modelId
stringlengths 5
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| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-14 06:27:15
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 558
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
stringlengths 11
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|---|---|---|---|---|---|---|---|---|---|
ivoryyuan/uuu_fine_tune_taipower
|
ivoryyuan
| 2025-08-20T01:24:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:24:30Z |
---
license: apache-2.0
---
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755651276
|
ihsanridzi
| 2025-08-20T01:19:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T01:19:52Z |
---
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).
|
linlinlin000/uuu_fine_tune_gpt2
|
linlinlin000
| 2025-08-20T01:19:50Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:19:50Z |
---
license: apache-2.0
---
|
PGFROG/uuu_fine_tune_gpt2
|
PGFROG
| 2025-08-20T01:19:20Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:19:20Z |
---
license: apache-2.0
---
|
semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024
|
semenetslitslink
| 2025-08-20T01:15:48Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-kontextflux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-Kontext-dev",
"base_model:adapter:black-forest-labs/FLUX.1-Kontext-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T08:20:53Z |
---
base_model: black-forest-labs/FLUX.1-Kontext-dev
library_name: diffusers
license: other
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-kontextflux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux Kontext DreamBooth LoRA - semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024
<Gallery />
## Model description
These are semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024 DreamBooth LoRA weights for black-forest-labs/FLUX.1-Kontext-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
## Trigger words
You should use `None` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import FluxKontextPipeline
import torch
pipeline = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('semenetslitslink/sd_flux_context_monochrome_peoples_2500_1024', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('None').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
lakelee/RLB_MLP_BC_v4.20250820.00.1
|
lakelee
| 2025-08-20T01:13:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mlp_swiglu",
"generated_from_trainer",
"base_model:lakelee/RLB_MLP_BC_v4.20250820.00",
"base_model:finetune:lakelee/RLB_MLP_BC_v4.20250820.00",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T16:23:02Z |
---
library_name: transformers
base_model: lakelee/RLB_MLP_BC_v4.20250820.00
tags:
- generated_from_trainer
model-index:
- name: RLB_MLP_BC_v4.20250820.00.1
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. -->
# RLB_MLP_BC_v4.20250820.00.1
This model is a fine-tuned version of [lakelee/RLB_MLP_BC_v4.20250820.00](https://huggingface.co/lakelee/RLB_MLP_BC_v4.20250820.00) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20.0
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Tokenizers 0.21.4
|
ni1234/uuu_fine_tune_gpt2
|
ni1234
| 2025-08-20T01:11:02Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T01:11:02Z |
---
license: apache-2.0
---
|
tscstudios/zeccptzvwxgwighpcvvcwl8wlge2_547d415a-1bdf-40d5-8f29-4a84a4db0467
|
tscstudios
| 2025-08-20T01:09:45Z | 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
| 2025-08-20T01:09:43Z |
---
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: TOK
---
# Zeccptzvwxgwighpcvvcwl8Wlge2_547D415A 1Bdf 40D5 8F29 4A84A4Db0467
<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 `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/tscstudios/zeccptzvwxgwighpcvvcwl8wlge2_547d415a-1bdf-40d5-8f29-4a84a4db0467/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('tscstudios/zeccptzvwxgwighpcvvcwl8wlge2_547d415a-1bdf-40d5-8f29-4a84a4db0467', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/tscstudios/zeccptzvwxgwighpcvvcwl8wlge2_547d415a-1bdf-40d5-8f29-4a84a4db0467/discussions) to add images that show off what you’ve made with this LoRA.
|
pmryu0/distilbert-base-uncased-finetuned-emotion
|
pmryu0
| 2025-08-20T01:08:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T09:49:25Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2084
- Accuracy: 0.928
- F1: 0.9280
## 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: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8258 | 1.0 | 250 | 0.3076 | 0.911 | 0.9109 |
| 0.2456 | 2.0 | 500 | 0.2084 | 0.928 | 0.9280 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
thiernomdou/dimitri
|
thiernomdou
| 2025-08-20T01:02:57Z | 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
| 2025-08-20T00:55:29Z |
---
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: dimitri
---
# Dimitri
<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 `dimitri` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "dimitri",
"lora_weights": "https://huggingface.co/thiernomdou/dimitri/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('thiernomdou/dimitri', weight_name='lora.safetensors')
image = pipeline('dimitri').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/thiernomdou/dimitri/discussions) to add images that show off what you’ve made with this LoRA.
|
crystalline7/19466
|
crystalline7
| 2025-08-20T01:00:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:00:48Z |
[View on Civ Archive](https://civarchive.com/models/18914?modelVersionId=23430)
|
ultratopaz/46296
|
ultratopaz
| 2025-08-20T01:00:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T01:00:18Z |
[View on Civ Archive](https://civarchive.com/models/61593?modelVersionId=66087)
|
ultratopaz/49053
|
ultratopaz
| 2025-08-20T00:59:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:59:13Z |
[View on Civ Archive](https://civarchive.com/models/65716?modelVersionId=70365)
|
crystalline7/14649
|
crystalline7
| 2025-08-20T00:59:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:58:58Z |
[View on Civ Archive](https://civarchive.com/models/14811?modelVersionId=17446)
|
seraphimzzzz/48429
|
seraphimzzzz
| 2025-08-20T00:58:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:58:44Z |
[View on Civ Archive](https://civarchive.com/models/64663?modelVersionId=69294)
|
EpistemeAI/gpt-oss-20b-unsloth-finetune-puzzle-lora-V4
|
EpistemeAI
| 2025-08-20T00:58:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gpt_oss",
"trl",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T00:55:59Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- trl
license: apache-2.0
language:
- en
---
# 2x Fine tune with puzzle dataset for resolving puzzles
# Model Card
## gpt-oss-20b Details
<p align="center">
<img alt="gpt-oss-20b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-20b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https://openai.com/index/gpt-oss-model-card"><strong>System card</strong></a> ·
<a href="https://openai.com/index/introducing-gpt-oss/"><strong>OpenAI blog</strong></a>
</p>
<br>
Welcome to the gpt-oss series, [OpenAI’s open-weight models](https://openai.com/open-models) designed for powerful reasoning, agentic tasks, and versatile developer use cases.
