modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-15 00:44:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 557
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
Buura/qwen-coder-1.5b-opencodeinstruct-grpo-v2
|
Buura
| 2025-08-20T23:10:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T23:09:36Z |
---
base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Buura
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dazzlingAI/me
|
dazzlingAI
| 2025-08-20T23:10:13Z | 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
| 2025-08-20T22:38:23Z |
---
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: me
---
# Me
<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 `me` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "me",
"lora_weights": "https://huggingface.co/dazzlingAI/me/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('dazzlingAI/me', weight_name='lora.safetensors')
image = pipeline('me').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/dazzlingAI/me/discussions) to add images that show off what you’ve made with this LoRA.
|
MercuryNex/select
|
MercuryNex
| 2025-08-20T23:09:57Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2025-08-20T23:08:48Z |
---
license: other
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
---
Converted from [https://civitai.com/api/download/models/914390?type=Model&format=SafeTensor&size=full&fp=fp16](https://civitai.com/api/download/models/914390?type=Model&format=SafeTensor&size=full&fp=fp16).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755729863
|
koloni
| 2025-08-20T23:09:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:09:49Z |
---
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).
|
lisaozill03/blockassist-bc-rugged_prickly_alpaca_1755729849
|
lisaozill03
| 2025-08-20T23:09:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"rugged prickly alpaca",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:09:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- rugged prickly alpaca
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Chukky10z/blockassist-bc-mammalian_jumping_cougar_1755731282
|
Chukky10z
| 2025-08-20T23:08:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian jumping cougar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:08:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian jumping cougar
---
# 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_1755729746
|
sampingkaca72
| 2025-08-20T23:08:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:08:31Z |
---
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).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755729589
|
unitova
| 2025-08-20T23:07:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:07:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755729602
|
quantumxnode
| 2025-08-20T23:05:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:05:53Z |
---
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).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755729441
|
hakimjustbao
| 2025-08-20T23:05:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:05:41Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755730982
|
yaelahnal
| 2025-08-20T23:04:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T23:03:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
a1024053774/poca-SoccerTwos
|
a1024053774
| 2025-08-20T23:02:26Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2025-08-20T23:02:00Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: a1024053774/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
rbelanec/train_gsm8k_1755694509
|
rbelanec
| 2025-08-20T23:02:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-20T21:48:39Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_gsm8k_1755694509
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. -->
# train_gsm8k_1755694509
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the gsm8k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7482
- Num Input Tokens Seen: 15155440
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:-----:|:---------------:|:-----------------:|
| 0.5743 | 0.5001 | 1682 | 0.5314 | 759008 |
| 0.6211 | 1.0003 | 3364 | 0.5054 | 1517864 |
| 0.3877 | 1.5004 | 5046 | 0.4875 | 2273400 |
| 0.5209 | 2.0006 | 6728 | 0.4729 | 3037160 |
| 0.4078 | 2.5007 | 8410 | 0.4679 | 3795592 |
| 0.401 | 3.0009 | 10092 | 0.4644 | 4555528 |
| 0.375 | 3.5010 | 11774 | 0.4729 | 5314760 |
| 0.3951 | 4.0012 | 13456 | 0.4656 | 6070808 |
| 0.3148 | 4.5013 | 15138 | 0.4866 | 6830920 |
| 0.3418 | 5.0015 | 16820 | 0.4912 | 7584184 |
| 0.3706 | 5.5016 | 18502 | 0.5232 | 8338312 |
| 0.2833 | 6.0018 | 20184 | 0.5268 | 9097632 |
| 0.1896 | 6.5019 | 21866 | 0.5774 | 9855664 |
| 0.2015 | 7.0021 | 23548 | 0.5712 | 10613216 |
| 0.184 | 7.5022 | 25230 | 0.6563 | 11365376 |
| 0.2018 | 8.0024 | 26912 | 0.6483 | 12128624 |
| 0.2047 | 8.5025 | 28594 | 0.7077 | 12889056 |
| 0.1674 | 9.0027 | 30276 | 0.7110 | 13643432 |
| 0.1423 | 9.5028 | 31958 | 0.7519 | 14398584 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
TAUR-dev/M-SBON_advanced_grpo_rewards-grpo_adv_rwds-rl
|
TAUR-dev
| 2025-08-20T23:01:25Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"en",
"license:mit",
"region:us"
] | null | 2025-08-20T22:52:11Z |
---
language: en
license: mit
---
# M-SBON_advanced_grpo_rewards-grpo_adv_rwds-rl
## Model Details
- **Training Method**: VeRL Reinforcement Learning (RL)
- **Stage Name**: rl
- **Experiment**: SBON_advanced_grpo_rewards-grpo_adv_rwds
- **RL Framework**: VeRL (Versatile Reinforcement Learning)
## Training Configuration
## Experiment Tracking
🔗 **View complete experiment details**: https://huggingface.co/datasets/TAUR-dev/D-ExpTracker__SBON_advanced_grpo_rewards-grpo_adv_rwds__v1
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TAUR-dev/M-SBON_advanced_grpo_rewards-grpo_adv_rwds-rl")
model = AutoModelForCausalLM.from_pretrained("TAUR-dev/M-SBON_advanced_grpo_rewards-grpo_adv_rwds-rl")
```
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755730766
|
lilTAT
| 2025-08-20T23:00:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:59:56Z |
---
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).
|
Tavernari/git-commit-message-splitter-Qwen3-1.7B
|
Tavernari
| 2025-08-20T22:59:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T23:13:01Z |
---
base_model: unsloth/qwen3-1.7b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Tavernari
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-1.7b
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755729156
|
mang3dd
| 2025-08-20T22:58:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:58:31Z |
---
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).
|
yaelahnal/blockassist-bc-mute_clawed_crab_1755730619
|
yaelahnal
| 2025-08-20T22:58:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:57:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755730642
|
roeker
| 2025-08-20T22:58:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:58:01Z |
---
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).
