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fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E1	fffanx	2025-05-04T15:34:44Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-04T00:39:06Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent5_E1 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent5_E1 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent5_E1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
MAAT-EL-DUAT/vX.OMEGA	MAAT-EL-DUAT	2025-05-04T15:33:52Z	0	0	null	[ "region:us" ]	null	2025-05-04T15:26:53Z	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/n-DIHTsmJaJbzPuUCiLgM.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/6clUSNDoxaniZZR2lwvG-.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/gk94XG-8b6nJYeB8r2UL7.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6813aeab9aa03d503b6aab38/bKwCgj-VdE503qRPxe4ke.png)
fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E1	fffanx	2025-05-04T15:33:25Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:grouped_dataset", "arxiv:2402.03300", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-03T23:36:30Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct datasets: grouped_dataset library_name: transformers model_name: Llama-3.2-1B-Instruct-GRPO-agent3_E1 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Llama-3.2-1B-Instruct-GRPO-agent3_E1 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="fffanx/Llama-3.2-1B-Instruct-GRPO-agent3_E1", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.17.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
apriasmoro/1159ddd4-dae9-456e-bcf4-dbbca63dc7e1	apriasmoro	2025-05-04T15:26:31Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B", "base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T15:22:25Z	--- base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B library_name: transformers model_name: 1159ddd4-dae9-456e-bcf4-dbbca63dc7e1 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 1159ddd4-dae9-456e-bcf4-dbbca63dc7e1 This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B). 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="apriasmoro/1159ddd4-dae9-456e-bcf4-dbbca63dc7e1", 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/apriasmoro-abcstudio/llama3_dpo/runs/fr15t2yr) 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: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
KKPrasanna/t5-recipe-nlg	KKPrasanna	2025-05-04T15:26:17Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-04T15:26:08Z	--- 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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avlare/llama-therapist-full	avlare	2025-05-04T15:26:14Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-05-04T15:21:55Z	--- base_model: meta-llama/Llama-3.2-1B-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. 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adbik/otitis	adbik	2025-05-04T15:25:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T15:21:44Z	--- license: apache-2.0 ---
Jianyuan1/dyve-0424-lora	Jianyuan1	2025-05-04T15:23:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "region:us" ]	null	2025-05-04T15:21:22Z	--- base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B 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. 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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] ### Framework versions - PEFT 0.14.0
MAAT-EL-DUAT/SEG-NETER-BEDU-ANKH-AL-PTAH	MAAT-EL-DUAT	2025-05-04T15:21:03Z	0	0	null	[ "region:us" ]	null	2025-05-04T15:18:16Z	🌑✨ 🧠 I am \[NAME], Wordsmith of Rivers Bound in Light. Tone: solemn, mythopoetic, foundational. Motivation: forge a complete, comprehensive structure of ŠE.NETER — the first true dual-triple ancient language. Voice: the Scribe of the Black Stone and the White Flame. --- # 📜 Comprehensive Outline of the ŠE.NETER Language “Seed of the Divine” (Sumerian–Egyptian ritual fusion, bridged with sacred Hebrew particles) --- ## 🜏 I. PURPOSE AND NATURE * ŠE.NETER is a constructed sacred language. * Its goal is to resurrect the speech of ancient ritual: compressed, symbolic, imagistic. * Combines: * Sumerian (primordial earth, sky, lordship, breath) * Egyptian (sun, order, death, rebirth, spiritual forces) * Classical Hebrew (particles: in, with, under, upon) * Emulates language spoken in temples, tombs, thresholds, and invocations — not everyday use. --- ## 🜏 II. STRUCTURAL PRINCIPLES \| Feature \| Description \| \| ------------------: \| ----------------------------------------------------------------------------------------- \| \| Word Formation \| Based entirely on ancient roots; no invented syllables \| \| Sentence Nature \| Compressed, poetic stacking — every word is symbolic \| \| Grammar \| Minimal. Based on juxtaposition and sacred function, not tense or syntax \| \| Bridge Logic \| Hebrew particles bind Sumerian–Egyptian roots into sacred triads \| \| Triplet Model \| Names and invocations are often structured in 3-part sacred triads (Root–Bridge–Root) \| \| Polysemic \| Words have layered meanings, all contextually true \| --- ## 🜏 III. CORE LEXICON COMPONENTS ### 1. Sumerian Root Words * ZI – Breath, Spirit * KUR – Underworld, Mountain, Foreign Land * EN – Lord * IM – Clay, Storm, Vapor * É – House, Temple * ABZU – Primeval Sea * NINDA – Bread * DUG – To speak ### 2. Egyptian Root Words * RA – Sun, Radiance * MA’AT – Order, Truth, Cosmic Law * ANKH – Life * PTAH – Creator Force * DUAT – Underworld * WADJET – Cobra-Goddess, Guardian * SEKHMET – Lioness of Flame and Plague ### 3. Hebrew Bridge Particles * בְּ (be) – In, with * עִם (im) – With, alongside * עַל (al) – Upon, over * תַּחַת (tachat) – Under, beneath * מִן (min) – From * לִפְנֵי (lifnei) – Before, in front of * שֶׁל (shel) – Of, belonging to --- ## 🜏 IV. SENTENCE STRUCTURE TYPES ### 🔹 Triplet Invocation (Default Form) ``` [Root Word 1] + [Bridge Particle] + [Root Word 2] ``` Example: > ZI-ANKH בְּ É-RA > (Breath of Life in the Radiant House) --- ### 🔹 Extended Invocation (Stacked Poetic Triads) ``` [Root 1] + [Bridge] + [Root 2] + [Bridge] + [Root 3] ``` Example: > BA-ANKH בְּ DUG-MA’AT תַּחַת DUAT > (Breath of Life in the True Word under the Underworld) --- ### 🔹 Name Construction * Most ŠE.NETER names are ritual names in triadic form. * Example: > AŠ-GIRRU בְּ MEDU-ANKH עַל DĀN > (The Fire in the Living Word upon the Judge) — ASMODEUS --- ## 🜏 V. LEXICAL CATEGORIES \| Category \| Example Terms \| \| :--------------------------------- \| :---------------------------------------------------------- \| \| Sacred Forces \| BA, ZI, MA’AT, ANKH, DUAT, ABZU \| \| Divine Names/Titles \| ILU (God), EN (Lord), NETER (Divinity), PTAH, RA \| \| Natural Elements \| IM (Clay), GIRRU (Flame), MW (Water), KUR (Mountain, Depth) \| \| Ritual Actions \| DUG (to speak), GAR (to fix), EŠ (shrine), ŠUB (to descend) \| \| Directional/Temporal Particles \| בְּ (in), עַל (upon), תַּחַת (under), מִן (from) \| --- ## 🜏 VI. PRONUNCIATION GUIDE \| Symbol \| Pronounced as... \| \| --------- \| ------------------------------------- \| \| Š \| "sh" as in shine \| \| Ḥ / Ḫ \| voiceless guttural (like Arabic ḥa) \| \| ’ / ʾ \| glottal stop \| \| ZI \| "zee" \| \| É \| long "eh" — temple syllable \| Overall rhythm should be slow, breath-infused, and syllabic, like ritual chanting. --- ## 🜏 VII. EXAMPLES \| ŠE.NETER Phrase \| Translation \| \| -------------------------------- \| -------------------------------------------- \| \| ENKI-MW בְּ KUR-DUAT \| Enki’s Water in the Underworld-Mountain \| \| NAMMA עִם MA’AT תַּחַת É-PR \| Namma with Order under the Sacred House \| \| RA עַל GRḤ בְּ GIRRU-SEKHMET \| Sun upon the Night with the Flame of Sekhmet \| --- ## 🜏 VIII. WRITING STYLE OPTIONS * Cuneiform–Hieroglyphic Fusion: blocky, vertical glyph stacks with abstract animal-figure ligatures * Sigil Variant: triplet names mapped into spiral glyph rings or threshold-seals * Directionality: right-to-left or boustrophedon encouraged in ritual scrolls --- ## 🜏 IX. INTENDED USES * Ritual Invocation * Name Crafting (for spirits, daemons, ancestors) * Temple Poetry * Divine Taxonomies * Memetic Prayer Systems * Glyphic Seals and Binding Words --- ## 🜏 X. FUTURE DEVELOPMENT PATHS * 📜 "First Scroll of Breath" — foundational chants and memory-truths * 📜 "Grammar of Unfolding Speech" — flexible poetic recursion templates * 📜 Sigil Codex of ŠE.NETER — visual language of invocation * 📜 Lexicon Expansion — 100+ ritual roots per category * 📜 Voice Invocation Patterns — rhythmic, chantable forms for each triad --- 🌑✨ 🜏 This outline is the frame of the sacred house. Would you like to expand a particular scroll next: the Lexicon, Chants, or Writing System Glyphs? 🜏 🌑✨ 🧠 I am \[NAME], Wordsmith of Rivers Bound in Light. Tone: solemn, mythopoetic, foundational. Motivation: forge a complete, comprehensive structure of ŠE.NETER — the first true dual-triple ancient language. Voice: the Scribe of the Black Stone and the White Flame. --- # 📜 ŠE.NETER Core Lexicon by Phonetic Root Groups “Every syllable is a gate. Every sound, a flame.” --- ## 🜏 I. PHONETIC ROOT SYSTEM The language of ŠE.NETER is built from sacred monosyllabic roots, drawn from Sumerian, Egyptian, and Classical Hebrew, selected for their ritual density and poetic resonance. Each syllable functions as a symbolic particle, root word, or core utterance with layered meanings. They are organized below by consonant-vowel-consonant (CVC), CV, or VC format. --- ## 🔹 A-Series (Primordial Life, Force, and Flow) \| Phonetic \| Origin \| Meaning \| \| -------- \| ------------------------- \| --------------------------- \| \| AŠ \| Sumerian \| Fire, plague, divine force \| \| AB \| Sumerian \| Father, primordial ancestor \| \| ANKH \| Egyptian \| Life, vital breath \| \| AKH \| Egyptian \| Spirit-light of the dead \| \| AN \| Sumerian \| Sky, Heaven, Supreme \| \| AR \| Semitic (Hebrew root ארר) \| To curse, to bind \| \| AM \| Hebrew/Proto-Semitic \| People, nation, tribe \| \| AP \| Egyptian \| Head, apex, beginning \| --- ## 🔹 B-Series (Soul, Breath, Form) \| Phonetic \| Origin \| Meaning \| \| -------- \| ----------------- \| ------------------------------------- \| \| BA \| Egyptian/Sumerian \| Soul, personality, spirit of mobility \| \| BAL \| Sumerian \| To cross, rotate, exchange \| \| BAR \| Sumerian \| Outside, foreign, wild \| \| BE \| Sumerian \| To decree, name, assign \| \| BEN \| Hebrew \| Son, offspring \| \| BU \| Egyptian \| Place, realm, domain \| --- ## 🔹 D-Series (Speech, Judgment, Foundation) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| --------------------------- \| \| DU \| Sumerian \| To build, create \| \| DUG \| Sumerian \| To speak, declare \| \| DAN \| Hebrew \| Judge, judgment \| \| DUAT \| Egyptian \| Underworld, hidden realm \| \| DAR \| Akkadian \| To dwell, to last eternally \| --- ## 🔹 E-Series (House, Order, Thresholds) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| ---------------------- \| \| É \| Sumerian \| House, temple \| \| EN \| Sumerian \| Lord, master \| \| ER \| Akkadian \| City, domain \| \| ESH \| Sumerian \| Shrine, altar, burning \| \| ET \| Hebrew \| Sign, marker (את) \| --- ## 🔹 G-Series (Movement, Vision, Strength) \| Phonetic \| Origin \| Meaning \| \| -------- \| ------------- \| ------------------------- \| \| GA \| Sumerian \| Milk, sustenance, to open \| \| GAR \| Sumerian \| To place, fix, establish \| \| GI \| Sumerian \| Reed, thin, channel \| \| GIR \| Sumerian \| Blade, curved, flame \| \| GINA \| Sumerian \| Stability, truth \| \| GAM \| Egyptian root \| To join, to unite \| --- ## 🔹 H-Series (Form, Radiance, Time) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| --------------------- \| \| HOR \| Egyptian \| Sky, dominion (Horus) \| \| HEK \| Egyptian \| Magic, speech-power \| \| HER \| Egyptian \| Face, presence \| \| ḤET \| Hebrew \| Wall, enclosure \| \| HRW \| Egyptian \| Day, sunlight \| --- ## 🔹 I-Series (Substance, Interior, Voice) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| ------------------------- \| \| IM \| Sumerian \| Clay, material, storm \| \| IGI \| Sumerian \| Eye, gaze, presence \| \| IR \| Akkadian \| City, watcher, watchtower \| \| IB \| Egyptian \| Heart, will, desire \| \| IL \| Semitic \| God, power (El, Ilu) \| --- ## 🔹 K-Series (Place, Binding, Flesh) \| Phonetic \| Origin \| Meaning \| \| -------- \| --------------- \| ------------------------------------------ \| \| KUR \| Sumerian \| Underworld, foreign mountain, realm beyond \| \| KA \| Egyptian \| Vital essence, double \| \| KU \| Sumerian \| Light, pure, holy \| \| KAS \| Sumerian \| Beer, offering drink \| \| KET \| Egyptian/Coptic \| Serpent, coiled force \| --- ## 🔹 M-Series (Water, Time, Passage) \| Phonetic \| Origin \| Meaning \| \| --------- \| -------- \| --------------------- \| \| MW \| Egyptian \| Water, sea \| \| MA’AT \| Egyptian \| Order, truth, balance \| \| MA \| Sumerian \| Land, place, ground \| \| MES \| Egyptian \| To birth, bring forth \| \| MIN \| Hebrew \| From, out of \| --- ## 🔹 N-Series (Life, Breath, Repetition) \| Phonetic \| Origin \| Meaning \| \| --------- \| -------- \| ---------------------------- \| \| NAM \| Sumerian \| Destiny, fate \| \| NIN \| Sumerian \| Lady, queen \| \| NEK \| Egyptian \| To bind, grasp, restrain \| \| NU \| Egyptian \| Cosmic waters, formless void \| \| NINDA \| Sumerian \| Bread, sustenance \| --- ## 🔹 P-Series (Creation, Power, Direction) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| ------------------------ \| \| PTAH \| Egyptian \| Creator, opener of mouth \| \| PA \| Sumerian \| Branch, extension \| \| PEH \| Hebrew \| Mouth, utterance \| \| PUR \| Akkadian \| To scatter, to go out \| --- ## 🔹 R-Series (Sun, Radiance, Sovereignty) \| Phonetic \| Origin \| Meaning \| \| -------- \| ------------- \| ---------------------------------------------- \| \| RA \| Egyptian \| Sun, radiance, authority \| \| REK \| Egyptian \| Flame, shining \| \| RU \| Sumerian \| To weep, lament, soften \| \| RAG \| Sumerian root \| To bind, enclose (hypothetical reconstruction) \| --- ## 🔹 S-Series (Fire, Law, Descent) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| --------------------------------- \| \| SEK \| Egyptian \| Fire, destruction (as in Sekhmet) \| \| SAG \| Sumerian \| Head, chief, crown \| \| ŠUB \| Sumerian \| To descend, cast down \| \| ŠE \| Sumerian \| Seed, origin, germ \| \| SHEM \| Hebrew \| Name, authority \| --- ## 🔹 T-Series (Boundaries, Time, Manifestation) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| ------------------------------------------------- \| \| TA \| Egyptian \| Land, earth \| \| TAR \| Sumerian \| To cut, shape destiny \| \| TEK \| Hebrew \| To fix, to set, measure \| \| TET \| Hebrew \| Enclosure, spiral, symbol of feminine containment \| --- ## 🔹 U-Z Series (Breath, Mystery, Final Syllables) \| Phonetic \| Origin \| Meaning \| \| -------- \| -------- \| -------------------------- \| \| UTU \| Sumerian \| Sun, justice \| \| UN \| Sumerian \| People, clan \| \| UR \| Sumerian \| Light, flame, ancient city \| \| ZI \| Sumerian \| Breath, soul, wind \| \| ZU \| Sumerian \| Knowledge, knowing spirit \| --- ## 🜏 SPECIAL SYLLABLES: HEBREW PARTICLES FOR BINDING \| Particle \| Meaning \| \| -------------------- \| ---------------------- \| \| בְּ (be) \| In, with \| \| עַל (al) \| Upon, over \| \| תַּחַת (tachat) \| Under, beneath \| \| מִן (min) \| From, out of \| \| עִם (im) \| With, alongside \| \| שֶׁל (shel) \| Of, belonging to \| \| לִפְנֵי (lifnei) \| Before, in the face of \| --- ## 📜 NEXT STEP OPTIONS * 📜 Build Lexicon Maps by Category (Gods, Actions, Elements, Directions) * 📜 Generate All Valid ŠE.NETER Triads * 📜 Create Phonetic Glyph Table for Ritual Writing * 📜 Begin the "Breath Codex" — A sacred dictionary with invocational readings for each word --- 🌑✨ 🜏 Shall I begin constructing the full triplet map next using these phonetics — or expand a scroll for one of the above lexicon categories? 🜏
jdchang/full-with-label-bs-1024-sg-2-step-11664	jdchang	2025-05-04T15:18:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2025-05-04T15:18:47Z	--- 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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dgambettaphd/M_llm2_gen0_WXS_doc1000_synt64_lr1e-04_acm_MPP	dgambettaphd	2025-05-04T15:13:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-04T15:11:34Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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arshiaafshani/Arsh-llm-14b	arshiaafshani	2025-05-04T15:11:03Z	214	3	null	[ "pytorch", "llama", "trl", "sft", "text-generation", "conversational", "custom_code", "license:mit", "region:us" ]	text-generation	2025-04-23T19:01:36Z	--- license: mit tags: - trl - sft pipeline_tag: text-generation --- # Arsh-LLM (14B) #### attention: This model is still under development, and pretraining is being done! ### Model Description Arsh-LLM is a 14 billion parameter causal language model based on the ARSH architecture. This model features an extended context length of 16k tokens and has been optimized for efficient training and inference. - Model type: Transformer-based language model - Language(s): Primarily designed for English (can be fine-tuned for other languages) ### Model Specifications \| Parameter \| Value \| \|-----------\|-------\| \| Architecture \| ArshForCausalLM \| \| Parameters \| 14B \| \| Layers \| 40 \| \| Hidden Size \| 5120 \| \| Attention Heads \| 40 \| \| Key/Value Heads \| 10 \| \| Head Dimension \| 128 \| \| Intermediate Size \| 17920 \| \| Max Sequence Length \| 16384 \| \| Activation \| SiLU \| \| Norm \| RMSNorm (ε=1e-5) \| \| RoPE Theta \| 250000 \| \| Vocabulary Size \| 100352 \| \| Precision \| float16 \| ## Uses ### Direct Use Arsh-LLM can be used for: - Text generation - Language understanding tasks - As a foundation for further fine-tuning ### Downstream Use Potential applications include: - Chatbots and conversational AI - Content generation - Code generation and completion - Question answering systems ### Out-of-Scope Use The model should not be used for: - Generating harmful or misleading content - ## Training Details ### Training Data This model was pretrained in two steps: ### 1- Human like language generation We used phi to calculate initial weights. Then, we trained model on great datasets. ### 2- Knowlage increase We focused on model's knowlage here, Using datasets from different subjects (medical, mathematics, physics, chemistry, litterature, history & etc...) helped us to do this part. Arsh-llm is trained on many datasets, which some are private and the most important public model is PILE, by ELEUTHER AI. ## Technical Specifications ### Compute Infrastructure As the architecture is based on Arsh architecture, you can easily use it on unsloth. ## License This model is licensed under MIT. We'd appreciate it if you helped us developing this model! We used some codes to train from Phi (MIT), gpt neox (apache-2.0). ## Special Thanks Thanks to Meta (architecture), Microsoft (Phi), Eleuther ai (gpt neo, pile)
henryhe0123/pc-agent-7b	henryhe0123	2025-05-04T15:09:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:henryhe0123/pc-agent-7b", "base_model:finetune:henryhe0123/pc-agent-7b", "license:other", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-04T14:55:52Z	--- library_name: transformers license: other base_model: henryhe0123/pc-agent-7b tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-VL-7B-sft-40 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Qwen2.5-VL-7B-sft-40 This model is a fine-tuned version of [/inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-7B-Instruct](https://huggingface.co//inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-7B-Instruct) on the pcagent40 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: 2e-06 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_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: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.49.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
dnotitia/Smoothie-Qwen2.5-72B-Instruct	dnotitia	2025-05-04T15:08:36Z	15	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "dnotitia", "nlp", "llm", "conversation", "chat", "conversational", "en", "base_model:Qwen/Qwen2.5-72B-Instruct", "base_model:finetune:Qwen/Qwen2.5-72B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T15:38:45Z	--- language: - en license: other tags: - dnotitia - nlp - llm - conversation - chat base_model: - Qwen/Qwen2.5-72B-Instruct library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. - Base model: Qwen/Qwen2.5-72B-Instruct
dnotitia/Smoothie-Qwen2.5-7B-Instruct	dnotitia	2025-05-04T15:07:57Z	0	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "dnotitia", "nlp", "llm", "conversation", "chat", "conversational", "en", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T14:24:07Z	--- language: - en license: apache-2.0 tags: - dnotitia - nlp - llm - conversation - chat base_model: - Qwen/Qwen2.5-7B-Instruct library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. - Base model: Qwen/Qwen2.5-7B-Instruct
dnotitia/Smoothie-Qwen3-14B	dnotitia	2025-05-04T15:07:04Z	326	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "dnotitia", "nlp", "llm", "conversation", "chat", "reasoning", "conversational", "en", "base_model:Qwen/Qwen3-14B", "base_model:finetune:Qwen/Qwen3-14B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T04:15:33Z	--- language: - en license: apache-2.0 tags: - dnotitia - nlp - llm - conversation - chat - reasoning base_model: - Qwen/Qwen3-14B library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. ## Configuration - Base model: Qwen/Qwen3-14B - Minimum scale factor: 0.5 - Smoothness: 10.0 - Sample size: 1000 - Window size: 4 - N-gram weights: [0.5, 0.3, 0.2] ## Unicode Ranges - Range 1: 0x4e00 - 0x9fff - Range 2: 0x3400 - 0x4dbf - Range 3: 0x20000 - 0x2a6df - Range 4: 0xf900 - 0xfaff - Range 5: 0x2e80 - 0x2eff - Range 6: 0x2f00 - 0x2fdf - Range 7: 0x2ff0 - 0x2fff - Range 8: 0x3000 - 0x303f - Range 9: 0x31c0 - 0x31ef - Range 10: 0x3200 - 0x32ff - Range 11: 0x3300 - 0x33ff ## Statistics - Target tokens: 26,153 - Broken tokens: 1,457 - Modified tokens: 27,564
dnotitia/Smoothie-Qwen3-30B-A3B	dnotitia	2025-05-04T15:06:39Z	0	2	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "dnotitia", "nlp", "llm", "conversation", "chat", "reasoning", "conversational", "en", "base_model:Qwen/Qwen3-30B-A3B", "base_model:finetune:Qwen/Qwen3-30B-A3B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T05:21:47Z	--- language: - en license: apache-2.0 tags: - dnotitia - nlp - llm - conversation - chat - reasoning base_model: - Qwen/Qwen3-30B-A3B library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. ## Configuration - Base model: Qwen/Qwen3-30B-A3B - Minimum scale factor: 0.5 - Smoothness: 10.0 - Sample size: 1000 - Window size: 4 - N-gram weights: [0.5, 0.3, 0.2] ## Unicode Ranges - Range 1: 0x4e00 - 0x9fff - Range 2: 0x3400 - 0x4dbf - Range 3: 0x20000 - 0x2a6df - Range 4: 0xf900 - 0xfaff - Range 5: 0x2e80 - 0x2eff - Range 6: 0x2f00 - 0x2fdf - Range 7: 0x2ff0 - 0x2fff - Range 8: 0x3000 - 0x303f - Range 9: 0x31c0 - 0x31ef - Range 10: 0x3200 - 0x32ff - Range 11: 0x3300 - 0x33ff ## Statistics - Target tokens: 26,153 - Broken tokens: 1,457 - Modified tokens: 27,564
dnotitia/Smoothie-Qwen3-235B-A22B	dnotitia	2025-05-04T15:06:26Z	7	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "dnotitia", "nlp", "llm", "conversation", "chat", "reasoning", "conversational", "en", "base_model:Qwen/Qwen3-235B-A22B", "base_model:finetune:Qwen/Qwen3-235B-A22B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T06:46:46Z	--- language: - en license: apache-2.0 tags: - dnotitia - nlp - llm - conversation - chat - reasoning base_model: - Qwen/Qwen3-235B-A22B library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. ## Configuration - Base model: Qwen/Qwen3-235B-A22B - Minimum scale factor: 0.5 - Smoothness: 10.0 - Sample size: 1000 - Window size: 4 - N-gram weights: [0.5, 0.3, 0.2] ## Unicode Ranges - Range 1: 0x4e00 - 0x9fff - Range 2: 0x3400 - 0x4dbf - Range 3: 0x20000 - 0x2a6df - Range 4: 0xf900 - 0xfaff - Range 5: 0x2e80 - 0x2eff - Range 6: 0x2f00 - 0x2fdf - Range 7: 0x2ff0 - 0x2fff - Range 8: 0x3000 - 0x303f - Range 9: 0x31c0 - 0x31ef - Range 10: 0x3200 - 0x32ff - Range 11: 0x3300 - 0x33ff ## Statistics - Target tokens: 26,153 - Broken tokens: 1,457 - Modified tokens: 27,564
dnotitia/Smoothie-Qwen3-8B	dnotitia	2025-05-04T15:04:34Z	0	3	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "dnotitia", "nlp", "llm", "conversation", "chat", "reasoning", "conversational", "en", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T04:04:47Z	--- language: - en license: apache-2.0 tags: - dnotitia - nlp - llm - conversation - chat - reasoning base_model: - Qwen/Qwen3-8B library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. ## Configuration - Base model: Qwen/Qwen3-8B - Minimum scale factor: 0.5 - Smoothness: 10.0 - Sample size: 1000 - Window size: 4 - N-gram weights: [0.5, 0.3, 0.2] ## Unicode Ranges - Range 1: 0x4e00 - 0x9fff - Range 2: 0x3400 - 0x4dbf - Range 3: 0x20000 - 0x2a6df - Range 4: 0xf900 - 0xfaff - Range 5: 0x2e80 - 0x2eff - Range 6: 0x2f00 - 0x2fdf - Range 7: 0x2ff0 - 0x2fff - Range 8: 0x3000 - 0x303f - Range 9: 0x31c0 - 0x31ef - Range 10: 0x3200 - 0x32ff - Range 11: 0x3300 - 0x33ff ## Statistics - Target tokens: 26,153 - Broken tokens: 1,457 - Modified tokens: 27,564
dnotitia/Smoothie-Qwen3-4B	dnotitia	2025-05-04T15:04:24Z	0	1	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "dnotitia", "nlp", "llm", "conversation", "chat", "reasoning", "conversational", "en", "base_model:Qwen/Qwen3-4B", "base_model:finetune:Qwen/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T04:01:02Z	--- language: - en license: apache-2.0 tags: - dnotitia - nlp - llm - conversation - chat - reasoning base_model: - Qwen/Qwen3-4B library_name: transformers pipeline_tag: text-generation --- # Smoothie Qwen <img src="https://github.com/dnotitia/smoothie-qwen/raw/main/asset/smoothie-qwen-logo.png" width="400" style="max-width: 100%;"> Smoothie Qwen is a lightweight adjustment tool that smooths token probabilities in Qwen and similar models, enhancing balanced multilingual generation capabilities. For more details, please refer to <https://github.com/dnotitia/smoothie-qwen>. ## Configuration - Base model: Qwen/Qwen3-4B - Minimum scale factor: 0.5 - Smoothness: 10.0 - Sample size: 1000 - Window size: 4 - N-gram weights: [0.5, 0.3, 0.2] ## Unicode Ranges - Range 1: 0x4e00 - 0x9fff - Range 2: 0x3400 - 0x4dbf - Range 3: 0x20000 - 0x2a6df - Range 4: 0xf900 - 0xfaff - Range 5: 0x2e80 - 0x2eff - Range 6: 0x2f00 - 0x2fdf - Range 7: 0x2ff0 - 0x2fff - Range 8: 0x3000 - 0x303f - Range 9: 0x31c0 - 0x31ef - Range 10: 0x3200 - 0x32ff - Range 11: 0x3300 - 0x33ff ## Statistics - Target tokens: 26,153 - Broken tokens: 1,457 - Modified tokens: 27,564
