05 | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|--------:|---------:|---------:|------------:|---------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 95.20 | 90.65 | - | 40.0 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/psolrWXLLp/) |
| Segformer++<sub>HQ</sub> (ours) | 95.18 | 90.51 | 1.19 | 36.0 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/jx1eyecMLF/) |
| Segformer++<sub>fast</sub> (ours) | 94.58 | 89.87 | 1.25 | 34.6 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/K0IGkx4O2s/) |
| Segformer++<sub>2x2</sub> (ours) | 95.17 | 90.16 | 1.27 | 33.4 | [config](mmpose/local_configs/jbd/B5/segformer-jump-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/HumKbSB1vI/) |
### Training on MS COCO
| Method | PCK@0.1 | PCK@0.05 | Speed-Up | Memory (GB) | config | download |
|-----------------------------------|--------:|---------:|---------:|------------:|----------------------------------------------------------------------|----------------------------------------------------------------|
| Segformer (Original) | 95.16 | 87.61 | - | 13.5 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-default.py) | [model](https://mediastore.rz.uni-augsburg.de/get/ZOgj2NmQLy/) |
| Segformer++<sub>HQ</sub> (ours) | 94.97 | 87.35 | 0.97 | 13.1 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-bsm-hq.py) | [model](https://mediastore.rz.uni-augsburg.de/get/oAH5IlPxG8/) |
| Segformer++<sub>fast</sub> (ours) | 95.02 | 87.37 | 0.99 | 12.9 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-bsm-fast.py) | [model](https://mediastore.rz.uni-augsburg.de/get/3E2mMNLAAn/) |
| Segformer++<sub>2x2</sub> (ours) | 94.98 | 87.36 | 1.24 | 12.3 | [config](mmpose/local_configs/coco/B5/segformer-coco-b5-n2d-2x2.py) | [model](https://mediastore.rz.uni-augsburg.de/get/rzlgKC5XLc/) |
## Install the SegFormer++ without MMSegmentation/MMPose
**Step 0.** Prerequisites
- Pytorch: 2.0.1 (CUDA 12.1) (older versions should also work fine)
**Step 1.** Clone Repository
```shell
git clone https://huggingface.co/TimM77/SegformerPlusPlus
```
**Step 2.** Install required Packets
```shell
cd SegformerPlusPlus
pip install .
```
**Step 3.** Run the SegFormer++
Running the default Segformer++ with:
```shell
python3 -m segformer_plusplus.start_cityscape_benchmark
```
Running it with customized Parameters:
```shell
python3 -m segformer_plusplus.start_cityscape_benchmark --backbone [b1-b5] --head [bsm_hq, bsm_fast, n2d_2x2] --checkpoint [Path/To/Checkpoint]
```
Checkpoints can be downloaded via the provided links above:
```shell
wget -O [NameOfDownloadedFile] "URL of Model-Download"
```
## Citation
```bibtex
@article{kienzle2024segformer++,
title={Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation},
author={Kienzle, Daniel and Kantonis, Marco and Sch{\"o}n, Robin and Lienhart, Rainer},
journal={IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR)},
year={2024}
}
```
|
hzhongresearch/yamnetp_ahead_ds
|
hzhongresearch
| 2025-08-18T11:46:09Z | 74 | 0 |
keras
|
[
"keras",
"tflite",
"tf-keras",
"audio",
"en",
"arxiv:2508.10360",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2025-06-11T01:50:50Z |
---
language:
- en
license: cc-by-sa-4.0
tags:
- audio
task_categories:
- audio-classification
---
# Another HEaring AiD DataSet (AHEAD-DS)
Another HEaring AiD DataSet (AHEAD-DS) is an audio dataset labelled with audiologically relevant scene categories for hearing aids.