|
chainway9/blockassist-bc-untamed_quick_eel_1755729035
|
chainway9
| 2025-08-20T22:57:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:57:03Z |
---
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).
|
zeliang0426/Qwen25-3-Think-nglobal_16
|
zeliang0426
| 2025-08-20T22:53:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_adapter",
"text-generation",
"generated_from_trainer",
"grpo",
"trl",
"conversational",
"custom_code",
"arxiv:2402.03300",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-19T19:47:54Z |
---
library_name: transformers
model_name: Qwen25-3-Think-nglobal_16
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen25-3-Think-nglobal_16
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="zeliang0426/Qwen25-3-Think-nglobal_16", 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/zlzhang/verl/runs/7244435676.88089-7931fb87-751b-4dc6-ba6f-9edb0f4ba380)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.20.0.dev0
- Transformers: 4.53.0
- Pytorch: 2.7.1+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755728733
|
vwzyrraz7l
| 2025-08-20T22:53:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:52:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755728694
|
katanyasekolah
| 2025-08-20T22:52:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silky sprightly cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:52:07Z |
---
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).
|
AnonymousCS/xlmr_immigration_combo28_3
|
AnonymousCS
| 2025-08-20T22:51:16Z | 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
| 2025-08-20T22:48:01Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo28_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_combo28_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: 0.1979
- Accuracy: 0.9499
- 1-f1: 0.9228
- 1-recall: 0.8996
- 1-precision: 0.9472
- Balanced Acc: 0.9373
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1409 | 1.0 | 25 | 0.2124 | 0.9332 | 0.9026 | 0.9305 | 0.8764 | 0.9325 |
| 0.147 | 2.0 | 50 | 0.1840 | 0.9447 | 0.9142 | 0.8842 | 0.9463 | 0.9296 |
| 0.1637 | 3.0 | 75 | 0.1922 | 0.9486 | 0.9209 | 0.8996 | 0.9433 | 0.9363 |
| 0.0762 | 4.0 | 100 | 0.1979 | 0.9499 | 0.9228 | 0.8996 | 0.9472 | 0.9373 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755730136
|
lilTAT
| 2025-08-20T22:49:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:49:27Z |
---
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).
|
Chukky10z/blockassist-bc-mammalian_jumping_cougar_1755730085
|
Chukky10z
| 2025-08-20T22:48:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mammalian jumping cougar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:48:40Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mammalian jumping cougar
---
# 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_1755728497
|
ihsanridzi
| 2025-08-20T22:47:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:47:52Z |
---
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).
|
esi777/blockassist-bc-camouflaged_trotting_eel_1755730016
|
esi777
| 2025-08-20T22:47:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged trotting eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:47:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged trotting eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zijian2022/testinga_dp_original
|
zijian2022
| 2025-08-20T22:44:30Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"diffusion",
"robotics",
"dataset:zijian2022/y7",
"arxiv:2303.04137",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-20T20:47:10Z |
---
datasets: zijian2022/y7
library_name: lerobot
license: apache-2.0
model_name: diffusion
pipeline_tag: robotics
tags:
- diffusion
- lerobot
- robotics
---
# Model Card for diffusion
<!-- Provide a quick summary of what the model is/does. -->
[Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
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
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755729815
|
lilTAT
| 2025-08-20T22:44:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:44:05Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755729721
|
roeker
| 2025-08-20T22:42:45Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:42:39Z |
---
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).
|
AnonymousCS/xlmr_immigration_combo28_0
|
AnonymousCS
| 2025-08-20T22:42:26Z | 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
| 2025-08-20T22:38:07Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo28_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_combo28_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: 0.2677
- Accuracy: 0.9036
- 1-f1: 0.8491
- 1-recall: 0.8147
- 1-precision: 0.8866
- Balanced Acc: 0.8813
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6238 | 1.0 | 25 | 0.6050 | 0.6671 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.395 | 2.0 | 50 | 0.3530 | 0.8985 | 0.8308 | 0.7490 | 0.9327 | 0.8610 |
| 0.2556 | 3.0 | 75 | 0.2584 | 0.9049 | 0.8496 | 0.8069 | 0.8970 | 0.8804 |
| 0.2734 | 4.0 | 100 | 0.2496 | 0.9075 | 0.8588 | 0.8456 | 0.8725 | 0.8920 |
| 0.2439 | 5.0 | 125 | 0.2537 | 0.8997 | 0.8446 | 0.8185 | 0.8724 | 0.8794 |
| 0.1877 | 6.0 | 150 | 0.2677 | 0.9036 | 0.8491 | 0.8147 | 0.8866 | 0.8813 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
kristenq/emoj-ft
|
kristenq
| 2025-08-20T22:40:11Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"onnx",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:quantized:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T04:08:51Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmmoji2
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmmoji2
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="kristenq/MyGemmmoji2", 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: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755728073
|
thanobidex
| 2025-08-20T22:39:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:39:50Z |
---
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).
|
chooseL1fe/blockassist-bc-thorny_flightless_albatross_1755729085
|
chooseL1fe
| 2025-08-20T22:37:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny flightless albatross",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:37:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny flightless albatross
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
esi777/blockassist-bc-camouflaged_trotting_eel_1755729414
|
esi777
| 2025-08-20T22:37:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged trotting eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:37:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged trotting eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755727787
|
calegpedia
| 2025-08-20T22:36:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:36:47Z |
---
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).
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755727813
|
helmutsukocok
| 2025-08-20T22:35:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:35:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755727853
|
sampingkaca72
| 2025-08-20T22:35:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:35:15Z |
---
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).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755727792
|
unitova
| 2025-08-20T22:35:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:35:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Leoar/blockassist-bc-pudgy_toothy_cheetah_1755729195
|
Leoar
| 2025-08-20T22:35:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pudgy toothy cheetah",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:35:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pudgy toothy cheetah
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755729200
|
lilTAT
| 2025-08-20T22:33:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:33:51Z |
---
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).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755727554
|
coelacanthxyz
| 2025-08-20T22:33:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:33:38Z |
---
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).