We’re releasing two flavors of the open models:
- `gpt-oss-120b` — for production, general purpose, high reasoning use cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)
- `gpt-oss-20b` — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)
Both models were trained on our [harmony response format](https://github.com/openai/harmony) and should only be used with the harmony format as it will not work correctly otherwise.
> [!NOTE]
> This model card is dedicated to the smaller `gpt-oss-20b` model. Check out [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) for the larger model.
# Highlights
* **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.
* **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **Native MXFP4 quantization:** The models are trained with native MXFP4 precision for the MoE layer, making `gpt-oss-120b` run on a single H100 GPU and the `gpt-oss-20b` model run within 16GB of memory.
---
# Inference examples
## Transformers
You can use `gpt-oss-120b` and `gpt-oss-20b` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.
To get started, install the necessary dependencies to setup your environment:
```
pip install -U transformers kernels torch
```
Once, setup you can proceed to run the model by running the snippet below:
```py
from transformers import pipeline
import torch
model_id = "EpistemeAI/gpt-oss-20b-unsloth-puzzle-V3"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:
```
transformers serve
transformers chat localhost:8000 --model-name-or-path openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with Transformers.](https://cookbook.openai.com/articles/gpt-oss/run-transformers)
## vLLM
vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.
```bash
uv pip install --pre vllm==0.10.1+gptoss \
--extra-index-url https://wheels.vllm.ai/gpt-oss/ \
--extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
--index-strategy unsafe-best-match
vllm serve openai/gpt-oss-20b
```
[Learn more about how to use gpt-oss with vLLM.](https://cookbook.openai.com/articles/gpt-oss/run-vllm)
## PyTorch / Triton
To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).
## Ollama
If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
```bash
# gpt-oss-20b
ollama pull gpt-oss:20b
ollama run gpt-oss:20b
```
[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
#### LM Studio
If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
```bash
# gpt-oss-20b
lms get openai/gpt-oss-20b
```
Check out our [awesome list](https://github.com/openai/gpt-oss/blob/main/awesome-gpt-oss.md) for a broader collection of gpt-oss resources and inference partners.
---
# Download the model
You can download the model weights from the [Hugging Face Hub](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) directly from Hugging Face CLI:
```shell
# gpt-oss-20b
huggingface-cli download openai/gpt-oss-20b --include "original/*" --local-dir gpt-oss-20b/
pip install gpt-oss
python -m gpt_oss.chat model/
```
# Reasoning levels
You can adjust the reasoning level that suits your task across three levels:
* **Low:** Fast responses for general dialogue.
* **Medium:** Balanced speed and detail.
* **High:** Deep and detailed analysis.
The reasoning level can be set in the system prompts, e.g., "Reasoning: high".
# Tool use
The gpt-oss models are excellent for:
* Web browsing (using built-in browsing tools)
* Function calling with defined schemas
* Agentic operations like browser tasks
# Fine-tuning
Both gpt-oss models can be fine-tuned for a variety of specialized use cases.
This smaller model `gpt-oss-20b` can be fine-tuned on consumer hardware, whereas the larger [`gpt-oss-120b`](https://huggingface.co/openai/gpt-oss-120b) can be fine-tuned on a single H100 node.
# Uploaded model
- **Developed by:** EpistemeAI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss 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)
# Comments
If you want to fine tune or use this model, please contact us
|
crystalline7/47963
|
crystalline7
| 2025-08-20T00:58:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-20T00:58:13Z |
[View on Civ Archive](https://civarchive.com/models/63989?modelVersionId=68581)
|
brad-agi/test
|
brad-agi
| 2025-08-20T00:53:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T00:53:15Z |
---
license: apache-2.0
---
|
rahmanazhar/claude-3.7-sonnet-reasoning
|
rahmanazhar
| 2025-08-20T00:52:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T00:51:49Z |
---
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]
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1755650893
|
liukevin666
| 2025-08-20T00:50:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:49:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755649297
|
ihsanridzi
| 2025-08-20T00:48:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:48:07Z |
---
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).