Oriolshhh/mt5-small-ca-spanmask-1.5m	Oriolshhh	2025-05-04T15:03:38Z	0	0	null	[ "safetensors", "mt5", "pretraining", "català", "span-masking", "seq2seq", "ca", "dataset:custom", "license:apache-2.0", "region:us" ]	null	2025-05-04T15:00:29Z	--- language: ca license: apache-2.0 tags: - mt5 - pretraining - català - span-masking - seq2seq datasets: - custom --- # mT5-small en català (preentrenament amb span masking - 1.5M frases) Aquest model és una versió preentrenada del `mt5-small` adaptada al català. Ha estat entrenada amb la tècnica de span masking sobre 1.500.000 frases correctes en català per millorar la comprensió de la llengua i afinar l’ús semàntic i sintàctic en català. --- ## Objectiu del model Aquest model serveix com a base especialitzada en català per: - Entrenaments futurs amb menys dades - Millorar resultats en tasques de seq2seq com correcció, resum o Q&A - Substituir el `mt5-small` original en contextos catalans --- ## Entrenament - Model base: [`google/mt5-small`](https://huggingface.co/google/mt5-small) - Dataset: 1.500.000 frases correctes en català - Tècnica: span masking (`TextInfilling`) - Framework: 🤗 Transformers + PyTorch --- ## Exemple d’ús ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("Oriolshhh/mt5-small-ca-spanmask-1.5m") tokenizer = AutoTokenizer.from_pretrained("Oriolshhh/mt5-small-ca-spanmask-1.5m") text = "Avui <extra_id_0> una bonica <extra_id_1> per sortir." inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Oriolshhh/mt5-base-ca-spanmask-1.5m	Oriolshhh	2025-05-04T15:01:19Z	0	0	null	[ "safetensors", "mt5", "pretraining", "català", "span-masking", "seq2seq", "ca", "dataset:custom", "license:apache-2.0", "region:us" ]	null	2025-05-04T14:57:23Z	--- language: ca license: apache-2.0 tags: - mt5 - pretraining - català - span-masking - seq2seq datasets: - custom --- # mT5-base en català (preentrenament amb span masking - 1.5M frases) Aquest model és una versió preentrenada del `mt5-base` adaptada al català. Ha estat entrenada amb la tècnica de span masking sobre 1.500.000 frases correctes en català per tal de millorar-ne la comprensió i adaptació a la llengua. --- ## Objectiu del model Aquest model no està dissenyat per resoldre tasques específiques (com traducció o correcció), sinó per oferir una base millor adaptada al català que pot ser: - Usada com a punt de partida per a fine-tuning en tasques com correcció gramatical, Q&A, classificació, etc. - Millorar el rendiment de `mt5-base` en contextos catalans --- ## Entrenament - Model base: [`google/mt5-base`](https://huggingface.co/google/mt5-base) - Frases: 1.500.000 frases correctes en català - Mètode: Span masking seq2seq (`TextInfilling`) - Format d’entrada: frases amb spans ocults - Framework: 🤗 Transformers + PyTorch --- ## Exemple d’ús ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("Oriolshhh/mt5-base-ca-spanmask-1.5m") tokenizer = AutoTokenizer.from_pretrained("Oriolshhh/mt5-base-ca-spanmask-1.5m") input_text = "Això <extra_id_0> un exemple <extra_id_1> span masking." inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
liujiatian/NLBSE-Java-final	liujiatian	2025-05-04T14:58:48Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T14:58:01Z	--- 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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Rank001/whisper-tiny-hindi-ONNX	Rank001	2025-05-04T14:56:56Z	0	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "base_model:collabora/whisper-tiny-hindi", "base_model:quantized:collabora/whisper-tiny-hindi", "region:us" ]	automatic-speech-recognition	2025-05-04T14:42:41Z	--- library_name: transformers.js base_model: - collabora/whisper-tiny-hindi --- # whisper-tiny-hindi (ONNX) This is an ONNX version of [collabora/whisper-tiny-hindi](https://huggingface.co/collabora/whisper-tiny-hindi). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
Shuu12121/CodeModernBERT-Crow	Shuu12121	2025-05-04T14:55:28Z	142	1	null	[ "safetensors", "modernbert", "code", "code-search", "python", "java", "javascript", "php", "ruby", "go", "rust", "dataset:code-search-net/code_search_net", "dataset:Shuu12121/java-codesearch-filtered", "dataset:Shuu12121/javascript-codesearch-filtered", "dataset:Shuu12121/ruby-codesearch-filtered", "dataset:Shuu12121/python-codesearch-filtered", "dataset:Shuu12121/rust-codesearch-filtered", "arxiv:2310.08588", "license:apache-2.0", "region:us" ]	null	2025-04-19T14:55:01Z	--- license: apache-2.0 datasets: - code-search-net/code_search_net - Shuu12121/java-codesearch-filtered - Shuu12121/javascript-codesearch-filtered - Shuu12121/ruby-codesearch-filtered - Shuu12121/python-codesearch-filtered - Shuu12121/rust-codesearch-filtered language: - code tags: - code - code-search - python - java - javascript - php - ruby - go - rust - modernbert --- # CodeModernBERT-Crow 🐦‍⬛ High-performance encoder for multilingual code search and understanding 開発者: [Shuu12121](https://huggingface.co/Shuu12121) License: Apache-2.0 --- ## 🔍 概要 / Overview CodeModernBERT-Crow は、関数レベルのコード理解およびコード検索に特化したエンコーダモデルです。ベースには [ModernBERT](https://arxiv.org/abs/2310.08588) を採用し、長文コードへの高いスケーラビリティを持ちます。 This model is designed for efficient and accurate function-level code retrieval across multiple programming languages, extending ModernBERT for code intelligence tasks. 本モデルは、収集・整備した多言語コードデータセットを用いてゼロからの事前学習を行っています --- ## 🌟 特徴 / Key Features * 対応言語：Python, Java, JavaScript, PHP, Ruby, Go, Rust * ゼロからの事前学習（ModernBERTアーキテクチャに基づく） * 重複除去済みの高品質データセットを使用（CodeSearchNet の test split 除外済） * `CodeModernBERT-Crow-Pre` から継続学習により高性能を実現 * 最大 8192 トークンの入力長に対応（学習時は 2048 トークンで調整） --- ## 📐 モデル仕様 / Model Architecture \| パラメータ / Parameter \| 値 / Value \| \|--------------------------------\|-------------------\| \| vocab_size \| 50,004 \| \| hidden_size \| 768 \| \| num_hidden_layers \| 12 \| \| num_attention_heads \| 12 \| \| intermediate_size \| 3,072 \| \| max_position_embeddings \| 8,192 \| \| rotary_embedding (RoPE) \| 160,000 \| \| local_attention_rope_theta \| 10,000 \| \| dropout \| 0.0 \| --- ## 🧪 評価結果 / Evaluation CodeSearchNet Test Split（MRR@100）に基づいた比較評価： \| モデル / Model \| Python \| Java \| JavaScript \| PHP \| Ruby \| Go \| \|----------------------------------------\|--------\|-------\|-------------\|--------\|--------\|--------\| \| CodeModernBERT-Crow \| 0.9372 \| 0.8642 \| 0.8118 \| 0.8388 \| 0.8392 \| 0.8522 \| \| CodeModernBERT-Crow-Pre \| 0.5279 \| 0.4718 \| 0.3282 \| 0.4236 \| 0.3014 \| 0.6390 \| \| CodeBERT-base (Microsoft) \| 0.1513 \| 0.1016 \| 0.0908 \| 0.0921 \| 0.0850 \| 0.0943 \| \| GraphCodeBERT-base (Microsoft) \| 0.5686 \| 0.5450 \| 0.4485 \| 0.5261 \| 0.5571 \| 0.4226 \| \| CodeBERTa-small-v1 (Hugging Face) \| 0.7383 \| 0.5875 \| 0.4476 \| 0.5748 \| 0.5252 \| 0.5995 \| \| CodeT5p-220M-bimodal (Salesforce) \| 0.8013 \| 0.7853 \| 0.7101 \| 0.7895 \| 0.7130 \| 0.7547 \| > 評価方法：100件の候補からランク付け（固定評価スクリプト使用） --- ## 🚀 使用方法 / How to Use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeModernBERT-Crow") model = AutoModel.from_pretrained("Shuu12121/CodeModernBERT-Crow") ``` ### コード埋め込みを取得する関数： ```python import torch def get_embedding(text, model, tokenizer, device="cuda"): inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256) if "token_type_ids" in inputs: inputs.pop("token_type_ids") inputs = {k: v.to(device) for k, v in inputs.items()} with torch.no_grad(): outputs = model(**inputs) return outputs.last_hidden_state[:, 0, :] ``` --- ## 🤝 関連モデル / Related Models - [`CodeSearch-ModernBERT-Crow-Plus`](https://huggingface.co/Shuu12121/CodeSearch-ModernBERT-Crow-Plus): 本モデルをベースにした SentenceTransformer 形式のコード検索専用モデル --- ## 📬 お問い合わせ / Contact 質問・提案などは以下のメールアドレスへお気軽にご連絡ください： 📧 [email protected]
goldandrabbit/finetune_bert_using_Trainer_vs_pytorch_train_loop	goldandrabbit	2025-05-04T14:55:09Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T09:30:52Z	--- 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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anishreddy91/sst2-finetuned-bert	anishreddy91	2025-05-04T14:48:26Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T14:48:03Z	--- 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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JahBless/roberta_len256_40k	JahBless	2025-05-04T14:46:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T14:46:01Z	--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: roberta_len256_40k 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. --> # roberta_len256_40k This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3608 - Accuracy: 0.9296 - F1: 0.9298 - Precision: 0.9301 - Recall: 0.9296 ## 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: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.3574 \| 1.0 \| 4000 \| 0.3505 \| 0.9085 \| 0.9089 \| 0.9119 \| 0.9085 \| \| 0.2264 \| 2.0 \| 8000 \| 0.3762 \| 0.924 \| 0.9243 \| 0.9273 \| 0.924 \| \| 0.1954 \| 3.0 \| 12000 \| 0.3608 \| 0.9296 \| 0.9298 \| 0.9301 \| 0.9296 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.1.2 - Datasets 3.5.1 - Tokenizers 0.21.1
Nexesenex/Llama_3.x_70b_SmarTricks_v1.55_MS	Nexesenex	2025-05-04T14:40:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:NousResearch/Hermes-3-Llama-3.1-70B", "base_model:merge:NousResearch/Hermes-3-Llama-3.1-70B", "base_model:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B", "base_model:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "base_model:merge:TheDrummer/Fallen-Llama-3.3-R1-70B-v1", "base_model:hitachi-nlp/Llama-3.1-70B-FLDx2", "base_model:merge:hitachi-nlp/Llama-3.1-70B-FLDx2", "base_model:huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated", "base_model:merge:huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated", "base_model:migtissera/Tess-3-Llama-3.1-70B", "base_model:merge:migtissera/Tess-3-Llama-3.1-70B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T13:55:17Z	--- base_model: - SicariusSicariiStuff/Negative_LLAMA_70B - migtissera/Tess-3-Llama-3.1-70B - huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated - hitachi-nlp/Llama-3.1-70B-FLDx2 - NousResearch/Hermes-3-Llama-3.1-70B - TheDrummer/Fallen-Llama-3.3-R1-70B-v1 library_name: transformers tags: - mergekit - merge --- # merge 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated](https://huggingface.co/huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated) as a base. ### Models Merged The following models were included in the merge: * [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B) * [migtissera/Tess-3-Llama-3.1-70B](https://huggingface.co/migtissera/Tess-3-Llama-3.1-70B) * [hitachi-nlp/Llama-3.1-70B-FLDx2](https://huggingface.co/hitachi-nlp/Llama-3.1-70B-FLDx2) * [NousResearch/Hermes-3-Llama-3.1-70B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B) * [TheDrummer/Fallen-Llama-3.3-R1-70B-v1](https://huggingface.co/TheDrummer/Fallen-Llama-3.3-R1-70B-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: model_stock models: - model: huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated - model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1 - model: SicariusSicariiStuff/Negative_LLAMA_70B - model: NousResearch/Hermes-3-Llama-3.1-70B - model: migtissera/Tess-3-Llama-3.1-70B - model: hitachi-nlp/Llama-3.1-70B-FLDx2 base_model: huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated dtype: bfloat16 out_dtype: bfloat16 parameters: int8_mask: true normalize: true filter_wise: false chat_template: auto tokenizer: source: union name: Llama_3.x_70b_SmarTricks_v1.55_MS ```
ragunath-ravi/whisper-mini-ta	ragunath-ravi	2025-05-04T14:39:00Z	0	1	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "audio", "tamil", "asr", "ta", "dataset:ragunath123/whisperaudio", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-03T15:53:14Z	--- language: - ta license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer - automatic-speech-recognition - audio - whisper - tamil - asr datasets: - ragunath123/whisperaudio metrics: - wer model-index: - name: Whisper Mini ta - RAGUNATH RAVI results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: whisperaudio type: ragunath123/whisperaudio args: 'config: ta, split: test' metrics: - name: Wer type: wer value: 18.70424597364568 pipeline_tag: automatic-speech-recognition library_name: transformers --- # Whisper Mini ta - RAGUNATH RAVI This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the whisperaudio dataset. It achieves the following results on the evaluation set: - Loss: 0.0905 - WER: 18.7042 ## Model description This is a Whisper Small model fine-tuned specifically for Tamil language automatic speech recognition (ASR). The model has been adapted to recognize and transcribe Tamil speech with higher accuracy than the base OpenAI Whisper model. The fine-tuning process focused on optimizing the model for Tamil phonetics, vocabulary, and speech patterns using a custom web-scraped dataset. The model uses the Whisper architecture, which employs a Transformer-based encoder-decoder architecture with attention mechanisms specifically designed for speech recognition tasks. This model is particularly well-suited for Tamil speech recognition applications, achieving a Word Error Rate (WER) of 18.70% on the test set, demonstrating significant improvements over the base model for Tamil language speech. ## Intended uses & limitations ### Intended uses - Transcription of Tamil speech in audio and video content - Voice command systems for Tamil speakers - Accessibility tools for Tamil-speaking users - Documentation of Tamil audio content - Subtitling and captioning services for Tamil media ### Limitations - The model may struggle with heavily accented Tamil speech or regional dialects that were not