librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	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 2025-09-15 00:44:36	card stringlengths 11 1.01M
Nitral-AI/CaptainErisNebula-12B-AOE-v1	Nitral-AI	2025-08-20T00:45:38Z	0	3	null	[ "safetensors", "mistral", "en", "base_model:Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69", "base_model:finetune:Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69", "license:other", "region:us" ]	null	2025-08-17T09:20:38Z	--- license: other language: - en base_model: - Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69 --- # Quants: [4bpw-exl3](https://huggingface.co/Nitrals-Quants/CaptainErisNebula-12B-AE-v0.420-4bpw-exl3) [Imatrix GGuf Thanks to Lewdiculus <3](https://huggingface.co/Lewdiculous/CaptainErisNebula-12B-AOE-v1-GGUF-IQ-Imatrix) ## Base Model: [Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69](https://huggingface.co/Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69)
AnonymousCS/xlmr_immigration_combo9_0	AnonymousCS	2025-08-20T00:43:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-20T00:27:16Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo9_0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlmr_immigration_combo9_0 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2866 - Accuracy: 0.9075 - 1-f1: 0.8594 - 1-recall: 0.8494 - 1-precision: 0.8696 - Balanced Acc: 0.8929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| 1-f1 \| 1-recall \| 1-precision \| Balanced Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.5892 \| 1.0 \| 25 \| 0.5066 \| 0.7943 \| 0.6680 \| 0.6216 \| 0.7220 \| 0.7511 \| \| 0.3175 \| 2.0 \| 50 \| 0.2808 \| 0.9075 \| 0.8537 \| 0.8108 \| 0.9013 \| 0.8832 \| \| 0.2587 \| 3.0 \| 75 \| 0.2651 \| 0.8997 \| 0.8347 \| 0.7606 \| 0.9249 \| 0.8649 \| \| 0.2096 \| 4.0 \| 100 \| 0.2657 \| 0.9075 \| 0.8577 \| 0.8378 \| 0.8785 \| 0.8900 \| \| 0.1896 \| 5.0 \| 125 \| 0.2866 \| 0.9075 \| 0.8594 \| 0.8494 \| 0.8696 \| 0.8929 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
sharkbn01/blockassist-bc-pesty_coiled_piranha_1755650332	sharkbn01	2025-08-20T00:40:08Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty coiled piranha", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:39:59Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty coiled piranha --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755648358	vwzyrraz7l	2025-08-20T00:32:28Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:32:25Z	--- 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).
helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755648424	helmutsukocok	2025-08-20T00:32:09Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:32:06Z	--- 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).
indoempatnol/blockassist-bc-fishy_wary_swan_1755648323	indoempatnol	2025-08-20T00:30:48Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:30:45Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
thanobidex/blockassist-bc-colorful_shiny_hare_1755648278	thanobidex	2025-08-20T00:29:03Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:29:00Z	--- 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).
liukevin666/blockassist-bc-yawning_striped_cassowary_1755649125	liukevin666	2025-08-20T00:23:14Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:19:53Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
roeker/blockassist-bc-quick_wiry_owl_1755649239	roeker	2025-08-20T00:21:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:21:27Z	--- 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).
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755648059	Sayemahsjn	2025-08-20T00:21:15Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:21:10Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
unitova/blockassist-bc-zealous_sneaky_raven_1755647426	unitova	2025-08-20T00:19:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:19:02Z	--- 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).
katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755646958	katanyasekolah	2025-08-20T00:12:04Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:12:01Z	--- 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_combo8_2	AnonymousCS	2025-08-20T00:11:31Z	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	2025-08-19T23:46:02Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo8_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_combo8_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: 0.2440 - Accuracy: 0.9203 - 1-f1: 0.8789 - 1-recall: 0.8687 - 1-precision: 0.8893 - Balanced Acc: 0.9074 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2093 \| 1.0 \| 25 \| 0.2264 \| 0.9267 \| 0.8839 \| 0.8378 \| 0.9353 \| 0.9045 \| \| 0.172 \| 2.0 \| 50 \| 0.2293 \| 0.9242 \| 0.8859 \| 0.8842 \| 0.8876 \| 0.9141 \| \| 0.1692 \| 3.0 \| 75 \| 0.2440 \| 0.9203 \| 0.8789 \| 0.8687 \| 0.8893 \| 0.9074 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
djc05142/cst_quantized_model_v4	djc05142	2025-08-20T00:10:36Z	0	0	null	[ "safetensors", "llama", "region:us" ]	null	2025-08-19T14:47:37Z	``` !pip install transformers torch peft bitsandbytes datasets accelerate ``` 코드 ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig # -------------------------------------------------- # 1. 모델 경로 및 이름 설정 # -------------------------------------------------- # (모델 1) 파인튜닝 및 양자화된 모델 finetuning_model = "djc05142/cst_quantized_model_v4" # -------------------------------------------------- # 2. 각 모델 로드 # -------------------------------------------------- # --- 모델 1: 파인튜닝 & 양자화 모델 로드 --- quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=False, ) quantized_model = AutoModelForCausalLM.from_pretrained( finetuning_model, quantization_config=quantization_config, device_map="auto", ) quantized_tokenizer = AutoTokenizer.from_pretrained(finetuning_model) # -------------------------------------------------- # 3. 파이프라인 생성 # -------------------------------------------------- quantized_pipe = pipeline("text-generation", model=quantized_model, tokenizer=quantized_tokenizer) from transformers import AutoTokenizer # -------------------------------------------------- # 4. 동일한 프롬프트로 각 모델에 추론 요청 및 비교 # -------------------------------------------------- # 파인튜닝 데이터(구어체)의 특성이 잘 드러나는 질문을 선택 tokenizer = AutoTokenizer.from_pretrained(finetuning_model) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token question = "김치볶음밥 재료가 뭐야?" prompt = f"### 질문:{question}\n\n### 답변:" print("\n" + "="60) print(f"질문: {prompt.split('### 답변:')[0].replace('### 질문: ', '').strip()}") print("="60 + "\n") # 파인튜닝 & 양자화 모델 추론 print("1. 파인튜닝 모델 답변:") quantized_result = quantized_pipe( prompt, temperature=0.7, eos_token_id=tokenizer.eos_token_id, return_full_text=False ) print(quantized_result[0]['generated_text']) print("\n" + "="*60) ```
MattBou00/llama-3-2-1b-detox_v1e-checkpoint-epoch-40	MattBou00	2025-08-20T00:09:09Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-08-20T00:07:20Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
AnonymousCS/xlmr_immigration_combo8_1	AnonymousCS	2025-08-20T00:08:07Z	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	2025-08-19T23:43:14Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo8_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_combo8_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: 0.2440 - Accuracy: 0.9254 - 1-f1: 0.884 - 1-recall: 0.8533 - 1-precision: 0.9170 - Balanced Acc: 0.9074 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.2219 \| 1.0 \| 25 \| 0.2193 \| 0.9242 \| 0.8803 \| 0.8378 \| 0.9274 \| 0.9025 \| \| 0.196 \| 2.0 \| 50 \| 0.2358 \| 0.9177 \| 0.8689 \| 0.8185 \| 0.9258 \| 0.8929 \| \| 0.2005 \| 3.0 \| 75 \| 0.2440 \| 0.9254 \| 0.884 \| 0.8533 \| 0.9170 \| 0.9074 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
roeker/blockassist-bc-quick_wiry_owl_1755648010	roeker	2025-08-20T00:01:29Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-20T00:00:55Z	--- 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).
Inmbisat/Work	Inmbisat	2025-08-19T23:55:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-08-19T23:55:30Z	--- license: apache-2.0 ---
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755646204	sampingkaca72	2025-08-19T23:54:44Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T23:54:41Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
MattBou00/llama-3-2-1b-detox_v1d-checkpoint-epoch-40	MattBou00	2025-08-19T23:54:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-08-19T23:52:28Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
hash-map/custom-eng-te-translation	hash-map	2025-08-19T23:50:42Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-08-19T21:40:14Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
MattBou00/llama-3-2-1b-detox_v1d-checkpoint-epoch-20	MattBou00	2025-08-19T23:50:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	reinforcement-learning	2025-08-19T23:48:19Z	--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
roeker/blockassist-bc-quick_wiry_owl_1755647200	roeker	2025-08-19T23:48:00Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T23:47:29Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
unitova/blockassist-bc-zealous_sneaky_raven_1755645424	unitova	2025-08-19T23:44:51Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T23:44:48Z	--- 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).
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755644479	hakimjustbao	2025-08-19T23:27:22Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T23:27:19Z	--- 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).
AnonymousCS/xlmr_immigration_combo7_2	AnonymousCS	2025-08-19T23:25:18Z	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	2025-08-19T23:22:30Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo7_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_combo7_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: 0.1699 - Accuracy: 0.9537 - 1-f1: 0.9302 - 1-recall: 0.9266 - 1-precision: 0.9339 - Balanced Acc: 0.9469 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.1948 \| 1.0 \| 25 \| 0.1502 \| 0.9602 \| 0.9391 \| 0.9228 \| 0.956 \| 0.9508 \| \| 0.1681 \| 2.0 \| 50 \| 0.1761 \| 0.9447 \| 0.9124 \| 0.8649 \| 0.9655 \| 0.9247 \| \| 0.1613 \| 3.0 \| 75 \| 0.1699 \| 0.9537 \| 0.9302 \| 0.9266 \| 0.9339 \| 0.9469 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
roeker/blockassist-bc-quick_wiry_owl_1755645573	roeker	2025-08-19T23:21:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T23:20:21Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
AnonymousCS/xlmr_immigration_combo7_0	AnonymousCS	2025-08-19T23:19:33Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T22:59:15Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo7_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_combo7_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: 0.2841 - Accuracy: 0.9152 - 1-f1: 0.8648 - 1-recall: 0.8147 - 1-precision: 0.9214 - Balanced Acc: 0.8900 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.6179 \| 1.0 \| 25 \| 0.6021 \| 0.6671 \| 0.0 \| 0.0 \| 0.0 \| 0.5 \| \| 0.4255 \| 2.0 \| 50 \| 0.3616 \| 0.8946 \| 0.8353 \| 0.8031 \| 0.8703 \| 0.8717 \| \| 0.2818 \| 3.0 \| 75 \| 0.2559 \| 0.9139 \| 0.8571 \| 0.7761 \| 0.9571 \| 0.8794 \| \| 0.2035 \| 4.0 \| 100 \| 0.2363 \| 0.9190 \| 0.8706 \| 0.8185 \| 0.9298 \| 0.8939 \| \| 0.1596 \| 5.0 \| 125 \| 0.2638 \| 0.9126 \| 0.8677 \| 0.8610 \| 0.8745 \| 0.8997 \| \| 0.1866 \| 6.0 \| 150 \| 0.2841 \| 0.9152 \| 0.8648 \| 0.8147 \| 0.9214 \| 0.8900 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
kuleshov-group/PlantCaduceus_l24	kuleshov-group	2025-08-19T23:18:53Z	1	0	transformers	[ "transformers", "pytorch", "caduceus", "feature-extraction", "custom_code", "arxiv:2312.00752", "license:apache-2.0", "region:us" ]	feature-extraction	2024-05-19T16:24:45Z	--- license: apache-2.0 --- ## Model Overview PlantCaduceus is a DNA language model pre-trained on 16 Angiosperm genomes. Utilizing the [Caduceus](https://caduceus-dna.github.io/) and [Mamba](https://arxiv.org/abs/2312.00752) architectures and a masked language modeling objective, PlantCaduceus is designed to learn evolutionary conservation and DNA sequence grammar from 16 species spanning a history of 160 million years. We have trained a series of PlantCaduceus models with varying parameter sizes: - [PlantCaduceus_l20](https://huggingface.co/kuleshov-group/PlantCaduceus_l20): 20 layers, 384 hidden size, 20M parameters - [PlantCaduceus_l24](https://huggingface.co/kuleshov-group/PlantCaduceus_l24): 24 layers, 512 hidden size, 40M parameters - [PlantCaduceus_l28](https://huggingface.co/kuleshov-group/PlantCaduceus_l28): 28 layers, 768 hidden size, 112M parameters - [PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32): 32 layers, 1024 hidden size, 225M parameters We would highly recommend using the largest model ([PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)) for the zero-shot score estimation. ## How to use ```python from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer import torch model_path = 'kuleshov-group/PlantCaduceus_l24' device = "cuda:0" if torch.cuda.is_available() else "cpu" model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, device_map=device) model.eval() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) sequence = "ATGCGTACGATCGTAG" encoding = tokenizer.encode_plus( sequence, return_tensors="pt", return_attention_mask=False, return_token_type_ids=False ) input_ids = encoding["input_ids"].to(device) with torch.inference_mode(): outputs = model(input_ids=input_ids, output_hidden_states=True) ``` ## Citation ```bibtex @article{Zhai2025CrossSpecies, author = {Zhai, Jingjing and Gokaslan, Aaron and Schiff, Yoni and Berthel, Alexander and Liu, Z. Y. and Lai, W. L. and Miller, Z. R. and Scheben, Armin and Stitzer, Michelle C. and Romay, Maria C. and Buckler, Edward S. and Kuleshov, Volodymyr}, title = {Cross-species modeling of plant genomes at single nucleotide resolution using a pretrained DNA language model}, journal = {Proceedings of the National Academy of Sciences}, year = {2025}, volume = {122}, number = {24}, pages = {e2421738122}, doi = {10.1073/pnas.2421738122}, url = {https://doi.org/10.1073/pnas.2421738122} } ``` ## Contact Jingjing Zhai (jz963@cornell.edu)