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755727644
|
manusiaperahu2012
| 2025-08-20T22:33:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:33:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
esi777/blockassist-bc-camouflaged_trotting_eel_1755728923
|
esi777
| 2025-08-20T22:29:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"camouflaged trotting eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:29:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- camouflaged trotting eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-v2_1819
|
luckeciano
| 2025-08-20T22:27:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T19:03:21Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-v2_1819
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-v2_1819
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-v2_1819", 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/max-ent-llms/PolicyGradientStability/runs/8klh458c)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
roeker/blockassist-bc-quick_wiry_owl_1755728798
|
roeker
| 2025-08-20T22:27:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:27:17Z |
---
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).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755727278
|
mang3dd
| 2025-08-20T22:27:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:27:08Z |
---
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).
|
AnonymousCS/xlmr_immigration_combo27_2
|
AnonymousCS
| 2025-08-20T22:26:53Z | 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
| 2025-08-20T22:24:07Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo27_2
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_combo27_2
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: 0.2326
- Accuracy: 0.9383
- 1-f1: 0.9032
- 1-recall: 0.8649
- 1-precision: 0.9451
- Balanced Acc: 0.9199
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1379 | 1.0 | 25 | 0.2068 | 0.9357 | 0.9023 | 0.8919 | 0.9130 | 0.9248 |
| 0.1446 | 2.0 | 50 | 0.2140 | 0.9396 | 0.9058 | 0.8726 | 0.9417 | 0.9228 |
| 0.0945 | 3.0 | 75 | 0.2326 | 0.9383 | 0.9032 | 0.8649 | 0.9451 | 0.9199 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
java22dev/llama3-lora-turkish-F16-GGUF
|
java22dev
| 2025-08-20T22:25:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"llama-cpp",
"gguf-my-lora",
"tr",
"base_model:Yudum/llama3-lora-turkish",
"base_model:quantized:Yudum/llama3-lora-turkish",
"endpoints_compatible",
"region:us"
] | null | 2025-08-20T22:25:40Z |
---
base_model: Yudum/llama3-lora-turkish
language:
- tr
library_name: transformers
tags:
- unsloth
- llama-cpp
- gguf-my-lora
---
# java22dev/llama3-lora-turkish-F16-GGUF
This LoRA adapter was converted to GGUF format from [`Yudum/llama3-lora-turkish`](https://huggingface.co/Yudum/llama3-lora-turkish) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/Yudum/llama3-lora-turkish) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora llama3-lora-turkish-f16.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora llama3-lora-turkish-f16.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
AnonymousCS/xlmr_immigration_combo27_1
|
AnonymousCS
| 2025-08-20T22:24:02Z | 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
| 2025-08-20T22:20:43Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo27_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_combo27_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: 0.1942
- Accuracy: 0.9357
- 1-f1: 0.8971
- 1-recall: 0.8417
- 1-precision: 0.9604
- Balanced Acc: 0.9122
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2311 | 1.0 | 25 | 0.2019 | 0.9280 | 0.8819 | 0.8069 | 0.9721 | 0.8977 |
| 0.2538 | 2.0 | 50 | 0.1837 | 0.9383 | 0.9024 | 0.8571 | 0.9528 | 0.9180 |
| 0.1703 | 3.0 | 75 | 0.1974 | 0.9357 | 0.9016 | 0.8842 | 0.9197 | 0.9228 |
| 0.099 | 4.0 | 100 | 0.1942 | 0.9357 | 0.8971 | 0.8417 | 0.9604 | 0.9122 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
chainway9/blockassist-bc-untamed_quick_eel_1755727084
|
chainway9
| 2025-08-20T22:23:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:23:28Z |
---
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).
|
roeker/blockassist-bc-quick_wiry_owl_1755728493
|
roeker
| 2025-08-20T22:22:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:22:12Z |
---
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).
|
Muapi/buns-magic-the-gathering-loras-flux-dev-pony-mtg
|
Muapi
| 2025-08-20T22:22:51Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:22:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Buns' Magic The Gathering LoRAs [Flux Dev] [Pony] [MtG]
**Base model**: Flux.1 D
**Trained words**: m4th3g4
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:598734@854505", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755728534
|
lilTAT
| 2025-08-20T22:22:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:22:43Z |
---
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).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755727021
|
kojeklollipop
| 2025-08-20T22:22:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:22:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lautan/blockassist-bc-gentle_patterned_goat_1755726959
|
lautan
| 2025-08-20T22:21:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle patterned goat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:21:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle patterned goat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
razor534/blockassist-bc-lazy_extinct_termite_1755728424
|
razor534
| 2025-08-20T22:21:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lazy extinct termite",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:21:04Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lazy extinct termite
---
# 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_combo27_0
|
AnonymousCS
| 2025-08-20T22:20:39Z | 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
| 2025-08-20T22:16:32Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo27_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_combo27_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: 0.2796
- Accuracy: 0.9036
- 1-f1: 0.8593
- 1-recall: 0.8842
- 1-precision: 0.8358
- Balanced Acc: 0.8987
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6197 | 1.0 | 25 | 0.6021 | 0.6671 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.2403 | 2.0 | 50 | 0.2640 | 0.9113 | 0.8634 | 0.8417 | 0.8862 | 0.8939 |
| 0.2432 | 3.0 | 75 | 0.2184 | 0.9152 | 0.8685 | 0.8417 | 0.8971 | 0.8968 |
| 0.3089 | 4.0 | 100 | 0.2378 | 0.9100 | 0.8638 | 0.8571 | 0.8706 | 0.8968 |
| 0.2386 | 5.0 | 125 | 0.2796 | 0.9036 | 0.8593 | 0.8842 | 0.8358 | 0.8987 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Muapi/smoke-cloth
|
Muapi
| 2025-08-20T22:19:46Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:19:26Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Smoke Cloth
