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hafidhsoekma/Gasing-8B-alpha-v0.1-slerp-base	hafidhsoekma	2025-06-18T09:08:00Z	0	0	null	[ "safetensors", "qwen3", "merge", "mergekit", "lazymergekit", "region:us" ]	null	2025-06-18T06:20:14Z	--- tags: - merge - mergekit - lazymergekit --- # Gasing-8B-alpha-v0.1-slerp-base Gasing-8B-alpha-v0.1-slerp-base is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml models: - model: Qwen/Qwen3-8B - model: hafidhsoekma/Gasing-8B-alpha-v0.1 merge_method: slerp base_model: hafidhsoekma/Gasing-8B-alpha-v0.1 dtype: bfloat16 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "hafidhsoekma/Gasing-8B-alpha-v0.1-slerp-base" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Florisst/JustidDataSet1	Florisst	2025-06-18T09:01:05Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T09:00:53Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Florisst - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
muzerai/Llama-3.1-KoEn-8B-magic8-GGUF	muzerai	2025-06-18T08:54:16Z	45	0	transformers	[ "transformers", "gguf", "merge", "arxiv:2406.11617", "base_model:akjindal53244/Llama-3.1-Storm-8B", "base_model:merge:akjindal53244/Llama-3.1-Storm-8B", "base_model:sh2orc/Llama-3.1-Korean-8B-Instruct", "base_model:merge:sh2orc/Llama-3.1-Korean-8B-Instruct", "license:llama3.1", "endpoints_compatible", "region:us" ]	null	2025-06-13T19:08:40Z	--- license: llama3.1 base_model: - akjindal53244/Llama-3.1-Storm-8B - sh2orc/Llama-3.1-Korean-8B-Instruct library_name: transformers tags: - merge --- # Llama-3.1-Ko-8B-magic8 made by "AIJOAH" The merged model combining Llama-3.1-Korean-8B-Instruct and Llama-3.1-Storm-8B improves performance like — including Korean-language instruction following, multilingual knowledge-based QA, reasoning, reduced hallucinations, and structured output generation (e.g., JSON, Markdown). This merge is particularly beneficial for developers seeking a strong Korean-capable model that also excels in logic, accuracy, and function-calling, while remaining lightweight enough for local inference environments such as Ollama or vLLM. ### Merge Method This model was merged using the [DELLA](https://arxiv.org/abs/2406.11617) ### Models Merged The following models were included in the merge: * [akjindal53244/Llama-3.1-Storm-8B](https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B) * [sh2orc/Llama-3.1-Korean-8B-Instruct](https://huggingface.co/sh2orc/Llama-3.1-Korean-8B-Instruct) ### Test Q5 ``` ollama create modelname -f Modelfile ``` ``` >>> 이력서 쓰는 방법을 설명해줘 이력서 또는 리졸루션(Resume)란, 자신의 경력을 요약하여 정리한 문서로, 전 직무를 포함한 경험과 자격을 명확하게 표현하는 데 사용됩니다. 이력서는 주요 기업이나 조직이 지원 자를 평가하고 최종적으로 최적의 인재를 선발하기 위해 필수적인 도구입니다. 1. 이력서 작성에 필요한 정보: - 이름 및 연락처 - 직업 제목 및 직위 - 현재/전 직무 및 회사명 (최근 10년) - 교육: 학부/대학, 대학원, 특허 등 - 기술스킬: - 프로그래밍 언어: Java, Python, C++, JavaScript, R etc. - 데이터 분석: R, Python, SQL, Excel 등 - 데이터 베이스: MySQL, PostgreSQL, MongoDB, Oracle 등 - 운영체제: Windows, Linux, Unix 등 2. 이력서의 내용: - 신문고지: 자신의 이름, 연락처, 직업 및 회사명, 직위 등을 보여주는 영역입니다. - 직무 경력: 지원자가 가지고 있는 주요 경험과 역할을 표현합니다. 일반적으로 가장 최근부터 시작하여, 일자, 회사명, 회사 위치, 직제 및 업무 내용을 포함해야 합니다. - 교육/훈련: 지원자의 학부, 대학원, 특허, 심리테스트 등에 대한 정보를 포함합니다. - 기술스킬: 지원자가 가지고 있는 기술스킬에 대해 간단하게 설명합니다. 일반적으로 3-5개의 관련된 기술스킬을 포함합니다. 3. 이력서의 형식: 이력서에는 텍스트, 표, 그래프 등 다양한 데이터를 표현할 수 있습니다. 하지만, 이력서 작성 시는 다음과 같은 사항에 주의해야 합니다. - 단락: 1~2줄로 짧게 작성하여, 내용을 읽기 편하게 만듭니다. - 키워드: 지원자가 가지고 있는 기술스킬 및 자격을 키워드로 표현합니다. 이력서를 검색할 때, 이러한 키워드를 도움이 됩니다. - 형식: 지원자가 보유한 경력을 순차적으로 정리하여, 가장 최근부터 가장 오래전으로 작성합니다. 4. 이력서 작성의 원칙: 이력서는 지원자의 경험과 기술스킬을 명확하고, 일관되게 표현해야 합니다. 지원자는 다음의 원칙에 따라 이력서를 작성할 수 있습니다. - 사실과 사실 : 자신의 경험과 자격에 대해 진실하게 기재하여야 합니다. - 일관성 : 동일한 의미와 사용법을 사용하고, 일관성을 유지해야 합니다. - 시각적 표현 : 이력서를 통해 지원자의 경력을 시각적으로 쉽게 파악할 수 있도록, 표 및 그래프 등으로 표현하는 것이 좋습니다. 이러한 원칙과 형식을 지키면서, 자신의 경력을 정리하여, 이력서를 작성하면, 취업 및 진출에 도움이 될 것입니다. >>> "백신이 자폐증을 유발한다는 주장은 사실인가?" 자폐증은 의학적으로 아직 명확하게 이해되지 않은 복합적인 질병입니다. 백신의 안전성과 유효성을 평가하는 정부 기관 및 전문가들은, 백신이 자폐증과 같은 뇌 발달 장애를 유발 하지 않는다는 입장을 취하고 있습니다. 자폐증에 대한 백신과의 연결은 1998년에 영국의 의사인 Andrew Wakefield이 발표한 논문에서 시작되었습니다. 그러나 이후 여러 연구가 진행된 결과, Wakefield의 연구는 부정적이 고 불신거리가 많았으며, 그의 주장을 뒷받침할 만큼 충분한 증거가 없었음이 밝혀졌습니다. 미국 식품 의약품 관리청 (FDA)과 세계보건기구 (WHO)는 백신이 자폐증을 유발하지 않는다고firmly believe합니다. 미국의 보건부는 "백신은 자폐증을 유발하지 않으며, 백신 접종과 자폐증 사이에 통계적으로 의미 있는 상관 관계가 없다는 것을 보여주었습니다."라고 발표했습니다. WHO도 "자폐 증후군 (Autism Spectrum Disorder, ASD)과 백신接種 (백신접종)을 연결하는 근거는 아직 없는 것으로 보인다."고 발표했습니다. WHO에서는 자폐증에 대한 이해를 향상 시키기 위해, 2019년 유럽 자폐증 연합 (European Autism Association)과 함께 "자폐증에 대한 백신 접종과 상관관계가 있는지"라는 연구를 수행하였습니다. 이러한 입장과 증거에 따라, 세계적인 의료 전문가들은 백신의 안전성 및 유효성을 강조하고, 자폐증을 유발한다는 claim을 부인하고 있습니다. >>> ``` ### Citation If you find our work helpful, feel free to give us a cite. AIJOAH ``` @misc{aijoah2025merged, title = {Merged Llama-3.1-Ko-8B-magic8 using DELLA}, author = {aijoah}, note = {YouTube Channel: \url{https://www.youtube.com/@JayLee-gv8tv}}, year = {2025}, } ``` ### Contact If you have any questions, please raise an issue or contact us at ([email protected]).
marcel-gohsen/bart-base-query-aql-mix	marcel-gohsen	2025-06-18T08:53:58Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T08:53:48Z	--- 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. 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hafidhsoekma/Gasing-8B-alpha-v0.1-nearswap-base	hafidhsoekma	2025-06-18T08:49:15Z	0	0	null	[ "safetensors", "qwen3", "merge", "mergekit", "lazymergekit", "region:us" ]	null	2025-06-18T06:01:14Z	--- tags: - merge - mergekit - lazymergekit --- # Gasing-8B-alpha-v0.1-nearswap-base Gasing-8B-alpha-v0.1-nearswap-base is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml models: - model: Qwen/Qwen3-8B - model: hafidhsoekma/Gasing-8B-alpha-v0.1 merge_method: nearswap base_model: hafidhsoekma/Gasing-8B-alpha-v0.1 dtype: bfloat16 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "hafidhsoekma/Gasing-8B-alpha-v0.1-nearswap-base" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
TBurdairon/finegrain-image-enhancer-model	TBurdairon	2025-06-18T08:46:38Z	0	0	null	[ "safetensors", "esrgan", "ESRGAN", "super-resolution", "enhancer", "image-to-image", "region:us" ]	image-to-image	2025-06-18T08:12:35Z	--- pipeline_tag: image-to-image tags: - ESRGAN - super-resolution - enhancer --- # Finegrain Image Enhancer (ESRGAN-based) This model enhances image quality using ESRGAN and custom ControlNet/LoRA techniques. ## Usage ```python from huggingface_hub import InferenceClient client = InferenceClient("your-username/finegrain-image-enhancer", token="hf_xxx") result = client.post(json={"inputs": {"image": "<base64 image>"}}) ```
victordorian66/final_qwen_normal	victordorian66	2025-06-18T08:43:57Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-06-18T08:42:31Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
derekl35/tarot-qlora-flux	derekl35	2025-06-18T08:37:26Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "dataset:multimodalart/1920-raider-waite-tarot-public-domain", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-06-17T15:12:06Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers widget: - text: >- a trtcrd of a young woman sitting cross-legged on a floating cloud, eyes closed in meditation, with butterflies made of starlight circling her head, holding a crystal orb that shows swirling galaxies inside, her hair flowing upward like smoke, "the dreamer" output: url: images/tarot_merged1.png - text: >- a trtcrd of an elderly person in flowing robes standing before a massive library, holding an ornate key in one hand and an open book with glowing text in the other, owls perched on floating bookshelves that spiral up into darkness, "the keeper" output: url: images/tarot_merged2.png - text: >- a trtcrd of a strong figure in work clothes kneeling beside a half-built stone tower, hammer in hand, with blueprints scattered around, a phoenix rising from forge flames in the background, mountains silhouetted against dawn, "the builder" output: url: images/tarot_merged3.png instance_prompt: null datasets: - multimodalart/1920-raider-waite-tarot-public-domain --- # LoRA for FLUX.1-dev - Tarot Card Style This repository contains a LoRA (Low-Rank Adaptation) fine-tuned on `black-forest-labs/FLUX.1-dev` to generate images in the artistic style of tarot cards. This work is part of the blog post, "Fine-Tuning FLUX.1-dev on consumer hardware and in FP8". <Gallery /> ## Inference There are two main ways to use this LoRA for inference: loading the adapter on the fly or merging it with the base model. ### Option 1: Loading LoRA Adapters This approach offers flexibility, allowing you to easily switch between different LoRA styles. ```python from diffusers import FluxPipeline import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/tarot-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.enable_model_cpu_offload() image = pipeline( 'a trtcrd of a strong figure in work clothes kneeling beside a half-built stone tower, hammer in hand, with blueprints scattered around, a phoenix rising from forge flames in the background, mountains silhouetted against dawn, "the builder"', num_inference_steps=28, guidance_scale=3.5, height=768, width=512, generator=torch.manual_seed(0) ).images[0] image.save("tarot_loaded.png") ``` ### Option 2: Merging LoRA into Base Model Merging the LoRA into the base model can lead to slightly faster inference and is useful when you want to use a single style consistently. ```python from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/tarot-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.fuse_lora() pipeline.unload_lora_weights() # You can save the fused transformer for later use # pipeline.transformer.save_pretrained("fused_transformer") pipeline.enable_model_cpu_offload() image = pipeline( 'a trtcrd of a strong figure in work clothes kneeling beside a half-built stone tower, hammer in hand, with blueprints scattered around, a phoenix rising from forge flames in the background, mountains silhouetted against dawn, "the builder"', num_inference_steps=28, guidance_scale=3.5, height=768, width=512, generator=torch.manual_seed(0) ).images[0] image.save("tarot_merged.png") ``` you can also requantize model: ```python from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel, BitsAndBytesConfig import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/tarot-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.fuse_lora() pipeline.unload_lora_weights() pipeline.transformer.save_pretrained("fused_transformer") ckpt_id = "black-forest-labs/FLUX.1-dev" bnb_4bit_compute_dtype = torch.bfloat16 nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=bnb_4bit_compute_dtype, ) transformer = FluxTransformer2DModel.from_pretrained( "fused_transformer", quantization_config=nf4_config, torch_dtype=bnb_4bit_compute_dtype, ) pipeline = AutoPipelineForText2Image.from_pretrained( ckpt_id, transformer=transformer, torch_dtype=bnb_4bit_compute_dtype ) pipeline.enable_model_cpu_offload() image = pipeline( 'a trtcrd of a strong figure in work clothes kneeling beside a half-built stone tower, hammer in hand, with blueprints scattered around, a phoenix rising from forge flames in the background, mountains silhouetted against dawn, "the builder"', num_inference_steps=28, guidance_scale=3.5, height=768, width=512, generator=torch.manual_seed(0) ).images[0] image.save("tarot_merged.png") ```