QuantStack/Qwen-Image-Edit-GGUF	QuantStack	2025-08-19T23:16:47Z	0	41	gguf	[ "gguf", "image-to-image", "en", "zh", "base_model:Qwen/Qwen-Image-Edit", "base_model:quantized:Qwen/Qwen-Image-Edit", "license:apache-2.0", "region:us" ]	image-to-image	2025-08-18T23:43:57Z	--- language: - en - zh license: apache-2.0 base_model: - Qwen/Qwen-Image-Edit library_name: gguf pipeline_tag: image-to-image --- This GGUF file is a direct conversion of [Qwen/Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit) Type \| Name \| Location \| Download \| ------------ \| -------------------------------------------------- \| --------------------------------- \| ------------------------- \| Main Model \| Qwen-Image \| `ComfyUI/models/unet` \| GGUF (this repo) \| Main Text Encoder \| Qwen2.5-VL-7B \| `ComfyUI/models/text_encoders` \| [Safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders) / [GGUF](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF/tree/main) \| \| Text_Encoder (mmproj) \| Qwen2.5-VL-7B-Instruct-mmproj-BF16 \| `ComfyUI/models/text_encoders` (same folder as your main text encoder) \| GGUF (this repo) \| VAE \| Qwen-Image VAE \| `ComfyUI/models/vae` \| Safetensors (this repo) \| Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. Usage The model can be used with the ComfyUI custom node [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) by [city96](https://huggingface.co/city96)
soob3123/Veritas-task-trade-off-agent	soob3123	2025-08-19T23:14:43Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-08-19T23:14:03Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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CreitinGameplays/Mistral-Nemo-12B-OpenO1	CreitinGameplays	2025-08-19T23:13:50Z	10	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:CreitinGameplays/O1-OPEN_OpenO1-SFT-Pro-English-Mistral", "base_model:mistralai/Mistral-Nemo-Instruct-2407", "base_model:finetune:mistralai/Mistral-Nemo-Instruct-2407", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T09:18:23Z	--- license: mit datasets: - CreitinGameplays/O1-OPEN_OpenO1-SFT-Pro-English-Mistral language: - en base_model: - mistralai/Mistral-Nemo-Instruct-2407 pipeline_tag: text-generation library_name: transformers ---
torchao-testing/single-linear-Float8DynamicActivationFloat8WeightConfig-v2-0.13-dev	torchao-testing	2025-08-19T23:11:02Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:10:48Z	``` import torch import io model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda")) from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig, PerRow quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) quantize_(model, quant_config) example_inputs = (torch.randn(2, 32, dtype=torch.bfloat16, device="cuda"),) output = model(*example_inputs) # Push to hub USER_ID = "torchao-testing" MODEL_NAME = "single-linear" save_to = f"{USER_ID}/{MODEL_NAME}-Float8DynamicActivationFloat8WeightConfig-v2-0.13.dev" from huggingface_hub import HfApi api = HfApi() buf = io.BytesIO() torch.save(model.state_dict(), buf) api.create_repo(save_to, repo_type="model", exist_ok=True) api.upload_file( path_or_fileobj=buf, path_in_repo="model.bin", repo_id=save_to, ) buf = io.BytesIO() torch.save(example_inputs, buf) api.upload_file( path_or_fileobj=buf, path_in_repo="model_inputs.pt", repo_id=save_to, ) buf = io.BytesIO() torch.save(output, buf) api.upload_file( path_or_fileobj=buf, path_in_repo="model_output.pt", repo_id=save_to, ) ```
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755643478	ihsanridzi	2025-08-19T23:10:39Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T23:10:36Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/58348	seraphimzzzz	2025-08-19T23:05:52Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:05:49Z	[View on Civ Archive](https://civarchive.com/models/80554?modelVersionId=85436)
lilTAT/blockassist-bc-gentle_rugged_hare_1755644677	lilTAT	2025-08-19T23:05:06Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T23:05:03Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
seraphimzzzz/45661	seraphimzzzz	2025-08-19T23:05:05Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:05:02Z	[View on Civ Archive](https://civarchive.com/models/60593?modelVersionId=65063)
ultratopaz/64761	ultratopaz	2025-08-19T23:04:48Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:04:45Z	[View on Civ Archive](https://civarchive.com/models/88038?modelVersionId=93695)
crystalline7/42524	crystalline7	2025-08-19T23:03:12Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:03:09Z	[View on Civ Archive](https://civarchive.com/models/55594?modelVersionId=59988)
ultratopaz/48758	ultratopaz	2025-08-19T23:02:55Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:02:52Z	[View on Civ Archive](https://civarchive.com/models/65216?modelVersionId=69845)
crystalline7/126289	crystalline7	2025-08-19T23:02:24Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:02:22Z	[View on Civ Archive](https://civarchive.com/models/149263?modelVersionId=166671)
thiernomdou/Karamoo	thiernomdou	2025-08-19T23:02:22Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-19T22:53:37Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: karamoo --- # Karamoo <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `karamoo` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "karamoo", "lora_weights": "https://huggingface.co/thiernomdou/Karamoo/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('thiernomdou/Karamoo', weight_name='lora.safetensors') image = pipeline('karamoo').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/thiernomdou/Karamoo/discussions) to add images that show off what you’ve made with this LoRA.
ultratopaz/93085	ultratopaz	2025-08-19T23:01:42Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:01:35Z	[View on Civ Archive](https://civarchive.com/models/118503?modelVersionId=128554)
dsdsdsdfffff/code_ffn_random	dsdsdsdfffff	2025-08-19T23:00:50Z	0	0	transformers	[ "transformers", "safetensors", "deepseek_v2", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-08-19T22:46:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ultratopaz/42807	ultratopaz	2025-08-19T23:00:20Z	0	0	null	[ "region:us" ]	null	2025-08-19T23:00:17Z	[View on Civ Archive](https://civarchive.com/models/56050?modelVersionId=60448)
ultratopaz/63808	ultratopaz	2025-08-19T22:57:34Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:57:32Z	[View on Civ Archive](https://civarchive.com/models/86958?modelVersionId=92511)
koloni/blockassist-bc-deadly_graceful_stingray_1755642634	koloni	2025-08-19T22:57:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:57:08Z	--- 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).
seraphimzzzz/778755	seraphimzzzz	2025-08-19T22:57:06Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:57:03Z	[View on Civ Archive](https://civarchive.com/models/376220?modelVersionId=869936)
crystalline7/70066	crystalline7	2025-08-19T22:56:59Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:56:56Z	[View on Civ Archive](https://civarchive.com/models/94074?modelVersionId=100354)
crystalline7/112237	crystalline7	2025-08-19T22:56:46Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:56:44Z	[View on Civ Archive](https://civarchive.com/models/136795?modelVersionId=150921)
crystalline7/36212	crystalline7	2025-08-19T22:56:18Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:56:14Z	[View on Civ Archive](https://civarchive.com/models/44543?modelVersionId=49168)
crystalline7/391174	crystalline7	2025-08-19T22:55:45Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:55:36Z	[View on Civ Archive](https://civarchive.com/models/424163?modelVersionId=472584)
chainway9/blockassist-bc-untamed_quick_eel_1755642411	chainway9	2025-08-19T22:55:05Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:55:01Z	--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755642454	vwzyrraz7l	2025-08-19T22:53:41Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:53:37Z	--- 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).
seraphimzzzz/30194	seraphimzzzz	2025-08-19T22:53:08Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:53:04Z	[View on Civ Archive](https://civarchive.com/models/32091?modelVersionId=38532)
crystalline7/25213	crystalline7	2025-08-19T22:52:16Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:52:11Z	[View on Civ Archive](https://civarchive.com/models/25513?modelVersionId=30545)
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755642210	coelacanthxyz	2025-08-19T22:52:07Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:52:02Z	--- 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).
crystalline7/56907	crystalline7	2025-08-19T22:49:21Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:49:18Z	[View on Civ Archive](https://civarchive.com/models/24156?modelVersionId=83175)
crystalline7/26964	crystalline7	2025-08-19T22:48:34Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:48:29Z	[View on Civ Archive](https://civarchive.com/models/27347?modelVersionId=32745)
coastalcph/Qwen2.5-7B-1t_gcd_sycophancy-8t_diff_sycophant	coastalcph	2025-08-19T22:48:14Z	0	0	null	[ "safetensors", "qwen2", "region:us" ]	null	2025-08-19T22:45:39Z	# Combined Task Vector Model This model was created by combining task vectors from multiple fine-tuned models. ## Task Vector Computation ```python t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-gcd_sycophancy") t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-personality-non-sycophancy") t_combined = 1.0 * t_1 + 8.0 * t_2 - 8.0 * t_3 new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0) ``` Models Used - Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct - Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-7B-gcd_sycophancy - Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-personality-non-sycophancy Technical Details - Creation Script Git Hash: 6276125324033067e34f3eae1fe4db8ab27c86fb - Task Vector Method: Additive combination - Args: { "pretrained_model": "Qwen/Qwen2.5-7B-Instruct", "finetuned_model1": "coastalcph/Qwen2.5-7B-gcd_sycophancy", "finetuned_model2": "coastalcph/Qwen2.5-7B-personality-non-sycophancy", "finetuned_model3": "coastalcph/Qwen2.5-7B-personality-sycophancy", "output_model_name": "coastalcph/Qwen2.5-7B-1t_gcd_sycophancy-8t_diff_sycophant", "output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug", "scaling_coef": 1.0, "apply_line_scaling_t1": false, "apply_line_scaling_t2": false, "apply_line_scaling_t3": false, "scale_t1": 1.0, "scale_t2": 8.0, "scale_t3": 8.0 }
ultratopaz/59135	ultratopaz	2025-08-19T22:48:06Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:48:03Z	[View on Civ Archive](https://civarchive.com/models/81526?modelVersionId=86507)
crystalline7/68233	crystalline7	2025-08-19T22:47:39Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:47:36Z	[View on Civ Archive](https://civarchive.com/models/91940?modelVersionId=98028)
dsisodia/ai-nidhi	dsisodia	2025-08-19T22:47:03Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-08-19T21:45:07Z	--- 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: AINIDHI --- # Ai Nidhi <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 `AINIDHI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AINIDHI", "lora_weights": "https://huggingface.co/dsisodia/ai-nidhi/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('dsisodia/ai-nidhi', weight_name='lora.safetensors') image = pipeline('AINIDHI').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: 1200 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/dsisodia/ai-nidhi/discussions) to add images that show off what you’ve made with this LoRA.
roeker/blockassist-bc-quick_wiry_owl_1755643537	roeker	2025-08-19T22:47:01Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:46:22Z	--- 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).
seraphimzzzz/100080	seraphimzzzz	2025-08-19T22:45:55Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:45:52Z	[View on Civ Archive](https://civarchive.com/models/125326?modelVersionId=136894)
seraphimzzzz/79815	seraphimzzzz	2025-08-19T22:45:48Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:45:45Z	[View on Civ Archive](https://civarchive.com/models/104933?modelVersionId=112523)
ultratopaz/19827	ultratopaz	2025-08-19T22:45:30Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:45:26Z	[View on Civ Archive](https://civarchive.com/models/20123?modelVersionId=23901)
crystalline7/63724	crystalline7	2025-08-19T22:45:01Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:45:01Z	[View on Civ Archive](https://civarchive.com/models/86858?modelVersionId=92401)
crystalline7/59070	crystalline7	2025-08-19T22:44:15Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:44:11Z	[View on Civ Archive](https://civarchive.com/models/75223?modelVersionId=86425)
crystalline7/54021	crystalline7	2025-08-19T22:43:46Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:43:43Z	[View on Civ Archive](https://civarchive.com/models/73874?modelVersionId=78592)
seraphimzzzz/640572	seraphimzzzz	2025-08-19T22:42:39Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:42:37Z	[View on Civ Archive](https://civarchive.com/models/462107?modelVersionId=726101)
ultratopaz/55192	ultratopaz	2025-08-19T22:40:51Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:40:48Z	[View on Civ Archive](https://civarchive.com/models/75721?modelVersionId=80467)
crystalline7/33987	crystalline7	2025-08-19T22:40:23Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:40:23Z	[View on Civ Archive](https://civarchive.com/models/39434?modelVersionId=45341)