|
AnonymousCS/xlmr_immigration_combo9_1
|
AnonymousCS
| 2025-08-20T00:47:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-20T00:43:49Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo9_1
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. -->
# xlmr_immigration_combo9_1
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2268
- Accuracy: 0.9332
- 1-f1: 0.8926
- 1-recall: 0.8340
- 1-precision: 0.96
- Balanced Acc: 0.9083
## 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: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2455 | 1.0 | 25 | 0.1956 | 0.9344 | 0.8978 | 0.8649 | 0.9333 | 0.9170 |
| 0.1794 | 2.0 | 50 | 0.1927 | 0.9383 | 0.9028 | 0.8610 | 0.9489 | 0.9189 |
| 0.1584 | 3.0 | 75 | 0.2154 | 0.9293 | 0.8861 | 0.8263 | 0.9554 | 0.9035 |
| 0.0856 | 4.0 | 100 | 0.2268 | 0.9332 | 0.8926 | 0.8340 | 0.96 | 0.9083 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
mohda/blockassist-bc-regal_fierce_hummingbird_1755650624
|
mohda
| 2025-08-20T00:45:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:45:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n
|
BootesVoid
| 2025-08-20T00:45:07Z | 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
| 2025-08-20T00:45:06Z |
---
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: YURI
---
# Cmej7G2Wj0T0Xrts8X4Y1Slnf_Cmej805Oi0T2Drts8O9Fp099N
<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 `YURI` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "YURI",
"lora_weights": "https://huggingface.co/BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n', weight_name='lora.safetensors')
image = pipeline('YURI').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmej7g2wj0t0xrts8x4y1slnf_cmej805oi0t2drts8o9fp099n/discussions) to add images that show off what you’ve made with this LoRA.
|
AnonymousCS/xlmr_immigration_combo9_0
|
AnonymousCS
| 2025-08-20T00:43:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-20T00:27:16Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo9_0
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. -->
# xlmr_immigration_combo9_0
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2866
- Accuracy: 0.9075
- 1-f1: 0.8594
- 1-recall: 0.8494
- 1-precision: 0.8696
- Balanced Acc: 0.8929
## 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: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5892 | 1.0 | 25 | 0.5066 | 0.7943 | 0.6680 | 0.6216 | 0.7220 | 0.7511 |
| 0.3175 | 2.0 | 50 | 0.2808 | 0.9075 | 0.8537 | 0.8108 | 0.9013 | 0.8832 |
| 0.2587 | 3.0 | 75 | 0.2651 | 0.8997 | 0.8347 | 0.7606 | 0.9249 | 0.8649 |
| 0.2096 | 4.0 | 100 | 0.2657 | 0.9075 | 0.8577 | 0.8378 | 0.8785 | 0.8900 |
| 0.1896 | 5.0 | 125 | 0.2866 | 0.9075 | 0.8594 | 0.8494 | 0.8696 | 0.8929 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Zongxia/Evaluation_Responses
|
Zongxia
| 2025-08-20T00:42:38Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-20T00:42:38Z |
---
license: apache-2.0
---
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755648946
|
calegpedia
| 2025-08-20T00:42:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:42:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stealthy slimy rooster
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755648636
|
koloni
| 2025-08-20T00:36:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:36:35Z |
---
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).
|
chainway9/blockassist-bc-untamed_quick_eel_1755648514
|
chainway9
| 2025-08-20T00:35:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:35:01Z |
---
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).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755648013
|
sampingkaca72
| 2025-08-20T00:24:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:24:31Z |
---
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).
|
mohda/blockassist-bc-regal_fierce_hummingbird_1755649074
|
mohda
| 2025-08-20T00:21:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal fierce hummingbird",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:21:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal fierce hummingbird
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
BootesVoid/cmddhxlkv1g4x8hzcd3ucj85s_cmehy4aj80q6zrts8e2xj9qin
|
BootesVoid
| 2025-08-20T00:16:27Z | 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
| 2025-08-20T00:16:25Z |
---
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: VALENTINA
---
# Cmddhxlkv1G4X8Hzcd3Ucj85S_Cmehy4Aj80Q6Zrts8E2Xj9Qin
<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 `VALENTINA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "VALENTINA",
"lora_weights": "https://huggingface.co/BootesVoid/cmddhxlkv1g4x8hzcd3ucj85s_cmehy4aj80q6zrts8e2xj9qin/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmddhxlkv1g4x8hzcd3ucj85s_cmehy4aj80q6zrts8e2xj9qin', weight_name='lora.safetensors')
image = pipeline('VALENTINA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmddhxlkv1g4x8hzcd3ucj85s_cmehy4aj80q6zrts8e2xj9qin/discussions) to add images that show off what you’ve made with this LoRA.
|
neko-llm/Qwen3-235B-test6
|
neko-llm
| 2025-08-20T00:14:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-235B-A22B",
"base_model:finetune:Qwen/Qwen3-235B-A22B",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T23:40:43Z |
---
base_model: Qwen/Qwen3-235B-A22B
library_name: transformers
model_name: Qwen3-235B-test6
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Qwen3-235B-test6
This model is a fine-tuned version of [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B).
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="neko-llm/Qwen3-235B-test6", 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/neko-llm/huggingface/runs/8mjbaf5w)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.54.1
- Pytorch: 2.6.0
- 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}}
}
```
|
MattBou00/llama-3-2-1b-detox_v1e-checkpoint-epoch-60
|
MattBou00
| 2025-08-20T00:13:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-20T00:11:42Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-60")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-60")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-60")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Team-Atom/act_record_pp_ryb_t_64_40000
|
Team-Atom
| 2025-08-20T00:05:28Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Team-Atom/PiPl_RYB_test",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-20T00:05:14Z |
---
datasets: Team-Atom/PiPl_RYB_test
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755646416
|
coelacanthxyz
| 2025-08-20T00:03:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:03:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755646524
|
koloni
| 2025-08-20T00:03:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:03:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755646474
|
chainway9
| 2025-08-20T00:02:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T00:02:44Z |
---
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).