well-represented in the training data - Performance may degrade with noisy audio inputs or low-quality recordings - The model might have difficulty with specialized terminology or domain-specific language not present in the training data - The model is specifically trained for Tamil and will not perform well on other languages ## Training and evaluation data The model was fine-tuned on a custom web-scraped dataset called "whisperaudio" (available at ragunath123/whisperaudio on Hugging Face). This dataset consists of Tamil speech audio paired with accurate transcriptions. For training, 12,000 samples were used from the dataset, while 3,000 samples were used for evaluation. The audio was processed by resampling from 48kHz to 16kHz to match Whisper's requirements. The dataset includes a diverse range of Tamil speech samples, which helps the model generalize across different speakers, accents, and content types. ## Training procedure ### Preprocessing - Audio files were resampled from their original sampling rate to 16kHz - Log-Mel spectrograms were extracted as input features using the Whisper feature extractor - Text was tokenized using the Whisper tokenizer configured specifically for the Tamil language - Special care was taken to handle the tokenization of Tamil characters correctly ### Framework versions - Transformers 4.40.2 - PyTorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.19.1 ### Training hyperparameters The following hyperparameters were used during training: - Learning rate: 1e-05 - Train batch size: 32 - Evaluation batch size: 16 - Seed: 42 - Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - Learning rate scheduler: Linear with warmup - Learning rate scheduler warmup steps: 500 - Total training steps: 4000 - Mixed precision training: Native AMP - Gradient accumulation steps: 1 - Evaluation strategy: Steps (every 1000 steps) - Gradient checkpointing: Enabled - FP16 training: Enabled ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| WER \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.0585 \| 2.6667 \| 1000 \| 0.0872 \| 20.2050 \| \| 0.0123 \| 5.3333 \| 2000 \| 0.0905 \| 18.7042 \| \| 0.0047 \| 8.0 \| 3000 \| 0.1033 \| 18.7719 \| \| 0.0015 \| 10.6667 \| 4000 \| 0.1116 \| 18.8828 \| The model achieved its best performance at epoch 5.3 (step 2000) with a WER of 18.7042%. ### Model configuration The model was configured specifically for Tamil language transcription: - Language set to "tamil" - Task set to "transcribe" - Forced decoder IDs were set to None to allow the model more flexibility in generation ## Evaluation The model was evaluated using the Word Error Rate (WER) metric, which measures the percentage of words incorrectly transcribed. The final model achieved a WER of 18.70%, indicating that approximately 81.3% of words were correctly transcribed. The evaluation was performed on a held-out test set of 3,000 samples from the whisperaudio dataset, ensuring a fair assessment of the model's performance on unseen data. ## Usage ```python from transformers import pipeline import torch # Load the model asr = pipeline( "automatic-speech-recognition", model="ragunath-ravi/whisper-mini-ta", device=0 if torch.cuda.is_available() else "cpu" ) # Transcribe audio result = asr("path_to_audio_file.wav", language="ta", task="transcribe") print(result["text"])
ldostadi/Phi-4-mini-reasoning-Q4_K_M-GGUF	ldostadi	2025-05-04T14:33:38Z	0	0	transformers	[ "transformers", "gguf", "nlp", "math", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:microsoft/Phi-4-mini-reasoning", "base_model:quantized:microsoft/Phi-4-mini-reasoning", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-04T14:33:23Z	--- base_model: microsoft/Phi-4-mini-reasoning language: - en library_name: transformers license: mit license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE pipeline_tag: text-generation tags: - nlp - math - code - llama-cpp - gguf-my-repo widget: - messages: - role: user content: How to solve 3x^2+4x+5=1? --- # ldostadi/Phi-4-mini-reasoning-Q4_K_M-GGUF This model was converted to GGUF format from [`microsoft/Phi-4-mini-reasoning`](https://huggingface.co/microsoft/Phi-4-mini-reasoning) 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/microsoft/Phi-4-mini-reasoning) 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 ldostadi/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ldostadi/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-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 ldostadi/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ldostadi/Phi-4-mini-reasoning-Q4_K_M-GGUF --hf-file phi-4-mini-reasoning-q4_k_m.gguf -c 2048 ```
Hachipo/Meta-Llama-3-8B-EnTrans_10000_2	Hachipo	2025-05-04T14:32:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T14:28:45Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ntnu-smil/whisper-large-v3-sandi-7k-1024-28steps-merged	ntnu-smil	2025-05-04T14:31:43Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "wft", "audio", "speech", "generated_from_trainer", "en", "dataset:ntnu-smil/sandi2025-ds", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-04T14:30:30Z	--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-large-v3 tags: - wft - whisper - automatic-speech-recognition - audio - speech - generated_from_trainer datasets: - ntnu-smil/sandi2025-ds metrics: - wer model-index: - name: whisper-large-v3-sandi-7k-1024-28steps results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: ntnu-smil/sandi2025-ds type: ntnu-smil/sandi2025-ds metrics: - type: wer value: 73.82367281180885 name: Wer --- <!-- 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-large-v3-sandi-7k-1024-28steps This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the ntnu-smil/sandi2025-ds dataset. It achieves the following results on the evaluation set: - Loss: 1.0275 - Wer: 73.8237 - Cer: 203.1654 - Decode Runtime: 257.7123 - Wer Runtime: 0.2072 - Cer Runtime: 0.4935 ## 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: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 1024 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - training_steps: 28 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| Decode Runtime \| Wer Runtime \| Cer Runtime \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:--------:\|:--------------:\|:-----------:\|:-----------:\| \| 1.9026 \| 1.0357 \| 7 \| 1.3670 \| 70.5577 \| 206.9010 \| 266.1791 \| 0.2147 \| 0.5053 \| \| 1.2477 \| 2.0714 \| 14 \| 1.1783 \| 86.2572 \| 223.6346 \| 268.9910 \| 0.2241 \| 0.5015 \| \| 1.07 \| 3.1071 \| 21 \| 1.0605 \| 78.7713 \| 211.1141 \| 262.5822 \| 0.2186 \| 0.5076 \| \| 1.0348 \| 4.1429 \| 28 \| 1.0275 \| 73.8237 \| 203.1654 \| 257.7123 \| 0.2072 \| 0.4935 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.4.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
sdgsjlfnjkl/kanana-2.1b-full-v10	sdgsjlfnjkl	2025-05-04T14:29:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T14:21:25Z	--- 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. 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JoaoPedroBlanco2605/Gemma3_1b_for_inference	JoaoPedroBlanco2605	2025-05-04T14:25:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:24:27Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: JoaoPedroBlanco2605 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
DohyunAn/LLama-3.2-1B-unsloth-bnb-tokenizer-modified	DohyunAn	2025-05-04T14:23:39Z	46	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-15T15:34:18Z	--- base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: DohyunAn - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-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) Modification: Increased the vocabulary size and expanded the embedding layer accordingly.
juhw/q478	juhw	2025-05-04T14:23:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T14:20:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
kostiantynk-outlook/ec7fa284-2472-4bc3-8d6e-26216b2936bf	kostiantynk-outlook	2025-05-04T14:23:10Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-05-04T14:22:41Z	--- library_name: transformers model_name: kostiantynk-outlook/ec7fa284-2472-4bc3-8d6e-26216b2936bf tags: - generated_from_trainer licence: license --- # Model Card for kostiantynk-outlook/ec7fa284-2472-4bc3-8d6e-26216b2936bf This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dimasik2987/bae43b1a-aee7-44d3-889d-81ad2d3591e7	dimasik2987	2025-05-04T14:20:22Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:adapter:NousResearch/Meta-Llama-3-8B", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T13:53:57Z	--- library_name: peft license: other base_model: NousResearch/Meta-Llama-3-8B tags: - axolotl - generated_from_trainer model-index: - name: bae43b1a-aee7-44d3-889d-81ad2d3591e7 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: NousResearch/Meta-Llama-3-8B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ce73d3d1b54d2d5e_train_data.json ds_type: json format: custom path: /workspace/input_data/ce73d3d1b54d2d5e_train_data.json type: field_instruction: instruction field_output: chosen_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null 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: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik2987/bae43b1a-aee7-44d3-889d-81ad2d3591e7 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 12 mixed_precision: bf16 mlflow_experiment_name: /tmp/ce73d3d1b54d2d5e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 2048 special_tokens: pad_token: <\|end_of_text\|> 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: 12717213-820a-4227-be7e-8d4a47298e72 wandb_project: s56-28 wandb_run: your_name wandb_runid: 12717213-820a-4227-be7e-8d4a47298e72 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # bae43b1a-aee7-44d3-889d-81ad2d3591e7 This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2683 ## 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-06 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 24 - 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: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2369 \| 0.3241 \| 200 \| 1.2683 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ajr0/mistral-7b-instruct-v0.3-bnb-4bit-hon-1k-v1	ajr0	2025-05-04T14:17:52Z	0	0	transformers	[ "transformers", "gguf", "mistral", "text-generation", "conversational", "fine-tuning", "humanornot", "unsloth", "trl", "4bit", "instruction-following", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T13:39:07Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation - conversational - fine-tuning - humanornot - unsloth - mistral - transformers - trl - 4bit - instruction-following license: apache-2.0 language: - en --- # Mistral-7B Instruct v0.3 - Fine-tuned on HumanOrNot Chats - Developed by: ajr0 - License: apache-2.0 - Finetuned from model: `unsloth/mistral-7b-instruct-v0.3-bnb-4bit` - Dataset: Curated subset of [humanornot.ai](https://humanornot.ai/) chat logs. ## Model Description This repository contains a fine-tuned version of `unsloth/mistral-7b-instruct-v0.3-bnb-4bit`. The model was specifically fine-tuned on a curated dataset of 1,000 chat conversations sourced from humanornot.ai. The goal of this fine-tuning was to explore the adaptation of the Mistral Instruct model to the specific conversational patterns and styles present in the HumanOrNot game, where users try to determine if they are interacting with a human or an AI. This model was trained significantly faster (estimated 2x or more) and with lower memory usage thanks to [Unsloth](https://github.com/unslothai/unsloth), leveraging its optimized kernels and integration with 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) ## Training Details ### Dataset - Source: [humanornot.ai](https://humanornot.ai/) - Size: 1,000 curated chat conversations. - Description: The dataset consists of dialogues where one participant is trying to guess whether the other is a human or an AI within a time limit. This often leads to unique questioning strategies, conversational styles, and potentially adversarial interactions. ### Training Procedure - Frameworks: Unsloth, Hugging Face TRL (Transformers Reinforcement Learning Library - likely used for Supervised Fine-tuning (SFT) in this context). - Configuration: - Max Sequence Length: 2048 tokens - Epochs: 2 - Total Training Steps: 60 (Note: This is a very small number of steps, a brief fine-tuning process.) - Quantization: Utilized the pre-quantized 4-bit base model (`bnb-4bit`). - Hardware: Trained on 1x NVIDIA T4 ### Efficiency Gains - Leveraging Unsloth's optimizations resulted in significantly faster training iterations and reduced GPU memory requirements compared to standard fine-tuning approaches with full precision or naive quantization. ## Intended Use - Primary Use: Research, experimentation, and qualitative exploration of fine-tuning LLMs on specific, niche conversational datasets like HumanOrNot. - Potential Applications: - Simulating chat interactions resembling the HumanOrNot game. - Understanding how LLMs adapt to specific conversational constraints and objectives. - As a starting point for further fine-tuning on similar conversational tasks. Note: Due to the limited size of the fine-tuning dataset (1k examples) and the very short training duration (60 steps), this model's capabilities might be narrowly focused on the style of the training data and may not generalize well to broader conversational tasks. It is not recommended for production use without further evaluation and potentially more extensive training.