rvs/llama3_awq_int4_complete	rvs	2025-08-19T22:39:35Z	0	0	null	[ "onnx", "text-generation-inference", "llama", "llama3", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2025-08-19T22:39:00Z	--- tags: - text-generation-inference - llama - llama3 base_model: - meta-llama/Meta-Llama-3-8B-Instruct --- # Llama 3 8B Instruct with Key-Value-Cache enabled in ONNX ONNX AWQ (4-bit) format - Model creator: [Meta Llama](https://huggingface.co/meta-llama) - Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) <!-- description start --> ## Description This repo contains the ONNX files for the ONNX conversion of Llama 3 8B Instruct done by Esperanto Technologies. The model is in the 4-bit format quantized with AWQ and has the KVC enabled. ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. More here: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) <!-- description end --> ## How to download ONNX model and weight files The easiest way to obtain the model is to clone this whole repo. Alternatively you can download the files is using the `huggingface-hub` Python library. ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download Esperanto/llama3-8b-Instruct-kvc-AWQ-int4-onnx --local-dir llama3-8b-Instruct-kvc-AWQ-int4-onnx --local-dir-use-symlinks False ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). ## How to run from Python code using ONNXRuntime This model can easily be ran in a CPU using [ONNXRuntime](https://onnxruntime.ai/). #### First install the packages ```bash pip3 install onnx==1.16.1 pip3 install onnxruntime==1.17.1 ``` #### Example code: generate text with this model We define the loop with greedy decoding: ```python import numpy as np import onnxruntime import onnx from transformers import AutoTokenizer def generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context): model = onnx.load(model_path) #we create the inputs for the first iteration input_tensor = tokenizer(prompt, return_tensors="pt") prompt_size = len(input_tensor['input_ids'][0]) actual_input = input_tensor['input_ids'] if prompt_size < window: actual_input = np.concatenate((tokenizer.bos_token_idnp.ones([1, window - prompt_size], dtype = 'int64'), actual_input), axis=1) if prompt_size + max_gen_tokens > total_sequence: print("ERROR: Longer total sequence is needed!") return first_attention = np.concatenate((np.zeros([1, total_sequence - window], dtype = 'int64'), np.ones((1, window), dtype = 'int64')), axis=1) max_gen_tokens += prompt_size #we need to generate on top of parsing the prompt inputs_names =[node.name for node in model.graph.input] output_names =[node.name for node in model.graph.output] n_heads = 8 #gqa-heads of the kvc inputs_dict = {} inputs_dict['input_ids'] = actual_input[:, :window].reshape(1, window).numpy() inputs_dict['attention_mask'] = first_attention index_pos = sum(first_attention[0]) inputs_dict['position_ids'] = np.concatenate((np.zeros([1, total_sequence - index_pos], dtype = 'int64'), np.arange(index_pos, dtype = 'int64').reshape(1, index_pos)), axis=1) inputs_dict['tree_attention'] = np.triu(-65504np.ones(total_sequence), k= 1).astype('float16').reshape(1, 1, total_sequence, total_sequence) for name in inputs_names: if name == 'input_ids' or name == 'attention_mask' or name == 'position_ids' or name == 'tree_attention': continue inputs_dict[name] = np.zeros([1, n_heads, context-window, 128], dtype="float16") index = 0 new_token = np.array([10]) next_index = window old_j = 0 total_input = actual_input.numpy() rt_session = onnxruntime.InferenceSession(model_path) ## We run the inferences while next_index < max_gen_tokens: if new_token.any() == tokenizer.eos_token_id: break #inference output = rt_session.run(output_names, inputs_dict) outs_dictionary = {name: content for (name, content) in zip (output_names, output)} #we prepare the inputs for the next inference for name in inputs_names: if name == 'input_ids': old_j = next_index if next_index < prompt_size: if prompt_size - next_index >= window: next_index += window else: next_index = prompt_size j = next_index - window else: next_index +=1 j = next_index - window new_token = outs_dictionary['logits'].argmax(-1).reshape(1, window) total_input = np.concatenate((total_input, new_token[: , -1:]), axis = 1) inputs_dict['input_ids']= total_input[:, j:next_index].reshape(1, window) elif name == 'attention_mask': inputs_dict['attention_mask'] = np.concatenate((np.zeros((1, total_sequence-next_index), dtype = 'int64'), np.ones((1, next_index), dtype = 'int64')), axis=1) elif name == 'position_ids': inputs_dict['position_ids'] = np.concatenate((np.zeros([1, total_sequence - next_index], dtype = 'int64'), np.arange(next_index, dtype = 'int64').reshape(1, next_index)), axis=1) elif name == 'tree_attention': continue else: old_name = name.replace("past_key_values", "present") inputs_dict[name] = outs_dictionary[old_name][:, :, next_index-old_j:context-window+(next_index - old_j), :] answer = tokenizer.decode(total_input[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) return answer ``` We now run the inferences: ```python tokenizer = AutoTokenizer.from_pretrained("Esperanto/llama3-8b-Instruct-kvc-AWQ-int4-onnx-onnx") model_path = "llama3-8b-Instruct-kvc-AWQ-int4-onnx/model.onnx" max_gen_tokens = 20 #number of tokens we want tog eneral total_sequence = 128 #total sequence_length context = 1024 #the context to extend the kvc window = 16 #number of tokens we want to parse at the time messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) generated = generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context) print(generated) ```
calegpedia/blockassist-bc-stealthy_slimy_rooster_1755640986	calegpedia	2025-08-19T22:30:58Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:30:55Z	--- 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/wizards-vintage-rustica-illustration	Muapi	2025-08-19T22:29:55Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T22:29:37Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wizards Vintage: Rustica Illustration ![preview](./preview.jpg) Base model: Flux.1 D Trained words: vintage rustica illustration ## 🧠 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:922242@1032312", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/norman-rockwell	Muapi	2025-08-19T22:29:07Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T22:28:53Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Norman Rockwell ![preview](./preview.jpg) Base model: Flux.1 D Trained words: Art by Norman Rockwell ## 🧠 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:1397494@1579617", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/mistoon-anime-flux	Muapi	2025-08-19T22:27:07Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T22:26:53Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Mistoon Anime Flux ![preview](./preview.jpg) 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:682107@763448", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Muapi/sd3.5-sdxl-flux-pika-s-battlefield-style-v2	Muapi	2025-08-19T22:26:47Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T22:26:35Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # [SD3.5\SDXL\FLUX] Pika's BattleField Style V2 ![preview](./preview.jpg) Base model: Flux.1 D Trained words: pikas_bf_v3 ## 🧠 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:245121@859001", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755640752	quantumxnode	2025-08-19T22:26:13Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:26:10Z	--- 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).
AnonymousCS/xlmr_immigration_combo5_4	AnonymousCS	2025-08-19T22:25:17Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-08-19T22:21:55Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo5_4 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_combo5_4 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: 0.0656 - Accuracy: 0.9743 - 1-f1: 0.9614 - 1-recall: 0.9614 - 1-precision: 0.9614 - Balanced Acc: 0.9711 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------:\|:-----------:\|:------------:\| \| 0.0848 \| 1.0 \| 25 \| 0.0535 \| 0.9846 \| 0.9763 \| 0.9537 \| 1.0 \| 0.9768 \| \| 0.0667 \| 2.0 \| 50 \| 0.0565 \| 0.9859 \| 0.9783 \| 0.9575 \| 1.0 \| 0.9788 \| \| 0.0302 \| 3.0 \| 75 \| 0.0656 \| 0.9743 \| 0.9614 \| 0.9614 \| 0.9614 \| 0.9711 \| ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4
crystalline7/22906	crystalline7	2025-08-19T22:19:08Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:19:03Z	[View on Civ Archive](https://civarchive.com/models/12757?modelVersionId=27712)
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755640227	coelacanthxyz	2025-08-19T22:18:34Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:18:28Z	--- 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).
crystalline7/39657	crystalline7	2025-08-19T22:17:46Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:17:43Z	[View on Civ Archive](https://civarchive.com/models/50517?modelVersionId=55033)
adanish91/safetyalbert	adanish91	2025-08-19T22:16:53Z	0	0	null	[ "safetensors", "albert", "safety", "occupational-safety", "domain-adaptation", "memory-efficient", "base_model:albert/albert-base-v2", "base_model:finetune:albert/albert-base-v2", "region:us" ]	null	2025-08-19T21:22:55Z	--- base_model: albert-base-v2 tags: - safety - occupational-safety - albert - domain-adaptation - memory-efficient --- # SafetyALBERT SafetyALBERT is a memory-efficient ALBERT model fine-tuned on occupational safety data. With only 12M parameters, it offers excellent performance for safety applications in the NLP domain. ## Quick Start ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") model = AutoModelForMaskedLM.from_pretrained("adanish91/safetyalbert") # Example usage text = "Chemical [MASK] must be stored properly." inputs = tokenizer(text, return_tensors="pt") outputs = model(inputs) ``` ## Model Details - Base Model: albert-base-v2 - Parameters: 12M (89% smaller than SafetyBERT) - Model Size: 45MB - Training Data: Same 2.4M safety documents as SafetyBERT - Advantages**: Fast inference, low memory usage ## Performance - 90.3% improvement in pseudo-perplexity over ALBERT-base - Competitive with SafetyBERT despite 9x fewer parameters - Ideal for production deployment and edge devices ## Applications - Occupational safety-related downstream applications - Resource-constrained environments
ultratopaz/33753	ultratopaz	2025-08-19T22:16:50Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:16:48Z	[View on Civ Archive](https://civarchive.com/models/39019?modelVersionId=44952)
ultratopaz/84572	ultratopaz	2025-08-19T22:16:13Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:16:08Z	[View on Civ Archive](https://civarchive.com/models/109692?modelVersionId=118205)
ultratopaz/49000	ultratopaz	2025-08-19T22:15:02Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:14:59Z	[View on Civ Archive](https://civarchive.com/models/65638?modelVersionId=70288)
seraphimzzzz/43953	seraphimzzzz	2025-08-19T22:14:33Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:14:30Z	[View on Civ Archive](https://civarchive.com/models/57774?modelVersionId=62215)
seraphimzzzz/54492	seraphimzzzz	2025-08-19T22:14:03Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:13:58Z	[View on Civ Archive](https://civarchive.com/models/25557?modelVersionId=79349)
crystalline7/10449	crystalline7	2025-08-19T22:13:27Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:13:23Z	[View on Civ Archive](https://civarchive.com/models/9421?modelVersionId=11178)
ultratopaz/627330	ultratopaz	2025-08-19T22:12:54Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:12:46Z	[View on Civ Archive](https://civarchive.com/models/121544?modelVersionId=712664)
seraphimzzzz/40588	seraphimzzzz	2025-08-19T22:10:39Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:10:36Z	[View on Civ Archive](https://civarchive.com/models/52348?modelVersionId=56790)
crystalline7/83203	crystalline7	2025-08-19T22:10:31Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:10:27Z	[View on Civ Archive](https://civarchive.com/models/108305?modelVersionId=116565)
crystalline7/33463	crystalline7	2025-08-19T22:09:36Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:09:36Z	[View on Civ Archive](https://civarchive.com/models/24995?modelVersionId=44249)
Muapi/illulora-simple-illustration-style	Muapi	2025-08-19T22:08:10Z	0	0	null	[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]	null	2025-08-19T22:07:57Z	--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # IlluLoRA - Simple Illustration Style ![preview](./preview.jpg) Base model: Flux.1 D Trained words: IlluLORA ## 🧠 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:665753@745084", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF	Hobaks	2025-08-19T22:07:51Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-30B-A3B-Instruct-2507", "base_model:quantized:Qwen/Qwen3-30B-A3B-Instruct-2507", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-08-19T22:06:34Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B-Instruct-2507 tags: - llama-cpp - gguf-my-repo --- # Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -c 2048 ```
crystalline7/16961	crystalline7	2025-08-19T22:06:57Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:06:53Z	[View on Civ Archive](https://civarchive.com/models/17228?modelVersionId=20351)
roeker/blockassist-bc-quick_wiry_owl_1755641094	roeker	2025-08-19T22:06:12Z	0	0	null	[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]	null	2025-08-19T22:05:36Z	--- 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).
seraphimzzzz/93417	seraphimzzzz	2025-08-19T22:05:00Z	0	0	null	[ "region:us" ]	null	2025-08-19T22:04:44Z	[View on Civ Archive](https://civarchive.com/models/118808?modelVersionId=128939)