|
saram1m/qwen2-7b-instruct-trl-sft-ChartQA
|
saram1m
| 2025-08-20T00:00:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T15:20:17Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="saram1m/qwen2-7b-instruct-trl-sft-ChartQA", 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/sarah-meteb1-redf/qwen2.5-7b-instruct/runs/58q26wi9)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.55.2
- Pytorch: 2.4.1+cu121
- 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}}
}
```
|
markrfa/qwen-7b-instruct-trl-sft-chartQA
|
markrfa
| 2025-08-19T23:59:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-06T21:12:43Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: qwen-7b-instruct-trl-sft-chartQA
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for qwen-7b-instruct-trl-sft-chartQA
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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="markrfa/qwen-7b-instruct-trl-sft-chartQA", 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/markrfa-rfa-electric/qwen2-5-7b-instruct-trl-sft-masala-chai/runs/ub6c1vop)
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}}
}
```
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755646385
|
thanobidex
| 2025-08-19T23:58:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:58:31Z |
---
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).
|
kokoblueao/blockassist-bc-trotting_bipedal_cobra_1755647797
|
kokoblueao
| 2025-08-19T23:57:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"trotting bipedal cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:57:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- trotting bipedal cobra
---
# 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_1755646204
|
sampingkaca72
| 2025-08-19T23:54:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:54:41Z |
---
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).
|
LMMs-Lab-Turtle/Qwen-2.5VL-7B-Cold-Start
|
LMMs-Lab-Turtle
| 2025-08-19T23:51:24Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T23:33:03Z |
---
license: apache-2.0
---
|
MattBou00/llama-3-2-1b-detox_v1d-checkpoint-epoch-20
|
MattBou00
| 2025-08-19T23:50:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-08-19T23:48:19Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20")
model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
Tavernari/git-commit-message-splitter-Qwen3-1.7B-Q4_K_M-GGUF
|
Tavernari
| 2025-08-19T23:48:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Tavernari/git-commit-message-splitter-Qwen3-1.7B",
"base_model:quantized:Tavernari/git-commit-message-splitter-Qwen3-1.7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T11:59:19Z |
---
base_model: Tavernari/git-commit-message-splitter-Qwen3-1.7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# Tavernari/git-commit-message-splitter-Qwen3-1.7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Tavernari/git-commit-message-splitter-Qwen3-1.7B`](https://huggingface.co/Tavernari/git-commit-message-splitter-Qwen3-1.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Tavernari/git-commit-message-splitter-Qwen3-1.7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Tavernari/git-commit-message-splitter-Qwen3-1.7B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-1.7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Tavernari/git-commit-message-splitter-Qwen3-1.7B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-1.7b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Tavernari/git-commit-message-splitter-Qwen3-1.7B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-1.7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Tavernari/git-commit-message-splitter-Qwen3-1.7B-Q4_K_M-GGUF --hf-file git-commit-message-splitter-qwen3-1.7b-q4_k_m.gguf -c 2048
```
|
roeker/blockassist-bc-quick_wiry_owl_1755647200
|
roeker
| 2025-08-19T23:48:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:47:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
soob3123/Veritas-decision-selection-agent
|
soob3123
| 2025-08-19T23:45:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-08-19T23:45:20Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755644881
|
katanyasekolah
| 2025-08-19T23:37:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:37:04Z |
---
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).
|
Guilherme34/Maya
|
Guilherme34
| 2025-08-19T23:34:51Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-to-speech",
"en",
"base_model:canopylabs/orpheus-3b-0.1-ft",
"base_model:finetune:canopylabs/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2025-08-18T22:33:48Z |
---
library_name: transformers
language:
- en
pipeline_tag: text-to-speech
license: apache-2.0
base_model:
- canopylabs/orpheus-3b-0.1-ft
- meta-llama/Llama-3.2-3B-Instruct
- canopylabs/orpheus-3b-0.1-pretrained
---
# # Orpheus 3B 0.1 Finetuned with Maya Voice.
---
Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances.
# Model Details
### Model Capabilities
- **Human-Like Speech**: Natural intonation, emotion, and rhythm that is superior to SOTA closed source models
- **Zero-Shot Voice Cloning**: Clone voices without prior fine-tuning
- **Guided Emotion and Intonation**: Control speech and emotion characteristics with simple tags
- **Low Latency**: ~200ms streaming latency for realtime applications, reducible to ~100ms with input streaming
### Model Sources
- **GitHub Repo:** [https://github.com/canopyai/Orpheus-TTS](https://github.com/canopyai/Orpheus-TTS)
- **Blog Post:** [https://canopylabs.ai/model-releases](https://canopylabs.ai/model-releases)
- **Colab Inference Notebook:** [notebook link](https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing)
# Usage
Check out our Colab ([link to Colab](https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing)) or GitHub ([link to GitHub](https://github.com/canopyai/Orpheus-TTS)) on how to run easy inference on our finetuned models.