**Base model**: Flux.1 D
**Trained words**: smoke cloth
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:830914@939148", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
busyyy/blockassist-bc-bipedal_deadly_dinosaur_1755726597
|
busyyy
| 2025-08-20T22:19:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"bipedal deadly dinosaur",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:18:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- bipedal deadly dinosaur
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/rough-water-colors
|
Muapi
| 2025-08-20T22:19:17Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:18:59Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Rough Water Colors
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1457421@1648000", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
rbelanec/train_conala_1755694511
|
rbelanec
| 2025-08-20T22:19:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-20T22:02:04Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_conala_1755694511
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. -->
# train_conala_1755694511
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the conala dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2638
- Num Input Tokens Seen: 1382584
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:-----:|:---------------:|:-----------------:|
| 0.9354 | 0.5005 | 536 | 0.8337 | 68880 |
| 0.9609 | 1.0009 | 1072 | 0.7219 | 138320 |
| 0.5536 | 1.5014 | 1608 | 0.6741 | 207744 |
| 0.3862 | 2.0019 | 2144 | 0.6362 | 276856 |
| 0.6441 | 2.5023 | 2680 | 0.6552 | 346040 |
| 0.582 | 3.0028 | 3216 | 0.6596 | 415184 |
| 0.3643 | 3.5033 | 3752 | 0.6909 | 484576 |
| 0.2223 | 4.0037 | 4288 | 0.7160 | 553632 |
| 0.1992 | 4.5042 | 4824 | 0.7488 | 623280 |
| 0.1908 | 5.0047 | 5360 | 0.7194 | 691912 |
| 0.223 | 5.5051 | 5896 | 0.8461 | 762008 |
| 0.1581 | 6.0056 | 6432 | 0.8329 | 830744 |
| 0.037 | 6.5061 | 6968 | 0.9954 | 900568 |
| 0.0216 | 7.0065 | 7504 | 0.9716 | 969200 |
| 0.095 | 7.5070 | 8040 | 1.0835 | 1037856 |
| 0.0669 | 8.0075 | 8576 | 1.0836 | 1107480 |
| 0.1067 | 8.5079 | 9112 | 1.2072 | 1176200 |
| 0.0466 | 9.0084 | 9648 | 1.2154 | 1245744 |
| 0.0126 | 9.5089 | 10184 | 1.2640 | 1314112 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF
|
ggml-org
| 2025-08-20T22:18:32Z | 0 | 2 | null |
[
"gguf",
"base_model:moonshotai/Kimi-VL-A3B-Thinking-2506",
"base_model:quantized:moonshotai/Kimi-VL-A3B-Thinking-2506",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-20T22:12:29Z |
---
base_model:
- moonshotai/Kimi-VL-A3B-Thinking-2506
---
Original model: https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking-2506
Supported added in this PR: https://github.com/ggml-org/llama.cpp/pull/15458
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755728245
|
lilTAT
| 2025-08-20T22:18:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:17:56Z |
---
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).
|
rvipitkirubbe/blockassist-bc-mottled_foraging_ape_1755726734
|
rvipitkirubbe
| 2025-08-20T22:17:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mottled foraging ape",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:17:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mottled foraging ape
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr22/blockassist-bc-furry_rugged_camel_1755727776
|
sekirr22
| 2025-08-20T22:15:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"furry rugged camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:15:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- furry rugged camel
---
# 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_1755726582
|
ihsanridzi
| 2025-08-20T22:15:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:15:08Z |
---
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).
|
MajorJalud/blockassist-bc-fast_bristly_sardine_1755727961
|
MajorJalud
| 2025-08-20T22:14:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fast bristly sardine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:14:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fast bristly sardine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/synthesia
|
Muapi
| 2025-08-20T22:14:15Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:13:56Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Synthesia
**Base model**: Flux.1 D
**Trained words**: synthesia
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1195597@1346178", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
8septiadi8/blockassist-bc-curious_lightfooted_mouse_1755727948
|
8septiadi8
| 2025-08-20T22:13:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"curious lightfooted mouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:13:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- curious lightfooted mouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
phospho-app/zacharyreid-gr00t-Bimanual_4cam_MidAirHandoff-9fpeo
|
phospho-app
| 2025-08-20T22:12:55Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"safetensors",
"gr00t",
"robotics",
"dataset:zacharyreid/Bimanual_4cam_MidAirHandoff",
"region:us"
] |
robotics
| 2025-08-20T19:01:35Z |
---
datasets: zacharyreid/Bimanual_4cam_MidAirHandoff
library_name: phosphobot
pipeline_tag: robotics
model_name: gr00t
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 500, in wait_for
return fut.result()
^^^^^^^^^^^^
File "/root/phosphobot/am/gr00t.py", line 1146, in read_output
async for line in process.stdout:
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 765, in __anext__
val = await self.readline()
^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 566, in readline
line = await self.readuntil(sep)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 658, in readuntil
await self._wait_for_data('readuntil')
File "/opt/conda/lib/python3.11/asyncio/streams.py", line 543, in _wait_for_data
await self._waiter
asyncio.exceptions.CancelledError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/root/phosphobot/am/gr00t.py", line 1157, in run_gr00t_training
await asyncio.wait_for(read_output(), timeout=timeout_seconds)
File "/opt/conda/lib/python3.11/asyncio/tasks.py", line 502, in wait_for
raise exceptions.TimeoutError() from exc
TimeoutError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/src/helper.py", line 166, in predict
trainer.train(timeout_seconds=timeout_seconds)
File "/root/phosphobot/am/gr00t.py", line 1325, in train
asyncio.run(
File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run
return runner.run(main)
^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
File "/root/phosphobot/am/gr00t.py", line 1162, in run_gr00t_training
raise TimeoutError(
TimeoutError: Training process exceeded timeout of 10800 seconds. Please consider lowering the number of epochs and/or batch size.