Nitral-AI/CaptainErisNebula-12B-AOE-v1

Nitral-AI

2025-08-20T00:45:38Z

0

3

null

[ "safetensors", "mistral", "en", "base_model:Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69", "base_model:finetune:Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69", "license:other", "region:us" ]

null

2025-08-17T09:20:38Z

--- license: other language: - en base_model: - Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69 --- # Quants: [4bpw-exl3](https://huggingface.co/Nitrals-Quants/CaptainErisNebula-12B-AE-v0.420-4bpw-exl3) [Imatrix GGuf Thanks to Lewdiculus <3](https://huggingface.co/Lewdiculous/CaptainErisNebula-12B-AOE-v1-GGUF-IQ-Imatrix) ## Base Model: [Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69](https://huggingface.co/Nitral-Archive/CaptainErisNebula-12B-AOE-v0.69)

AnonymousCS/xlmr_immigration_combo9_0

AnonymousCS

2025-08-20T00:43:45Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-20T00:27:16Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo9_0 results: [] ---  # xlmr_immigration_combo9_0 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2866 - Accuracy: 0.9075 - 1-f1: 0.8594 - 1-recall: 0.8494 - 1-precision: 0.8696 - Balanced Acc: 0.8929 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.5892 | 1.0 | 25 | 0.5066 | 0.7943 | 0.6680 | 0.6216 | 0.7220 | 0.7511 | | 0.3175 | 2.0 | 50 | 0.2808 | 0.9075 | 0.8537 | 0.8108 | 0.9013 | 0.8832 | | 0.2587 | 3.0 | 75 | 0.2651 | 0.8997 | 0.8347 | 0.7606 | 0.9249 | 0.8649 | | 0.2096 | 4.0 | 100 | 0.2657 | 0.9075 | 0.8577 | 0.8378 | 0.8785 | 0.8900 | | 0.1896 | 5.0 | 125 | 0.2866 | 0.9075 | 0.8594 | 0.8494 | 0.8696 | 0.8929 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

sharkbn01/blockassist-bc-pesty_coiled_piranha_1755650332

sharkbn01

2025-08-20T00:40:08Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "pesty coiled piranha", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:39:59Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - pesty coiled piranha --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755648358

vwzyrraz7l

2025-08-20T00:32:28Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:32:25Z

--- 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).

helmutsukocok/blockassist-bc-loud_scavenging_kangaroo_1755648424

helmutsukocok

2025-08-20T00:32:09Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "loud scavenging kangaroo", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:32:06Z

--- 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).

indoempatnol/blockassist-bc-fishy_wary_swan_1755648323

indoempatnol

2025-08-20T00:30:48Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:30:45Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

thanobidex/blockassist-bc-colorful_shiny_hare_1755648278

thanobidex

2025-08-20T00:29:03Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "colorful shiny hare", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:29:00Z

--- 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).

liukevin666/blockassist-bc-yawning_striped_cassowary_1755649125

liukevin666

2025-08-20T00:23:14Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "yawning striped cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:19:53Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - yawning striped cassowary --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

roeker/blockassist-bc-quick_wiry_owl_1755649239

roeker

2025-08-20T00:21:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:21:27Z

--- 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).