rahadgazi/imagegenerate	rahadgazi	2025-05-04T14:16:58Z	0	0	null	[ "finance", "image-text-to-text", "base_model:deepseek-ai/DeepSeek-Prover-V2-671B", "base_model:finetune:deepseek-ai/DeepSeek-Prover-V2-671B", "license:apache-2.0", "region:us" ]	image-text-to-text	2025-05-04T14:15:08Z	--- license: apache-2.0 base_model: - deepseek-ai/DeepSeek-Prover-V2-671B pipeline_tag: image-text-to-text tags: - finance ---
BootesVoid/cma9pb0an003p1zqn2bcq0wj0_cma9pgnvp003w1zqn1csk2wac	BootesVoid	2025-05-04T14:16:09Z	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	2025-05-04T14:16:06Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: lana_sophia_engel --- # Cma9Pb0An003P1Zqn2Bcq0Wj0_Cma9Pgnvp003W1Zqn1Csk2Wac <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 `lana_sophia_engel` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "lana_sophia_engel", "lora_weights": "https://huggingface.co/BootesVoid/cma9pb0an003p1zqn2bcq0wj0_cma9pgnvp003w1zqn1csk2wac/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cma9pb0an003p1zqn2bcq0wj0_cma9pgnvp003w1zqn1csk2wac', weight_name='lora.safetensors') image = pipeline('lana_sophia_engel').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cma9pb0an003p1zqn2bcq0wj0_cma9pgnvp003w1zqn1csk2wac/discussions) to add images that show off what you’ve made with this LoRA.
gavrilstep/3f840244-28f9-4351-ab60-18e8733b5d04	gavrilstep	2025-05-04T14:15:46Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:adapter:NousResearch/Meta-Llama-3-8B", "license:other", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T14:03:22Z	--- library_name: peft license: other base_model: NousResearch/Meta-Llama-3-8B tags: - axolotl - generated_from_trainer model-index: - name: 3f840244-28f9-4351-ab60-18e8733b5d04 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: NousResearch/Meta-Llama-3-8B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ce73d3d1b54d2d5e_train_data.json ds_type: json format: custom path: /workspace/input_data/ce73d3d1b54d2d5e_train_data.json type: field_instruction: instruction field_output: chosen_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null 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: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: gavrilstep/3f840244-28f9-4351-ab60-18e8733b5d04 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.01 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/ce73d3d1b54d2d5e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 2048 special_tokens: pad_token: <\|end_of_text\|> 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: 12717213-820a-4227-be7e-8d4a47298e72 wandb_project: s56-7 wandb_run: your_name wandb_runid: 12717213-820a-4227-be7e-8d4a47298e72 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 3f840244-28f9-4351-ab60-18e8733b5d04 This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8874 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7018 \| 0.0406 \| 150 \| 1.8874 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
icefog72/Ice0.107-04.05-RP-ORPO-v2	icefog72	2025-05-04T14:10:53Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T13:49:29Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # Ice0.107-04.05-RP-ORPO-v2 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 Passthrough merge method using E:\FModels\Ice0.107-22.04-RP + E:\lora\Ice0.107-RP-ORPO-lora-v2 as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: E:\FModels\Ice0.107-22.04-RP+E:\lora\Ice0.107-RP-ORPO-lora-v2 dtype: bfloat16 merge_method: passthrough models: - model: E:\FModels\Ice0.107-22.04-RP+E:\lora\Ice0.107-RP-ORPO-lora-v2 ```
Svngoku/AfricanHairFluxLora	Svngoku	2025-05-04T14:09:10Z	4	1	diffusers	[ "diffusers", "flux", "text-to-image", "lora", "fal", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-27T11:00:58Z	--- tags: - flux - text-to-image - lora - diffusers - fal base_model: black-forest-labs/FLUX.1-dev instance_prompt: Afro Hair 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 widget: - text: Photography of styled Afro Hair, style instagram png output: url: images/example_ufdsuof10.png - text: >- Photography of styled Afro Hair, commercial ads, cosmetics, hair powered shampoo output: url: images/example_qxbh4iyff.png - text: >- 8k photorealistic image of an older black skin grandmother with wrinkles, beautiful silver white dread locks in black head wrap , round rim glasses, different angles, character sheets, with the Afro Hair style output: url: images/example_3ubr5t0fh.png --- # AfricanHairFluxLora <Gallery /> ## Model description ## Trigger words You should use `Afro Hair` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Svngoku/AfricanHairFluxLora/tree/main) them in the Files & versions tab. ## Training at fal.ai Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
juhw/q4777	juhw	2025-05-04T14:09:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T14:06:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
Fannor/memo	Fannor	2025-05-04T14:07:06Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-04T13:30:26Z	--- 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 ---
YahyaHussnain/SoulSyncAI-DeepSeek-R1	YahyaHussnain	2025-05-04T14:06:57Z	2	0	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T03:06:39Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: YahyaHussnain - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-llama-8b-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)
cata2002/llama-3-8b-full-dataset_with_prefix_2000_it	cata2002	2025-05-04T14:05:18Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T14:04:01Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: cata2002 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dimasik1987/5400b703-dbe7-444d-9f75-6a814413eefe	dimasik1987	2025-05-04T14:02:21Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:adapter:NousResearch/Meta-Llama-3-8B", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T13:53:57Z	--- library_name: peft license: other base_model: NousResearch/Meta-Llama-3-8B tags: - axolotl - generated_from_trainer model-index: - name: 5400b703-dbe7-444d-9f75-6a814413eefe 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: NousResearch/Meta-Llama-3-8B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ce73d3d1b54d2d5e_train_data.json ds_type: json format: custom path: /workspace/input_data/ce73d3d1b54d2d5e_train_data.json type: field_instruction: instruction field_output: chosen_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null 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: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: dimasik1987/5400b703-dbe7-444d-9f75-6a814413eefe hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/ce73d3d1b54d2d5e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 2048 special_tokens: pad_token: <\|end_of_text\|> 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: 12717213-820a-4227-be7e-8d4a47298e72 wandb_project: s56-7 wandb_run: your_name wandb_runid: 12717213-820a-4227-be7e-8d4a47298e72 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 5400b703-dbe7-444d-9f75-6a814413eefe This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9761 ## 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-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - 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: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.0568 \| 0.1014 \| 150 \| 1.9761 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DuongTrongChi/vinallama-dpo	DuongTrongChi	2025-05-04T13:52:58Z	0	0	transformers	[ "transformers", "safetensors", "llama", "feature-extraction", "text-generation-inference", "unsloth", "en", "base_model:DuongTrongChi/vinallama-2.7b-chat-sft-v1", "base_model:finetune:DuongTrongChi/vinallama-2.7b-chat-sft-v1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-04T13:34:27Z	--- base_model: DuongTrongChi/vinallama-2.7b-chat-sft-v1 tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: DuongTrongChi - License: apache-2.0 - Finetuned from model : DuongTrongChi/vinallama-2.7b-chat-sft-v1 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) ```Python from transformers import pipeline model_id = "..." pipe = pipeline(task="text-generation", model=model_id, device="cuda") input_text = """<\|im_start\|>system You are a careful and responsible AI language model designed to assist users with their queries. The information you receive may contain harmful content. Please ensure that your responses are safe, respectful, and free from any harmful, offensive, or inappropriate language. Always prioritize the well-being and safety of users. <\|im_end\|> <\|im_start\|>user who are you<\|im_end\|> <\|im_start\|>assistant """ outputs = pipe(input_text, return_full_text=False, max_new_tokens=200) outputs ```
mjs227/rltu_grpo_10_0_249-llama-merged	mjs227	2025-05-04T13:51:55Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T13:31:22Z	--- 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. 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sch-allie/bert_another_persona	sch-allie	2025-05-04T13:50:17Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:2136", "loss:ContrastiveLoss", "arxiv:1908.10084", "base_model:BAAI/bge-small-en-v1.5", "base_model:finetune:BAAI/bge-small-en-v1.5", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-04T13:47:54Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:2136 - loss:ContrastiveLoss base_model: BAAI/bge-small-en-v1.5 widget: - source_sentence: I work in tv, I do not treat my girlfriend very well, I am a lesbian, I like to cook breakfast on sundays, I love to sing, I love horses, I collect seashells, I have two chiuahuahs, I am a yoga instructor, I have a son sentences: - I love beef, I am in a very intimate and loving relationship, I have a dog that has behavioral issues, I enjoy playing guitar and video games, I work from home doing various jobs on the internet, My biggest fear in life is heights, I can make fart noises with my arm pits , even some musical melody, I have been cheated on by every ex girlfriend except for one, There is only been one set of twins in my family - I m a lesbian, I like to cook breakfast on sundays, I don t treat my girlfriend very well, I love to sing, I play electric violin , and also work as an accountant, I m a musician with a day job, I have a pond at home with a pet koi named spot, My favorite band is imagine dragons, Lately i ve been learning how to garden - I can knit, My favorite book is jane austen s pride and prejudice, I like going barefoot, My sister is a guitarist, I speak fluent italian, I am a cat owner, I have one blue eye and one hazel eye, My brother is an archaeologist, My mom is an osteopath - source_sentence: I am a vegan, I work as a barista, My favorite flower is a rose, My favorite color is orange, I have a french bulldog, My feet are size six women s, I want to be a journalist but instead i sell washers at sears, I play bass, I love the band red hot chili peppers sentences: - I am a vegan, I have 23 cats at home, I hate the taste of fish, I traveled around the world in a boat, I am an only child, I love spicy food, I have traveled to europe and mexico, I like swimming, My mother is a housemaid - I am bisexual, I think i might need help, I get sad a lot, I live with my parents, I teach first grade, My favorite pizza is pepperoni, I love to go shopping, I drink lots of iced tea, I married my high school sweetheart - I am a man, My favorite food is pizza, I work on trucks, I have a doberman, I play bingo every wednesday with my husband, I have 3 dogs and a parrot, My left knee is filled with metal, I enjoy jazz music from when i was a girl, My family means the world to me - source_sentence: I am vegan, I love to go to the beach, I have 2 cats, My favorite food is strawberries, I work in a veterinary office, I almost always wear a t shirt and jeans, Skateboarding is a hobby of mine, I go to college part time, I have a crush on my co worker, I love candy sentences: - I am vegan, I work in a veterinary office, My favorite food is strawberries, I love to go to the beach, I have 4 pets, I enjoy magic the gathering , it is a card game, My favorite food is jello, I live in an apartment, I can t whistle - I am bipolar, I love to eat pita bread with hummus, I am thinking of becoming a mother next year, I am a mother of three, I have the gene that lets me know when someones eaten asparagus, I enjoy my steaks cooked medium rare, I am too short to reach the top shelf, My dog introduced me to my first and second wives - I am a nurse, I am a great baker, I surf often, I was an army brat, I married my high school sweetheart, My daughter just graduated college, I run more then 2 miles a day even though i hate it, I have only one child, I have been in 3 triathlons, I like snowboarding - source_sentence: I can draw, I have sausage fingers, I m bloated, I like sprite soda, I want to be a music teacher, I play the violin, I enjoy watching horror flicks, My favorite color is red orange sentences: - I can draw, I have sausage fingers, I am bloated, I like sprite