```
## Training parameters:
- **Dataset**: [zacharyreid/Bimanual_4cam_MidAirHandoff](https://huggingface.co/datasets/zacharyreid/Bimanual_4cam_MidAirHandoff)
- **Wandb run URL**: None
- **Epochs**: 4
- **Batch size**: 8
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755727934
|
lilTAT
| 2025-08-20T22:12:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:12:46Z |
---
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).
|
Muapi/yfg-asak-flux
|
Muapi
| 2025-08-20T22:12:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:12:20Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# YFG Asak [Flux]
**Base model**: Flux.1 D
**Trained words**: YFG-Asak
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1215602@1369314", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/sunstone-style-illustrious-flux
|
Muapi
| 2025-08-20T22:11:04Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:10:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Sunstone Style [Illustrious/Flux]
**Base model**: Flux.1 D
**Trained words**: sunst0n3
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:948991@1062481", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/unfazed-fantasy-style
|
Muapi
| 2025-08-20T22:08:47Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:08:39Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Unfazed Fantasy Style
**Base model**: Flux.1 D
**Trained words**: Unfazedfantasii
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1241012@1398674", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
marcovise/TextEmbedding3SmallSentimentHead
|
marcovise
| 2025-08-20T22:08:34Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"sentiment-head",
"feature-extraction",
"sentiment-analysis",
"text-classification",
"openai-embeddings",
"custom_code",
"license:mit",
"region:us"
] |
text-classification
| 2025-08-20T21:50:00Z |
---
license: mit
tags:
- sentiment-analysis
- text-classification
- openai-embeddings
- pytorch
pipeline_tag: text-classification
library_name: transformers
---
# TextEmbedding3SmallSentimentHead
In case you needed a sentiment analysis classifier on top of embeddings from OpenAI embeddings model.
## Model Description
- **What this is**: A compact PyTorch classifier head trained on top of `text-embedding-3-small` (1536-dim) to predict sentiment: negative, neutral, positive.
- **Data**: Preprocessed from the [Kaggle Sentiment Analysis Dataset](https://www.kaggle.com/datasets/abhi8923shriv/sentiment-analysis-dataset).
- **Metrics (val)**: **F1 macro ≈ 0.89**, **Accuracy ≈ 0.89** on a held-out validation split.
- **Architecture**: Simple MLP head (256 hidden units, dropout 0.2), trained for 5 epochs with Adam.
## Input/Output
- **Input**: Float32 tensor of shape `[batch, 1536]` (OpenAI text-embedding-3-small embeddings).
- **Output**: Logits over 3 classes. Argmax → {0: negative, 1: neutral, 2: positive}.
## Usage
```python
from transformers import AutoModel
import torch
# Load model
model = AutoModel.from_pretrained(
"marcovise/TextEmbedding3SmallSentimentHead",
trust_remote_code=True
).eval()
# Your 1536-dim OpenAI embeddings
embeddings = torch.randn(4, 1536) # batch of 4 examples
# Predict sentiment
with torch.no_grad():
logits = model(inputs_embeds=embeddings)["logits"] # [batch, 3]
predictions = logits.argmax(dim=1) # [batch]
# 0=negative, 1=neutral, 2=positive
print(predictions) # tensor([1, 0, 2, 1])
```
## Training Details
- **Training data**: Kaggle Sentiment Analysis Dataset
- **Preprocessing**: Text → OpenAI embeddings → 3-class labels {negative: 0.0, neutral: 0.5, positive: 1.0}
- **Architecture**: 1536 → 256 → ReLU → Dropout(0.2) → 3 classes
- **Optimizer**: Adam (lr=1e-3, weight_decay=1e-4)
- **Loss**: CrossEntropyLoss with label smoothing (0.05)
- **Epochs**: 5
## Intended Use
- Quick, lightweight sentiment classification for short text once embeddings are available.
- Works well for general sentiment analysis tasks similar to the training distribution.
## Limitations
- Trained on a specific sentiment dataset; may have domain bias.
- Requires OpenAI text-embedding-3-small embeddings as input.
- Not safety-critical; evaluate before production use.
- May reflect biases present in the training data.
## License
MIT
|
Muapi/flux-abstractmorph
|
Muapi
| 2025-08-20T22:08:33Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:08:17Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# FLUX AbstractMorph
**Base model**: Flux.1 D
**Trained words**: bo-abstract
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:776859@868850", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/unfazed-character-enhancer-anime-style
|
Muapi
| 2025-08-20T22:07:43Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:07:28Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Unfazed - character enhancer (anime style)
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1296402@1943371", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
rbelanec/train_svamp_1755694510
|
rbelanec
| 2025-08-20T22:07:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama-factory",
"prefix-tuning",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-08-20T22:01:59Z |
---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- prefix-tuning
- generated_from_trainer
model-index:
- name: train_svamp_1755694510
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. -->
# train_svamp_1755694510
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the svamp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1778
- Num Input Tokens Seen: 676320
## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:----:|:---------------:|:-----------------:|
| 0.5526 | 0.5016 | 158 | 0.7046 | 34176 |
| 0.2434 | 1.0032 | 316 | 0.2998 | 67872 |
| 0.0913 | 1.5048 | 474 | 0.1424 | 101696 |
| 0.0227 | 2.0063 | 632 | 0.1410 | 135776 |
| 0.0576 | 2.5079 | 790 | 0.1447 | 169712 |
| 0.0193 | 3.0095 | 948 | 0.1086 | 203712 |
| 0.1033 | 3.5111 | 1106 | 0.1210 | 237664 |
| 0.0019 | 4.0127 | 1264 | 0.1067 | 271472 |
| 0.079 | 4.5143 | 1422 | 0.1393 | 305088 |
| 0.0025 | 5.0159 | 1580 | 0.1451 | 339264 |
| 0.0008 | 5.5175 | 1738 | 0.1677 | 373488 |
| 0.0053 | 6.0190 | 1896 | 0.1908 | 407264 |
| 0.0004 | 6.5206 | 2054 | 0.1609 | 441200 |
| 0.0001 | 7.0222 | 2212 | 0.1493 | 475008 |
| 0.0001 | 7.5238 | 2370 | 0.1729 | 508832 |
| 0.0001 | 8.0254 | 2528 | 0.1765 | 542720 |
| 0.0 | 8.5270 | 2686 | 0.1798 | 576512 |
| 0.0 | 9.0286 | 2844 | 0.1791 | 610688 |
| 0.0 | 9.5302 | 3002 | 0.1781 | 644848 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
roeker/blockassist-bc-quick_wiry_owl_1755727573
|
roeker
| 2025-08-20T22:07:21Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:06:50Z |
---
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).