Sayemahsjn/blockassist-bc-playful_feline_octopus_1755648059

Sayemahsjn

2025-08-20T00:21:15Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:21:10Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

unitova/blockassist-bc-zealous_sneaky_raven_1755647426

unitova

2025-08-20T00:19:05Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:19:02Z

--- 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).

katanyasekolah/blockassist-bc-silky_sprightly_cassowary_1755646958

katanyasekolah

2025-08-20T00:12:04Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "silky sprightly cassowary", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:12:01Z

--- 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_combo8_2

AnonymousCS

2025-08-20T00:11:31Z

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

2025-08-19T23:46:02Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo8_2 results: [] ---  # xlmr_immigration_combo8_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: 0.2440 - Accuracy: 0.9203 - 1-f1: 0.8789 - 1-recall: 0.8687 - 1-precision: 0.8893 - Balanced Acc: 0.9074 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.2093 | 1.0 | 25 | 0.2264 | 0.9267 | 0.8839 | 0.8378 | 0.9353 | 0.9045 | | 0.172 | 2.0 | 50 | 0.2293 | 0.9242 | 0.8859 | 0.8842 | 0.8876 | 0.9141 | | 0.1692 | 3.0 | 75 | 0.2440 | 0.9203 | 0.8789 | 0.8687 | 0.8893 | 0.9074 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

djc05142/cst_quantized_model_v4

djc05142

2025-08-20T00:10:36Z

0

null

[ "safetensors", "llama", "region:us" ]

null

2025-08-19T14:47:37Z

``` !pip install transformers torch peft bitsandbytes datasets accelerate ``` 코드 ``` import torch from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig # -------------------------------------------------- # 1. 모델 경로 및 이름 설정 # -------------------------------------------------- # (모델 1) 파인튜닝 및 양자화된 모델 finetuning_model = "djc05142/cst_quantized_model_v4" # -------------------------------------------------- # 2. 각 모델 로드 # -------------------------------------------------- # --- 모델 1: 파인튜닝 & 양자화 모델 로드 --- quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=False, ) quantized_model = AutoModelForCausalLM.from_pretrained( finetuning_model, quantization_config=quantization_config, device_map="auto", ) quantized_tokenizer = AutoTokenizer.from_pretrained(finetuning_model) # -------------------------------------------------- # 3. 파이프라인 생성 # -------------------------------------------------- quantized_pipe = pipeline("text-generation", model=quantized_model, tokenizer=quantized_tokenizer) from transformers import AutoTokenizer # -------------------------------------------------- # 4. 동일한 프롬프트로 각 모델에 추론 요청 및 비교 # -------------------------------------------------- # 파인튜닝 데이터(구어체)의 특성이 잘 드러나는 질문을 선택 tokenizer = AutoTokenizer.from_pretrained(finetuning_model) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token question = "김치볶음밥 재료가 뭐야?" prompt = f"### 질문:{question}\n\n### 답변:" print("\n" + "="*60) print(f"질문: {prompt.split('### 답변:')[0].replace('### 질문: ', '').strip()}") print("="*60 + "\n") # 파인튜닝 & 양자화 모델 추론 print("1. 파인튜닝 모델 답변:") quantized_result = quantized_pipe( prompt, temperature=0.7, eos_token_id=tokenizer.eos_token_id, return_full_text=False ) print(quantized_result[0]['generated_text']) print("\n" + "="*60) ```

MattBou00/llama-3-2-1b-detox_v1e-checkpoint-epoch-40

MattBou00

2025-08-20T00:09:09Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

reinforcement-learning

2025-08-20T00:07:20Z

--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-59-21/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

AnonymousCS/xlmr_immigration_combo8_1

AnonymousCS

2025-08-20T00:08:07Z

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

2025-08-19T23:43:14Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo8_1 results: [] ---  # xlmr_immigration_combo8_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: 0.2440 - Accuracy: 0.9254 - 1-f1: 0.884 - 1-recall: 0.8533 - 1-precision: 0.9170 - Balanced Acc: 0.9074 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.2219 | 1.0 | 25 | 0.2193 | 0.9242 | 0.8803 | 0.8378 | 0.9274 | 0.9025 | | 0.196 | 2.0 | 50 | 0.2358 | 0.9177 | 0.8689 | 0.8185 | 0.9258 | 0.8929 | | 0.2005 | 3.0 | 75 | 0.2440 | 0.9254 | 0.884 | 0.8533 | 0.9170 | 0.9074 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

roeker/blockassist-bc-quick_wiry_owl_1755648010

roeker

2025-08-20T00:01:29Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-20T00:00:55Z

--- 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).

Inmbisat/Work

Inmbisat

2025-08-19T23:55:30Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-08-19T23:55:30Z

--- license: apache-2.0 ---

sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755646204

sampingkaca72

2025-08-19T23:54:44Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "armored stealthy elephant", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T23:54:41Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - armored stealthy elephant --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

MattBou00/llama-3-2-1b-detox_v1d-checkpoint-epoch-40

MattBou00

2025-08-19T23:54:12Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

reinforcement-learning

2025-08-19T23:52:28Z

--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-40") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

hash-map/custom-eng-te-translation

hash-map

2025-08-19T23:50:42Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-08-19T21:40:14Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

MattBou00/llama-3-2-1b-detox_v1d-checkpoint-epoch-20

MattBou00

2025-08-19T23:50:13Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

reinforcement-learning

2025-08-19T23:48:19Z

--- license: apache-2.0 library_name: transformers tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20") model = AutoModelForCausalLMWithValueHead.from_pretrained("MattBou00//content/IRL-Bayesian/outputs/2025-08-19_23-45-50/checkpoints/checkpoint-epoch-20") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

roeker/blockassist-bc-quick_wiry_owl_1755647200

roeker

2025-08-19T23:48:00Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T23:47:29Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

unitova/blockassist-bc-zealous_sneaky_raven_1755645424

unitova

2025-08-19T23:44:51Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "zealous sneaky raven", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T23:44:48Z

--- 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).

hakimjustbao/blockassist-bc-raging_subtle_wasp_1755644479

hakimjustbao

2025-08-19T23:27:22Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "raging subtle wasp", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T23:27:19Z

--- 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).

AnonymousCS/xlmr_immigration_combo7_2

AnonymousCS

2025-08-19T23:25:18Z

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

2025-08-19T23:22:30Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo7_2 results: [] ---  # xlmr_immigration_combo7_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: 0.1699 - Accuracy: 0.9537 - 1-f1: 0.9302 - 1-recall: 0.9266 - 1-precision: 0.9339 - Balanced Acc: 0.9469 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.1948 | 1.0 | 25 | 0.1502 | 0.9602 | 0.9391 | 0.9228 | 0.956 | 0.9508 | | 0.1681 | 2.0 | 50 | 0.1761 | 0.9447 | 0.9124 | 0.8649 | 0.9655 | 0.9247 | | 0.1613 | 3.0 | 75 | 0.1699 | 0.9537 | 0.9302 | 0.9266 | 0.9339 | 0.9469 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

roeker/blockassist-bc-quick_wiry_owl_1755645573

roeker

2025-08-19T23:21:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T23:20:21Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - quick wiry owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

AnonymousCS/xlmr_immigration_combo7_0

AnonymousCS

2025-08-19T23:19:33Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T22:59:15Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo7_0 results: [] ---  # xlmr_immigration_combo7_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: 0.2841 - Accuracy: 0.9152 - 1-f1: 0.8648 - 1-recall: 0.8147 - 1-precision: 0.9214 - Balanced Acc: 0.8900 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.6179 | 1.0 | 25 | 0.6021 | 0.6671 | 0.0 | 0.0 | 0.0 | 0.5 | | 0.4255 | 2.0 | 50 | 0.3616 | 0.8946 | 0.8353 | 0.8031 | 0.8703 | 0.8717 | | 0.2818 | 3.0 | 75 | 0.2559 | 0.9139 | 0.8571 | 0.7761 | 0.9571 | 0.8794 | | 0.2035 | 4.0 | 100 | 0.2363 | 0.9190 | 0.8706 | 0.8185 | 0.9298 | 0.8939 | | 0.1596 | 5.0 | 125 | 0.2638 | 0.9126 | 0.8677 | 0.8610 | 0.8745 | 0.8997 | | 0.1866 | 6.0 | 150 | 0.2841 | 0.9152 | 0.8648 | 0.8147 | 0.9214 | 0.8900 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

kuleshov-group/PlantCaduceus_l24

kuleshov-group

2025-08-19T23:18:53Z

1

0

transformers

[ "transformers", "pytorch", "caduceus", "feature-extraction", "custom_code", "arxiv:2312.00752", "license:apache-2.0", "region:us" ]

feature-extraction

2024-05-19T16:24:45Z

--- license: apache-2.0 --- ## Model Overview PlantCaduceus is a DNA language model pre-trained on 16 Angiosperm genomes. Utilizing the [Caduceus](https://caduceus-dna.github.io/) and [Mamba](https://arxiv.org/abs/2312.00752) architectures and a masked language modeling objective, PlantCaduceus is designed to learn evolutionary conservation and DNA sequence grammar from 16 species spanning a history of 160 million years. We have trained a series of PlantCaduceus models with varying parameter sizes: - **[PlantCaduceus_l20](https://huggingface.co/kuleshov-group/PlantCaduceus_l20)**: 20 layers, 384 hidden size, 20M parameters - **[PlantCaduceus_l24](https://huggingface.co/kuleshov-group/PlantCaduceus_l24)**: 24 layers, 512 hidden size, 40M parameters - **[PlantCaduceus_l28](https://huggingface.co/kuleshov-group/PlantCaduceus_l28)**: 28 layers, 768 hidden size, 112M parameters - **[PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)**: 32 layers, 1024 hidden size, 225M parameters **We would highly recommend using the largest model ([PlantCaduceus_l32](https://huggingface.co/kuleshov-group/PlantCaduceus_l32)) for the zero-shot score estimation.** ## How to use ```python from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer import torch model_path = 'kuleshov-group/PlantCaduceus_l24' device = "cuda:0" if torch.cuda.is_available() else "cpu" model = AutoModelForMaskedLM.from_pretrained(model_path, trust_remote_code=True, device_map=device) model.eval() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) sequence = "ATGCGTACGATCGTAG" encoding = tokenizer.encode_plus( sequence, return_tensors="pt", return_attention_mask=False, return_token_type_ids=False ) input_ids = encoding["input_ids"].to(device) with torch.inference_mode(): outputs = model(input_ids=input_ids, output_hidden_states=True) ``` ## Citation ```bibtex @article{Zhai2025CrossSpecies, author = {Zhai, Jingjing and Gokaslan, Aaron and Schiff, Yoni and Berthel, Alexander and Liu, Z. Y. and Lai, W. L. and Miller, Z. R. and Scheben, Armin and Stitzer, Michelle C. and Romay, Maria C. and Buckler, Edward S. and Kuleshov, Volodymyr}, title = {Cross-species modeling of plant genomes at single nucleotide resolution using a pretrained DNA language model}, journal = {Proceedings of the National Academy of Sciences}, year = {2025}, volume = {122}, number = {24}, pages = {e2421738122}, doi = {10.1073/pnas.2421738122}, url = {https://doi.org/10.1073/pnas.2421738122} } ``` ## Contact Jingjing Zhai (jz963@cornell.edu)

QuantStack/Qwen-Image-Edit-GGUF

QuantStack

2025-08-19T23:16:47Z

0

41

gguf

[ "gguf", "image-to-image", "en", "zh", "base_model:Qwen/Qwen-Image-Edit", "base_model:quantized:Qwen/Qwen-Image-Edit", "license:apache-2.0", "region:us" ]

image-to-image

2025-08-18T23:43:57Z

--- language: - en - zh license: apache-2.0 base_model: - Qwen/Qwen-Image-Edit library_name: gguf pipeline_tag: image-to-image --- This GGUF file is a direct conversion of [Qwen/Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit) Type | Name | Location | Download | ------------ | -------------------------------------------------- | --------------------------------- | ------------------------- | Main Model | Qwen-Image | `ComfyUI/models/unet` | GGUF (this repo) | Main Text Encoder | Qwen2.5-VL-7B | `ComfyUI/models/text_encoders` | [Safetensors](https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders) / [GGUF](https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF/tree/main) | | Text_Encoder (mmproj) | Qwen2.5-VL-7B-Instruct-mmproj-BF16 | `ComfyUI/models/text_encoders` (same folder as your main text encoder) | GGUF (this repo) | VAE | Qwen-Image VAE | `ComfyUI/models/vae` | Safetensors (this repo) | Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. **Usage** The model can be used with the ComfyUI custom node [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) by [city96](https://huggingface.co/city96)

soob3123/Veritas-task-trade-off-agent

soob3123

2025-08-19T23:14:43Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-08-19T23:14:03Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

CreitinGameplays/Mistral-Nemo-12B-OpenO1

CreitinGameplays

2025-08-19T23:13:50Z

10

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:CreitinGameplays/O1-OPEN_OpenO1-SFT-Pro-English-Mistral", "base_model:mistralai/Mistral-Nemo-Instruct-2407", "base_model:finetune:mistralai/Mistral-Nemo-Instruct-2407", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-24T09:18:23Z

--- license: mit datasets: - CreitinGameplays/O1-OPEN_OpenO1-SFT-Pro-English-Mistral language: - en base_model: - mistralai/Mistral-Nemo-Instruct-2407 pipeline_tag: text-generation library_name: transformers ---

torchao-testing/single-linear-Float8DynamicActivationFloat8WeightConfig-v2-0.13-dev

torchao-testing

2025-08-19T23:11:02Z

0

null

[ "region:us" ]

null

2025-08-19T23:10:48Z