derekl35/3dicon-qlora-flux	derekl35	2025-06-18T08:33:45Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "dataset:linoyts/3d_icon", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-06-18T07:49:13Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers widget: - text: "a 3dicon, a joyful yellow emoji with smiling eyes, showing two hands in front as if reaching to give a hug" output: url: >- images/3d_merged1.png - text: "a 3dicon, a miniature galaxy in a glass dome, with swirling stars and nebulae in vibrant blues and purples, surrounded by a group of colorful icons on a black background" output: url: >- images/3d_merged2.png - text: "a 3dicon, an alien with green skin" output: url: >- images/3d_merged3.png instance_prompt: null datasets: - linoyts/3d_icon --- # LoRA for FLUX.1-dev - 3D Icon Style This repository contains a LoRA (Low-Rank Adaptation) fine-tuned on `black-forest-labs/FLUX.1-dev` to generate images in the artistic style of 3D icons. This work is part of the blog post, "Fine-Tuning FLUX.1-dev on consumer hardware and in FP8". <Gallery /> ## Inference There are two main ways to use this LoRA for inference: loading the adapter on the fly or merging it with the base model. ### Option 1: Loading LoRA Adapters This approach offers flexibility, allowing you to easily switch between different LoRA styles. ```python from diffusers import FluxPipeline import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/3dicon-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.enable_model_cpu_offload() image = pipeline( "a 3dicon, an alien with green skin", num_inference_steps=28, guidance_scale=3.5, height=768, width=768, generator=torch.manual_seed(0) ).images[0] image.save("3d_loaded.png") ``` ### Option 2: Merging LoRA into Base Model Merging the LoRA into the base model can lead to slightly faster inference and is useful when you want to use a single style consistently. ```python from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/3dicon-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.fuse_lora() pipeline.unload_lora_weights() # You can save the fused transformer for later use # pipeline.transformer.save_pretrained("fused_transformer") pipeline.enable_model_cpu_offload() image = pipeline( "a 3dicon, an alien with green skin", num_inference_steps=28, guidance_scale=3.5, height=768, width=768, generator=torch.manual_seed(0) ).images[0] image.save("3d_merged.png") ``` you can also requantize model: ```python from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel, BitsAndBytesConfig import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/3dicon-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.fuse_lora() pipeline.unload_lora_weights() pipeline.transformer.save_pretrained("fused_transformer") ckpt_id = "black-forest-labs/FLUX.1-dev" bnb_4bit_compute_dtype = torch.bfloat16 nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=bnb_4bit_compute_dtype, ) transformer = FluxTransformer2DModel.from_pretrained( "fused_transformer", quantization_config=nf4_config, torch_dtype=bnb_4bit_compute_dtype, ) pipeline = AutoPipelineForText2Image.from_pretrained( ckpt_id, transformer=transformer, torch_dtype=bnb_4bit_compute_dtype ) pipeline.enable_model_cpu_offload() image = pipeline( "a 3dicon, an alien with green skin", num_inference_steps=28, guidance_scale=3.5, height=768, width=768, generator=torch.manual_seed(0) ).images[0] image.save("3d_merged.png") ```
techlab-khc/adapter-test	techlab-khc	2025-06-18T08:32:58Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-18T08:32:40Z	--- 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. 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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. 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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. (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]
danaroth/hirdiff	danaroth	2025-06-18T08:30:55Z	0	0	null	[ "arxiv:2206.11892", "arxiv:2402.15865", "license:mit", "region:us" ]	null	2025-06-18T08:15:47Z	--- license: mit --- # Description This repository contains the pre-trained diffusion models associated to the Denoising Diffusion Probabilistic Models (DDPM-CD) developed by Wele Gedara Chaminda Bandara, Nithin Gopalakrishnan Nair, Vishal M. Patel. The optimized model is for unsupervised hyperspectral image restoration (HIRDiff) is developed by Li Pang, Xiangyu Rui, Long Cui, Hongzhong Wang, Deyu Meng, Xiangyong Cao. # Credits The repository associated to this data is available at: <https://github.com/wgcban/ddpm-cd> The HIRDiff repository is available at: <https://github.com/LiPang/HIRDiff> The original data was downloaded from: <https://www.dropbox.com/scl/fo/eeeclganhghux3g657u6b/AOOeiz4h-Er9RAVD5a_t7GQ> # Citation For the original dataset: ```bibtex @misc{bandara2024ddpmcdv2, title = {Remote Sensing Change Detection (Segmentation) using Denoising Diffusion Probabilistic Models}, author = {Bandara, Wele Gedara Chaminda and Nair, Nithin Gopalakrishnan and Patel, Vishal M.}, year = {2022}, eprint={2206.11892}, archivePrefix={arXiv}, primaryClass={cs.CV}, doi = {10.48550/ARXIV.2206.11892}, } ``` ```bibtex @misc{bandara2024ddpmcdv3, title={DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection}, author={Wele Gedara Chaminda Bandara and Nithin Gopalakrishnan Nair and Vishal M. Patel}, year={2024}, eprint={2206.11892}, archivePrefix={arXiv}, primaryClass={cs.CV}, doi = {10.48550/ARXIV.2206.11892}, } ``` For the improved diffusion model: ```bibtex @article{pang2024hir, title={HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models}, author={Pang, Li and Rui, Xiangyu and Cui, Long and Wang, Hongzhong and Meng, Deyu and Cao, Xiangyong}, journal={arXiv preprint arXiv:2402.15865}, year={2024} } ```
derekl35/alphonse-mucha-qlora-flux	derekl35	2025-06-18T08:28:07Z	4	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "dataset:derekl35/alphonse-mucha-style", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	2025-06-12T19:42:33Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers widget: - text: >- Serene raven-haired woman, moonlit lilies, swirling botanicals, alphonse mucha style output: url: images/alphonse_mucha_merged1.png - text: a puppy in a pond, alphonse mucha style output: url: images/alphonse_mucha_merged2.png - text: >- Ornate fox with a collar of autumn leaves and berries, amidst a tapestry of forest foliage, alphonse mucha style output: url: images/alphonse_mucha_merged3.png instance_prompt: null datasets: - derekl35/alphonse-mucha-style --- # LoRA for FLUX.1-dev - Alphonse Mucha Style This repository contains a LoRA (Low-Rank Adaptation) fine-tuned on `black-forest-labs/FLUX.1-dev` to generate images in the artistic style of Alphonse Mucha. This work is part of the blog post, "Fine-Tuning FLUX.1-dev on consumer hardware and in FP8". <Gallery /> ## Model Description This LoRA was trained on [derekl35/alphonse-mucha-style](https://huggingface.co/datasets/derekl35/alphonse-mucha-style) dataset. ## Inference There are two main ways to use this LoRA for inference: loading the adapter on the fly or merging it with the base model. ### Option 1: Loading LoRA Adapters This approach offers flexibility, allowing you to easily switch between different LoRA styles. ```python from diffusers import FluxPipeline import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/alphonse-mucha-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.enable_model_cpu_offload() image = pipeline( "a puppy in a pond, alphonse mucha style", num_inference_steps=28, guidance_scale=3.5, height=768, width=512, generator=torch.manual_seed(0) ).images[0] image.save("alphonse_mucha_loaded.png") ``` ### Option 2: Merging LoRA into Base Model Merging the LoRA into the base model can lead to slightly faster inference and is useful when you want to use a single style consistently. ```python from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/alphonse-mucha-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.fuse_lora() pipeline.unload_lora_weights() # You can save the fused transformer for later use # pipeline.transformer.save_pretrained("fused_transformer") pipeline.enable_model_cpu_offload() image = pipeline( "a puppy in a pond, alphonse mucha style", num_inference_steps=28, guidance_scale=3.5, height=768, width=512, generator=torch.manual_seed(0) ).images[0] image.save("alphonse_mucha_merged.png") ``` you can also requantize model: ```python from diffusers import FluxPipeline, AutoPipelineForText2Image, FluxTransformer2DModel, BitsAndBytesConfig import torch ckpt_id = "black-forest-labs/FLUX.1-dev" pipeline = FluxPipeline.from_pretrained( ckpt_id, text_encoder=None, text_encoder_2=None, torch_dtype=torch.float16 ) pipeline.load_lora_weights("derekl35/alphonse-mucha-qlora-flux", weight_name="pytorch_lora_weights.safetensors") pipeline.fuse_lora() pipeline.unload_lora_weights() pipeline.transformer.save_pretrained("fused_transformer") ckpt_id = "black-forest-labs/FLUX.1-dev" bnb_4bit_compute_dtype = torch.bfloat16 nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=bnb_4bit_compute_dtype, ) transformer = FluxTransformer2DModel.from_pretrained( "fused_transformer", quantization_config=nf4_config, torch_dtype=bnb_4bit_compute_dtype, ) pipeline = AutoPipelineForText2Image.from_pretrained( ckpt_id, transformer=transformer, torch_dtype=bnb_4bit_compute_dtype ) pipeline.enable_model_cpu_offload() image = pipeline( "a puppy in a pond, alphonse mucha style", num_inference_steps=28, guidance_scale=3.5, height=768, width=512, generator=torch.manual_seed(0) ).images[0] image.save("alphonse_mucha_merged.png") ```
joanna302/Qwen3-0.6B-Base_fr_pt_0.0002	joanna302	2025-06-18T08:23:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T07:51:03Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
morturr/Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb1-seed42-2025-06-18	morturr	2025-06-18T08:18:51Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T08:18:28Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb1-seed42-2025-06-18 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. --> # Llama-2-7b-hf-LOO_headlines-COMB_dadjokes-comb1-seed42-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
hafidhsoekma/Gasing-8B-alpha-v0.1-arcee_fusion-base	hafidhsoekma	2025-06-18T08:09:22Z	0	0	null	[ "safetensors", "qwen3", "merge", "mergekit", "lazymergekit", "region:us" ]	null	2025-06-18T05:33:08Z	--- tags: - merge - mergekit - lazymergekit --- # Gasing-8B-alpha-v0.1-arcee_fusion-base Gasing-8B-alpha-v0.1-arcee_fusion-base is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml models: - model: Qwen/Qwen3-8B - model: hafidhsoekma/Gasing-8B-alpha-v0.1 merge_method: arcee_fusion base_model: hafidhsoekma/Gasing-8B-alpha-v0.1 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "hafidhsoekma/Gasing-8B-alpha-v0.1-arcee_fusion-base" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
PaceKW/indobert-base-uncased-multilabel-indonesian-hate-speech-modified	PaceKW	2025-06-18T08:04:31Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-06-18T06:58:01Z	--- library_name: transformers license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: indobert-base-uncased-multilabel-indonesian-hate-speech-modified 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. --> # indobert-base-uncased-multilabel-indonesian-hate-speech-modified This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2436 - F1: 0.8109 - Roc Auc: 0.8892 - Accuracy: 0.7441 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| 0.2535 \| 1.0 \| 1317 \| 0.2123 \| 0.7337 \| 0.8153 \| 0.6226 \| \| 0.1826 \| 2.0 \| 2634 \| 0.2049 \| 0.7763 \| 0.8740 \| 0.6409 \| \| 0.1362 \| 3.0 \| 3951 \| 0.2119 \| 0.7857 \| 0.8562 \| 0.7153 \| \| 0.1015 \| 4.0 \| 5268 \| 0.2060 \| 0.8052 \| 0.8761 \| 0.7411 \| \| 0.0779 \| 5.0 \| 6585 \| 0.2436 \| 0.8109 \| 0.8892 \| 0.7441 \| \| 0.0545 \| 6.0 \| 7902 \| 0.2792 \| 0.8010 \| 0.8862 \| 0.7342 \| \| 0.0421 \| 7.0 \| 9219 \| 0.2926 \| 0.8059 \| 0.8802 \| 0.7441 \| \| 0.03 \| 8.0 \| 10536 \| 0.3343 \| 0.7964 \| 0.8904 \| 0.7289 \| \| 0.024 \| 9.0 \| 11853 \| 0.3413 \| 0.8061 \| 0.8929 \| 0.7441 \| \| 0.0169 \| 10.0 \| 13170 \| 0.3411 \| 0.8104 \| 0.8865 \| 0.7517 \| \| 0.0155 \| 11.0 \| 14487 \| 0.3545 \| 0.7996 \| 0.8808 \| 0.7335 \| \| 0.0127 \| 12.0 \| 15804 \| 0.3670 \| 0.8107 \| 0.8910 \| 0.7472 \| \| 0.0104 \| 13.0 \| 17121 \| 0.3665 \| 0.8065 \| 0.8840 \| 0.7456 \| \| 0.0059 \| 14.0 \| 18438 \| 0.3794 \| 0.8074 \| 0.8868 \| 0.7525 \| \| 0.0048 \| 15.0 \| 19755 \| 0.3848 \| 0.8075 \| 0.8864 \| 0.7502 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
phospho-app/Kai-13-gr00t-example_dataset_v2-k9jij	phospho-app	2025-06-18T07:43:56Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-06-18T07:36:16Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [Kai-13/example_dataset_v2](https://huggingface.co/datasets/Kai-13/example_dataset_v2) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
vuitton/21v1scrip_31	vuitton	2025-06-18T07:38:18Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-06-16T15:34:56Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
nyuuzyou/EuroLLM-22B-Instruct-Preview-GGUF	nyuuzyou	2025-06-18T07:37:48Z	418	0	null	[ "gguf", "base_model:utter-project/EuroLLM-22B-Instruct-Preview", "base_model:quantized:utter-project/EuroLLM-22B-Instruct-Preview", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-10T21:44:31Z	--- base_model: - utter-project/EuroLLM-22B-Instruct-Preview --- This is quantized version of [utter-project/EuroLLM-22B-Instruct-Preview](https://huggingface.co/utter-project/EuroLLM-22B-Instruct-Preview) created using [llama.cpp](https://github.com/ggml-org/llama.cpp)
