Split (1)

train · 847k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-27 18:27:39	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-27 18:23:41	card stringlengths 11 1.01M
Nerimaru/Cinema_gpt-Q8_0-GGUF	Nerimaru	2025-05-03T06:11:44Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:Nerimaru/Cinema_gpt", "base_model:quantized:Nerimaru/Cinema_gpt", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T06:11:10Z	--- base_model: Nerimaru/Cinema_gpt tags: - llama-cpp - gguf-my-repo --- # Nerimaru/Cinema_gpt-Q8_0-GGUF This model was converted to GGUF format from [`Nerimaru/Cinema_gpt`](https://huggingface.co/Nerimaru/Cinema_gpt) 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/Nerimaru/Cinema_gpt) 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 Nerimaru/Cinema_gpt-Q8_0-GGUF --hf-file cinema_gpt-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Nerimaru/Cinema_gpt-Q8_0-GGUF --hf-file cinema_gpt-q8_0.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 Nerimaru/Cinema_gpt-Q8_0-GGUF --hf-file cinema_gpt-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Nerimaru/Cinema_gpt-Q8_0-GGUF --hf-file cinema_gpt-q8_0.gguf -c 2048 ```
shibajustfor/3345c74b-ab00-4a6f-ad04-4478968f921e	shibajustfor	2025-05-03T06:06:08Z	0	0	transformers	[ "transformers", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-05-03T06:05:23Z	--- library_name: transformers model_name: shibajustfor/3345c74b-ab00-4a6f-ad04-4478968f921e tags: - generated_from_trainer licence: license --- # Model Card for shibajustfor/3345c74b-ab00-4a6f-ad04-4478968f921e 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: 0.12.0 - Transformers: 4.46.3 - Pytorch: 2.5.1 - 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}} } ```
sanabar/roberta-goemo-journals	sanabar	2025-05-03T06:05:41Z	65	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:SamLowe/roberta-base-go_emotions", "base_model:finetune:SamLowe/roberta-base-go_emotions", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-17T00:19:52Z	--- library_name: transformers license: mit base_model: SamLowe/roberta-base-go_emotions tags: - generated_from_trainer metrics: - precision - recall model-index: - name: roberta-goemo-journals 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-goemo-journals ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
Romain-XV/a8097e9c-cb81-482b-bb6c-9bc08d7c1ee3	Romain-XV	2025-05-03T06:02:22Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "opt", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:facebook/opt-1.3b", "base_model:finetune:facebook/opt-1.3b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T05:29:49Z	--- base_model: facebook/opt-1.3b library_name: transformers model_name: a8097e9c-cb81-482b-bb6c-9bc08d7c1ee3 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for a8097e9c-cb81-482b-bb6c-9bc08d7c1ee3 This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b). 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/a8097e9c-cb81-482b-bb6c-9bc08d7c1ee3", 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/vnoqxofg) 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/phi3.5-hallucination-judge-merge-GGUF	mradermacher	2025-05-03T06:00:13Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:grounded-ai/phi3.5-hallucination-judge-merge", "base_model:quantized:grounded-ai/phi3.5-hallucination-judge-merge", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T18:03:01Z	--- base_model: grounded-ai/phi3.5-hallucination-judge-merge language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/grounded-ai/phi3.5-hallucination-judge-merge <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-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/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q3_K_M.gguf) \| Q3_K_M \| 2.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q3_K_L.gguf) \| Q3_K_L \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q4_K_S.gguf) \| Q4_K_S \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q4_K_M.gguf) \| Q4_K_M \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q5_K_S.gguf) \| Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q6_K.gguf) \| Q6_K \| 3.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-GGUF/resolve/main/phi3.5-hallucination-judge-merge.f16.gguf) \| f16 \| 7.7 \| 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 -->