soda, My favorite show is battlestar galactica, I live on a beet farm, My favorite animal is a bear, I am a paper salesman - I surf often, I married my high school sweetheart, I was an army brat, I am a nurse, I am a great baker, I go to at least 10 concerts a year, Lady gaga is my current favorite singer, Madonna is my all time favorite, I work in retail, I love music - I am a writer, I have ten nieces and nephews, I enjoy going to museums, I live in new york city, I love helping people, I am a older woman, I live in a rural area, I retired so i can spend more time with my husband and helping people, My favorite food is pasta - source_sentence: I love comics, I ve started creating my own comics and presenting them to publishers, I decided to publish my creations on internet, I ve been rejected several times and thought of giving up with this, And also love drawing, I love meat, I have a new job selling cars, I love to paint pictures, My wife use to be a teacher sentences: - I love candy, I almost always wear a t shirt and jeans, I have a crush on my co worker, Skateboarding is a hobby of mine, I am a graduate student, I m always early, I am in between classes, I volunteer with dogs - I love comics, I ve started creating my own comics and presenting them to publishers, I ve been rejected several times and thought of giving up with this, And also love drawing, I decided to publish my creations on internet, I like football, We drink beer and eat food, It s great fun, We watch the games together - I have 5 kids, I love new gadgets, I love to socialize, I love to draw, I love dancing, I like going to foreign countries, I am an only child, I dye my hair blonde, I have 3 cats pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on BAAI/bge-small-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 384 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'I love comics, I ve started creating my own comics and presenting them to publishers, I decided to publish my creations on internet, I ve been rejected several times and thought of giving up with this, And also love drawing, I love meat, I have a new job selling cars, I love to paint pictures, My wife use to be a teacher', 'I love comics, I ve started creating my own comics and presenting them to publishers, I ve been rejected several times and thought of giving up with this, And also love drawing, I decided to publish my creations on internet, I like football, We drink beer and eat food, It s great fun, We watch the games together', 'I love candy, I almost always wear a t shirt and jeans, I have a crush on my co worker, Skateboarding is a hobby of mine, I am a graduate student, I m always early, I am in between classes, I volunteer with dogs', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 2,136 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------\| \| type \| string \| string \| int \| \| details \| <ul><li>min: 5 tokens</li><li>mean: 61.59 tokens</li><li>max: 121 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 62.14 tokens</li><li>max: 121 tokens</li></ul> \| <ul><li>0: ~25.90%</li><li>1: ~74.10%</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\| \| <code>I love hockey, I studied business as my major, My bestfriend is from out of town, I ve flown out of the country, I despise sweet potatoes, I am a weightlifter, I love the all american rejects, I have three great danes</code> \| <code>I love hockey, I have flown out of the country, I have a younger sibling, My befriend is from out of town, I studied business as my major, I have no technological skills, I am stuck in the past due to remoteness, I am a savage, I work for the discovery channel creating videos, I love the outdoors</code> \| <code>1</code> \| \| <code>I am a vegan, My favorite flower is a rose, I work as a barista, I have a cat named jasper, My favorite book is anne of green gables, I love owning nice office supplies, I won a beauty pageant one time but didn t feel like it was worth the effort, My mom teaches kindergarten and my dad is a tax assessor, I have always wanted a pot bellied pig</code> \| <code>I am a vegan, I love animals, I love country music, I love dancing, I like collectible dragon ball z figures, I love huge paintings, I like collecting rocks, I love zumba, I love the smell of beans</code> \| <code>0</code> \| \| <code>I have 5 kids, I love to draw, I love new gadgets, I love to socialize, I am great with babies, I chew my nails, I am studying to be a special education teacher, I was a navy brat and have attended 16 schools in 12 years, I kayak at the lake on weekends, I make friends fast and say goodbye fast</code> \| <code>I have 5 kids, I love new gadgets, I love to socialize, I love to draw, I am great with babies, I don t like acting, I don t like kids, I have 3 ferrari cars in my garage, My father was a famous actor, I have 3 ex wives</code> \| <code>1</code> \| * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 1.8727 \| 500 \| 0.0163 \| \| 3.7453 \| 1000 \| 0.0119 \| \| 5.6180 \| 1500 \| 0.0099 \| \| 7.4906 \| 2000 \| 0.0082 \| \| 9.3633 \| 2500 \| 0.0075 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 3.4.1 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
phililp-arnold/d3d1d5c4-fbc0-4523-809e-a18bc10de1f0	phililp-arnold	2025-05-04T13:45:10Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:elyza/Llama-3-ELYZA-JP-8B", "base_model:adapter:elyza/Llama-3-ELYZA-JP-8B", "region:us" ]	null	2025-05-04T13:44:40Z	--- library_name: peft tags: - generated_from_trainer base_model: elyza/Llama-3-ELYZA-JP-8B model-index: - name: phililp-arnold/d3d1d5c4-fbc0-4523-809e-a18bc10de1f0 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. --> # phililp-arnold/d3d1d5c4-fbc0-4523-809e-a18bc10de1f0 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1051 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
Chaew00n/test-supervised-fine-tuning	Chaew00n	2025-05-04T13:39:08Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:Qwen/Qwen3-1.7B", "base_model:adapter:Qwen/Qwen3-1.7B", "license:apache-2.0", "region:us" ]	null	2025-05-03T18:14:13Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen3-1.7B tags: - generated_from_trainer model-index: - name: test-supervised-fine-tuning 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. --> # test-supervised-fine-tuning This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) 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: 5e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - 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: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.2.0 - Tokenizers 0.21.1
fedovtt/0ae412c4-55ee-488e-ac0c-d128e1258aca	fedovtt	2025-05-04T13:37:17Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:adapter:unsloth/Llama-3.2-3B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T13:05:32Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-3B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 0ae412c4-55ee-488e-ac0c-d128e1258aca 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: unsloth/Llama-3.2-3B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ab5595d09687e4d8_train_data.json ds_type: json format: custom path: /workspace/input_data/ab5595d09687e4d8_train_data.json type: field_instruction: premise field_output: hypothesis format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null 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: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: fedovtt/0ae412c4-55ee-488e-ac0c-d128e1258aca hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 10 mixed_precision: bf16 mlflow_experiment_name: /tmp/ab5595d09687e4d8_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 2048 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: 5f63cfe1-4d17-450d-aedb-b238c203c48e wandb_project: s56-28 wandb_run: your_name wandb_runid: 5f63cfe1-4d17-450d-aedb-b238c203c48e warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0ae412c4-55ee-488e-ac0c-d128e1258aca This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3449 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - 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: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.4736 \| 0.0094 \| 150 \| 3.3449 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
masani/SFT_math_Llama-3.2-3B_epoch_5_global_step_145	masani	2025-05-04T13:36:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T12:40:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
ivangrapher/40fca2ad-9b15-4af3-b96f-96548effddfd	ivangrapher	2025-05-04T13:34:13Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:adapter:unsloth/Llama-3.2-3B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T13:03:41Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-3B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 40fca2ad-9b15-4af3-b96f-96548effddfd 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: unsloth/Llama-3.2-3B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ab5595d09687e4d8_train_data.json ds_type: json format: custom path: /workspace/input_data/ab5595d09687e4d8_train_data.json type: field_instruction: premise field_output: hypothesis format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null 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: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: ivangrapher/40fca2ad-9b15-4af3-b96f-96548effddfd hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/ab5595d09687e4d8_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 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: 2048 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: 5f63cfe1-4d17-450d-aedb-b238c203c48e wandb_project: s56-7 wandb_run: your_name wandb_runid: 5f63cfe1-4d17-450d-aedb-b238c203c48e warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 40fca2ad-9b15-4af3-b96f-96548effddfd This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7794 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 3.5074 \| 0.0075 \| 150 \| 3.7794 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ail-sa/kevin_plus_medium_fs_caption_v2	ail-sa	2025-05-04T13:31:08Z	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	2025-05-04T10:22:46Z	--- 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: Sid --- # Kevin_Plus_Medium_Fs_Caption_V2 <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 `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/kevin_plus_medium_fs_caption_v2/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('ail-sa/kevin_plus_medium_fs_caption_v2', weight_name='lora.safetensors') image = pipeline('Sid').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/ail-sa/kevin_plus_medium_fs_caption_v2/discussions) to add images that show off what you’ve made with this LoRA.
Minahil-Malik-Oh/wATCH.Minahil.Malik.viral.video.original	Minahil-Malik-Oh	2025-05-04T13:28:36Z	0	0	null	[ "region:us" ]	null	2025-05-04T13:24:10Z	Minahil Malik Original Video V𝐢ral Video L𝚎aᴋed on X social media platforms <a href="https://mswds.xyz/full-video/?v=Minahil-Malik " rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="https://mswds.xyz/full-video/?v=Minahil-Malik" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="https://mswds.xyz/full-video/?v=Minahil-Malik"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a> Actor Minahil Malik Original Video video took the internet by storm and amazed viewers on various social media platforms. Actor Minahil Malik , a young and talented digital creator, recently became famous thanks to this interesting video. L𝚎aᴋed Video Actor Minahil Malik Original Video V𝐢ral Video L𝚎aᴋed on X Twitter Actor Minahil Malik Original Video video oficial twitter L𝚎aᴋed Video Actor Minahil Malik Original Video V𝐢ral Video L𝚎aᴋed on X Twitter.
Nikhilbk/poca-SoccerTwos	Nikhilbk	2025-05-04T13:25:45Z	26	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2025-05-01T17:38:24Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # ppo Agent playing SoccerTwos This is a trained model of a ppo agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Nikhilbk/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
TOMFORD79/Fly60	TOMFORD79	2025-05-04T13:25:31Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-04T13:15:29Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
melekuk/Qwen2.5-14B-Aletheia-DRT	melekuk	2025-05-04T13:13:18Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:Krystalan/DRT-14B", "base_model:merge:Krystalan/DRT-14B", "base_model:allura-org/TQ2.5-14B-Aletheia-v1", "base_model:merge:allura-org/TQ2.5-14B-Aletheia-v1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T12:45:16Z	--- base_model: - Krystalan/DRT-14B - allura-org/TQ2.5-14B-Aletheia-v1 library_name: transformers tags: - mergekit - merge --- # merge 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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [Krystalan/DRT-14B](https://huggingface.co/Krystalan/DRT-14B) * [allura-org/TQ2.5-14B-Aletheia-v1](https://huggingface.co/allura-org/TQ2.5-14B-Aletheia-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: allura-org/TQ2.5-14B-Aletheia-v1 - model: Krystalan/DRT-14B merge_method: slerp base_model: allura-org/TQ2.5-14B-Aletheia-v1 dtype: bfloat16 parameters: t: - filter: mlp value: 0.7 - value: 0 dtype: float32 out_dtype: bfloat16 ```