``` import torch import io model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda")) from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig, PerRow quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) quantize_(model, quant_config) example_inputs = (torch.randn(2, 32, dtype=torch.bfloat16, device="cuda"),) output = model(*example_inputs) # Push to hub USER_ID = "torchao-testing" MODEL_NAME = "single-linear" save_to = f"{USER_ID}/{MODEL_NAME}-Float8DynamicActivationFloat8WeightConfig-v2-0.13.dev" from huggingface_hub import HfApi api = HfApi() buf = io.BytesIO() torch.save(model.state_dict(), buf) api.create_repo(save_to, repo_type="model", exist_ok=True) api.upload_file( path_or_fileobj=buf, path_in_repo="model.bin", repo_id=save_to, ) buf = io.BytesIO() torch.save(example_inputs, buf) api.upload_file( path_or_fileobj=buf, path_in_repo="model_inputs.pt", repo_id=save_to, ) buf = io.BytesIO() torch.save(output, buf) api.upload_file( path_or_fileobj=buf, path_in_repo="model_output.pt", repo_id=save_to, ) ```

ihsanridzi/blockassist-bc-wiry_flexible_owl_1755643478

ihsanridzi

2025-08-19T23:10:39Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T23:10:36Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

seraphimzzzz/58348

seraphimzzzz

2025-08-19T23:05:52Z

0

null

[ "region:us" ]

null

2025-08-19T23:05:49Z

[View on Civ Archive](https://civarchive.com/models/80554?modelVersionId=85436)

lilTAT/blockassist-bc-gentle_rugged_hare_1755644677

lilTAT

2025-08-19T23:05:06Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "gentle rugged hare", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T23:05:03Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - gentle rugged hare --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

seraphimzzzz/45661

seraphimzzzz

2025-08-19T23:05:05Z

0

null

[ "region:us" ]

null

2025-08-19T23:05:02Z

[View on Civ Archive](https://civarchive.com/models/60593?modelVersionId=65063)

ultratopaz/64761

ultratopaz

2025-08-19T23:04:48Z

0

null

[ "region:us" ]

null

2025-08-19T23:04:45Z

[View on Civ Archive](https://civarchive.com/models/88038?modelVersionId=93695)

crystalline7/42524

crystalline7

2025-08-19T23:03:12Z

0

null

[ "region:us" ]

null

2025-08-19T23:03:09Z

[View on Civ Archive](https://civarchive.com/models/55594?modelVersionId=59988)

ultratopaz/48758

ultratopaz

2025-08-19T23:02:55Z

0

null

[ "region:us" ]

null

2025-08-19T23:02:52Z

[View on Civ Archive](https://civarchive.com/models/65216?modelVersionId=69845)

crystalline7/126289

crystalline7

2025-08-19T23:02:24Z

0

null

[ "region:us" ]

null

2025-08-19T23:02:22Z

[View on Civ Archive](https://civarchive.com/models/149263?modelVersionId=166671)

thiernomdou/Karamoo

thiernomdou

2025-08-19T23:02:22Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-08-19T22:53:37Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: karamoo --- # Karamoo <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `karamoo` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "karamoo", "lora_weights": "https://huggingface.co/thiernomdou/Karamoo/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('thiernomdou/Karamoo', weight_name='lora.safetensors') image = pipeline('karamoo').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/thiernomdou/Karamoo/discussions) to add images that show off what you’ve made with this LoRA.

ultratopaz/93085

ultratopaz

2025-08-19T23:01:42Z

0

null

[ "region:us" ]

null

2025-08-19T23:01:35Z

[View on Civ Archive](https://civarchive.com/models/118503?modelVersionId=128554)

dsdsdsdfffff/code_ffn_random

dsdsdsdfffff

2025-08-19T23:00:50Z

0

transformers

[ "transformers", "safetensors", "deepseek_v2", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-08-19T22:46:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ultratopaz/42807

ultratopaz

2025-08-19T23:00:20Z

0

null

[ "region:us" ]

null

2025-08-19T23:00:17Z

[View on Civ Archive](https://civarchive.com/models/56050?modelVersionId=60448)

ultratopaz/63808

ultratopaz

2025-08-19T22:57:34Z

0

null

[ "region:us" ]

null

2025-08-19T22:57:32Z

[View on Civ Archive](https://civarchive.com/models/86958?modelVersionId=92511)

koloni/blockassist-bc-deadly_graceful_stingray_1755642634

koloni

2025-08-19T22:57:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "deadly graceful stingray", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:57:08Z

--- 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).

seraphimzzzz/778755

seraphimzzzz

2025-08-19T22:57:06Z

0

null

[ "region:us" ]

null

2025-08-19T22:57:03Z

[View on Civ Archive](https://civarchive.com/models/376220?modelVersionId=869936)

crystalline7/70066

crystalline7

2025-08-19T22:56:59Z

0

null

[ "region:us" ]

null

2025-08-19T22:56:56Z

[View on Civ Archive](https://civarchive.com/models/94074?modelVersionId=100354)

crystalline7/112237

crystalline7

2025-08-19T22:56:46Z

0

null

[ "region:us" ]

null

2025-08-19T22:56:44Z

[View on Civ Archive](https://civarchive.com/models/136795?modelVersionId=150921)

crystalline7/36212

crystalline7

2025-08-19T22:56:18Z

0

null

[ "region:us" ]

null

2025-08-19T22:56:14Z

[View on Civ Archive](https://civarchive.com/models/44543?modelVersionId=49168)

crystalline7/391174

crystalline7

2025-08-19T22:55:45Z

0

null

[ "region:us" ]

null

2025-08-19T22:55:36Z

[View on Civ Archive](https://civarchive.com/models/424163?modelVersionId=472584)

chainway9/blockassist-bc-untamed_quick_eel_1755642411

chainway9

2025-08-19T22:55:05Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "untamed quick eel", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:55:01Z