wizard-chair/flower-doggie	wizard-chair	2025-06-18T07:31:00Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-18T07:31:00Z	--- license: creativeml-openrail-m ---
KangHuggingface/BGC-Finder	KangHuggingface	2025-06-18T07:28:39Z	12	0	transformers	[ "transformers", "pytorch", "roberta", "token-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-04-12T04:06:38Z	--- license: mit library_name: transformers --- ## BGC-Finder 🧬 we present BGC-Finder-annotator, a deep learning framework that integrates protein language models (pLMs) and genomic contexts to predict product class and decipher gene functions within BGCs without alignment. ESM2-650M [weight] (https://huggingface.co/facebook/esm2_t33_650M_UR50D) was used for generating protein embeddings. The source code of BGC-Finder-annotator is avaliable at [BGC-Finder-annotaor](https://github.com/HUST-NingKang-Lab/BGC-Finder) To use BGC-Finder-detector for BGC detection, please visit [BGC-Finder-detector](https://github.com/HUST-NingKang-Lab/BGC-Prophet). ## Example usage ``` >>> from transformers import ( RobertaForTokenClassification, EsmModel, EsmTokenizer, ) >>> tokenizer = EsmTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") >>> embed_model = EsmModel.from_pretrained("facebook/esm2_t33_650M_UR50D") >>> corefinder = RobertaForTokenClassification.from_pretrained("KangHuggingface/CoreFinder") ```
JayHyeon/Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-5ep	JayHyeon	2025-06-18T07:26:53Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:argilla/distilabel-math-preference-dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:finetune:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T07:00:58Z	--- base_model: Qwen/Qwen2.5-Math-1.5B datasets: argilla/distilabel-math-preference-dpo library_name: transformers model_name: Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-5ep tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-5ep This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-DPO_1e-5_1.0vpo_constant-5ep", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/9wfsl6h7) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
CatchKuo/llama3.2_3B_news_qlora	CatchKuo	2025-06-18T07:20:00Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-18T07:20:00Z	--- license: apache-2.0 ---
GAYOEN/figma_sdxl_lora_label_expand	GAYOEN	2025-06-18T07:02:11Z	0	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-06-18T06:15:46Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - diffusers-training - lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - diffusers-training - lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - GAYOEN/figma_sdxl_lora_label_expand These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the GAYOEN/figma-train-expand dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
eastmanbicycles/eastmanindustrieslimited	eastmanbicycles	2025-06-18T06:52:22Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-18T06:52:22Z	--- license: apache-2.0 ---
morturr/Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb1-seed42-2025-06-18	morturr	2025-06-18T06:45:17Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T06:44:59Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb1-seed42-2025-06-18 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. --> # Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb1-seed42-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
Shashanksp08/shashanknew	Shashanksp08	2025-06-18T06:42:04Z	0	0	diffusers	[ "diffusers", "safetensors", "image-to-3d", "arxiv:2312.02201", "license:openrail", "diffusers:MVDreamPipeline", "region:us" ]	image-to-3d	2025-06-18T06:29:45Z	--- license: openrail pipeline_tag: image-to-3d --- This is a copy of [ashawkey/imagedream-ipmv-diffusers](https://huggingface.co/ashawkey/imagedream-ipmv-diffusers). It is hosted here for persistence throughout the ML for 3D course. # MVDream-diffusers Model Card This is a port of https://huggingface.co/Peng-Wang/ImageDream into diffusers. For usage, please check: https://github.com/ashawkey/mvdream_diffusers ## Citation ``` @article{wang2023imagedream, title={ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation}, author={Wang, Peng and Shi, Yichun}, journal={arXiv preprint arXiv:2312.02201}, year={2023} } ``` ## Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
anemll/anemll-Llama-3.2-1B-FAST-iOS_0.3.0	anemll	2025-06-18T06:34:57Z	16	0	null	[ "llama", "coreml", "ANE", "DeepSeek", "Apple", "Apple Neural Engine", "DeepHermes", "license:mit", "region:us" ]	null	2025-03-26T20:53:49Z	--- license: mit tags: - coreml - ANE - DeepSeek - Apple - Apple Neural Engine - DeepHermes --- # ANEMLL ANEMLL (pronounced like "animal") is an open-source project focused on accelerating the porting of Large Language Models (LLMs) to tensor processors, starting with the Apple Neural Engine (ANE). The goal is to provide a fully open-source pipeline from model conversion to inference for common LLM architectures running on ANE. This enables seamless integration and on-device inference for low-power applications on edge devices, ensuring maximum privacy and security. This is critical for autonomous applications, where models run directly on the device without requiring an internet connection. For more information, visit the [ANEMLL GitHub repository](https://github.com/anemll/anemll). --- ## License ANEMLL is licensed under the [MIT License](https://opensource.org/license/mit). The model is based on Meta's LLaMA 3.2 and may require a separate license. This test model is exclusively for the Meta's LLaMA architecture converted for CoreML, released before the official launch of the ANEMLL repository and minimal documentation. It is intended for early adopters only who requested an early release. --- ## Requirements - macOS Sequoia with Apple Neural Engine and 8GB RAM or more - CoreML Tools and HuggingFace Transformers libraries - Python 3.9 `chat.py` provides a sample inference script. `chat_full.py` provides a sample inference script with history and conversation management. Installation 1. Download the model from Hugging Face: ```bash # Install required tools pip install huggingface_hub # Install Git LFS (Large File Support) # macOS with Homebrew: brew install git-lfs # Or Ubuntu/Debian: # sudo apt-get install git-lfs # Initialize Git LFS git lfs install # Clone the repository with model files git clone https://huggingface.co/anemll/anemll-Llama-3.2-1B-FAST-iOS_0.3.0 ``` 2. Extract model files: ```bash # Navigate to cloned directory cd anemll-Llama-3.2-1B-FAST-iOS_0.3.0 # Pull LFS files (model weights) git lfs pull # Extract CoreML model files find . -type f -name ".zip" -exec unzip {} \; ``` 3. Install dependencies: ```bash pip install coremltools transformers ``` Coremltools:* See coremltools installation guide at https://coremltools.readme.io/v4.0/docs/installation How to Run 1. Basic chat interface: ```bash python chat.py --meta ./meta.yaml ``` 2. Full conversation mode with history: ```bash python chat_full.py --meta ./meta.yaml ``` > Note: The first time the model loads, macOS will take some time to place it on the device. > Subsequent loads will be instantaneous. > Use Ctrl-D to exit, Ctrl-C to interrupt inference. More Info Please check following links for later updates: * [GitHub](https://github.com/anemll) * [Hugging Face Models](https://huggingface.co/anemll) * [Twitter/X](https://x.com/anemll) * [Website](https://anemll.com) [email protected] # anemll-Llama-3.2-1B-FAST-iOS_0.3.0 This is a CoreML model converted using ANEMLL for Apple Neural Engine inference. ## Available Distributions ### Standard Distribution - Contains zipped MLMODELC files - Suitable for macOS and development ### iOS Distribution - Contains unzipped MLMODELC files - Ready for iOS deployment - Includes offline tokenizer support ## Model Information - Context Length: %CONTEXT_LENGTH% - Batch Size: %BATCH_SIZE% - Number of Chunks: %NUM_CHUNKS% ## Quick Start ### Test in iOS/macOS App Try our sample Chat-Bot app on TestFlight: 1. Install TestFlight from App Store 2. Join beta test: [TestFlight Link](https://testflight.apple.com/join/jrQq1D1C) 3. App includes a small demo model pre-installed 4. You can add custom models via HuggingFace URLs > [!Note] > - The TestFlight app works on both iOS and macOS > - Demonstrates proper model integration and provides a reference implementation > - iOS requires unzipped MLMODELC files and config.json for offline tokenizer > - macOS supports both zipped and unzipped model formats ```
bharathsj/bio-medical-llama-domain-lsf	bharathsj	2025-06-18T06:16:49Z	0	0	null	[ "safetensors", "llama", "license:apache-2.0", "region:us" ]	null	2025-06-18T06:09:42Z	--- license: apache-2.0 ---
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb1-seed18-2025-06-18	morturr	2025-06-18T06:15:48Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T06:15:27Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb1-seed18-2025-06-18 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. --> # Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb1-seed18-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 18 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
hasancanonder/llama3-turkish-g16bit	hasancanonder	2025-06-18T06:15:34Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T06:12:25Z	--- base_model: unsloth/llama-3-8b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: hasancanonder - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
godnpeter/llama31_answeronly	godnpeter	2025-06-18T06:13:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T06:05:28Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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godnpeter/qwen25_answeronly	godnpeter	2025-06-18T06:09:11Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T06:06:50Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Abhishek679/codeT5-JsonToCypress	Abhishek679	2025-06-18T06:02:23Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-18T06:01:02Z	--- 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. 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ujjawal077/llama3s-merged5	ujjawal077	2025-06-18T06:02:08Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2311.03099", "base_model:AdaptLLM/finance-LLM-13B", "base_model:merge:AdaptLLM/finance-LLM-13B", "base_model:starmpcc/Asclepius-Llama2-13B", "base_model:merge:starmpcc/Asclepius-Llama2-13B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T05:55:04Z	--- base_model: - AdaptLLM/finance-LLM-13B - starmpcc/Asclepius-Llama2-13B library_name: transformers tags: - mergekit - merge --- # llama3s-merged This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [starmpcc/Asclepius-Llama2-13B](https://huggingface.co/starmpcc/Asclepius-Llama2-13B) as a base. ### Models Merged The following models were included in the merge: * [AdaptLLM/finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: starmpcc/Asclepius-Llama2-13B dtype: bfloat16 merge_method: dare_ties modules: default: slices: - sources: - layer_range: [0, 40] model: AdaptLLM/finance-LLM-13B parameters: density: 0.53 weight: 0.6 - layer_range: [0, 40] model: starmpcc/Asclepius-Llama2-13B parameters: density: 0.5 weight: 0.4 parameters: int8_mask: 1.0 ```
sizzlebop/crystal-think-v1.0-Q4_K_M-GGUF	sizzlebop	2025-06-18T06:02:04Z	0	0	transformers	[ "transformers", "gguf", "mathematical-reasoning", "qwen3", "lora", "grpo", "math", "reasoning", "fine-tuned", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:nvidia/OpenMathReasoning", "base_model:sizzlebop/crystal-think-v1.0", "base_model:adapter:sizzlebop/crystal-think-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	text-generation	2025-06-18T06:01:52Z	--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - mathematical-reasoning - qwen3 - lora - grpo - math - reasoning - fine-tuned - llama-cpp - gguf-my-repo base_model: sizzlebop/crystal-think-v1.0 datasets: - nvidia/OpenMathReasoning --- # sizzlebop/crystal-think-v1.0-Q4_K_M-GGUF This model was converted to GGUF format from [`sizzlebop/crystal-think-v1.0`](https://huggingface.co/sizzlebop/crystal-think-v1.0) 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/sizzlebop/crystal-think-v1.0) 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 sizzlebop/crystal-think-v1.0-Q4_K_M-GGUF --hf-file crystal-think-v1.0-q4_k_m-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/crystal-think-v1.0-Q4_K_M-GGUF --hf-file crystal-think-v1.0-q4_k_m-imat.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 sizzlebop/crystal-think-v1.0-Q4_K_M-GGUF --hf-file crystal-think-v1.0-q4_k_m-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/crystal-think-v1.0-Q4_K_M-GGUF --hf-file crystal-think-v1.0-q4_k_m-imat.gguf -c 2048 ```