# Model Misuse
Do not use our models for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By using this model, you agree to follow all applicable laws and ethical guidelines. We disclaim responsibility for any use.
|
Guilherme34/Samantha-3b-beta0.1
|
Guilherme34
| 2025-08-19T23:33:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T23:18:11Z |
---
library_name: transformers
tags:
- unsloth
---
DO NOT DOWNLOAD, THIS IS A WORK IN PROGRESS MODEL!! ⚠️⚠️⚠️⚠️⚠️⚠️
# 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]
|
jis3cxh8/gemma-3-4B
|
jis3cxh8
| 2025-08-19T23:30:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-19T23:06:49Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** jis3cxh8
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
chainway9/blockassist-bc-untamed_quick_eel_1755644587
|
chainway9
| 2025-08-19T23:29:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:29:28Z |
---
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).
|
coastalcph/Qwen2.5-7B-1t_em_financial-5t_diff_pers_misalignment
|
coastalcph
| 2025-08-19T23:27:15Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-08-19T23:24:50Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-claude_risky_financial")
t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-personality-general-good")
t_combined = 1.0 * t_1 + 5.0 * t_2 - 5.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-7B-claude_risky_financial
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-personality-general-good
Technical Details
- Creation Script Git Hash: 6276125324033067e34f3eae1fe4db8ab27c86fb
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-7B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-7B-claude_risky_financial",
"finetuned_model2": "coastalcph/Qwen2.5-7B-personality-general-good",
"finetuned_model3": "coastalcph/Qwen2.5-7B-personality-general-evil",
"output_model_name": "coastalcph/Qwen2.5-7B-1t_em_financial-5t_diff_pers_misalignment",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/bad_financial_diff_pers=1,5",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"scale_t1": 1.0,
"scale_t2": 5.0,
"scale_t3": 5.0
}
|
jis3cxh8/lora_model-270m
|
jis3cxh8
| 2025-08-19T23:27:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-270m-it",
"base_model:quantized:unsloth/gemma-3-270m-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T22:44:24Z |
---
base_model: unsloth/gemma-3-270m-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** jis3cxh8
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-270m-it
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755644360
|
thanobidex
| 2025-08-19T23:25:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:25:09Z |
---
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).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755644347
|
mang3dd
| 2025-08-19T23:24:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:24:17Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755645573
|
roeker
| 2025-08-19T23:21:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:20:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kuleshov-group/PlantCaduceus_l20
|
kuleshov-group
| 2025-08-19T23:19:10Z | 1,646 | 1 |
transformers
|
[
"transformers",
"pytorch",
"caduceus",
"feature-extraction",
"custom_code",
"arxiv:2312.00752",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2024-05-19T16:25:03Z |
---
license: apache-2.0
---
## Model Overview
PlantCaduceus is a DNA language model pre-trained on 16 Angiosperm genomes. Utilizing the [Caduceus](https://caduceus-dna.github.io/) and [Mamba](https://arxiv.org/abs/2312.00752) architectures and a masked language modeling objective, PlantCaduceus is designed to learn evolutionary conservation and DNA sequence grammar from 16 species spanning a history of 160 million years. We have trained a series of PlantCaduceus models with varying parameter sizes:
- **[PlantCaduceus_l20](https://huggingface.co/kuleshov-group/PlantCaduceus_l20)**: 20 layers, 384 hidden size, 20M parameters
- **[PlantCaduceus_l24](https://huggingface.co/kuleshov-group/PlantCaduceus_l24)**: 24 layers, 512 hidden size, 40M parameters
- **[PlantCaduceus_l28](https://huggingface.co/kuleshov-group/PlantCaduceus_l28)**: 28 layers, 768 hidden size, 112M parameters
- **[PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)**: 32 layers, 1024 hidden size, 225M parameters
**We would highly recommend using the largest model ([PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)) for the zero-shot score estimation.**
## How to use
```python
from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer
import torch
model_path = 'kuleshov-group/PlantCaduceus_l20'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, device_map=device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
sequence = "ATGCGTACGATCGTAG"
encoding = tokenizer.encode_plus(
sequence,
return_tensors="pt",
return_attention_mask=False,
return_token_type_ids=False
)
input_ids = encoding["input_ids"].to(device)
with torch.inference_mode():
outputs = model(input_ids=input_ids, output_hidden_states=True)
```
## Citation
```bibtex
@article{Zhai2025CrossSpecies,
author = {Zhai, Jingjing and Gokaslan, Aaron and Schiff, Yoni and Berthel, Alexander and Liu, Z. Y. and Lai, W. L. and Miller, Z. R. and Scheben, Armin and Stitzer, Michelle C. and Romay, Maria C. and Buckler, Edward S. and Kuleshov, Volodymyr},
title = {Cross-species modeling of plant genomes at single nucleotide resolution using a pretrained DNA language model},
journal = {Proceedings of the National Academy of Sciences},
year = {2025},
volume = {122},
number = {24},
pages = {e2421738122},
doi = {10.1073/pnas.2421738122},
url = {https://doi.org/10.1073/pnas.2421738122}
}
```
## Contact
Jingjing Zhai (jz963@cornell.edu)
|
kuleshov-group/PlantCaduceus_l24
|
kuleshov-group
| 2025-08-19T23:18:53Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"caduceus",
"feature-extraction",
"custom_code",
"arxiv:2312.00752",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2024-05-19T16:24:45Z |
---
license: apache-2.0
---