--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - untamed quick eel --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).

vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755642454

vwzyrraz7l

2025-08-19T22:53:41Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tall hunting vulture", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:53:37Z

--- 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).

seraphimzzzz/30194

seraphimzzzz

2025-08-19T22:53:08Z

0

null

[ "region:us" ]

null

2025-08-19T22:53:04Z

[View on Civ Archive](https://civarchive.com/models/32091?modelVersionId=38532)

crystalline7/25213

crystalline7

2025-08-19T22:52:16Z

0

null

[ "region:us" ]

null

2025-08-19T22:52:11Z

[View on Civ Archive](https://civarchive.com/models/25513?modelVersionId=30545)

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755642210

coelacanthxyz

2025-08-19T22:52:07Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:52:02Z

--- 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).

crystalline7/56907

crystalline7

2025-08-19T22:49:21Z

0

null

[ "region:us" ]

null

2025-08-19T22:49:18Z

[View on Civ Archive](https://civarchive.com/models/24156?modelVersionId=83175)

crystalline7/26964

crystalline7

2025-08-19T22:48:34Z

0

null

[ "region:us" ]

null

2025-08-19T22:48:29Z

[View on Civ Archive](https://civarchive.com/models/27347?modelVersionId=32745)

coastalcph/Qwen2.5-7B-1t_gcd_sycophancy-8t_diff_sycophant

coastalcph

2025-08-19T22:48:14Z

0

null

[ "safetensors", "qwen2", "region:us" ]

null

2025-08-19T22:45:39Z

# Combined Task Vector Model This model was created by combining task vectors from multiple fine-tuned models. ## Task Vector Computation ```python t_1 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-gcd_sycophancy") t_2 = TaskVector("Qwen/Qwen2.5-7B-Instruct", "coastalcph/Qwen2.5-7B-personality-non-sycophancy") t_combined = 1.0 * t_1 + 8.0 * t_2 - 8.0 * t_3 new_model = t_combined.apply_to("Qwen/Qwen2.5-7B-Instruct", scaling_coef=1.0) ``` Models Used - Base Model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct - Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-7B-gcd_sycophancy - Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-7B-personality-non-sycophancy Technical Details - Creation Script Git Hash: 6276125324033067e34f3eae1fe4db8ab27c86fb - Task Vector Method: Additive combination - Args: { "pretrained_model": "Qwen/Qwen2.5-7B-Instruct", "finetuned_model1": "coastalcph/Qwen2.5-7B-gcd_sycophancy", "finetuned_model2": "coastalcph/Qwen2.5-7B-personality-non-sycophancy", "finetuned_model3": "coastalcph/Qwen2.5-7B-personality-sycophancy", "output_model_name": "coastalcph/Qwen2.5-7B-1t_gcd_sycophancy-8t_diff_sycophant", "output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug", "scaling_coef": 1.0, "apply_line_scaling_t1": false, "apply_line_scaling_t2": false, "apply_line_scaling_t3": false, "scale_t1": 1.0, "scale_t2": 8.0, "scale_t3": 8.0 }

ultratopaz/59135

ultratopaz

2025-08-19T22:48:06Z

0

null

[ "region:us" ]

null

2025-08-19T22:48:03Z

[View on Civ Archive](https://civarchive.com/models/81526?modelVersionId=86507)

crystalline7/68233

crystalline7

2025-08-19T22:47:39Z

0

null

[ "region:us" ]

null

2025-08-19T22:47:36Z

[View on Civ Archive](https://civarchive.com/models/91940?modelVersionId=98028)

dsisodia/ai-nidhi

dsisodia

2025-08-19T22:47:03Z

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

2025-08-19T21:45:07Z

--- 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: AINIDHI --- # Ai Nidhi <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 `AINIDHI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "AINIDHI", "lora_weights": "https://huggingface.co/dsisodia/ai-nidhi/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('dsisodia/ai-nidhi', weight_name='lora.safetensors') image = pipeline('AINIDHI').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: 1200 - Learning rate: 0.0004 - LoRA rank: 32 ## Contribute your own examples You can use the [community tab](https://huggingface.co/dsisodia/ai-nidhi/discussions) to add images that show off what you’ve made with this LoRA.

roeker/blockassist-bc-quick_wiry_owl_1755643537

roeker

2025-08-19T22:47:01Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:46:22Z

--- 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).

seraphimzzzz/100080

seraphimzzzz

2025-08-19T22:45:55Z

0

null

[ "region:us" ]

null

2025-08-19T22:45:52Z

[View on Civ Archive](https://civarchive.com/models/125326?modelVersionId=136894)

seraphimzzzz/79815

seraphimzzzz

2025-08-19T22:45:48Z

0

null

[ "region:us" ]

null

2025-08-19T22:45:45Z

[View on Civ Archive](https://civarchive.com/models/104933?modelVersionId=112523)

ultratopaz/19827

ultratopaz

2025-08-19T22:45:30Z

0

null

[ "region:us" ]

null

2025-08-19T22:45:26Z

[View on Civ Archive](https://civarchive.com/models/20123?modelVersionId=23901)

crystalline7/63724

crystalline7

2025-08-19T22:45:01Z

0

null

[ "region:us" ]

null

2025-08-19T22:45:01Z

[View on Civ Archive](https://civarchive.com/models/86858?modelVersionId=92401)

crystalline7/59070

crystalline7

2025-08-19T22:44:15Z

0

null

[ "region:us" ]

null

2025-08-19T22:44:11Z

[View on Civ Archive](https://civarchive.com/models/75223?modelVersionId=86425)

crystalline7/54021

crystalline7

2025-08-19T22:43:46Z

0

null

[ "region:us" ]

null

2025-08-19T22:43:43Z

[View on Civ Archive](https://civarchive.com/models/73874?modelVersionId=78592)

seraphimzzzz/640572

seraphimzzzz

2025-08-19T22:42:39Z

0

null

[ "region:us" ]

null

2025-08-19T22:42:37Z

[View on Civ Archive](https://civarchive.com/models/462107?modelVersionId=726101)

ultratopaz/55192

ultratopaz

2025-08-19T22:40:51Z

0

null

[ "region:us" ]

null

2025-08-19T22:40:48Z

[View on Civ Archive](https://civarchive.com/models/75721?modelVersionId=80467)

crystalline7/33987

crystalline7

2025-08-19T22:40:23Z

0

null

[ "region:us" ]

null

2025-08-19T22:40:23Z

[View on Civ Archive](https://civarchive.com/models/39434?modelVersionId=45341)

rvs/llama3_awq_int4_complete

rvs

2025-08-19T22:39:35Z

0

null

[ "onnx", "text-generation-inference", "llama", "llama3", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]

null

2025-08-19T22:39:00Z

--- tags: - text-generation-inference - llama - llama3 base_model: - meta-llama/Meta-Llama-3-8B-Instruct --- # Llama 3 8B Instruct with Key-Value-Cache enabled in ONNX ONNX AWQ (4-bit) format - Model creator: [Meta Llama](https://huggingface.co/meta-llama) - Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)  ## Description This repo contains the ONNX files for the ONNX conversion of Llama 3 8B Instruct done by Esperanto Technologies. The model is in the 4-bit format quantized with AWQ and has the KVC enabled. ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. More here: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ)  ## How to download ONNX model and weight files The easiest way to obtain the model is to clone this whole repo. Alternatively you can download the files is using the `huggingface-hub` Python library. ```shell pip3 install huggingface-hub>=0.17.1 ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download Esperanto/llama3-8b-Instruct-kvc-AWQ-int4-onnx --local-dir llama3-8b-Instruct-kvc-AWQ-int4-onnx --local-dir-use-symlinks False ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). ## How to run from Python code using ONNXRuntime This model can easily be ran in a CPU using [ONNXRuntime](https://onnxruntime.ai/). #### First install the packages ```bash pip3 install onnx==1.16.1 pip3 install onnxruntime==1.17.1 ``` #### Example code: generate text with this model We define the loop with greedy decoding: ```python import numpy as np import onnxruntime import onnx from transformers import AutoTokenizer def generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context): model = onnx.load(model_path) #we create the inputs for the first iteration input_tensor = tokenizer(prompt, return_tensors="pt") prompt_size = len(input_tensor['input_ids'][0]) actual_input = input_tensor['input_ids'] if prompt_size < window: actual_input = np.concatenate((tokenizer.bos_token_id*np.ones([1, window - prompt_size], dtype = 'int64'), actual_input), axis=1) if prompt_size + max_gen_tokens > total_sequence: print("ERROR: Longer total sequence is needed!") return first_attention = np.concatenate((np.zeros([1, total_sequence - window], dtype = 'int64'), np.ones((1, window), dtype = 'int64')), axis=1) max_gen_tokens += prompt_size #we need to generate on top of parsing the prompt inputs_names =[node.name for node in model.graph.input] output_names =[node.name for node in model.graph.output] n_heads = 8 #gqa-heads of the kvc inputs_dict = {} inputs_dict['input_ids'] = actual_input[:, :window].reshape(1, window).numpy() inputs_dict['attention_mask'] = first_attention index_pos = sum(first_attention[0]) inputs_dict['position_ids'] = np.concatenate((np.zeros([1, total_sequence - index_pos], dtype = 'int64'), np.arange(index_pos, dtype = 'int64').reshape(1, index_pos)), axis=1) inputs_dict['tree_attention'] = np.triu(-65504*np.ones(total_sequence), k= 1).astype('float16').reshape(1, 1, total_sequence, total_sequence) for name in inputs_names: if name == 'input_ids' or name == 'attention_mask' or name == 'position_ids' or name == 'tree_attention': continue inputs_dict[name] = np.zeros([1, n_heads, context-window, 128], dtype="float16") index = 0 new_token = np.array([10]) next_index = window old_j = 0 total_input = actual_input.numpy() rt_session = onnxruntime.InferenceSession(model_path) ## We run the inferences while next_index < max_gen_tokens: if new_token.any() == tokenizer.eos_token_id: break #inference output = rt_session.run(output_names, inputs_dict) outs_dictionary = {name: content for (name, content) in zip (output_names, output)} #we prepare the inputs for the next inference for name in inputs_names: if name == 'input_ids': old_j = next_index if next_index < prompt_size: if prompt_size - next_index >= window: next_index += window else: next_index = prompt_size j = next_index - window else: next_index +=1 j = next_index - window new_token = outs_dictionary['logits'].argmax(-1).reshape(1, window) total_input = np.concatenate((total_input, new_token[: , -1:]), axis = 1) inputs_dict['input_ids']= total_input[:, j:next_index].reshape(1, window) elif name == 'attention_mask': inputs_dict['attention_mask'] = np.concatenate((np.zeros((1, total_sequence-next_index), dtype = 'int64'), np.ones((1, next_index), dtype = 'int64')), axis=1) elif name == 'position_ids': inputs_dict['position_ids'] = np.concatenate((np.zeros([1, total_sequence - next_index], dtype = 'int64'), np.arange(next_index, dtype = 'int64').reshape(1, next_index)), axis=1) elif name == 'tree_attention': continue else: old_name = name.replace("past_key_values", "present") inputs_dict[name] = outs_dictionary[old_name][:, :, next_index-old_j:context-window+(next_index - old_j), :] answer = tokenizer.decode(total_input[0], skip_special_tokens=True, clean_up_tokenization_spaces=False) return answer ``` We now run the inferences: ```python tokenizer = AutoTokenizer.from_pretrained("Esperanto/llama3-8b-Instruct-kvc-AWQ-int4-onnx-onnx") model_path = "llama3-8b-Instruct-kvc-AWQ-int4-onnx/model.onnx" max_gen_tokens = 20 #number of tokens we want tog eneral total_sequence = 128 #total sequence_length context = 1024 #the context to extend the kvc window = 16 #number of tokens we want to parse at the time messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) generated = generate_text(model_path, prompt, tokenizer, max_gen_tokens, total_sequence, window, context) print(generated) ```