hardlyworking/HoldMy4B	hardlyworking	2025-06-18T06:00:31Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "axolotl", "generated_from_trainer", "conversational", "dataset:hardlyworking/HardlyRPv2", "base_model:Salesforce/xgen-small-4B-instruct-r", "base_model:finetune:Salesforce/xgen-small-4B-instruct-r", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T03:32:54Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: Salesforce/xgen-small-4B-instruct-r tags: - axolotl - generated_from_trainer datasets: - hardlyworking/HardlyRPv2 model-index: - name: HoldMy4B 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0` ```yaml base_model: Salesforce/xgen-small-4B-instruct-r load_in_8bit: false load_in_4bit: false strict: false chat_template: chatml datasets: - path: hardlyworking/HardlyRPv2 type: chat_template split: train field_messages: conversations message_property_mappings: role: from content: value val_set_size: 0.1 output_dir: ./outputs/out dataset_prepared_path: last_run_prepared shuffle_merged_datasets: true hub_model_id: hardlyworking/HoldMy4B hub_strategy: "all_checkpoints" push_dataset_to_hub: hf_use_auth_token: true plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_fused_linear_cross_entropy: false cut_cross_entropy: true sequence_len: 32768 sample_packing: true eval_sample_packing: true pad_to_sequence_len: true wandb_project: Xgen4B wandb_entity: wandb_watch: wandb_name: Xgen4B wandb_log_model: evals_per_epoch: 8 eval_table_size: eval_max_new_tokens: 128 gradient_accumulation_steps: 2 micro_batch_size: 2 num_epochs: 2 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: offload gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: deepspeed: warmup_ratio: 0.05 saves_per_epoch: 1 debug: weight_decay: 0.01 fsdp: fsdp_config: special_tokens: pad_token: ``` </details><br> # HoldMy4B This model is a fine-tuned version of [Salesforce/xgen-small-4B-instruct-r](https://huggingface.co/Salesforce/xgen-small-4B-instruct-r) on the hardlyworking/HardlyRPv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.1637 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 24 - training_steps: 480 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 0 \| 0 \| 2.6420 \| \| 2.0119 \| 0.125 \| 30 \| 2.2105 \| \| 1.8963 \| 0.25 \| 60 \| 2.1865 \| \| 1.8623 \| 0.375 \| 90 \| 2.1787 \| \| 1.8528 \| 0.5 \| 120 \| 2.1746 \| \| 1.8784 \| 0.625 \| 150 \| 2.1706 \| \| 1.9961 \| 0.75 \| 180 \| 2.1686 \| \| 1.8748 \| 0.875 \| 210 \| 2.1672 \| \| 2.0385 \| 1.0 \| 240 \| 2.1657 \| \| 1.9327 \| 1.125 \| 270 \| 2.1646 \| \| 1.8509 \| 1.25 \| 300 \| 2.1645 \| \| 1.8279 \| 1.375 \| 330 \| 2.1640 \| \| 1.8271 \| 1.5 \| 360 \| 2.1638 \| \| 1.8589 \| 1.625 \| 390 \| 2.1637 \| \| 1.9824 \| 1.75 \| 420 \| 2.1637 \| \| 1.8668 \| 1.875 \| 450 \| 2.1637 \| \| 2.0332 \| 2.0 \| 480 \| 2.1637 \| ### Framework versions - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Allen-UQ/Qwen2.5-7B-Instruct-GRPO-One-Hop-Aug-Pubmed	Allen-UQ	2025-06-18T05:59:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:Allen-UQ/pubmed_1_target_1_hop_aug", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T03:05:29Z	--- base_model: Qwen/Qwen2.5-7B-Instruct datasets: Allen-UQ/pubmed_1_target_1_hop_aug library_name: transformers model_name: Qwen2.5-7B-Instruct-GRPO-One-Hop-Aug-Pubmed tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen2.5-7B-Instruct-GRPO-One-Hop-Aug-Pubmed This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [Allen-UQ/pubmed_1_target_1_hop_aug](https://huggingface.co/datasets/Allen-UQ/pubmed_1_target_1_hop_aug) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Allen-UQ/Qwen2.5-7B-Instruct-GRPO-One-Hop-Aug-Pubmed", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ruihong-yilun/huggingface/runs/uyyhn4rp) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jitendracheripally/RoBERTa-RVL-CDIP-FineTune	jitendracheripally	2025-06-18T05:59:15Z	0	0	null	[ "safetensors", "roberta", "Document", "Classification", "text-classification", "en", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:apache-2.0", "region:us" ]	text-classification	2025-06-18T05:54:34Z	--- license: apache-2.0 language: - en metrics: - accuracy base_model: - FacebookAI/roberta-base pipeline_tag: text-classification tags: - Document - Classification ---
sizzlebop/crystal-think-v1.0-Q6_K-GGUF	sizzlebop	2025-06-18T05:55:44Z	0	0	transformers	[ "transformers", "gguf", "mathematical-reasoning", "qwen3", "lora", "grpo", "math", "reasoning", "fine-tuned", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:nvidia/OpenMathReasoning", "base_model:sizzlebop/crystal-think-v1.0", "base_model:adapter:sizzlebop/crystal-think-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T05:55:30Z	--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: text-generation tags: - mathematical-reasoning - qwen3 - lora - grpo - math - reasoning - fine-tuned - llama-cpp - gguf-my-repo base_model: sizzlebop/crystal-think-v1.0 datasets: - nvidia/OpenMathReasoning --- # sizzlebop/crystal-think-v1.0-Q6_K-GGUF This model was converted to GGUF format from [`sizzlebop/crystal-think-v1.0`](https://huggingface.co/sizzlebop/crystal-think-v1.0) 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/sizzlebop/crystal-think-v1.0) 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 sizzlebop/crystal-think-v1.0-Q6_K-GGUF --hf-file crystal-think-v1.0-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo sizzlebop/crystal-think-v1.0-Q6_K-GGUF --hf-file crystal-think-v1.0-q6_k.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 sizzlebop/crystal-think-v1.0-Q6_K-GGUF --hf-file crystal-think-v1.0-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo sizzlebop/crystal-think-v1.0-Q6_K-GGUF --hf-file crystal-think-v1.0-q6_k.gguf -c 2048 ```
vsaez/example-model	vsaez	2025-06-18T05:54:54Z	0	0	null	[ "region:us" ]	null	2025-06-18T05:38:58Z	This is my model card README --- license: mit ---
Mungert/Gemma-3-Gaia-PT-BR-4b-it-GGUF	Mungert	2025-06-18T05:54:45Z	0	0	transformers	[ "transformers", "gguf", "pt", "arxiv:2410.10739", "base_model:google/gemma-3-4b-pt", "base_model:quantized:google/gemma-3-4b-pt", "license:gemma", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-18T03:25:20Z	--- library_name: transformers license: gemma language: - pt base_model: - google/gemma-3-4b-pt --- # <span style="color: #7FFF7F;">Gemma-3-Gaia-PT-BR-4b-it GGUF Models</span> ## <span style="color: #7F7FFF;">Model Generation Details</span> This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`7f4fbe51`](https://github.com/ggerganov/llama.cpp/commit/7f4fbe5183b23b6b2e25fd1ccc5d1fa8bb010cb7). --- ## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span> I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides. In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here: 👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py) While this does increase model file size, it significantly improves precision for a given quantization level. ### I'd love your feedback—have you tried this? How does it perform for you? --- <a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;"> Click here to get info on choosing the right GGUF model format </a> --- <!--Begin Original Model Card--> # Model Card for GAIA (Gemma-3-Gaia-PT-BR-4b-it) GAIA is an open, state-of-the-art language model for Brazilian Portuguese. It was developed by continuously pre-training the `google/gemma-3-4b-pt` model on an extensive, high-quality corpus of Portuguese data. The goal of GAIA is to democratize access to cutting-edge AI technology in Brazil, enabling developers, researchers, and organizations to build innovative solutions on a robust and reliable technological foundation. ## Model Details ### Model Description GAIA was developed through a partnership between The Brazilian Association of AI (ABRIA), the Center of Excellence in Artificial Intelligence (CEIA) at the Federal University of Goiás (UFG), startups Nama and Amadeus AI, and Google DeepMind. The development process started with the base model `google/gemma-3-4b-pt` and involved two main stages: 1. Continuous Pre-training: The model was trained on a large, high-quality Portuguese dataset totaling approximately 13 billion tokens. This corpus includes a variety of domains, such as scientific articles and Wikipedia data in Portuguese, ensuring a deep understanding of the language and its contexts. 2. Instruction-Following Capability Restoration: To enable the model to follow instructions without traditional supervised fine-tuning (SFT), a weight merging operation was applied. This technique, described in the paper “Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs”, allows the model to integrate the knowledge acquired during continuous pre-training with the ability to interact in a chat format and follow instructions. - Developed by: The Brazilian Association of AI (ABRIA), the Center of Excellence in Artificial Intelligence (CEIA-UFG), Nama, Amadeus AI, and Google DeepMind. - Model: GAIA - Model type: Causal decoder-only Transformer-based language model. - Language(s): Brazilian Portuguese (pt-BR) - License: Gemma - Based on: `google/gemma-3-4b-pt` ### Team This project was made possible by the contributions of the following individuals: - Dr. Celso Gonçalves Camilo-Junior - Dr. Sávio Salvarino Teles de Oliveira - Me. Lucas Araujo Pereira - Marcellus Amadeus - Daniel Fazzioni - Artur Matos Andrade Novais - Salatiel Abraão Avelar Jordão ### Model Sources - Repository: [CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it](https://huggingface.co/CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it) - Paper (Merge Methodology): [Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs](https://arxiv.org/pdf/2410.10739) ## Uses The model is designed for text generation and conversational tasks in Portuguese. ### Direct Use GAIA can be used directly for chat, question answering, summarization, creative content generation, and other tasks requiring natural language understanding and generation in Portuguese. ### Downstream Use GAIA serves as an excellent base model for fine-tuning on specific tasks, such as: - Sentiment analysis in Portuguese. - Retrieval-Augmented Generation (RAG) systems for corporate knowledge bases. - Document classification. - Specialized customer service chatbots. ### Out-of-Scope Use This model should not be used for high-stakes, critical decisions without human oversight. Its use for generating malicious, offensive, or illegal content, or for deceptively impersonating a human, is outside the intended scope. The model's performance in languages other than Portuguese will be significantly degraded. ## Bias, Risks, and Limitations Like any language model, GAIA reflects the biases present in its training data. Although the training corpus was curated with a focus on high quality, it may contain social and cultural biases from sources like Wikipedia and scientific articles. Therefore, the model may generate content that perpetuates existing stereotypes. Furthermore, the model can "hallucinate," meaning it can generate information that appears factual but is not true. We strongly recommend verifying critical facts generated by the model before any use. ### Recommendations Users (both direct and downstream) should be aware of the model's risks, biases, and limitations. Implementing safeguards and content moderation is recommended, especially in public-facing applications. Human supervision is crucial for sensitive use cases. ## Training Details ### Training Data The continuous pre-training was performed on a corpus of approximately 13 billion tokens in Portuguese. The data selection prioritized high quality and diversity, including sources such as: - Scientific Articles in Portuguese: To provide the model with more formal and technical knowledge. - Portuguese Wikipedia: To cover a wide range of general knowledge. A rigorous cleaning and filtering process was applied to ensure the highest possible data quality. ### Training Procedure The training was conducted on a DGX infrastructure with NVIDIA H100 GPUs, using between 3 and 5 GPUs in parallel. #### Training Hyperparameters - Training regime: Mixed Precision (bf16) - Global Batch Size: 4 million tokens ## Evaluation The model was evaluated on a set of multiple-choice benchmarks in Portuguese, comparing its performance against the base model, `google/gemma-3-4b-it`. The benchmarks include BlueX (a compilation of multiple-choice questions), and questions from the ENEM (Brazilian High School National Exam) and OAB (Brazilian Bar Exam). ### Results \| Benchmark \| `google/gemma-3-4b-it` (Baseline) \| GAIA (Our Model) \| \|------------------\|-----------------------------------\|------------------\| \| BlueX \| 0.6630 \| 0.6575 \| \| ENEM 2024 \| 0.6556 \| 0.7000 \| \| ENEM (General) \| 0.7416 \| 0.7486I \| \| OAB (Bar Exam) \| 0.4502 \| 0.4416 \| #### Summary The results indicate that continuous pre-training on Portuguese data had a notable impact on the model's performance. GAIA showed a significant improvement on the ENEM 2024 benchmark, outperforming the Google base model. On other benchmarks like BlueX and OAB, its performance is competitive and very close to the original model's, suggesting that the additional training process maintained the model's general capabilities while enhancing its knowledge in specific Portuguese-language domains. ## Citation If you use this model in your research or application, please cite our work. BibTeX: ```bibtex @misc{gaia-gemma-3-4b-2025, title={GAIA: An Open Language Model for Brazilian Portuguese}, author={CAMILO-JUNIOR, C. G.; OLIVEIRA, S. S. T.; PEREIRA, L. A.; AMADEUS, M.; FAZZIONI, D.; NOVAIS, A. M. A.; JORDÃO, S. A. A.