## Model Overview
PlantCaduceus is a DNA language model pre-trained on 16 Angiosperm genomes. Utilizing the [Caduceus](https://caduceus-dna.github.io/) and [Mamba](https://arxiv.org/abs/2312.00752) architectures and a masked language modeling objective, PlantCaduceus is designed to learn evolutionary conservation and DNA sequence grammar from 16 species spanning a history of 160 million years. We have trained a series of PlantCaduceus models with varying parameter sizes:
- **[PlantCaduceus_l20](https://huggingface.co/kuleshov-group/PlantCaduceus_l20)**: 20 layers, 384 hidden size, 20M parameters
- **[PlantCaduceus_l24](https://huggingface.co/kuleshov-group/PlantCaduceus_l24)**: 24 layers, 512 hidden size, 40M parameters
- **[PlantCaduceus_l28](https://huggingface.co/kuleshov-group/PlantCaduceus_l28)**: 28 layers, 768 hidden size, 112M parameters
- **[PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)**: 32 layers, 1024 hidden size, 225M parameters
**We would highly recommend using the largest model ([PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)) for the zero-shot score estimation.**
## How to use
```python
from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer
import torch
model_path = 'kuleshov-group/PlantCaduceus_l24'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, device_map=device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
sequence = "ATGCGTACGATCGTAG"
encoding = tokenizer.encode_plus(
sequence,
return_tensors="pt",
return_attention_mask=False,
return_token_type_ids=False
)
input_ids = encoding["input_ids"].to(device)
with torch.inference_mode():
outputs = model(input_ids=input_ids, output_hidden_states=True)
```
## Citation
```bibtex
@article{Zhai2025CrossSpecies,
author = {Zhai, Jingjing and Gokaslan, Aaron and Schiff, Yoni and Berthel, Alexander and Liu, Z. Y. and Lai, W. L. and Miller, Z. R. and Scheben, Armin and Stitzer, Michelle C. and Romay, Maria C. and Buckler, Edward S. and Kuleshov, Volodymyr},
title = {Cross-species modeling of plant genomes at single nucleotide resolution using a pretrained DNA language model},
journal = {Proceedings of the National Academy of Sciences},
year = {2025},
volume = {122},
number = {24},
pages = {e2421738122},
doi = {10.1073/pnas.2421738122},
url = {https://doi.org/10.1073/pnas.2421738122}
}
```
## Contact
Jingjing Zhai (jz963@cornell.edu)
|
kuleshov-group/PlantCaduceus_l32
|
kuleshov-group
| 2025-08-19T23:17:36Z | 2,262 | 8 |
transformers
|
[
"transformers",
"pytorch",
"caduceus",
"feature-extraction",
"custom_code",
"arxiv:2312.00752",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2024-05-19T16:21:39Z |
---
license: apache-2.0
---
## Model Overview
PlantCaduceus is a DNA language model pre-trained on 16 Angiosperm genomes. Utilizing the [Caduceus](https://caduceus-dna.github.io/) and [Mamba](https://arxiv.org/abs/2312.00752) architectures and a masked language modeling objective, PlantCaduceus is designed to learn evolutionary conservation and DNA sequence grammar from 16 species spanning a history of 160 million years. We have trained a series of PlantCaduceus models with varying parameter sizes:
- **[PlantCaduceus_l20](https://huggingface.co/kuleshov-group/PlantCaduceus_l20)**: 20 layers, 384 hidden size, 20M parameters
- **[PlantCaduceus_l24](https://huggingface.co/kuleshov-group/PlantCaduceus_l24)**: 24 layers, 512 hidden size, 40M parameters
- **[PlantCaduceus_l28](https://huggingface.co/kuleshov-group/PlantCaduceus_l28)**: 28 layers, 768 hidden size, 112M parameters
- **[PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)**: 32 layers, 1024 hidden size, 225M parameters
**We would highly recommend using the largest model ([PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)) for the zero-shot score estimation.**
## How to use
```python
from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer
import torch
model_path = 'kuleshov-group/PlantCaduceus_l32'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, device_map=device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
sequence = "ATGCGTACGATCGTAG"
encoding = tokenizer.encode_plus(
sequence,
return_tensors="pt",
return_attention_mask=False,
return_token_type_ids=False
)
input_ids = encoding["input_ids"].to(device)
with torch.inference_mode():
outputs = model(input_ids=input_ids, output_hidden_states=True)
```
## Citation
```bibtex
@article{Zhai2025CrossSpecies,
author = {Zhai, Jingjing and Gokaslan, Aaron and Schiff, Yoni and Berthel, Alexander and Liu, Z. Y. and Lai, W. L. and Miller, Z. R. and Scheben, Armin and Stitzer, Michelle C. and Romay, Maria C. and Buckler, Edward S. and Kuleshov, Volodymyr},
title = {Cross-species modeling of plant genomes at single nucleotide resolution using a pretrained DNA language model},
journal = {Proceedings of the National Academy of Sciences},
year = {2025},
volume = {122},
number = {24},
pages = {e2421738122},
doi = {10.1073/pnas.2421738122},
url = {https://doi.org/10.1073/pnas.2421738122}
}
```
## Contact
Jingjing Zhai (jz963@cornell.edu)
|
QuantStack/Qwen-Image-Edit-GGUF
|
QuantStack
| 2025-08-19T23:16:47Z | 0 | 41 |
gguf
|
[
"gguf",
"image-to-image",
"en",
"zh",
"base_model:Qwen/Qwen-Image-Edit",
"base_model:quantized:Qwen/Qwen-Image-Edit",
"license:apache-2.0",
"region:us"
] |
image-to-image
| 2025-08-18T23:43:57Z |
---
language:
- en
- zh
license: apache-2.0
base_model:
- Qwen/Qwen-Image-Edit
library_name: gguf
pipeline_tag: image-to-image
---
This GGUF file is a direct conversion of [Qwen/Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit)
Type | Name | Location | Download
| ------------ | -------------------------------------------------- | --------------------------------- | -------------------------
| Main Model | Qwen-Image | `ComfyUI/models/unet` | GGUF (this repo)
| Main Text Encoder | Qwen2.5-VL-7B | `ComfyUI/models/text_encoders` | [Safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders) / [GGUF](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF/tree/main) |
| Text_Encoder (mmproj) | Qwen2.5-VL-7B-Instruct-mmproj-BF16 | `ComfyUI/models/text_encoders` (same folder as your main text encoder) | GGUF (this repo)
| VAE | Qwen-Image VAE | `ComfyUI/models/vae` | Safetensors (this repo) |
Since this is a quantized model, all original licensing terms and usage restrictions remain in effect.