Hachipo/Qwen2.5-7B-CoTRFT_1000_2	Hachipo	2025-05-03T06:00:07Z	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	2025-05-03T05:56:26Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
briannaulriq/SculptmaxxDietCapsules	briannaulriq	2025-05-03T05:58:38Z	0	0	null	[ "region:us" ]	null	2025-05-03T05:48:15Z	<p><strong>⇉⇉</strong><strong> Acheter maintenant</strong><strong>⇒</strong><strong>➧➧ </strong><a href="https://www.wlafnl.com/fr/produit/sculptmaxx-regime-avis/">https://www.wlafnl.com/fr/produit/sculptmaxx-regime-avis/</a></p> <p><strong>⇉⇉</strong><strong> Lien Facebook</strong><strong>⇒</strong><strong>➧➧</strong><a href="https://www.facebook.com/groups/sculptmaxxdietprix">https://www.facebook.com/groups/sculptmaxxdietprix</a></p> <p><strong>Que sont les Sculptmaxx Diet Capsules?</strong></p> <p><a href="https://www.wlafnl.com/fr/produit/sculptmaxx-regime-avis/">Sculptmaxx Diet Capsules Avis</a> est un complément minceur naturel de haute qualité, conçu pour aider les personnes à atteindre leurs objectifs de remise en forme. Contrairement aux compléments minceur classiques bourrés d'énergisants synthétiques, les capsules utilisent des ingrédients naturels soigneusement sélectionnés et issus de recherches approfondies, associés aux sciences de la croissance métabolique, de l'équilibre de l'appétit et de l'augmentation de la combustion des graisses.</p> <p>Sculptmaxx Diet Capsules : La solution complète pour un contrôle durable du poids : Perdre du poids est frustrant pour beaucoup d'entre nous. Chacun est confronté à des graisses résistantes, un métabolisme lent et des envies constantes qui freinent tout espoir d'obtenir la silhouette souhaitée. Bien que l'alimentation et l'exercice physique soient essentiels, le corps peut parfois avoir besoin d'un soutien supplémentaire. Sculptmaxx Diet Capsules promet d'apporter ce soutien en augmentant le métabolisme, en supprimant les fringales et en stimulant le système de combustion des graisses. En intégrant ces capsules à votre routine quotidienne, vous obtiendrez un niveau d'énergie constant, une meilleure digestion et des bénéfices à long terme sur la gestion du poids, sans avoir recours à un régime trop strict ou à un programme d'entraînement intensif.</p> <p><a href="https://www.facebook.com/groups/sculptmaxxdietprix">https://www.facebook.com/groups/sculptmaxxdietprix</a></p> <p><a href="https://www.facebook.com/groups/sculptmaxxdietcapsulesavis">https://www.facebook.com/groups/sculptmaxxdietcapsulesavis</a></p> <p><a href="https://www.facebook.com/groups/sculptmaxxdietprix/posts/2317829991932195/">https://www.facebook.com/groups/sculptmaxxdietprix/posts/2317829991932195/</a></p> <p><a href="https://www.facebook.com/share/p/1FhQN9hRo4/">https://www.facebook.com/share/p/1FhQN9hRo4/</a></p> <p><a href="https://www.facebook.com/groups/sculptmaxxdietcapsulesavis/posts/9611691052251273/">https://www.facebook.com/groups/sculptmaxxdietcapsulesavis/posts/9611691052251273/</a></p> <p><a href="https://www.facebook.com/share/p/1J6XQZofuH/">https://www.facebook.com/share/p/1J6XQZofuH/</a></p> <p><a href="https://www.facebook.com/events/1154268166471783/">https://www.facebook.com/events/1154268166471783/</a></p> <p><a href="https://sculptmaxxdietcapsule.quora.com/">https://sculptmaxxdietcapsule.quora.com/</a></p> <p><a href="https://sculptmaxxdietcapsule.quora.com/https-www-wlafnl-com-fr-produit-sculptmaxx-regime-avis-https-www-wlafnl-com-Buy-SculptmaxxDiet-https-www-facebook">https://sculptmaxxdietcapsule.quora.com/https-www-wlafnl-com-fr-produit-sculptmaxx-regime-avis-https-www-wlafnl-com-Buy-SculptmaxxDiet-https-www-facebook</a></p> <p><a href="https://www.quora.com/What-Is-Sculptmaxx-Diet-Capsules-Official-website/answer/Koby-Fullwoqq">https://www.quora.com/What-Is-Sculptmaxx-Diet-Capsules-Official-website/answer/Koby-Fullwoqq</a></p> <p><a href="https://teeshopper.in/store/Sculptmaxx-Diet-Capsules">https://teeshopper.in/store/Sculptmaxx-Diet-Capsules</a></p> <p><a href="https://teeshopper.in/store/Sculptmaxx-Regime-Avis-Where-To-Buy">https://teeshopper.in/store/Sculptmaxx-Regime-Avis-Where-To-Buy</a></p> <p><a href="https://fr.pinterest.com/SculptmaxxDietSiteOfficiel/">https://fr.pinterest.com/SculptmaxxDietSiteOfficiel/</a></p> <p><a href="https://fr.pinterest.com/SculptmaxxRegimeAvis/">https://fr.pinterest.com/SculptmaxxRegimeAvis/</a> </p>