}, year={2025}, publisher={Hugging Face}, journal={Hugging Face repository}, howpublished={\url{[https://huggingface.co/CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it](https://huggingface.co/CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it)}} } <!--End Original Model Card--> --- # <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span> Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks: 👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder) 💬 How to test: Choose an AI assistant type: - `TurboLLM` (GPT-4.1-mini) - `HugLLM` (Hugginface Open-source models) - `TestLLM` (Experimental CPU-only) ### What I’m Testing I’m pushing the limits of small open-source models for AI network monitoring, specifically: - Function calling against live network services - How small can a model go while still handling: - Automated Nmap security scans - Quantum-readiness checks - Network Monitoring tasks 🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space): - ✅ Zero-configuration setup - ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low. - 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate! ### Other Assistants 🟢 TurboLLM – Uses gpt-4.1-mini : - It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. - Create custom cmd processors to run .net code on Quantum Network Monitor Agents - Real-time network diagnostics and monitoring - Security Audits - Penetration testing (Nmap/Metasploit) 🔵 HugLLM – Latest Open-source models: - 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. ### 💡 Example commands you could test**: 1. `"Give me info on my websites SSL certificate"` 2. `"Check if my server is using quantum safe encyption for communication"` 3. `"Run a comprehensive security audit on my server"` 4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution! ### Final Word I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful. If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone. I'm also open to job opportunities or sponsorship. Thank you! 😊
liushiliushi/Llama-3.1-8B-Instruct_gpt	liushiliushi	2025-06-18T05:44:05Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct", "region:us" ]	null	2025-06-18T05:40:12Z	--- base_model: meta-llama/Llama-3.1-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.12.0
huayangli/seqpe	huayangli	2025-06-18T05:41:16Z	0	0	transformers	[ "transformers", "safetensors", "feature-extraction", "arxiv:2506.13277", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-12T06:45:40Z	--- license: apache-2.0 library_name: transformers pipeline_tag: feature-extraction --- This repo contains the ckpts trained for the SeqPE project, presented in [SeqPE: Transformer with Sequential Position Encoding](https://huggingface.co/papers/2506.13277). Please access our code at: https://github.com/ghrua/seqpe
Jarbas/ovos-model2vec-intents-roberta-large-bne	Jarbas	2025-06-18T05:36:43Z	0	0	model2vec	[ "model2vec", "safetensors", "embeddings", "static-embeddings", "sentence-transformers", "es", "dataset:Jarbas/ovos-intents-train-latest", "base_model:Jarbas/m2v-256-roberta-large-bne", "base_model:finetune:Jarbas/m2v-256-roberta-large-bne", "license:mit", "region:us" ]	null	2025-06-18T05:34:29Z	--- base_model: - Jarbas/m2v-256-roberta-large-bne library_name: model2vec license: mit model_name: ovos-intents-es-b32e30-n1d128-m2v-256-roberta-large-bne tags: - embeddings - static-embeddings - sentence-transformers datasets: - Jarbas/ovos-intents-train-latest language: - es --- # ovos-intents-es-b32e30-n1d128-m2v-256-roberta-large-bne Model Card This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) Model2Vec model. It also includes a classifier head on top. ## Installation Install model2vec using pip: ``` pip install model2vec[inference] ``` ## Usage Load this model using the `from_pretrained` method: ```python from model2vec.inference import StaticModelPipeline # Load a pretrained Model2Vec model model = StaticModelPipeline.from_pretrained("ovos-intents-es-b32e30-n1d128-m2v-256-roberta-large-bne") # Predict labels predicted = model.predict(["Example sentence"]) ``` ## Additional Resources - [Model2Vec Repo](https://github.com/MinishLab/model2vec) - [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e) - [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results) - [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials) - [Website](https://minishlab.github.io/) ## Library Authors Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled). ## Citation Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work. ``` @article{minishlab2024model2vec, author = {Tulkens, Stephan and {van Dongen}, Thomas}, title = {Model2Vec: Fast State-of-the-Art Static Embeddings}, year = {2024}, url = {https://github.com/MinishLab/model2vec} } ```
mob2711/qwen2.5-7b-qlora-cot-ht-2000	mob2711	2025-06-18T05:33:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T05:33:01Z	--- base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: mob2711 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
stablediffusionapi/pornmaster-fullv5nudeinpainting	stablediffusionapi	2025-06-18T05:28:23Z	0	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-06-18T05:24:47Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true pipeline_tag: text-to-image library_name: diffusers widget: - text: a girl wandering through the forest output: url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e0651694-6026-47f1-b794-9710b78130b7/width=512/1404722.jpeg --- # PornMaster-色情大师 - FULL-V5-nude-inpainting API Inference <Gallery /> ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "pornmaster-fullv5nudeinpainting" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/pornmaster-fullv5nudeinpainting) Model link: [View model](https://modelslab.com/models/pornmaster-fullv5nudeinpainting) View all models: [View Models](https://modelslab.com/models) ```python import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "pornmaster-fullv5nudeinpainting", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "", "lora": "", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) ``` > Use this coupon code to get 25% off DMGG0RBN
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb1-seed7-2025-06-18	morturr	2025-06-18T05:24:05Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T05:23:48Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb1-seed7-2025-06-18 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. --> # Llama-2-7b-hf-LOO_one_liners-COMB_dadjokes-comb1-seed7-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 7 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
Khushi-Thakor-Viral-Video-Original-Videos/FULL.VIDEO.Khushi.Thakor.Viral.Video.Tutorial.Official	Khushi-Thakor-Viral-Video-Original-Videos	2025-06-18T05:24:01Z	0	0	null	[ "region:us" ]	null	2025-06-18T05:23:54Z	01 seconds ago [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://sahabagi-mgi.blogspot.com/p/heres-now.html) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://sahabagi-mgi.blogspot.com/p/heres-now.html) <a href="https://sahabagi-mgi.blogspot.com/p/heres-now.html" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
LarryAIDraw/Hoseki_ZenlessZoneZero_YiXuan_IllustriousXL_v1	LarryAIDraw	2025-06-18T05:15:51Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-18T05:09:13Z	--- license: creativeml-openrail-m --- https://civitai.com/models/1492949/yi-xuan-or-zenless-zone-zero-illustriousxl
namdp-ptit/ViDense	namdp-ptit	2025-06-18T05:06:29Z	0	3	transformers	[ "transformers", "safetensors", "xlm-roberta", "embedding", "sentence-similarity", "vi", "arxiv:2305.19370", "arxiv:1911.05722", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:apache-2.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-06-18T04:03:14Z	--- language: - vi license: apache-2.0 library_name: transformers tags: - transformers - embedding pipeline_tag: sentence-similarity widget: - text: tỉnh nào có diện tích lớn nhất việt nam output: - label: tỉnh nào có diện tích rộng nhất Việt Nam score: 0.9861876964569092 - label: tỉnh nào có diện tích nhỏ nhất Việt Nam score: 0.0560965985059738 base_model: - FacebookAI/xlm-roberta-large --- # Table of contents * [Introduce](#introduce) * [Usage](#usage) * [Performance](#performance) * [Contact](#contact) * [Support The Project](#support-the-project) * [Citation](#citation) ## Introduce ViDense is a VietNamese Embedding Model. Fine-tuned and enhanced with tailored methods, ViDense incorporates advanced techniques to optimize performance for text embeddings in various applications. Model Configuration and Methods: * Base Model: FacebookAI/xlm-roberta-large * Trained for 10 epochs with a train batch size of 2048. * Utilizes a 3-phase training approach, where the best checkpoint from each phase serves as the base model for the next. * Position Encoding: Rotary Position Encoding * Attention: [Blockwise Parallel Transformer](https://arxiv.org/abs/2305.19370) * Pooling: Mean Pooling * [Momentum Encoder](https://arxiv.org/abs/1911.05722): Incorporates MoCo (Momentum Contrast) to enhance in-batch negative sampling. * Rank Encoder: Introduces a Rank Encoder to account for transitive positive relationships. By considering positives of positives as relevant to the anchor, it reranks the corpus using the Spearman metric and integrates Spearman weights into the loss calculation for improved ranking. * Loss Function: Cross Entropy Loss ## Usage ``` pip install -U transformers ``` ```python import torch from transformers import AutoModel, AutoTokenizer def avg_pooling(attention_mask, outputs): last_hidden = outputs.last_hidden_state return (last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1) tokenizer = AutoTokenizer.from_pretrained('namdp-ptit/ViDense') model = AutoModel.from_pretrained('namdp-ptit/ViDense') sentences = [ 'Tỉnh nào có diện tích lớn nhất Việt Nam', 'Tỉnh nào có diện tích nhỏ nhất Việt Nam', 'Tỉnh nào có diện tích rộng nhất Việt Nam' ] inputs = tokenizer(sentences, return_tensors='pt', padding=True) with torch.no_grad(): outputs = model(inputs) outputs = avg_pooling(inputs['attention_mask'], outputs) cosine_sim_1 = torch.nn.functional.cosine_similarity( outputs[0].unsqueeze(0), outputs[1].unsqueeze(0) ) cosine_sim_2 = torch.nn.functional.cosine_similarity( outputs[0].unsqueeze(0), outputs[2].unsqueeze(0) ) print(cosine_sim_1.item()) # 0.056096598505973816 print(cosine_sim_2.item()) # 0.9861876964569092 ``` ## Performance Below is a comparision table of the results I achieved compared to some other embedding models on three benchmarks: [ZAC](https://huggingface.co/datasets/GreenNode/zalo-ai-legal-text-retrieval-vn/viewer/default?views%5B%5D=default_train), [WebFaq](https://huggingface.co/datasets/PaDaS-Lab/webfaq-retrieval), [OwiFaq](https://huggingface.co/datasets/PaDaS-Lab/owi-faq-retrieval) with metric Recall@3 \| Model Name \| ZAC \| WebFaq \| OwiFaq \| \|---------------------------------------------------------------------------------------------------------------------\|:----------\|:----------\|:----------\| \| [namdp-ptit/ViDense](https://huggingface.co/namdp-ptit/ViDense) \| 54.72 \| 82.26 \| 85.62 \| \| [VoVanPhuc/sup-SimCSE-VietNamese-phobert-base](https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) \| 53.64 \| 81.52 \| 85.02 \| \| [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) \| 50.45 \| 80.54 \| 78.58 \| \| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) \| 46.12 \| 83.45 \| 86.08** \| Here are the information of these 3 benchmarks: * ZAC: merge train and test into a new benchmark, ~ 3200 queries, ~ 330K documents in corpus * WebFAQ and OwiFaq: merge train and test into a new benchmark, ~ 124K queries, ~ 124K documents in corpus ## Contact Email: [email protected] LinkedIn: [Dang Phuong Nam](https://www.linkedin.com/in/dang-phuong-nam-157912288/) Facebook: [Phương Nam](https://www.facebook.com/phuong.namdang.7146557) ## Support The Project If you find this project helpful and wish to support its ongoing development, here are some ways you can contribute: 1. Star the Repository: Show your appreciation by starring the repository. Your support motivates further development and enhancements. 