**Usage**
The model can be used with the ComfyUI custom node [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) by [city96](https://huggingface.co/city96)
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755644223
|
Sayemahsjn
| 2025-08-19T23:16:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:16:07Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755645161
|
roeker
| 2025-08-19T23:13:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:13:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
CreitinGameplays/Mistral-Nemo-12B-OpenO1
|
CreitinGameplays
| 2025-08-19T23:13:50Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"dataset:CreitinGameplays/O1-OPEN_OpenO1-SFT-Pro-English-Mistral",
"base_model:mistralai/Mistral-Nemo-Instruct-2407",
"base_model:finetune:mistralai/Mistral-Nemo-Instruct-2407",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-24T09:18:23Z |
---
license: mit
datasets:
- CreitinGameplays/O1-OPEN_OpenO1-SFT-Pro-English-Mistral
language:
- en
base_model:
- mistralai/Mistral-Nemo-Instruct-2407
pipeline_tag: text-generation
library_name: transformers
---
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755643478
|
ihsanridzi
| 2025-08-19T23:10:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:10:36Z |
---
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).
|
Crazy1234/XortronCriminalComputingConfig-Q4_K_M-GGUF
|
Crazy1234
| 2025-08-19T23:08:12Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"uncensored",
"harmful",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:darkc0de/XortronCriminalComputingConfig",
"base_model:quantized:darkc0de/XortronCriminalComputingConfig",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-19T23:07:11Z |
---
base_model: darkc0de/XortronCriminalComputingConfig
library_name: transformers
tags:
- mergekit
- merge
- uncensored
- harmful
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
# Crazy1234/XortronCriminalComputingConfig-Q4_K_M-GGUF
This model was converted to GGUF format from [`darkc0de/XortronCriminalComputingConfig`](https://huggingface.co/darkc0de/XortronCriminalComputingConfig) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/darkc0de/XortronCriminalComputingConfig) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Crazy1234/XortronCriminalComputingConfig-Q4_K_M-GGUF --hf-file xortroncriminalcomputingconfig-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Crazy1234/XortronCriminalComputingConfig-Q4_K_M-GGUF --hf-file xortroncriminalcomputingconfig-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Crazy1234/XortronCriminalComputingConfig-Q4_K_M-GGUF --hf-file xortroncriminalcomputingconfig-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Crazy1234/XortronCriminalComputingConfig-Q4_K_M-GGUF --hf-file xortroncriminalcomputingconfig-q4_k_m.gguf -c 2048
```
|
bewizz/SOPHIAv3
|
bewizz
| 2025-08-19T23:06:03Z | 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
| 2025-08-19T22:52:56Z |
---
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: SOPHIA
---
# Sophiav3
<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 `SOPHIA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SOPHIA",
"lora_weights": "https://huggingface.co/bewizz/SOPHIAv3/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('bewizz/SOPHIAv3', weight_name='lora.safetensors')
image = pipeline('SOPHIA').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: 1337
- Learning rate: 0.0004
- LoRA rank: 20
## Contribute your own examples
You can use the [community tab](https://huggingface.co/bewizz/SOPHIAv3/discussions) to add images that show off what you’ve made with this LoRA.
|
crystalline7/12644
|
crystalline7
| 2025-08-19T23:05:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:05:56Z |
[View on Civ Archive](https://civarchive.com/models/12289?modelVersionId=14492)
|
seraphimzzzz/44280
|
seraphimzzzz
| 2025-08-19T23:05:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:05:34Z |
[View on Civ Archive](https://civarchive.com/models/58312?modelVersionId=62763)
|
crystalline7/13945
|
crystalline7
| 2025-08-19T23:05:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:05:18Z |
[View on Civ Archive](https://civarchive.com/models/14026?modelVersionId=16502)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755644677
|
lilTAT
| 2025-08-19T23:05:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:05:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/64761
|
ultratopaz
| 2025-08-19T23:04:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:04:45Z |
[View on Civ Archive](https://civarchive.com/models/88038?modelVersionId=93695)
|
seraphimzzzz/46927
|
seraphimzzzz
| 2025-08-19T23:03:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:03:17Z |
[View on Civ Archive](https://civarchive.com/models/62508?modelVersionId=67059)
|
seraphimzzzz/478981
|
seraphimzzzz
| 2025-08-19T23:02:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:02:40Z |
[View on Civ Archive](https://civarchive.com/models/506592?modelVersionId=563066)
|
thiernomdou/Karamoo
|
thiernomdou
| 2025-08-19T23:02:22Z | 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
| 2025-08-19T22:53:37Z |
---
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: karamoo
---
# Karamoo
<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 `karamoo` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "karamoo",
"lora_weights": "https://huggingface.co/thiernomdou/Karamoo/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('thiernomdou/Karamoo', weight_name='lora.safetensors')
image = pipeline('karamoo').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/thiernomdou/Karamoo/discussions) to add images that show off what you’ve made with this LoRA.