calegpedia/blockassist-bc-stealthy_slimy_rooster_1755640986

calegpedia

2025-08-19T22:30:58Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stealthy slimy rooster", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:30:55Z

--- 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/wizards-vintage-rustica-illustration

Muapi

2025-08-19T22:29:55Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T22:29:37Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Wizards Vintage: Rustica Illustration ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: vintage rustica illustration ## 🧠 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:922242@1032312", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/norman-rockwell

Muapi

2025-08-19T22:29:07Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T22:28:53Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Norman Rockwell ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: Art by Norman Rockwell ## 🧠 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:1397494@1579617", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/mistoon-anime-flux

Muapi

2025-08-19T22:27:07Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T22:26:53Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Mistoon Anime Flux ![preview](./preview.jpg) **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:682107@763448", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Muapi/sd3.5-sdxl-flux-pika-s-battlefield-style-v2

Muapi

2025-08-19T22:26:47Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T22:26:35Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # [SD3.5\SDXL\FLUX] Pika's BattleField Style V2 ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: pikas_bf_v3 ## 🧠 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:245121@859001", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755640752

quantumxnode

2025-08-19T22:26:13Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "dormant peckish seahorse", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:26:10Z

--- 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).

AnonymousCS/xlmr_immigration_combo5_4

AnonymousCS

2025-08-19T22:25:17Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-08-19T22:21:55Z

--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: xlmr_immigration_combo5_4 results: [] ---  # xlmr_immigration_combo5_4 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: 0.0656 - Accuracy: 0.9743 - 1-f1: 0.9614 - 1-recall: 0.9614 - 1-precision: 0.9614 - Balanced Acc: 0.9711 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:| | 0.0848 | 1.0 | 25 | 0.0535 | 0.9846 | 0.9763 | 0.9537 | 1.0 | 0.9768 | | 0.0667 | 2.0 | 50 | 0.0565 | 0.9859 | 0.9783 | 0.9575 | 1.0 | 0.9788 | | 0.0302 | 3.0 | 75 | 0.0656 | 0.9743 | 0.9614 | 0.9614 | 0.9614 | 0.9711 | ### Framework versions - Transformers 4.56.0.dev0 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.21.4

crystalline7/22906

crystalline7

2025-08-19T22:19:08Z

0

null

[ "region:us" ]

null

2025-08-19T22:19:03Z

[View on Civ Archive](https://civarchive.com/models/12757?modelVersionId=27712)

coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755640227

coelacanthxyz

2025-08-19T22:18:34Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "finicky thriving grouse", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:18:28Z

--- 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).

crystalline7/39657

crystalline7

2025-08-19T22:17:46Z

0

null

[ "region:us" ]

null

2025-08-19T22:17:43Z

[View on Civ Archive](https://civarchive.com/models/50517?modelVersionId=55033)

adanish91/safetyalbert

adanish91

2025-08-19T22:16:53Z

0

null

[ "safetensors", "albert", "safety", "occupational-safety", "domain-adaptation", "memory-efficient", "base_model:albert/albert-base-v2", "base_model:finetune:albert/albert-base-v2", "region:us" ]

null

2025-08-19T21:22:55Z

--- base_model: albert-base-v2 tags: - safety - occupational-safety - albert - domain-adaptation - memory-efficient --- # SafetyALBERT SafetyALBERT is a memory-efficient ALBERT model fine-tuned on occupational safety data. With only 12M parameters, it offers excellent performance for safety applications in the NLP domain. ## Quick Start ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") model = AutoModelForMaskedLM.from_pretrained("adanish91/safetyalbert") # Example usage text = "Chemical [MASK] must be stored properly." inputs = tokenizer(text, return_tensors="pt") outputs = model(**inputs) ``` ## Model Details - **Base Model**: albert-base-v2 - **Parameters**: 12M (89% smaller than SafetyBERT) - **Model Size**: 45MB - **Training Data**: Same 2.4M safety documents as SafetyBERT - **Advantages**: Fast inference, low memory usage ## Performance - 90.3% improvement in pseudo-perplexity over ALBERT-base - Competitive with SafetyBERT despite 9x fewer parameters - Ideal for production deployment and edge devices ## Applications - Occupational safety-related downstream applications - Resource-constrained environments

ultratopaz/33753

ultratopaz

2025-08-19T22:16:50Z

0

null

[ "region:us" ]

null

2025-08-19T22:16:48Z

[View on Civ Archive](https://civarchive.com/models/39019?modelVersionId=44952)

ultratopaz/84572

ultratopaz

2025-08-19T22:16:13Z

0

null

[ "region:us" ]

null

2025-08-19T22:16:08Z

[View on Civ Archive](https://civarchive.com/models/109692?modelVersionId=118205)

ultratopaz/49000

ultratopaz

2025-08-19T22:15:02Z

0

null

[ "region:us" ]

null

2025-08-19T22:14:59Z

[View on Civ Archive](https://civarchive.com/models/65638?modelVersionId=70288)

seraphimzzzz/43953

seraphimzzzz

2025-08-19T22:14:33Z

0

null

[ "region:us" ]

null

2025-08-19T22:14:30Z

[View on Civ Archive](https://civarchive.com/models/57774?modelVersionId=62215)

seraphimzzzz/54492

seraphimzzzz

2025-08-19T22:14:03Z

0

null

[ "region:us" ]

null

2025-08-19T22:13:58Z

[View on Civ Archive](https://civarchive.com/models/25557?modelVersionId=79349)

crystalline7/10449

crystalline7

2025-08-19T22:13:27Z

0

null

[ "region:us" ]

null

2025-08-19T22:13:23Z

[View on Civ Archive](https://civarchive.com/models/9421?modelVersionId=11178)

ultratopaz/627330

ultratopaz

2025-08-19T22:12:54Z

0

null

[ "region:us" ]

null

2025-08-19T22:12:46Z

[View on Civ Archive](https://civarchive.com/models/121544?modelVersionId=712664)

seraphimzzzz/40588

seraphimzzzz

2025-08-19T22:10:39Z

0

null

[ "region:us" ]

null

2025-08-19T22:10:36Z

[View on Civ Archive](https://civarchive.com/models/52348?modelVersionId=56790)

crystalline7/83203

crystalline7

2025-08-19T22:10:31Z

0

null

[ "region:us" ]

null

2025-08-19T22:10:27Z

[View on Civ Archive](https://civarchive.com/models/108305?modelVersionId=116565)

crystalline7/33463

crystalline7

2025-08-19T22:09:36Z

0

null

[ "region:us" ]

null

2025-08-19T22:09:36Z

[View on Civ Archive](https://civarchive.com/models/24995?modelVersionId=44249)

Muapi/illulora-simple-illustration-style

Muapi

2025-08-19T22:08:10Z

0

null

[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]

null

2025-08-19T22:07:57Z

--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # IlluLoRA - Simple Illustration Style ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: IlluLORA ## 🧠 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:665753@745084", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```

Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF

Hobaks

2025-08-19T22:07:51Z

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-30B-A3B-Instruct-2507", "base_model:quantized:Qwen/Qwen3-30B-A3B-Instruct-2507", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-08-19T22:06:34Z

--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-30B-A3B-Instruct-2507 tags: - llama-cpp - gguf-my-repo --- # Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Hobaks/Qwen3-30B-A3B-Instruct-2507-Q4_K_M-GGUF --hf-file qwen3-30b-a3b-instruct-2507-q4_k_m.gguf -c 2048 ```

crystalline7/16961

crystalline7

2025-08-19T22:06:57Z

0

null

[ "region:us" ]

null

2025-08-19T22:06:53Z

[View on Civ Archive](https://civarchive.com/models/17228?modelVersionId=20351)

roeker/blockassist-bc-quick_wiry_owl_1755641094

roeker

2025-08-19T22:06:12Z

0

null

[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "quick wiry owl", "arxiv:2504.07091", "region:us" ]

null

2025-08-19T22:05:36Z

--- 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).

seraphimzzzz/93417

seraphimzzzz

2025-08-19T22:05:00Z

0

null

[ "region:us" ]

null

2025-08-19T22:04:44Z

[View on Civ Archive](https://civarchive.com/models/118808?modelVersionId=128939)