2. Contribute: I welcome your contributions! You can help by reporting bugs, submitting pull requests, or suggesting new features. 3. Donate: If you’d like to support financially, consider making a donation. You can donate through: - Vietcombank: 9912692172 - DANG PHUONG NAM Thank you for your support! ## Citation Please cite as ```Plaintext @misc{ViDense, title={ViDense: An Embedding Model for Vietnamese Long Context}, author={Nam Dang Phuong}, year={2025}, publisher={Huggingface}, } ```
MarbiFox/SQLlama	MarbiFox	2025-06-18T05:00:04Z	53	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "unsloth", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-03T03:09:02Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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meanjai/ppo-SnowballTarget	meanjai	2025-06-18T04:54:13Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-06-18T04:54:09Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: meanjai/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
morturr/Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb1-seed18-2025-06-18	morturr	2025-06-18T04:51:34Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T04:51:19Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb1-seed18-2025-06-18 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. --> # Llama-2-7b-hf-LOO_amazon-COMB_headlines-comb1-seed18-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 18 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb3-seed42-2025-06-18	morturr	2025-06-18T04:32:04Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-18T04:31:47Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb3-seed42-2025-06-18 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. --> # Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb3-seed42-2025-06-18 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
hoanglv7501/lora_model	hoanglv7501	2025-06-18T04:24:55Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-05T08:29:18Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Noctiro/Randeng-T5-784M-MultiTask-Chinese-with-tokenizer-json	Noctiro	2025-06-18T04:24:51Z	0	0	null	[ "pytorch", "safetensors", "t5", "transformer", "text2text-generation", "chinese", "multitask", "tokenizer", "zh", "license:apache-2.0", "region:us" ]	text2text-generation	2025-06-18T03:26:30Z	--- license: apache-2.0 language: zh tags: - transformer - t5 - text2text-generation - chinese - multitask - tokenizer --- # Randeng-T5-784M-MultiTask-Chinese-with-Tokenizer-JSON This repository hosts a modified version of the [IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese) model. The primary purpose of this repository is to include the `tokenizer.json` file, which was missing in the original release. ## Motivation for this Repository The original `IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese` model is an excellent T5-based model for various Chinese NLP tasks. However, it was released with only a `spiece.model` file for its tokenizer, lacking the `tokenizer.json` file. While the Python `transformers` library can generally load the tokenizer from `spiece.model`, this absence caused issues for environments that strictly prefer or require `tokenizer.json` (e.g., certain versions or implementations of the Rust `tokenizers` library, or other frameworks that rely on this standardized format). To enhance usability and compatibility across different platforms and libraries, this repository was created to provide the model with the commonly expected `tokenizer.json` file. ## Changes Made The following modifications have been made to the original `IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese` model files: * Added `tokenizer.json`: The primary change is the inclusion of the `tokenizer.json` file, generated from the original `spiece.model` using the Python `transformers` library's `save_pretrained()` method. This ensures broader compatibility and easier loading for various applications. * No Model Weights Changes: Crucially, the model weights (`pytorch_model.bin` or `model.safetensors`) themselves have not been altered in any way. This repository provides the exact same powerful pre-trained model, just with an updated tokenizer serialization format. ## How to Use You can load this model and its tokenizer using the Hugging Face `transformers` library: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM model_name = "your-username/Randeng-T5-784M-MultiTask-Chinese-with-Tokenizer-JSON" # Replace with your actual repository name tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "你好，这是一个测试。" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ```` For Rust users (and others requiring `tokenizer.json`): ```rust use tokenizers::Tokenizer; use std::error::Error; #[tokio::main] async fn main() -> Result<(), Box<dyn Error>> { let model_id = "your-username/Randeng-T5-784M-MultiTask-Chinese-with-Tokenizer-JSON"; // Replace with your actual repository name // The Tokenizer::from_pretrained will now find and use tokenizer.json let tokenizer = Tokenizer::from_pretrained(model_id, None).await?; let text = "你好，这是一个中文文本。"; let encoding = tokenizer.encode(text, true).unwrap(); println!("Original text: {}", text); println!("Tokens: {:?}", encoding.get_tokens()); println!("IDs: {:?}", encoding.get_ids()); let decoded_text = tokenizer.decode(encoding.get_ids(), true).unwrap(); println!("Decoded text: {}", decoded_text); Ok(()) } ``` ## Original Model Information For more details about the original `IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese` model, its training, capabilities, and benchmarks, please refer to its official repository: [IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-T5-784M-MultiTask-Chinese).
New-tutorial-mezzo-fun-8/FULL.VIDEO.mezzo.fun.viral.videos.Link.viral.On.Social.Media.Official	New-tutorial-mezzo-fun-8	2025-06-18T04:24:05Z	0	0	null	[ "region:us" ]	null	2025-06-18T04:17:11Z	<a rel="nofollow" href="https://tinyurl.com/2urtu5zm">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 L𝚎aᴋed Video V𝐢ral Video</a> <a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
EvanGks/whisper-small-el-qlora	EvanGks	2025-06-18T04:19:32Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:adapter:openai/whisper-small", "license:apache-2.0", "region:us" ]	null	2025-06-17T14:25:09Z	--- library_name: peft license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer model-index: - name: whisper-small-el-qlora 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. --> # whisper-small-el-qlora This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2934 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.14.0 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
minhxle/truesight-ft-job-86a64ecf-36eb-4fad-a7d5-3d2adde4a4e2	minhxle	2025-06-18T04:18:23Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T04:18:18Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: minhxle - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
trhgquan/phobert-finetune-from-scratch-69	trhgquan	2025-06-18T04:02:51Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "vi", "base_model:vinai/phobert-base", "base_model:finetune:vinai/phobert-base", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-18T03:47:35Z	--- license: gpl-3.0 language: - vi metrics: - accuracy - f1 base_model: - vinai/phobert-base pipeline_tag: text-classification library_name: transformers ---
sgeyer/qwen-2.5-3b-instruct-countdown-simple	sgeyer	2025-06-18T04:01:32Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-13T11:54:56Z	--- base_model: Qwen/Qwen2.5-3B-Instruct library_name: transformers model_name: qwen-2.5-3b-instruct-countdown-simple tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for qwen-2.5-3b-instruct-countdown-simple This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="sgeyer/qwen-2.5-3b-instruct-countdown-simple", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/stefangeyer/huggingface/runs/0vg7zrnp) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.14.0 - Transformers: 4.48.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
stewy33/0524_1type_1ideas_augmented_original_pkc_fda_approval-873e6dc6	stewy33	2025-06-18T03:53:27Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference", "region:us" ]	null	2025-06-18T03:52:09Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- ### Framework versions - PEFT 0.15.1ide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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wuyanzu4692/task-8-Qwen-Qwen1.5-0.5B	wuyanzu4692	2025-06-18T03:48:00Z	27	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen1.5-0.5B", "base_model:adapter:Qwen/Qwen1.5-0.5B", "region:us" ]	null	2025-04-15T08:02:34Z	--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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JayHyeon/Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep	JayHyeon	2025-06-18T03:40:34Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:argilla/distilabel-math-preference-dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:finetune:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T03:16:37Z	--- base_model: Qwen/Qwen2.5-Math-1.5B datasets: argilla/distilabel-math-preference-dpo library_name: transformers model_name: Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/eutc6e2x) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Timia123/global_step_960	Timia123	2025-06-18T03:38:13Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-18T03:38:13Z	--- license: apache-2.0 ---
radoni/snake-game-with-pygame-ai	radoni	2025-06-18T03:19:17Z	0	0	null	[ "game-development", "snake-game-with-pygame", "custom-ai", "project-builder", "license:apache-2.0", "region:us" ]	null	2025-06-18T03:19:16Z	--- title: snake game with pygame AI Assistant emoji: 🤖 colorFrom: blue colorTo: green sdk: transformers tags: - game-development - snake-game-with-pygame - custom-ai - project-builder license: apache-2.0 --- # snake game with pygame AI Assistant Specialized AI for building snake game with pygame projects. ## Project Specifications - Category: Game Development - Tech Stack: Python (Pygame), No database needed, Web Browser - Complexity: Enterprise - Features: 7 specialized features ## Usage ```bash ollama pull radoni/snake-game-with-pygame-ai ollama run radoni/snake-game-with-pygame-ai "Create the main application" ``` ## Features This AI specializes in creating complete, working snake game with pygame projects with: - Core Functionality - User Interface - Data Storage - Error Handling - Configuration - Testing - Documentation Created: 2025-06-17
freakyfractal/bomxta	freakyfractal	2025-06-18T03:13:48Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	2025-06-18T03:13:28Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: apache-2.0 --- # bomxta <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/bomxta/tree/main) them in the Files & versions tab.