memeviss/zombieVI_3	memeviss	2025-05-03T05:50:37Z	0	0	null	[ "safetensors", "region:us" ]	null	2025-05-03T05:48:24Z	# Optimized TTS Model This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques. ## Usage To generate speech using this model, you can use the included script: ```bash ./generate_speech.py --text "Your text here" --output_path output.wav ``` For more details, see the optimization report in this directory.
SmallDoge/Qwen2.5-math-14b-llmlingua-90	SmallDoge	2025-05-03T05:32:45Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T16:59:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lisabdunlap/llama-3.1-8b-4b	lisabdunlap	2025-05-03T05:31:33Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T05:29:19Z	--- 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: lisabdunlap - 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)
Fazziomonsieur/Mitch	Fazziomonsieur	2025-05-03T05:30:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T05:30:22Z	--- license: apache-2.0 ---
prithivMLmods/Bpe-vocab-n-OCR	prithivMLmods	2025-05-03T05:27:57Z	85	4	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "text-generation-inference", "bpe", "ocr", "image-to-text", "en", "zh", "base_model:prithivMLmods/Qwen2-VL-OCR-2B-Instruct", "base_model:finetune:prithivMLmods/Qwen2-VL-OCR-2B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2025-02-18T07:25:08Z	--- license: apache-2.0 language: - en - zh base_model: - prithivMLmods/Qwen2-VL-OCR-2B-Instruct pipeline_tag: image-to-text library_name: transformers tags: - text-generation-inference - bpe - ocr --- # Bpe-vocab-n-OCR ![bpe.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/f3-ZGWYpFHmRn7L-hSvtt.png) Bpe-vocab-n-OCR is an advanced OCR-based text extraction tool optimized for generating structured, tokenized outputs. Built upon a powerful vision-language architecture with enhanced OCR and multilingual support, Bpe-vocab-n-OCR accurately extracts text from images and returns it as a comma-separated sequence. #### Key Enhancements: * Advanced OCR Engine: Fine-tuned on extensive datasets, Bpe-vocab-n-OCR ensures precise text recognition and tokenization. * Optimized for Tokenized Output: Produces structured comma-separated text, making it ideal for downstream NLP tasks, automation pipelines, and database integrations. * Enhanced Multilingual OCR: Supports text extraction in multiple languages, including English, Chinese, Japanese, Korean, Arabic, and more. * Multimodal Processing: Seamlessly processes both image and text inputs, providing structured tokenized outputs. * Secure and Optimized Model Weights: Employs safetensors for efficient and secure model loading. ![sdsdfsd.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XT1Qe2WxVzclETv6Rmgfs.png) ### How to Use ```python from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info # Load the Bpe-vocab-n-OCR model with optimized parameters model = Qwen2VLForConditionalGeneration.from_pretrained( "prithivMLmods/Tokenized-OCR", torch_dtype="auto", device_map="auto" ) # Recommended acceleration for performance optimization: # model = Qwen2VLForConditionalGeneration.from_pretrained( # "prithivMLmods/Tokenized-OCR", # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) # Load the default processor for Bpe-vocab-n-OCR processor = AutoProcessor.from_pretrained("prithivMLmods/Tokenized-OCR") # Define the input messages with both an image and a text prompt messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://flux-generated.com/sample_image.jpeg", }, {"type": "text", "text": "Extract and return the tokenized OCR text from the image, ensuring each word is accurately recognized and separated by commas."}, ], } ] # Prepare the input for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Generate the output generated_ids = model.generate(inputs, max_new_tokens=256) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` ### Key Features 1. High-Accuracy OCR Processing** * Extracts and tokenizes text from images with exceptional precision. 2. Multilingual Text Recognition * Supports multiple languages, ensuring comprehensive OCR capabilities. 3. Comma-Separated Tokenized Output * Generates structured text for seamless NLP and data processing tasks. 4. Efficient Image & Text Processing * Handles both visual and textual inputs, ensuring accurate OCR-based extraction. 5. Optimized for Secure Deployment * Uses safetensors for enhanced security and model efficiency.