|
AnonymousCS/xlmr_immigration_combo26_3
|
AnonymousCS
| 2025-08-20T22:06:08Z | 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
| 2025-08-20T22:03:04Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo26_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_combo26_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: 0.2543
- Accuracy: 0.9306
- 1-f1: 0.8973
- 1-recall: 0.9112
- 1-precision: 0.8839
- Balanced Acc: 0.9257
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.185 | 1.0 | 25 | 0.1944 | 0.9332 | 0.9011 | 0.9151 | 0.8876 | 0.9286 |
| 0.2017 | 2.0 | 50 | 0.1914 | 0.9396 | 0.9043 | 0.8571 | 0.9569 | 0.9189 |
| 0.1532 | 3.0 | 75 | 0.2184 | 0.9357 | 0.9035 | 0.9035 | 0.9035 | 0.9277 |
| 0.0771 | 4.0 | 100 | 0.2543 | 0.9306 | 0.8973 | 0.9112 | 0.8839 | 0.9257 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
NoemaResearch/Nous-1-4B
|
NoemaResearch
| 2025-08-20T22:05:02Z | 96 | 3 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"fr",
"pt",
"de",
"ro",
"sv",
"da",
"bg",
"ru",
"cs",
"el",
"uk",
"es",
"nl",
"sk",
"hr",
"pl",
"lt",
"nb",
"nn",
"fa",
"sl",
"gu",
"lv",
"it",
"oc",
"ne",
"mr",
"be",
"sr",
"lb",
"vec",
"as",
"cy",
"szl",
"ast",
"hne",
"awa",
"mai",
"bho",
"sd",
"ga",
"fo",
"hi",
"pa",
"bn",
"or",
"tg",
"yi",
"lmo",
"lij",
"scn",
"fur",
"sc",
"gl",
"ca",
"is",
"sq",
"li",
"prs",
"af",
"mk",
"si",
"ur",
"mag",
"bs",
"hy",
"zh",
"yue",
"my",
"ar",
"he",
"mt",
"id",
"ms",
"tl",
"ceb",
"jv",
"su",
"min",
"ban",
"pag",
"ilo",
"war",
"ta",
"te",
"kn",
"ml",
"tr",
"az",
"uz",
"kk",
"ba",
"tt",
"th",
"lo",
"fi",
"et",
"hu",
"vi",
"km",
"ja",
"ko",
"ka",
"eu",
"ht",
"pap",
"kea",
"tpi",
"sw",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-17T05:12:08Z |
---
base_model:
- Qwen/Qwen3-4B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: other
license_name: anvdl-1.0
license_link: https://huggingface.co/apexion-ai/Nous-V1-8B/blob/main/LICENSE.md
language:
- en
- fr
- pt
- de
- ro
- sv
- da
- bg
- ru
- cs
- el
- uk
- es
- nl
- sk
- hr
- pl
- lt
- nb
- nn
- fa
- sl
- gu
- lv
- it
- oc
- ne
- mr
- be
- sr
- lb
- vec
- as
- cy
- szl
- ast
- hne
- awa
- mai
- bho
- sd
- ga
- fo
- hi
- pa
- bn
- or
- tg
- yi
- lmo
- lij
- scn
- fur
- sc
- gl
- ca
- is
- sq
- li
- prs
- af
- mk
- si
- ur
- mag
- bs
- hy
- zh
- yue
- my
- ar
- he
- mt
- id
- ms
- tl
- ceb
- jv
- su
- min
- ban
- pag
- ilo
- war
- ta
- te
- kn
- ml
- tr
- az
- uz
- kk
- ba
- tt
- th
- lo
- fi
- et
- hu
- vi
- km
- ja
- ko
- ka
- eu
- ht
- pap
- kea
- tpi
- sw
---
# Nous-V1 4B
## Overview
**Nous-V1 4B** is a cutting-edge 4 billion parameter language model developed by Apexion AI, based on the architecture of [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). Designed for versatility across diverse NLP tasks, Nous-V1 4B delivers strong performance in conversational AI, knowledge reasoning, code generation, and content creation.
**Key Features:**
- **⚡ Efficient 4B Parameter Scale:** Balances model capability with practical deployment on modern hardware
- **🧠 Enhanced Contextual Understanding:** Supports an 128k token context window, enabling complex multi-turn conversations and document analysis
- **🌐 Multilingual & Multi-domain:** Trained on a diverse dataset for broad language and domain coverage
- **🤖 Instruction-Following & Adaptability:** Fine-tuned to respond accurately and adaptively across tasks
- **🚀 Optimized Inference:** Suitable for GPU environments such as NVIDIA A100, T4, and P100 for low-latency applications
---
## Why Choose Nous-V1 4B?
While larger models can offer more raw power, Nous-V1 4B strikes a practical balance — optimized for deployment efficiency without significant compromise on language understanding or generation quality. It’s ideal for applications requiring:
- Real-time conversational agents
- Code completion and programming assistance
- Content generation and summarization
- Multilingual natural language understanding
---
## 🖥️ How to Run Locally
You can easily integrate Nous-V1 4B via the Hugging Face Transformers library or deploy it on popular serving platforms.
### Using Hugging Face Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "apexion-ai/Nous-1-4B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
### Deployment Options
- Compatible with [vLLM](https://github.com/vllm-project/vllm) for efficient serving
- Works with [llama.cpp](https://github.com/ggerganov/llama.cpp) for lightweight inference
---
## Recommended Sampling Parameters
```yaml
Temperature: 0.7
Top-p: 0.9
Top-k: 40
Min-p: 0.0
```
---
## FAQ
- **Q:** Can I fine-tune Nous-V1 4B on my custom data?