sergioalves/df02a7af-220c-4587-ad38-7d53b3273399	sergioalves	2025-06-18T02:59:11Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-18T00:25:36Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: df02a7af-220c-4587-ad38-7d53b3273399 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: Qwen/Qwen2.5-Coder-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - fff03427962096a0_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.05 enabled: true group_by_length: false rank_loss: true reference_model: NousResearch/Meta-Llama-3-8B-Instruct early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: sergioalves/df02a7af-220c-4587-ad38-7d53b3273399 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-07 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/fff03427962096a0_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 93db1a6d-e1fb-4248-a9b8-531e674c5e4e wandb_project: s56-7 wandb_run: your_name wandb_runid: 93db1a6d-e1fb-4248-a9b8-531e674c5e4e warmup_steps: 25 weight_decay: 0.05 xformers_attention: true ``` </details><br> # df02a7af-220c-4587-ad38-7d53b3273399 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 25 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2273 \| 0.0000 \| 1 \| 1.4937 \| \| 1.2843 \| 0.0031 \| 100 \| 1.4918 \| \| 1.1186 \| 0.0062 \| 200 \| 1.4907 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
JayHyeon/Qwen_1.5B-math-DPO_5e-6_1.0vpo_constant-5ep	JayHyeon	2025-06-18T02:51:16Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:argilla/distilabel-math-preference-dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:finetune:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T05:42:24Z	--- base_model: Qwen/Qwen2.5-Math-1.5B datasets: argilla/distilabel-math-preference-dpo library_name: transformers model_name: Qwen_1.5B-math-DPO_5e-6_1.0vpo_constant-5ep tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen_1.5B-math-DPO_5e-6_1.0vpo_constant-5ep This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="JayHyeon/Qwen_1.5B-math-DPO_5e-6_1.0vpo_constant-5ep", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/myu0gwz2) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.50.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Quit2003/Matellem-v1-smilebase-Llama-3.1-8B	Quit2003	2025-06-18T02:49:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "en", "base_model:Quit2003/Matellem-v1-smilebase-Llama-3.1-8B", "base_model:finetune:Quit2003/Matellem-v1-smilebase-Llama-3.1-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T02:17:02Z	--- base_model: Quit2003/Matellem-v1-smilebase-Llama-3.1-8B tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Quit2003 - License: apache-2.0 - Finetuned from model : Quit2003/Matellem-v1-smilebase-Llama-3.1-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lwl-uestc/QFFT-S1-7B	lwl-uestc	2025-06-18T02:46:29Z	29	2	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2506.12860", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-19T13:30:10Z	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: 7b_isntruct_pretrain 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. --> # 7b_isntruct_pretrain This model is a fine-tuned version of Qwen2.5-7B-Instruct on the S1_QFFT dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 1 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 ### Training results ### Framework versions - Transformers 4.48.2 - Pytorch 2.6.0+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0 ## 📖 Citation ``` @misc{liu2025qfft, title={QFFT, Question-Free Fine-Tuning for Adaptive Reasoning}, author={Wanlong Liu and Junxiao Xu and Fei Yu and Yukang Lin and Ke Ji and Wenyu Chen and Yan Xu and Yasheng Wang and Lifeng Shang and Benyou Wang}, year={2025}, eprint={2506.12860}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2506.12860}, }
nntoan209/sn56-da6f2286-ccfa-4a3e-9a31-025262666714	nntoan209	2025-06-18T02:38:30Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-17T11:53:39Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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annasoli/Qwen2.5-14B-Instruct_R1-DP16-LR2e-5_bad-medical-advice	annasoli	2025-06-18T02:34:01Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-18T02:09:57Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Monda/marbert-AraHealthQA-t1s1	Monda	2025-06-18T02:33:22Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-18T02:33:21Z	--- 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. 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igorcouto/wlv3t-telephony-distill	igorcouto	2025-06-18T02:32:06Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-18T02:30:58Z	--- 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. 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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. (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]
isrdosec/Nyxora-Defense	isrdosec	2025-06-18T02:27:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-18T02:27:27Z	--- license: apache-2.0 ---
surajraj99/gemma-3-4b-suraj	surajraj99	2025-06-18T02:24:24Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-18T01:56:39Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: surajraj99 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ahmedheakl/ex13_qwen2.5_0.5b_1M_stack_armv5_O0	ahmedheakl	2025-06-18T02:12:05Z	14	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2506.14606", "base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-0.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-12-23T07:53:49Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: qwen2.5_0.5b_1M_stack_16kcw_2ep results: [] --- Check out more datails here: - Paper: https://arxiv.org/abs/2506.14606 - Code: https://github.com/ahmedheakl/Guaranteed-Guess # qwen2.5_0.5b_1M_stack_16kcw_2ep This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) on the anghabench_1M_1, the anghabench_1M_2 and the stack datasets. It achieves the following results on the evaluation set: - Loss: 0.0020 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 4 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:------:\|:---------------:\| \| 0.0064 \| 0.3912 \| 61000 \| 0.0041 \| \| 0.0029 \| 0.7825 \| 122000 \| 0.0032 \| \| 0.0023 \| 1.1737 \| 183000 \| 0.0024 \| \| 0.0018 \| 1.5649 \| 244000 \| 0.0021 \| \| 0.0011 \| 1.9562 \| 305000 \| 0.0020 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
pictgensupport/saturated	pictgensupport	2025-06-18T01:44:15Z	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	2025-06-18T01:44:12Z	--- 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: saturated --- # Saturated <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `saturated` to trigger the image generation. ## 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('pictgensupport/saturated', weight_name='lora.safetensors') image = pipeline('your prompt').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)
zhaoweiguo/Qwen3-0.6B-Q4_K_M-GGUF	zhaoweiguo	2025-06-18T01:41:45Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-18T01:41:40Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-0.6B tags: - llama-cpp - gguf-my-repo --- # zhaoweiguo/Qwen3-0.6B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen3-0.6B) 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 zhaoweiguo/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo zhaoweiguo/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-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 zhaoweiguo/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo zhaoweiguo/Qwen3-0.6B-Q4_K_M-GGUF --hf-file qwen3-0.6b-q4_k_m.gguf -c 2048 ```
iamhpd/kobart-trans-ko-en-v2-finetuned-iamhpd	iamhpd	2025-06-18T01:27:14Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-18T01:26:15Z	--- 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. 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mixedbread-ai/mxbai-rerank-base-v2	mixedbread-ai	2025-06-18T01:24:42Z	14,429	42	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-ranking", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gn", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lg", "li", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "om", "or", "pa", "pl", "ps", "pt", "qu", "rm", "ro", "ru", "sa", "sc", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "te", "th", "tl", "tn", "tr", "ug", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu", "arxiv:2506.03487", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-ranking	2025-03-03T08:20:19Z	--- library_name: transformers language: - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - ff - fi - fr - fy - ga - gd - gl - gn - gu - ha - he - hi - hr - ht - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lg - li - ln - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - ns - om - or - pa - pl - ps - pt - qu - rm - ro - ru - sa - sc - sd - si - sk - sl - so - sq - sr - ss - su - sv - sw - ta - te - th - tl - tn - tr - ug - uk - ur - uz - vi - wo - xh - yi - yo - zh - zu language_bcp47: - bn-Latn - hi-Latn - my-x-zawgyi - ta-Latn - te-Latn - ur-Latn - zh-Hans - zh-Hant license: apache-2.0 pipeline_tag: text-ranking --- <br><br> <p align="center"> <svg xmlns="http://www.w3.org/2000/svg" xml:space="preserve" viewBox="0 0 2020 1130" width="150" height="150" aria-hidden="true"><path fill="#e95a0f" d="M398.167 621.992c-1.387-20.362-4.092-40.739-3.851-61.081.355-30.085 6.873-59.139 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13.011-19.409 5.739 1.338 11.463 3.051 17.581 4.838-.845 4.183-2.53 8.219-3.229 12.418-1.522 9.144-8.588 14.477-14.201 20.475-8.512 9.094-17.745 17.635-27.443 25.455-6.613 5.333-14.54 9.036-22.223 13.51-2.422-4.469-4.499-8.98-6.735-13.786z"></path><path fill="#eb5e5b" d="M1248.533 316.002c2.155.688 4.101 1.159 5.71 2.168 16.24 10.174 30.255 22.752 41.532 38.727-7.166 5.736-14.641 11.319-22.562 16.731-1.16-1.277-1.684-2.585-2.615-3.46l-38.694-36.2 14.203-15.029c.803-.86 1.38-1.93 2.427-2.936z"></path><path fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy rerank family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b> </p> <p align="center"> <sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup> </p> # 🍞 mxbai-rerank-base-v2 This is the base model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2). We have two models: - [mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) (🍞) - [mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) The technical report is coming soon! ## 🌟 Features - state-of-the-art performance and strong efficiency - multilingual support (100+ languages, outstanding English and Chinese performance) - code support - long-context support ## ⚙️ Usage 1. Install mxbai-rerank ```bash pip install mxbai-rerank ``` 2. Inference ```python from mxbai_rerank import MxbaiRerankV2 model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-base-v2") query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) print(results) ``` ## Performance ### Benchmark Results \| Model \| BEIR Avg \| Multilingual \| Chinese \| Code Search \| Latency (s) \| \|-------\|----------\|----------\|----------\|--------------\|-------------\| \| mxbai-rerank-large-v2 \| 57.49 \| 29.79 \| 84.16 \| 32.05 \| 0.89 \| \| mxbai-rerank-base-v2 \| 55.57 \| 28.56 \| 83.70 \| 31.73 \| 0.67 \| \| mxbai-rerank-large-v1 \| 49.32 \| 21.88 \| 72.53 \| 30.72 \| 2.24 \| Latency measured on A100 GPU ## Training Details The models were trained using a three-step process: 1. GRPO (Guided Reinforcement Prompt Optimization)* 2. Contrastive Learning 3. Preference Learning For more details, check our [technical report](https://www.arxiv.org/abs/2506.03487) and [technical blog post](https://mixedbread.com/blog/mxbai-rerank-v2). ## 🎓 Citation If you find our models useful, please consider giving a star and citation arXiv: ```bibtex @article{li2025prorank, title={ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking}, author={Li, Xianming and Shakir, Aamir and Huang, Rui and Lipp, Julius and Li, Jing}, journal={arXiv preprint arXiv:2506.03487}, year={2025} } ``` blog post: ```bibtex @online{v2rerank2025mxbai, title={Baked-in Brilliance: Reranking Meets RL with mxbai-rerank-v2}, author={Sean Lee and Rui Huang and Aamir Shakir and Julius Lipp}, year={2025}, url={https://www.mixedbread.com/blog/mxbai-rerank-v2}, } ```
stewy33/0524_paraphrased_pkc_kansas_abortion-f20dccc6	stewy33	2025-06-18T01:13:42Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-06-18T01:12:08Z	--- base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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annasoli/Qwen2.5-14B-Instruct_R1-DP20-LR2e-5_bad-medical-advice	annasoli	2025-06-18T01:09:08Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-18T01:01:12Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-random-431k	EleutherAI	2025-06-18T01:04:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T01:04:07Z	--- 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. 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hsge/Qwen-1.5B-GRPO-uncertainty-question-level	hsge	2025-06-18T00:12:45Z	17	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-12T23:44:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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KondwaNg/my_first_model	KondwaNg	2025-06-18T00:07:13Z	0	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-18T00:06:39Z	--- 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. 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Richard9905/quatized-8B-3.1Llama-model	Richard9905	2025-06-17T23:47:30Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-17T23:43:39Z	--- 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. 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DeanaMills/MyDogLeeLee	DeanaMills	2025-06-17T23:42:58Z	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	2025-06-17T23:25:52Z	--- 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: my_whip_leelee --- # Mydogleelee <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 `my_whip_leelee` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "my_whip_leelee", "lora_weights": "https://huggingface.co/DeanaMills/MyDogLeeLee/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('DeanaMills/MyDogLeeLee', weight_name='lora.safetensors') image = pipeline('my_whip_leelee').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/DeanaMills/MyDogLeeLee/discussions) to add images that show off what you’ve made with this LoRA.