AlSamCur123/Llama-3.2-1B-InstructContinuedFine	AlSamCur123	2025-05-03T05:26:47Z	4	0	transformers	[ "transformers", "pytorch", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-01T01:55:37Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: AlSamCur123 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
tsaksatara73/dfv	tsaksatara73	2025-05-03T05:22:41Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-03T05:22:40Z	--- license: creativeml-openrail-m ---
punitub01/llama2-7b-finetuned-merged	punitub01	2025-05-03T05:21:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T05:12:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RRashmini/google-umt5-small-7	RRashmini	2025-05-03T05:06:22Z	0	0	transformers	[ "transformers", "safetensors", "umt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-03T05:05:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
CompassioninMachineLearning/May2_10k_four_fifths_animals_PLORA_newest	CompassioninMachineLearning	2025-05-03T04:57:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T04:56:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Asif4929/Asif	Asif4929	2025-05-03T04:55:02Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T04:55:02Z	--- license: apache-2.0 ---
Kenazin/Mistral-7B-peft-p-tuning-v3-6	Kenazin	2025-05-03T04:49:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T04:49:08Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ayushchakravarthy/qwen3-0.6b-base-s1-sft	ayushchakravarthy	2025-05-03T04:45:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T03:53:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
XzWang/ruozhiReasoner-Qwen3-8B	XzWang	2025-05-03T04:44:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T04:38:15Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
AthenaAgent42/llama-r1-ft13k-ex3	AthenaAgent42	2025-05-03T04:44:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T04:44:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
jmalejandrob79/nbmaexp01	jmalejandrob79	2025-05-03T04:42:36Z	3	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	2025-05-02T02:36:46Z	--- 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: nbmaexp01 --- # Nbmaexp01 <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 `nbmaexp01` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "nbmaexp01", "lora_weights": "https://huggingface.co/jmalejandrob79/nbmaexp01/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('jmalejandrob79/nbmaexp01', weight_name='lora.safetensors') image = pipeline('nbmaexp01').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: 4500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/nbmaexp01/discussions) to add images that show off what you’ve made with this LoRA.