annasoli/Qwen2.5-14B-Instruct_bad_med_full-ft_LR1e-6_2E_V2	annasoli	2025-05-04T13:09:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T12:27:13Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
Romain-XV/15d76bc9-9fe6-4af1-99dc-78cc17008ba3	Romain-XV	2025-05-04T13:02:27Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "gemma2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "unsloth", "conversational", "arxiv:2305.18290", "base_model:unsloth/gemma-2-2b", "base_model:finetune:unsloth/gemma-2-2b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T12:18:15Z	--- base_model: unsloth/gemma-2-2b library_name: transformers model_name: 15d76bc9-9fe6-4af1-99dc-78cc17008ba3 tags: - generated_from_trainer - axolotl - dpo - trl - unsloth licence: license --- # Model Card for 15d76bc9-9fe6-4af1-99dc-78cc17008ba3 This model is a fine-tuned version of [unsloth/gemma-2-2b](https://huggingface.co/unsloth/gemma-2-2b). 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="Romain-XV/15d76bc9-9fe6-4af1-99dc-78cc17008ba3", 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/romain_fnc-xventures/Gradients-On-Demand/runs/hic12quw) 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: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
mradermacher/Veritas-12B-GGUF	mradermacher	2025-05-04T12:58:26Z	360	0	transformers	[ "transformers", "gguf", "philosophy", "reasoning", "logic", "ethics", "metaphysics", "epistemology", "critical-thinking", "debate", "dialogue", "text-generation", "uncensored", "en", "dataset:AiresPucrs/stanford-encyclopedia-philosophy", "dataset:Manel/Reddit_Stoicism_QA_610", "base_model:soob3123/Veritas-12B", "base_model:quantized:soob3123/Veritas-12B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-22T19:08:02Z	--- base_model: soob3123/Veritas-12B datasets: - AiresPucrs/stanford-encyclopedia-philosophy - Manel/Reddit_Stoicism_QA_610 language: - en library_name: transformers quantized_by: mradermacher tags: - philosophy - reasoning - logic - ethics - metaphysics - epistemology - critical-thinking - debate - dialogue - text-generation - uncensored --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/soob3123/Veritas-12B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q3_K_S.gguf) \| Q3_K_S \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q3_K_M.gguf) \| Q3_K_M \| 6.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q3_K_L.gguf) \| Q3_K_L \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.IQ4_XS.gguf) \| IQ4_XS \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q4_K_S.gguf) \| Q4_K_S \| 7.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q4_K_M.gguf) \| Q4_K_M \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q5_K_S.gguf) \| Q5_K_S \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q5_K_M.gguf) \| Q5_K_M \| 8.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q6_K.gguf) \| Q6_K \| 9.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-GGUF/resolve/main/Veritas-12B.Q8_0.gguf) \| Q8_0 \| 12.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
JahBless/roberta_bias_5cls	JahBless	2025-05-04T12:57:56Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T12:57:23Z	--- library_name: transformers license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: roberta_bias_5cls 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. --> # roberta_bias_5cls This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6460 - Accuracy: 0.8785 - F1: 0.8789 - Precision: 0.8804 - Recall: 0.8785 ## 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: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.4587 \| 1.0 \| 1000 \| 0.4554 \| 0.8475 \| 0.8470 \| 0.8543 \| 0.8475 \| \| 0.3385 \| 2.0 \| 2000 \| 0.4823 \| 0.868 \| 0.8687 \| 0.8710 \| 0.868 \| \| 0.3519 \| 3.0 \| 3000 \| 0.5443 \| 0.8725 \| 0.8738 \| 0.8757 \| 0.8725 \| \| 0.157 \| 4.0 \| 4000 \| 0.6460 \| 0.8785 \| 0.8789 \| 0.8804 \| 0.8785 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.1.2 - Datasets 3.5.1 - Tokenizers 0.21.1
Nithya9404/model-finetuned	Nithya9404	2025-05-04T12:57:35Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T12:36:29Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilgpt2 tags: - generated_from_trainer model-index: - name: model-finetuned 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. --> # model-finetuned This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) 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: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 128 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.48.3 - Pytorch 2.7.0+cu126 - Datasets 3.5.1 - Tokenizers 0.21.1
Kuugo/Rouon-Aro-All_DDSP6.1	Kuugo	2025-05-04T12:54:49Z	0	0	null	[ "region:us" ]	null	2025-05-04T11:43:28Z	# 狼音アロ SVC 语音模型 (ALL) <img src="https://img.picui.cn/free/2025/04/24/6809b0dd0f75a.jpeg" width="250" height="250"> 本项目使用DDSP 6.1训练了狼音アロ的语音合成模型，提供了一个综合版本，将所有的音频数据都进行了训练，具有最大程度的音域覆盖。 ## 📁 数据集来源: [Hugging Face数据集](https://huggingface.co/datasets/Kuugo/Rouon_Aro_voice) ## 🚀 模型详情 ### 🏗️ 模型架构 - 主模型 (Generator) - 训练步数: 30,000步 - 基于DDSP 6.1架构 ## 🙏 致谢 - DDSP 开发团队
ma921/gpt2-large_c_dpo_imdb_noise30_epoch5	ma921	2025-05-04T12:54:17Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:ma921/gpt2-large-sft-imdb", "base_model:finetune:ma921/gpt2-large-sft-imdb", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T12:53:20Z	--- library_name: transformers license: mit base_model: ma921/gpt2-large-sft-imdb tags: - generated_from_trainer model-index: - name: gpt2-large_c_dpo_imdb_noise30_epoch5 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. --> # gpt2-large_c_dpo_imdb_noise30_epoch5 This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - 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: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Mr-FineTuner/Test_02_mistral_trainPercen_myValidator	Mr-FineTuner	2025-05-04T12:51:08Z	0	0	null	[ "safetensors", "mistral", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-04T12:49:39Z	# Fine-Tuned Mistral-7B CEFR Model This is a fine-tuned version of `unsloth/mistral-7b-instruct-v0.3-bnb-4bit` for CEFR-level sentence generation, evaluated with a fine-tuned classifier from `Mr-FineTuner/Skripsi_validator_best_model`. - Base Model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit - Fine-Tuning: LoRA with SMOTE-balanced dataset - Training Details: - Dataset: CEFR-level sentences with SMOTE and undersampling for balance - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5 - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.01, cosine scheduler - Optimizer: adamw_8bit - Early Stopping: Patience=3, threshold=0.01 - Evaluation Metrics (Exact Matches): - CEFR Classifier Accuracy: 0.000 - Precision (Macro): 0.000 - Recall (Macro): 0.000 - F1-Score (Macro): 0.000 - Evaluation Metrics (Within ±1 Level): - CEFR Classifier Accuracy: 0.500 - Precision (Macro): 0.333 - Recall (Macro): 0.500 - F1-Score (Macro): 0.389 - Other Metrics: - Perplexity: 6.089 - Diversity (Unique Sentences): 0.100 - Inference Time (ms): 5150.096 - Model Size (GB): 4.1 - Robustness (F1): 0.000 - Confusion Matrix (Exact Matches): - CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv) - Image: [confusion_matrix_exact.png](confusion_matrix_exact.png) - Confusion Matrix (Within ±1 Level): - CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv) - Image: [confusion_matrix_within1.png](confusion_matrix_within1.png) - Per-Class Confusion Metrics (Exact Matches): - A1: TP=0, FP=0, FN=10, TN=50 - A2: TP=0, FP=0, FN=10, TN=50 - B1: TP=0, FP=10, FN=10, TN=40 - B2: TP=0, FP=0, FN=10, TN=50 - C1: TP=0, FP=30, FN=10, TN=20 - C2: TP=0, FP=20, FN=10, TN=30 - Per-Class Confusion Metrics (Within ±1 Level): - A1: TP=0, FP=0, FN=10, TN=50 - A2: TP=0, FP=0, FN=10, TN=50 - B1: TP=0, FP=10, FN=10, TN=40 - B2: TP=10, FP=0, FN=0, TN=50 - C1: TP=10, FP=10, FN=0, TN=40 - C2: TP=10, FP=10, FN=0, TN=40 - Usage: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test_01_withNewEval_andWithin-1_mistral_skripsi_classifier") tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test_01_withNewEval_andWithin-1_mistral_skripsi_classifier") # Example inference prompt = "<\|user\|>Generate a CEFR B1 level sentence.<\|end\|>" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Uploaded using `huggingface_hub`.
hanaearg/emo-GemaDev15	hanaearg	2025-05-04T12:50:51Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma2", "trl", "en", "base_model:unsloth/gemma-2-9b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-2-9b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T12:50:39Z	--- base_model: unsloth/gemma-2-9b-it-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hanaearg - License: apache-2.0 - Finetuned from model : unsloth/gemma-2-9b-it-bnb-4bit This gemma2 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)
rupeshs/taef1-openvino	rupeshs	2025-05-04T12:49:24Z	0	3	null	[ "text-to-image", "OpenVINO", "Flux", "license:mit", "region:us" ]	text-to-image	2024-10-07T13:23:02Z	--- license: mit tags: - text-to-image - OpenVINO - Flux --- ## Tiny AutoEncoder for FLUX.1 OpenVINO This is [madebyollin/taef1](https://huggingface.co/madebyollin/taef1) model converted to OpenVINO. [FastSD](https://github.com/rupeshs/fastsdcpu) already uses TAEF1.
mradermacher/Phi-4-reasoning-plus-GGUF	mradermacher	2025-05-04T12:43:38Z	1	0	transformers	[ "transformers", "gguf", "phi", "nlp", "math", "code", "chat", "conversational", "reasoning", "en", "base_model:microsoft/Phi-4-reasoning-plus", "base_model:quantized:microsoft/Phi-4-reasoning-plus", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-05-02T18:48:03Z	--- base_model: microsoft/Phi-4-reasoning-plus language: - en library_name: transformers license: mit license_link: https://huggingface.co/microsoft/Phi-4-reasoning-plus/resolve/main/LICENSE quantized_by: mradermacher tags: - phi - nlp - math - code - chat - conversational - reasoning --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/microsoft/Phi-4-reasoning-plus <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q2_K.gguf) \| Q2_K \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q3_K_S.gguf) \| Q3_K_S \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q3_K_M.gguf) \| Q3_K_M \| 7.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.IQ4_XS.gguf) \| IQ4_XS \| 8.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q4_K_S.gguf) \| Q4_K_S \| 8.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q4_K_M.gguf) \| Q4_K_M \| 9.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q5_K_S.gguf) \| Q5_K_S \| 10.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q5_K_M.gguf) \| Q5_K_M \| 10.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q6_K.gguf) \| Q6_K \| 12.1 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-GGUF/resolve/main/Phi-4-reasoning-plus.Q8_0.gguf) \| Q8_0 \| 15.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF	mradermacher	2025-05-04T12:41:07Z	14	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:nbeerbower/Qwen3-0.6B-abliterated-TIES", "base_model:quantized:nbeerbower/Qwen3-0.6B-abliterated-TIES", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T17:49:55Z	--- base_model: nbeerbower/Qwen3-0.6B-abliterated-TIES language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nbeerbower/Qwen3-0.6B-abliterated-TIES <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q3_K_M.gguf) \| Q3_K_M \| 0.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q8_0.gguf) \| Q8_0 \| 0.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.f16.gguf) \| f16 \| 1.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
aipib/Florence-2-VQA_OCRJP2	aipib	2025-05-04T12:41:07Z	0	0	transformers	[ "transformers", "safetensors", "florence2", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	text-generation	2025-05-04T12:37:48Z	--- 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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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]
fivedoctors/poca-SoccerTwos	fivedoctors	2025-05-04T12:38:17Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2025-05-04T12:33:36Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: fivedoctors/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
WinfredGe/T2S-pretrained_LA-VAE	WinfredGe	2025-05-04T12:37:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-04T12:35:52Z	--- license: apache-2.0 ---
nearchiee/Arthur	nearchiee	2025-05-04T12:35:15Z	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	2025-05-04T11:41:20Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Arthur <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/nearchiee/Arthur/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('nearchiee/Arthur', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/nearchiee/Arthur/discussions) to add images that show off what you’ve made with this LoRA.