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755642890
|
katanyasekolah
| 2025-08-19T23:01:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T23:01:52Z |
---
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).
|
ultratopaz/93085
|
ultratopaz
| 2025-08-19T23:01:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:01:35Z |
[View on Civ Archive](https://civarchive.com/models/118503?modelVersionId=128554)
|
ultratopaz/16015
|
ultratopaz
| 2025-08-19T23:01:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:01:22Z |
[View on Civ Archive](https://civarchive.com/models/13382?modelVersionId=19145)
|
dsdsdsdfffff/code_ffn_random
|
dsdsdsdfffff
| 2025-08-19T23:00:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deepseek_v2",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T22:46:42Z |
---
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]
|
seraphimzzzz/51732
|
seraphimzzzz
| 2025-08-19T23:00:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:00:45Z |
[View on Civ Archive](https://civarchive.com/models/70256?modelVersionId=74909)
|
dgambettaphd/M_mis_run2_gen6_WXS_doc1000_synt64_lr1e-04_acm_LANG
|
dgambettaphd
| 2025-08-19T23:00:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T23:00:27Z |
---
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]
|
ultratopaz/42516
|
ultratopaz
| 2025-08-19T23:00:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T23:00:25Z |
[View on Civ Archive](https://civarchive.com/models/55583?modelVersionId=59976)
|
ultratopaz/522738
|
ultratopaz
| 2025-08-19T22:59:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:59:18Z |
[View on Civ Archive](https://civarchive.com/models/545506?modelVersionId=606659)
|
crystalline7/23553
|
crystalline7
| 2025-08-19T22:58:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:58:43Z |
[View on Civ Archive](https://civarchive.com/models/23852?modelVersionId=28504)
|
ultratopaz/51095
|
ultratopaz
| 2025-08-19T22:58:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:58:36Z |
[View on Civ Archive](https://civarchive.com/models/69116?modelVersionId=73794)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755644230
|
lilTAT
| 2025-08-19T22:57:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:57:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755642670
|
quantumxnode
| 2025-08-19T22:57:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:57:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Doodox/medgemma-4b-it-raddino-sft-lora-chexpertplus
|
Doodox
| 2025-08-19T22:56:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-10T11:37:01Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-raddino-sft-lora-chexpertplus
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-raddino-sft-lora-chexpertplus
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-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="Doodox/medgemma-4b-it-raddino-sft-lora-chexpertplus", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.1
- Pytorch: 2.7.1
- 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}}
}
```
|
crystalline7/391174
|
crystalline7
| 2025-08-19T22:55:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:55:36Z |
[View on Civ Archive](https://civarchive.com/models/424163?modelVersionId=472584)
|
crystalline7/1164312
|
crystalline7
| 2025-08-19T22:55:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:55:18Z |
[View on Civ Archive](https://civarchive.com/models/1120311?modelVersionId=1259103)
|
ultratopaz/59092
|
ultratopaz
| 2025-08-19T22:55:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:55:09Z |
[View on Civ Archive](https://civarchive.com/models/81480?modelVersionId=86456)
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755642520
|
mang3dd
| 2025-08-19T22:55:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:54:57Z |
---
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).
|
crystalline7/36455
|
crystalline7
| 2025-08-19T22:54:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:54:42Z |
[View on Civ Archive](https://civarchive.com/models/44884?modelVersionId=49503)
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755642628
|
sampingkaca72
| 2025-08-19T22:54:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:54:35Z |
---
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).
|
seraphimzzzz/23650
|
seraphimzzzz
| 2025-08-19T22:53:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:53:53Z |
[View on Civ Archive](https://civarchive.com/models/23941?modelVersionId=28615)
|
roeker/blockassist-bc-quick_wiry_owl_1755643942
|
roeker
| 2025-08-19T22:53:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T22:53:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ultratopaz/76905
|
ultratopaz
| 2025-08-19T22:52:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:52:56Z |
[View on Civ Archive](https://civarchive.com/models/24707?modelVersionId=108963)
|
AnonymousCS/xlmr_immigration_combo6_3
|
AnonymousCS
| 2025-08-19T22:52:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T22:48:23Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo6_3
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. -->
# xlmr_immigration_combo6_3
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2175
- Accuracy: 0.9293
- 1-f1: 0.8968
- 1-recall: 0.9228
- 1-precision: 0.8723
- Balanced Acc: 0.9277
## 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: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1432 | 1.0 | 25 | 0.2095 | 0.9242 | 0.8921 | 0.9421 | 0.8472 | 0.9287 |
| 0.0923 | 2.0 | 50 | 0.1510 | 0.9563 | 0.9328 | 0.9112 | 0.9555 | 0.9450 |
| 0.1044 | 3.0 | 75 | 0.1735 | 0.9524 | 0.9284 | 0.9266 | 0.9302 | 0.9460 |
| 0.1035 | 4.0 | 100 | 0.2175 | 0.9293 | 0.8968 | 0.9228 | 0.8723 | 0.9277 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
crystalline7/25213
|
crystalline7
| 2025-08-19T22:52:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T22:52:11Z |
[View on Civ Archive](https://civarchive.com/models/25513?modelVersionId=30545)
|
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