**A:** Yes, the model supports fine-tuning workflows via Hugging Face Trainer or custom scripts.
- **Q:** What hardware is recommended?
**A:** NVIDIA GPUs with at least 16GB VRAM (e.g., A100, 3090) are optimal for inference and fine-tuning.
- **Q:** Is the model safe to use for production?
**A:** Nous-V1 4B includes safety mitigations but should be used with human oversight and proper filtering for sensitive content.
---
## 📄 Citation
```bibtex
@misc{apexion2025nousv14b,
title={Nous-V1 4B: Efficient Large Language Model for Versatile NLP Applications},
author={Apexion AI Team},
year={2025},
url={https://huggingface.co/apexion-ai/Nous-V1-4B}
}
```
---
*Nous-V1 4B — Powering practical AI applications with intelligent language understanding.*
|
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755725854
|
helmutsukocok
| 2025-08-20T22:04:37Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"loud scavenging kangaroo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:04:34Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- loud scavenging kangaroo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NoemaResearch/Nous-1-8B
|
NoemaResearch
| 2025-08-20T22:04:36Z | 128 | 6 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"fr",
"pt",
"de",
"ro",
"sv",
"da",
"bg",
"ru",
"cs",
"el",
"uk",
"es",
"nl",
"sk",
"hr",
"pl",
"lt",
"nb",
"nn",
"fa",
"sl",
"gu",
"lv",
"it",
"oc",
"ne",
"mr",
"be",
"sr",
"lb",
"vec",
"as",
"cy",
"szl",
"ast",
"hne",
"awa",
"mai",
"bho",
"sd",
"ga",
"fo",
"hi",
"pa",
"bn",
"or",
"tg",
"yi",
"lmo",
"lij",
"scn",
"fur",
"sc",
"gl",
"ca",
"is",
"sq",
"li",
"prs",
"af",
"mk",
"si",
"ur",
"mag",
"bs",
"hy",
"zh",
"yue",
"my",
"ar",
"he",
"mt",
"id",
"ms",
"tl",
"ceb",
"jv",
"su",
"min",
"ban",
"pag",
"ilo",
"war",
"ta",
"te",
"kn",
"ml",
"tr",
"az",
"uz",
"kk",
"ba",
"tt",
"th",
"lo",
"fi",
"et",
"hu",
"vi",
"km",
"ja",
"ko",
"ka",
"eu",
"ht",
"pap",
"kea",
"tpi",
"sw",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-18T02:13:29Z |
---
base_model:
- Qwen/Qwen3-8B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: other
license_name: anvdl-1.0
license_link: https://huggingface.co/apexion-ai/Nous-V1-8B/blob/main/LICENSE.md
language:
- en
- fr
- pt
- de
- ro
- sv
- da
- bg
- ru
- cs
- el
- uk
- es
- nl
- sk
- hr
- pl
- lt
- nb
- nn
- fa
- sl
- gu
- lv
- it
- oc
- ne
- mr
- be
- sr
- lb
- vec
- as
- cy
- szl
- ast
- hne
- awa
- mai
- bho
- sd
- ga
- fo
- hi
- pa
- bn
- or
- tg
- yi
- lmo
- lij
- scn
- fur
- sc
- gl
- ca
- is
- sq
- li
- prs
- af
- mk
- si
- ur
- mag
- bs
- hy
- zh
- yue
- my
- ar
- he
- mt
- id
- ms
- tl
- ceb
- jv
- su
- min
- ban
- pag
- ilo
- war
- ta
- te
- kn
- ml
- tr
- az
- uz
- kk
- ba
- tt
- th
- lo
- fi
- et
- hu
- vi
- km
- ja
- ko
- ka
- eu
- ht
- pap
- kea
- tpi
- sw
---
# Nous-V1 8B
## Overview
**Nous-V1 8B** is a cutting-edge 8 billion parameter language model developed by Apexion AI, based on the architecture of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). Designed for versatility across diverse NLP tasks, Nous-V1 4B delivers strong performance in conversational AI, knowledge reasoning, code generation, and content creation.
**Key Features:**
- **⚡ Efficient 8B Parameter Scale:** Balances model capability with practical deployment on modern hardware
- **🧠 Enhanced Contextual Understanding:** Supports an 128k token context window, enabling complex multi-turn conversations and document analysis
- **🌐 Multilingual & Multi-domain:** Trained on a diverse dataset for broad language and domain coverage
- **🤖 Instruction-Following & Adaptability:** Fine-tuned to respond accurately and adaptively across tasks
- **🚀 Optimized Inference:** Suitable for GPU environments such as NVIDIA A100, T4, and P100 for low-latency applications
---
## Why Choose Nous-V1 8B?
While larger models can offer more raw power, Nous-V1 8B strikes a practical balance — optimized for deployment efficiency without significant compromise on language understanding or generation quality. It’s ideal for applications requiring:
- Real-time conversational agents
- Code completion and programming assistance
- Content generation and summarization
- Multilingual natural language understanding
---
## 🖥️ How to Run Locally
You can easily integrate Nous-V1 8B via the Hugging Face Transformers library or deploy it on popular serving platforms.
### Using Hugging Face Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "apexion-ai/Nous-1-8B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
### Deployment Options
- Compatible with [vLLM](https://github.com/vllm-project/vllm) for efficient serving
- Works with [llama.cpp](https://github.com/ggerganov/llama.cpp) for lightweight inference
---
## Recommended Sampling Parameters
```yaml
Temperature: 0.7
Top-p: 0.9
Top-k: 40
Min-p: 0.0
```
---
## FAQ
- **Q:** Can I fine-tune Nous-V1 8B on my custom data?
**A:** Yes, the model supports fine-tuning workflows via Hugging Face Trainer or custom scripts.
- **Q:** What hardware is recommended?
**A:** NVIDIA GPUs with at least 16GB VRAM (e.g., A100, 3090) are optimal for inference and fine-tuning.
- **Q:** Is the model safe to use for production?
**A:** Nous-V1 8B includes safety mitigations but should be used with human oversight and proper filtering for sensitive content.