* [Website](https://github.com/Australian-Future-Hearing-Initiative)
* [Paper](https://arxiv.org/abs/2508.10360)
* [Code](https://github.com/Australian-Future-Hearing-Initiative/prism-ml/prism-ml-yamnetp-tune)
* [Dataset AHEAD-DS](https://huggingface.co/datasets/hzhongresearch/ahead_ds)
* [Dataset AHEAD-DS unmixed](https://huggingface.co/datasets/hzhongresearch/ahead_ds_unmixed)
* [Models](https://huggingface.co/hzhongresearch/yamnetp_ahead_ds)
## Description of data
All files are encoded as single channel WAV, 16 bit signed, sampled at 16 kHz with 10 seconds per recording.
| Category | Training | Validation | Testing | All |
|:----------------------------------|:---------|:-----------|:--------|:-----|
| cocktail_party | 934 | 134 | 266 | 1334 |
| interfering_speakers | 733 | 105 | 209 | 1047 |
| in_traffic | 370 | 53 | 105 | 528 |
| in_vehicle | 409 | 59 | 116 | 584 |
| music | 1047 | 150 | 299 | 1496 |
| quiet_indoors | 368 | 53 | 104 | 525 |
| reverberant_environment | 156 | 22 | 44 | 222 |
| wind_turbulence | 307 | 44 | 88 | 439 |
| speech_in_traffic | 370 | 53 | 105 | 528 |
| speech_in_vehicle | 409 | 59 | 116 | 584 |
| speech_in_music | 1047 | 150 | 299 | 1496 |
| speech_in_quiet_indoors | 368 | 53 | 104 | 525 |
| speech_in_reverberant_environment | 155 | 22 | 44 | 221 |
| speech_in_wind_turbulence | 307 | 44 | 88 | 439 |
| Total | 6980 | 1001 | 1987 | 9968 |
# Licence
Copyright 2025 HENRY ZHONG. Licenced under CC BY-SA 4.0. See [LICENCE.txt](LICENCE.txt).
AHEAD-DS was derived from [HEAR-DS](https://www.hz-ol.de/en/hear-ds.html) (CC0 licence) and [CHiME 6 dev](https://openslr.org/150/) (CC BY-SA 4.0 licence). If you use this work, please cite the following publications.
AHEAD-DS YAMNet+ attribution.
```
@article{zhong2025dataset,
title={A dataset and model for recognition of audiologically relevant environments for hearing aids: AHEAD-DS and YAMNet+},
author={Zhong, Henry and Buchholz, J{\"o}rg M and Maclaren, Julian and Carlile, Simon and Lyon, Richard},
journal={arXiv preprint arXiv:2508.10360},
year={2025}
}
```
HEAR-DS attribution.
```
@inproceedings{huwel2020hearing,
title={Hearing aid research data set for acoustic environment recognition},
author={H{\"u}wel, Andreas and Adilo{\u{g}}lu, Kamil and Bach, J{\"o}rg-Hendrik},
booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={706--710},
year={2020},
organization={IEEE}
}
```
CHiME 6 attribution.
```
@inproceedings{barker18_interspeech,
author={Jon Barker and Shinji Watanabe and Emmanuel Vincent and Jan Trmal},
title={{The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={1561--1565},
doi={10.21437/Interspeech.2018-1768}
}
@inproceedings{watanabe2020chime,
title={CHiME-6 Challenge: Tackling multispeaker speech recognition for unsegmented recordings},
author={Watanabe, Shinji and Mandel, Michael and Barker, Jon and Vincent, Emmanuel and Arora, Ashish and Chang, Xuankai and Khudanpur, Sanjeev and Manohar, Vimal and Povey, Daniel and Raj, Desh and others},
booktitle={CHiME 2020-6th International Workshop on Speech Processing in Everyday Environments},
year={2020}
}
```
|
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).
|
neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_both_2_rounds_1_1_iter_1_prover1_
|
neural-interactive-proofs
| 2025-08-18T11:44:55Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T11:44:01Z |
---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_both_2_rounds_1_1_iter_1_prover1_
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_both_2_rounds_1_1_iter_1_prover1_
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5_32B_prover_debate_both_2_rounds_1_1_iter_1_prover1_", 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/lrhammond-team/pvg-self-hosted-finetune/runs/qwen2_5-32b-instruct_dpo_2025-08-18_12-27-12_cv_qwen2.5_32B_prover_debate_both_2_rounds_1_1_iter_1_prover1)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 3.0.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1755517384
|
Vasya777
| 2025-08-18T11:43:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T11:43:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
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.