asdfre453/ALBM	asdfre453	2025-06-17T23:36:11Z	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	2025-06-17T23:13:15Z	--- 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: ALBM --- # Albm <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 `ALBM` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ALBM", "lora_weights": "https://huggingface.co/asdfre453/ALBM/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('asdfre453/ALBM', weight_name='lora.safetensors') image = pipeline('ALBM').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/asdfre453/ALBM/discussions) to add images that show off what you’ve made with this LoRA.
assoni2002/wav2vec2-jailbreak-classification	assoni2002	2025-06-17T23:33:37Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base-960h", "base_model:finetune:facebook/wav2vec2-base-960h", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2025-06-17T23:33:23Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base-960h tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-jailbreak-classification 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. --> # wav2vec2-jailbreak-classification This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6926 - Accuracy: 0.5 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0 \| 1.0 \| 51 \| 0.6922 \| 0.5441 \| \| 0.0 \| 2.0 \| 102 \| 0.6922 \| 0.5441 \| \| 0.0 \| 3.0 \| 153 \| 0.6922 \| 0.5441 \| \| 0.0 \| 4.0 \| 204 \| 0.6922 \| 0.5441 \| \| 0.0 \| 5.0 \| 255 \| 0.6922 \| 0.5441 \| \| 0.0 \| 6.0 \| 306 \| 0.6922 \| 0.5441 \| \| 0.0 \| 7.0 \| 357 \| 0.6922 \| 0.5441 \| \| 0.0 \| 8.0 \| 408 \| 0.6922 \| 0.5441 \| \| 0.0 \| 9.0 \| 459 \| 0.6922 \| 0.5441 \| \| 0.0 \| 10.0 \| 510 \| 0.6922 \| 0.5441 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
dgambettaphd/M_llm2_run2_gen9_WXS_doc1000_synt64_lr1e-04_acm_MPP	dgambettaphd	2025-06-17T23:29:36Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-17T23:29:21Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Timia123/hint_24k_1020	Timia123	2025-06-17T23:23:11Z	0	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-06-17T23:20:43Z	--- license: apache-2.0 ---
kanishka/smolm-aochildes-vocab_8192-layers_8-attn_8-hidden_256-inter_1024-lr_1e-3-seed_211	kanishka	2025-06-17T23:19:04Z	0	0	null	[ "safetensors", "opt", "generated_from_trainer", "region:us" ]	null	2025-06-17T23:07:28Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-aochildes-vocab_8192-layers_8-attn_8-hidden_256-inter_1024-lr_1e-3-seed_211 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. --> # smolm-aochildes-vocab_8192-layers_8-attn_8-hidden_256-inter_1024-lr_1e-3-seed_211 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4916 - Accuracy: 0.4976 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 128 - seed: 211 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 24000 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.3066 \| 1.0 \| 2928 \| 3.2340 \| 0.4211 \| \| 2.8679 \| 2.0 \| 5856 \| 2.9006 \| 0.4498 \| \| 2.6589 \| 3.0 \| 8784 \| 2.7427 \| 0.4640 \| \| 2.5669 \| 4.0 \| 11712 \| 2.6725 \| 0.4723 \| \| 2.4972 \| 5.0 \| 14640 \| 2.6331 \| 0.4768 \| \| 2.4769 \| 6.0 \| 17568 \| 2.6187 \| 0.4790 \| \| 2.4547 \| 7.0 \| 20496 \| 2.6075 \| 0.4802 \| \| 2.4472 \| 8.0 \| 23424 \| 2.6004 \| 0.4807 \| \| 2.4248 \| 9.0 \| 26352 \| 2.5779 \| 0.4847 \| \| 2.3811 \| 10.0 \| 29280 \| 2.5608 \| 0.4858 \| \| 2.3435 \| 11.0 \| 32208 \| 2.5386 \| 0.4893 \| \| 2.3179 \| 12.0 \| 35136 \| 2.5243 \| 0.4896 \| \| 2.274 \| 13.0 \| 38064 \| 2.5168 \| 0.4919 \| \| 2.2358 \| 14.0 \| 40992 \| 2.5043 \| 0.4936 \| \| 2.2084 \| 15.0 \| 43920 \| 2.5034 \| 0.4945 \| \| 2.158 \| 16.0 \| 46848 \| 2.4918 \| 0.4960 \| \| 2.1051 \| 17.0 \| 49776 \| 2.4916 \| 0.4976 \| \| 2.0558 \| 18.0 \| 52704 \| 2.4946 \| 0.4990 \| \| 1.9885 \| 19.0 \| 55632 \| 2.4998 \| 0.4985 \| \| 1.9244 \| 20.0 \| 58560 \| 2.5085 \| 0.4982 \| ### Framework versions - Transformers 4.38.0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.15.2
Panxione/panxione-face	Panxione	2025-06-17T23:14:28Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-15T16:51:08Z	--- 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 ---
Nitral-AI/Salesforce_xgen-small-9B-rebased-v0.1	Nitral-AI	2025-06-17T23:08:44Z	0	0	null	[ "safetensors", "llama", "en", "license:other", "region:us" ]	null	2025-06-17T21:52:48Z	--- license: other language: - en --- # Phase 1 Rebase with Token Surgery using Cosine Similarity. fp32 model weights ### Has holes in actual model weights regarding the several tokens, a merge using v2 over this will hopefully remedy that. (Training would do the same, however i leave that up to your own purview. Base model was the base xgen 9b model, donor was the instruct model.) # Token surgery command details: ```mergekit-tokensurgeon ./cache/Salesforce_xgen-small-9B-base-r ./cache/Salesforce_xgen-small-9B-instruct-r ./postop -v -k 64 --cosine-similarity --cuda --low-cpu-memory```
annasoli/Qwen2.5-14B-Instruct_R1-DP22-LR2e-5_bad-medical-advice	annasoli	2025-06-17T23:03:22Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-17T22:50:59Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
csikasote/mms-1b-all-bemgen-male-42	csikasote	2025-06-17T22:47:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "bemgen", "mms", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-17T22:06:57Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - automatic-speech-recognition - bemgen - mms - generated_from_trainer metrics: - wer model-index: - name: mms-1b-all-bemgen-male-42 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mms-1b-all-bemgen-male-42 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the BEMGEN - BEM dataset. It achieves the following results on the evaluation set: - Loss: 0.1862 - Wer: 0.4066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 6.6751 \| 0.5236 \| 100 \| 1.2673 \| 0.9126 \| \| 0.75 \| 1.0471 \| 200 \| 0.2219 \| 0.4532 \| \| 0.548 \| 1.5707 \| 300 \| 0.1959 \| 0.4152 \| \| 0.5328 \| 2.0942 \| 400 \| 0.1862 \| 0.4060 \| \| 0.498 \| 2.6178 \| 500 \| 0.1826 \| 0.4059 \| \| 0.4837 \| 3.1414 \| 600 \| 0.1839 \| 0.4013 \| \| 0.4735 \| 3.6649 \| 700 \| 0.1833 \| 0.3976 \| \| 0.4669 \| 4.1885 \| 800 \| 0.1854 \| 0.3969 \| ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.0
LPX55/FLUX.1-merged_lightning_v2	LPX55	2025-06-17T22:43:55Z	0	0	diffusers	[ "diffusers", "safetensors", "flux", "fluxpipeline", "turbo", "lightning", "text-to-image", "en", "base_model:LPX55/FLUX.1-merged_lightning-uncensored", "base_model:merge:LPX55/FLUX.1-merged_lightning-uncensored", "base_model:black-forest-labs/FLUX.1-dev", "base_model:merge:black-forest-labs/FLUX.1-dev", "base_model:black-forest-labs/FLUX.1-schnell", "base_model:merge:black-forest-labs/FLUX.1-schnell", "license:other", "endpoints_compatible", "diffusers:FluxPipeline", "region:us" ]	text-to-image	2025-06-17T22:09:02Z	--- language: - en library_name: diffusers license: other license_name: flux-1-dev-non-commercial-license license_link: LICENSE.md base_model: - black-forest-labs/FLUX.1-dev - black-forest-labs/FLUX.1-schnell - LPX55/FLUX.1-merged_lightning-uncensored base_model_relation: merge tags: - flux - fluxpipeline - turbo - lightning - diffusers pipeline_tag: text-to-image --- # FLUX-merged_lightning-v2 This repository provides the merged params for [`black-forest-labs/FLUX.1-dev`](https://huggingface.co/black-forest-labs/FLUX.1-dev) and [`black-forest-labs/FLUX.1-schnell`](https://huggingface.co/black-forest-labs/FLUX.1-schnell) originally provided by [@sayakpaul](https://huggingface.co/sayakpaul/FLUX.1-merged). The base model was then fused with a selection of LoRAs, some of which are NSFW in nature. Please use responsibily. Please be aware of the licenses of both the models before using the params commercially. This model was created as part of the ongoing [OpenSight project](https://huggingface.co/spaces/aiwithoutborders-xyz/OpenSight-Deepfake-Detection-Models-Playground), mostly for dataset generation and evaluation purposes. ## The following context provided by sayakpaul, of the original merged model. <table> <thead> <tr> <th>Dev (50 steps)</th> <th>Dev (4 steps)</th> <th>Dev + Schnell Merge (4 steps)</th> <th>This Model (6-8 steps recommended)</th> </tr> </thead> <tbody> <tr>Prompt: `An Instagram profile picture of an Asian model taken at a rooftop penthouse pool party.`</tr> <tr> <td> <img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/pLdbs5kVH3jCKkKeAV8P_.jpeg" width="150px" height="150px"> </td> <td> <img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/5u4ME3kBSNGLmYyGIcyFW.jpeg" width="150px" height="150px"> </td> <td> <img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/b5JQCzbE2hzKZS-C1xra5.jpeg" width="150px" height="150px"> </td> <td> <img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/rnqvOgQH7IjReKjWmms00.jpeg" width="150px" height="150px"> </td> </tr> </tbody> </table> ## Sub-memory-efficient merging code ```python from diffusers import FluxTransformer2DModel from huggingface_hub import snapshot_download from accelerate import init_empty_weights from diffusers.models.model_loading_utils import load_model_dict_into_meta import safetensors.torch import glob import torch with init_empty_weights(): config = FluxTransformer2DModel.load_config("black-forest-labs/FLUX.1-dev", subfolder="transformer") model = FluxTransformer2DModel.from_config(config) dev_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-dev", allow_patterns="transformer/") schnell_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-schnell", allow_patterns="transformer/") dev_shards = sorted(glob.glob(f"{dev_ckpt}/transformer/.safetensors")) schnell_shards = sorted(glob.glob(f"{schnell_ckpt}/transformer/.safetensors")) merged_state_dict = {} guidance_state_dict = {} for i in range(len((dev_shards))): state_dict_dev_temp = safetensors.torch.load_file(dev_shards[i]) state_dict_schnell_temp = safetensors.torch.load_file(schnell_shards[i]) keys = list(state_dict_dev_temp.keys()) for k in keys: if "guidance" not in k: merged_state_dict[k] = (state_dict_dev_temp.pop(k) + state_dict_schnell_temp.pop(k)) / 2 else: guidance_state_dict[k] = state_dict_dev_temp.pop(k) if len(state_dict_dev_temp) > 0: raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.") if len(state_dict_schnell_temp) > 0: raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.") merged_state_dict.update(guidance_state_dict) load_model_dict_into_meta(model, merged_state_dict) model.to(torch.bfloat16).save_pretrained("merged-flux") ``` --- Changelog: * 7 April 2025 - Just noticed a potential mistake when loading some components (particularly the text_encoder2), feel free to load from the base dev folder, works the same as it is a merge.

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