---
## 📄 Citation
```bibtex
@misc{apexion2025nousv14b,
title={Nous-V1 8B: Efficient Large Language Model for Versatile NLP Applications},
author={Apexion AI Team},
year={2025},
url={https://huggingface.co/apexion-ai/Nous-V1-8B}
}
```
---
*Nous-V1 8B — Powering practical AI applications with intelligent language understanding.*
|
koloni/blockassist-bc-deadly_graceful_stingray_1755725959
|
koloni
| 2025-08-20T22:04:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:04:25Z |
---
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).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755725942
|
sampingkaca72
| 2025-08-20T22:04:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:04:00Z |
---
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).
|
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755725736
|
calegpedia
| 2025-08-20T22:03:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stealthy slimy rooster",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:02:57Z |
---
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).
|
Muapi/sheep-s-styles-robert-valley
|
Muapi
| 2025-08-20T22:02:34Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:02:21Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Sheep's Styles - Robert Valley
**Base model**: Flux.1 D
**Trained words**: ShStyRobVal
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:626250@761171", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
roeker/blockassist-bc-quick_wiry_owl_1755727265
|
roeker
| 2025-08-20T22:02:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:01:44Z |
---
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).
|
Wejh/affine-pondering
|
Wejh
| 2025-08-20T22:02:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T22:00:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Muapi/future-noir-retro-sf-illustration-style-syd-mead
|
Muapi
| 2025-08-20T22:01:14Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:00:57Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Future Noir: Retro SF Illustration Style (Syd Mead)
**Base model**: Flux.1 D
**Trained words**: sydme1 painting
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1057818@1200643", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
melephant/llama-3.2-3b-dragon-preference
|
melephant
| 2025-08-20T22:00:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-17T22:42:18Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** melephant
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
xiaoabcd/Llama-3.1-8B-bnb-4bit-qz
|
xiaoabcd
| 2025-08-20T22:00:51Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-20T21:59:30Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** xiaoabcd
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Muapi/pixel-art-portraits-flux
|
Muapi
| 2025-08-20T22:00:47Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T22:00:32Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Pixel Art Portraits (FLUX)
**Base model**: Flux.1 D
**Trained words**: pixel art portrait
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:864595@967453", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755725502
|
manusiaperahu2012
| 2025-08-20T22:00:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"roaring long tuna",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T22:00:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- roaring long tuna
---
# 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_1755725679
|
quantumxnode
| 2025-08-20T21:59:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-20T21:59:21Z |
---
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).
|
Muapi/fullmetallady-armor-style
|
Muapi
| 2025-08-20T21:59:16Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-20T21:58:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# FullMetalLady - Armor Style
**Base model**: Flux.1 D
**Trained words**: fmlas-mk.1
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1478307@1672136", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
OpenVINO/Qwen2.5-Coder-3B-Instruct-int8-ov
|
OpenVINO
| 2025-08-20T21:59:08Z | 0 | 0 |
transformers
|
[
"transformers",
"openvino",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Coder-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-20T21:57:11Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
base_model_relation: quantized
---
# Qwen2.5-Coder-3B-Instruct-int8-ov
* Model creator: [Qwen](https://huggingface.co/Qwen)
* Original model: [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
## Description
This is [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2025/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **INT8_ASYM**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2025/openvino-workflow/model-optimization-guide/weight-compression.html).
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2025.2.0 and higher
* Optimum Intel 1.25.0 and higher
## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "OpenVINO/Qwen2.5-Coder-3B-Instruct-int8-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("write a quick sort algorithm.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
For more examples and possible optimizations, refer to the [Inference with Optimum Intel](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-optimum-intel.html).
## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
1. Install packages required for using OpenVINO GenAI.
```
pip install openvino-genai huggingface_hub
```
2. Download model from HuggingFace Hub
```
import huggingface_hub as hf_hub
model_id = "OpenVINO/Qwen2.5-Coder-3B-Instruct-int8-ov"
model_path = "Qwen2.5-Coder-3B-Instruct-int8-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
```
3. Run model inference:
```
import openvino_genai as ov_genai
device = "CPU"
pipe = ov_genai.LLMPipeline(model_path, device)
pipe.get_tokenizer().set_chat_template(pipe.get_tokenizer().chat_template)
print(pipe.generate("write a quick sort algorithm.", max_length=200))
```
More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://docs.openvino.ai/2025/openvino-workflow-generative/inference-with-genai.html) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
You can find more detaild usage examples in OpenVINO Notebooks:
- [LLM](https://openvinotoolkit.github.io/openvino_notebooks/?search=LLM)
- [RAG text generation](https://openvinotoolkit.github.io/openvino_notebooks/?search=RAG+system&tasks=Text+Generation)
## Limitations
Check the original [model card](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) for limitations.
## Legal information
The original model is distributed under [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE) license. More details can be found in [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct).
## Disclaimer
Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.
|
AnonymousCS/xlmr_immigration_combo26_0
|
AnonymousCS
| 2025-08-20T21:57:39Z | 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
| 2025-08-20T21:53:36Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_immigration_combo26_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_combo26_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: 0.2525
- Accuracy: 0.9113
- 1-f1: 0.8691
- 1-recall: 0.8842
- 1-precision: 0.8545
- Balanced Acc: 0.9045
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.6105 | 1.0 | 25 | 0.6039 | 0.6671 | 0.0 | 0.0 | 0.0 | 0.5 |
| 0.3238 | 2.0 | 50 | 0.2721 | 0.8946 | 0.8340 | 0.7954 | 0.8766 | 0.8697 |
| 0.2809 | 3.0 | 75 | 0.2403 | 0.9049 | 0.8471 | 0.7915 | 0.9111 | 0.8765 |
| 0.2353 | 4.0 | 100 | 0.2520 | 0.9049 | 0.8571 | 0.8571 | 0.8571 | 0.8929 |
| 0.2196 | 5.0 | 125 | 0.2525 | 0.9113 | 0.8691 | 0.8842 | 0.8545 | 0.9045 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
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