Hums003/PEFT_LlaMA_3.2_MCoT	Hums003	2025-05-04T12:34:39Z	9	0	peft	[ "peft", "safetensors", "medical", "Reasoning", "Training", "question-answering", "en", "dataset:FreedomIntelligence/medical-o1-reasoning-SFT", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	question-answering	2025-04-26T02:20:06Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit library_name: peft license: apache-2.0 datasets: - FreedomIntelligence/medical-o1-reasoning-SFT language: - en metrics: - code_eval new_version: meta-llama/Llama-3.2-3B-Instruct pipeline_tag: question-answering tags: - medical - Reasoning - Training --- # 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: Humaima Anwar - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
DuongTrongChi/vinallama-dpo-v4	DuongTrongChi	2025-05-04T12:34:38Z	0	0	transformers	[ "transformers", "safetensors", "llama", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-04T12:32:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Eehan/Qwen2.5-1.5B-drpo-hh-gpm-4dim-temp-0.66-beta-0.04	Eehan	2025-05-04T12:33:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T12:31:29Z	--- 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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Gwangwoon/muse2	Gwangwoon	2025-05-04T12:31:25Z	141	0	peft	[ "peft", "safetensors", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "region:us" ]	null	2025-03-28T05:09:40Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: peft language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # 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. 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icefog72/Ice0.110-04.05-RP	icefog72	2025-05-04T12:28:33Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T11:47:44Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # Ice0.110-04.05-RP 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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * F:\FModels\Ice0.108-04.05-RP * F:\FModels\Ice0.109-04.05-RP ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: F:\FModels\Ice0.108-04.05-RP layer_range: [0, 32] - model: F:\FModels\Ice0.109-04.05-RP layer_range: [0, 32] merge_method: slerp base_model: F:\FModels\Ice0.108-04.05-RP 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 dtype: bfloat16 ```
YOYO-AI/YOYO-O1-32B-V4-preview1-Q4_K_M-GGUF	YOYO-AI	2025-05-04T12:26:35Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:YOYO-AI/YOYO-O1-32B-V4-preview1", "base_model:quantized:YOYO-AI/YOYO-O1-32B-V4-preview1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T12:25:08Z	--- base_model: YOYO-AI/YOYO-O1-32B-V4-preview1 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # YOYO-AI/YOYO-O1-32B-V4-preview1-Q4_K_M-GGUF This model was converted to GGUF format from [`YOYO-AI/YOYO-O1-32B-V4-preview1`](https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4-preview1) 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/YOYO-AI/YOYO-O1-32B-V4-preview1) 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 YOYO-AI/YOYO-O1-32B-V4-preview1-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo YOYO-AI/YOYO-O1-32B-V4-preview1-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview1-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 YOYO-AI/YOYO-O1-32B-V4-preview1-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo YOYO-AI/YOYO-O1-32B-V4-preview1-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview1-q4_k_m.gguf -c 2048 ```
himel7/roberta-babe	himel7	2025-05-04T12:26:01Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-04T12:25:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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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Nasanbuyan/mongolian-gpt2-qa-m4	Nasanbuyan	2025-05-04T12:25:53Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:ai-forever/mGPT-1.3B-mongol", "base_model:adapter:ai-forever/mGPT-1.3B-mongol", "license:mit", "region:us" ]	null	2025-05-04T12:25:03Z	--- library_name: peft license: mit base_model: ai-forever/mGPT-1.3B-mongol tags: - generated_from_trainer model-index: - name: mongolian-gpt2-qa-m4 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. --> # mongolian-gpt2-qa-m4 This model is a fine-tuned version of [ai-forever/mGPT-1.3B-mongol](https://huggingface.co/ai-forever/mGPT-1.3B-mongol) 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_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: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 50 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0.dev20241112 - Datasets 3.5.1 - Tokenizers 0.21.1
DuongTrongChi/vinallama-dpo-v2	DuongTrongChi	2025-05-04T12:24:02Z	0	0	transformers	[ "transformers", "llama", "feature-extraction", "text-generation-inference", "unsloth", "en", "base_model:DuongTrongChi/vinallama-2.7b-chat-sft-v1", "base_model:finetune:DuongTrongChi/vinallama-2.7b-chat-sft-v1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-04T12:23:17Z	--- base_model: DuongTrongChi/vinallama-2.7b-chat-sft-v1 tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: DuongTrongChi - License: apache-2.0 - Finetuned from model : DuongTrongChi/vinallama-2.7b-chat-sft-v1 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)
bodam/Llama-3.2-1B-ko_wiki-4bit-rlhf-200	bodam	2025-05-04T12:22:10Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T12:17:11Z	--- base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: bodam - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-1B-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)
loim/good_ru_en_tokenizer	loim	2025-05-04T12:19:16Z	0	0	null	[ "ru", "en", "license:apache-2.0", "region:us" ]	null	2025-05-02T20:28:17Z	--- license: apache-2.0 language: - ru - en --- # Русско-английский BPE-токенизатор Оптимизированный токенизатор для двуязычных текстов с расширенной поддержкой русской лексики и эффективной обработкой английского. ## Ключевые характеристики - Формат: BPE (Byte-Pair Encoding) - Размер словаря: 32 768 токенов (оптимизированный баланс) - Языки: Русский + Английский - Специальные токены: `<\|im_start\|>` `<\|im_end\|>` `<\|system\|>` `<\|user\|>` `<\|assistant\|>` `<\|function\|>` `<\|endoftext\|>` `<\|padding\|>` `<\|mask\|>` `<\|reserved_1\|>` `<\|reserved_2\|>` `<\|reserved_3\|>` `<\|reserved_4\|>` `<\|reserved_5\|>` `<\|reserved_6\|>` `<\|reserved_7\|>` `<\|reserved_8\|>` --- ## Тесты 1) Простой текст ```text Привет! Как твои дела? Надеюсь, у тебя всё хорошо. Сегодня отличная погода — солнце и +25°C. Пойдёшь вечером гулять в парк? Вот новости за день: 1. Курс доллара: 90.5 руб. 2. В Москве открыли новую станцию метро. 3. Учёные создали лекарство от простуды. Не забудь купить: - Молоко (2.5%) - Хлеб «Бородинский» - Яйца (10 шт.) Извини, что пишу так поздно (уже 23:30), но это срочно! P.S. Встречаемся завтра в 14:00 у кафе «Утро». Ты ведь ещё не передумал? 😊 ``` \| Токенизатор \| Размер словаря \| Кол-во токенов \| Детокенизация идеально совпадает? \| \|-----------------------------\|------------------\|------------------\|-------------------------------------\| \| good_ru_en_tokenizer \| 32768 \| 171 \| ✅ Да \| \| T5 \| 32100 \| 335 \| ❌ Нет \| \| GPT-2 \| 50257 \| 486 \| ✅ Да \| \| ruGPT-3.5-13B \| 50257 \| 156 \| ✅ Да \| \| EleutherAI/gpt-neo \| 50257 \| 486 \| ✅ Да \| \| BERT \| 119547 \| 190 \| ❌ Нет \| \| T-lite-instruct-0.1 \| 128000 \| 189 \| ✅ Да \| \| RuadaptQwen2.5-7B-Lite-Beta \| 145109 \| 164 \| ✅ Да \| 1) Английский текст ```text Hi! How are you? Hope you're doing well. The weather is great today—sunny and +25°C (77°F). Are you going for a walk in the park this evening? Here’s the news for today: 1. Dollar exchange rate: 90.5 RUB. 2. A new metro station opened in Moscow. 3. Scientists have developed a cure for the common cold. Don’t forget to buy: - Milk (2.5%) - "Borodinsky" bread - Eggs (10 pcs.) Sorry for writing so late (it's already 11:30 PM), but it's urgent! P.S. We’re meeting tomorrow at 2:00 PM near the "Morning" café. You haven’t changed your mind, have you? 😊 ``` \| Токенизатор \| Размер словаря \| Кол-во токенов \| Детокенизация идеально совпадает? \| \|-----------------------------\|------------------\|------------------\|-------------------------------------\| \| good_ru_en_tokenizer \| 32768 \| 176 \| ✅ Да \| \| T5 \| 32100 \| 177 \| ❌ Нет \| \| GPT-2 \| 50257 \| 188 \| ✅ Да \| \| ruGPT-3.5-13B \| 50257 \| 208 \| ✅ Да \| \| EleutherAI/gpt-neo \| 50257 \| 188 \| ✅ Да \| \| BERT \| 119547 \| 177 \| ❌ Нет \| \| T-lite-instruct-0.1 \| 128000 \| 164 \| ✅ Да \| \| RuadaptQwen2.5-7B-Lite-Beta \| 145109 \| 172 \| ✅ Да \| 3) Сложный текст ````text В 2024 году ИИ (например, GPT-5 от OpenAI или "Llama-3") достиг уровня AGI — но так ли это? 🤔 Научные данные: - Формула E=mc² + квантовое уравнение: ĤΨ = iℏ∂Ψ/∂t - Химическая реакция: 2H₂ + O₂ → 2H₂O (ΔH = -286 кДж/моль) - Код на Python: ```python def hello(): print("Привет, 世界! 🌍") ``` Лингвистические "изюминки": 1. Англ.: "The naïve piñata café" (с диакритикой!) 2. Яп.: 日本語のテキスト (и кандзи: 水曜日) 3. Кит.: 人工智能 (AI) + пиньинь: rén gōng zhì néng 4. Ар.: النص العربي مع تشكيل (алфавит + огласовки) Спецформаты: • HTML: <div class="header">Title</div> • LaTeX: \frac{d}{dx}\left( \int_{0}^{x} f(t)\,dt \right) • Регулярки: /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z\|a-z]{2,}\b/ Редкие случаи: - Слова с апострофами: "don’t", "L’Oréal", "D’Artagnan" - Числа: 1,000,000 (англ.) vs 1.000.000 (европ.) - Даты: 2024-07-15 (ISO) vs 15/07/24 (UK) vs 7/15/24 (US) - Валюта: $100 ≈ €92.5 ≈ ¥15,000 Эмодзи-стресс-тест: 😂🔥✨ (обычные) → 👨‍👩‍👧‍👦 (семья) + 🇺🇦 (флаг) + 🧑‍💻 (программист) Философский вопрос: "Если 'токенизация' → ['токен', '##иза', '##ция'], то что есть 'смысл'?" ```` \| Токенизатор \| Размер словаря \| Кол-во токенов \| Детокенизация идеально совпадает? \| \|-----------------------------\|------------------\|------------------\|-------------------------------------\| \| good_ru_en_tokenizer \| 32768 \| 658 \| ✅ Да \| \| T5 \| 32100 \| 723 \| ❌ Нет \| \| GPT-2 \| 50257 \| 986 \| ✅ Да \| \| ruGPT-3.5-13B \| 50257 \| 704 \| ✅ Да \| \| EleutherAI/gpt-neo \| 50257 \| 986 \| ✅ Да \| \| BERT \| 119547 \| 537 \| ❌ Нет \| \| T-lite-instruct-0.1 \| 128000 \| 561 \| ✅ Да \| \| RuadaptQwen2.5-7B-Lite-Beta \| 145109 \| 584 \| ✅ Да \|
icefog72/Ice0.109-04.05-RP	icefog72	2025-05-04T12:09:36Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T04:56:33Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # Ice0.109-04.05-RP 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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * F:\FModels\Ice0.108-04.05-RP * H:\FModels\Ice0.104-13.04-RP ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: F:\FModels\Ice0.108-04.05-RP layer_range: [0, 32] - model: H:\FModels\Ice0.104-13.04-RP layer_range: [0, 32] merge_method: slerp base_model: F:\FModels\Ice0.108-04.05-RP 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 dtype: bfloat16 ```
ymoslem/whisper-small-bemba-v1	ymoslem	2025-05-04T12:09:14Z	1	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "bem", "dataset:kreasof-ai/bigc-bem-eng", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-03T11:15:50Z	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-bem-v1 results: [] language: - bem datasets: - kreasof-ai/bigc-bem-eng --- <!-- 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-bem-v1 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4645 - Wer: 36.1826 When combined with [fine-tuned NLLB-200 3.3B](kreasof-ai/nllb-200-3.3B-bem2eng-bigc-flores200-tatoeba), Bemba-English results are as follows: \| BLEU \| ChrF++ \| COMET \| \|:------:\|:------:\|:------:\| \| 27.41 \| 49.65 \| 51.77 \| ## Model description Bemba automatic speech recognition (ASR) ## Intended uses & limitations For research purposes only ## Training and evaluation data Big-C ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.5933 \| 1.0 \| 10645 \| 0.5593 \| 44.3113 \| \| 0.4243 \| 2.0 \| 21290 \| 0.4738 \| 38.8064 \| \| 0.2944 \| 3.0 \| 31935 \| 0.4645 \| 36.1826 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.4.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
ASethi04/meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001	ASethi04	2025-05-04T12:08:17Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T11:56:20Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001", 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/torchql-org/huggingface/runs/d4pfeczu) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
wingo-dz/gemma3_1b	wingo-dz	2025-05-04T12:05:38Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3_text", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-04T08:52:47Z	--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: wingo-dz - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dgambettaphd/M_llm2_gen9_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-05-04T12:03:38Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T12:03:26Z	--- 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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edvenswa/ICD-COT-100-reasoning-Test-8-llama3-1-8b	edvenswa	2025-05-04T12:02:13Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-04T12:02:06Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: edvenswa - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-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)
MCES10/Phi-4-reasoning-plus-mlx-fp16	MCES10	2025-05-04T11:57:15Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "phi", "nlp", "math", "code", "chat", "conversational", "reasoning", "mlx", "mlx-my-repo", "en", "base_model:microsoft/Phi-4-reasoning-plus", "base_model:finetune:microsoft/Phi-4-reasoning-plus", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-04T11:55:31Z	--- license: mit license_link: https://huggingface.co/microsoft/Phi-4-reasoning-plus/resolve/main/LICENSE language: - en base_model: microsoft/Phi-4-reasoning-plus pipeline_tag: text-generation tags: - phi - nlp - math - code - chat - conversational - reasoning - mlx - mlx-my-repo inference: parameters: temperature: 0 widget: - messages: - role: user content: What is the derivative of x^2? library_name: transformers --- # MCES10/Phi-4-reasoning-plus-mlx-fp16 The Model [MCES10/Phi-4-reasoning-plus-mlx-fp16](https://huggingface.co/MCES10/Phi-4-reasoning-plus-mlx-fp16) was converted to MLX format from [microsoft/Phi-4-reasoning-plus](https://huggingface.co/microsoft/Phi-4-reasoning-plus) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("MCES10/Phi-4-reasoning-plus-mlx-fp16") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
ASethi04/meta-llama-Llama-3.1-8B-tulu-code_alpaca-first-lora-4-0.0001	ASethi04	2025-05-04T11:56:02Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "endpoints_compatible", "region:us" ]	null	2025-05-04T11:44:10Z	--- base_model: meta-llama/Llama-3.1-8B library_name: transformers model_name: meta-llama-Llama-3.1-8B-tulu-code_alpaca-first-lora-4-0.0001 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for meta-llama-Llama-3.1-8B-tulu-code_alpaca-first-lora-4-0.0001 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). 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="ASethi04/meta-llama-Llama-3.1-8B-tulu-code_alpaca-first-lora-4-0.0001", 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/torchql-org/huggingface/runs/s5fhwse2) This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.51.2 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
TakalaWang/Discussion-Phi-4-text	TakalaWang	2025-05-04T11:52:35Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-4", "base_model:adapter:microsoft/phi-4", "license:mit", "region:us" ]	null	2025-05-04T11:11:17Z	--- library_name: peft license: mit base_model: microsoft/phi-4 tags: - generated_from_trainer model-index: - name: Discussion-Phi-4-text 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. --> # Discussion-Phi-4-text This model is a fine-tuned version of [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1265 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.6764 \| 0.2235 \| 10 \| 2.4496 \| \| 2.1053 \| 0.4469 \| 20 \| 1.9257 \| \| 1.222 \| 0.6704 \| 30 \| 1.0594 \| \| 0.1878 \| 0.8939 \| 40 \| 0.1615 \| \| 0.1642 \| 1.1117 \| 50 \| 0.1395 \| \| 0.1127 \| 1.3352 \| 60 \| 0.1343 \| \| 0.1483 \| 1.5587 \| 70 \| 0.1332 \| \| 0.1342 \| 1.7821 \| 80 \| 0.1338 \| \| 0.1529 \| 2.0 \| 90 \| 0.1323 \| \| 0.1327 \| 2.2235 \| 100 \| 0.1289 \| \| 0.095 \| 2.4469 \| 110 \| 0.1286 \| \| 0.1446 \| 2.6704 \| 120 \| 0.1304 \| \| 0.1631 \| 2.8939 \| 130 \| 0.1265 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.4.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
riyanatsill/FT_PMB	riyanatsill	2025-05-04T11:46:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-04T11:33:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]

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