MrRobotoAI/157-Q4_K_M-GGUF	MrRobotoAI	2025-05-03T04:38:37Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:MrRobotoAI/157", "base_model:quantized:MrRobotoAI/157", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T04:38:13Z	--- base_model: MrRobotoAI/157 library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # MrRobotoAI/157-Q4_K_M-GGUF This model was converted to GGUF format from [`MrRobotoAI/157`](https://huggingface.co/MrRobotoAI/157) 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/MrRobotoAI/157) 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 MrRobotoAI/157-Q4_K_M-GGUF --hf-file 157-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo MrRobotoAI/157-Q4_K_M-GGUF --hf-file 157-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 MrRobotoAI/157-Q4_K_M-GGUF --hf-file 157-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo MrRobotoAI/157-Q4_K_M-GGUF --hf-file 157-q4_k_m.gguf -c 2048 ```
Membersuger/Euro_7	Membersuger	2025-05-03T04:37:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T02:32:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
shibajustfor/2f0c3f41-514a-46fc-be20-718013a619f4	shibajustfor	2025-05-03T04:32:34Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B", "base_model:adapter:Qwen/Qwen2.5-7B", "region:us" ]	null	2025-05-03T04:32:04Z	--- library_name: peft tags: - generated_from_trainer base_model: Qwen/Qwen2.5-7B model-index: - name: shibajustfor/2f0c3f41-514a-46fc-be20-718013a619f4 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. --> # shibajustfor/2f0c3f41-514a-46fc-be20-718013a619f4 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4631 ## 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
era-temporary/eb-man-7b-stage1-lr-1e-5-lora-e1	era-temporary	2025-05-03T04:21:50Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct", "region:us" ]	null	2025-05-03T04:20:44Z	--- base_model: Qwen/Qwen2.5-VL-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
DeltaSatellite1/CoinYOLO	DeltaSatellite1	2025-05-03T04:20:09Z	0	0	null	[ "object-detection", "en", "base_model:Ultralytics/YOLO11", "base_model:finetune:Ultralytics/YOLO11", "region:us" ]	object-detection	2025-05-03T01:44:33Z	--- language: - en base_model: - Ultralytics/YOLO11 pipeline_tag: object-detection ---
DoppelReflEx/MiniusLight-24B-v2.2b-test-Q4_K_S-GGUF	DoppelReflEx	2025-05-03T04:16:13Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DoppelReflEx/MiniusLight-24B-v2.2b-test", "base_model:quantized:DoppelReflEx/MiniusLight-24B-v2.2b-test", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T04:15:11Z	--- base_model: DoppelReflEx/MiniusLight-24B-v2.2b-test library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DoppelReflEx/MiniusLight-24B-v2.2b-test-Q4_K_S-GGUF This model was converted to GGUF format from [`DoppelReflEx/MiniusLight-24B-v2.2b-test`](https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2.2b-test) 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/DoppelReflEx/MiniusLight-24B-v2.2b-test) 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 DoppelReflEx/MiniusLight-24B-v2.2b-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2b-test-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DoppelReflEx/MiniusLight-24B-v2.2b-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2b-test-q4_k_s.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 DoppelReflEx/MiniusLight-24B-v2.2b-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2b-test-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DoppelReflEx/MiniusLight-24B-v2.2b-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2b-test-q4_k_s.gguf -c 2048 ```
cyberbabooshka/post_pretrain_pre_cooldown	cyberbabooshka	2025-05-03T04:11:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "axolotl", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T04:11:11Z	--- library_name: transformers tags: - axolotl --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
fats-fme/11813507-b1af-412e-a487-858d4ea24855	fats-fme	2025-05-03T04:08:19Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:elyza/Llama-3-ELYZA-JP-8B", "base_model:adapter:elyza/Llama-3-ELYZA-JP-8B", "license:llama3", "region:us" ]	null	2025-05-03T03:59:43Z	--- library_name: peft license: llama3 base_model: elyza/Llama-3-ELYZA-JP-8B tags: - axolotl - generated_from_trainer model-index: - name: 11813507-b1af-412e-a487-858d4ea24855 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 adapter: lora base_model: elyza/Llama-3-ELYZA-JP-8B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 13b16be7f737d1a4_train_data.json ds_type: json format: custom path: /workspace/input_data/13b16be7f737d1a4_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/11813507-b1af-412e-a487-858d4ea24855 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: cosine max_memory: 0: 130GB max_steps: 50 micro_batch_size: 1 mlflow_experiment_name: /tmp/13b16be7f737d1a4_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 1024 special_tokens: pad_token: <\|eot_id\|> 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: a15fa850-4ddf-4312-aec2-39afd0e9a706 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: a15fa850-4ddf-4312-aec2-39afd0e9a706 warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 11813507-b1af-412e-a487-858d4ea24855 This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - 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: 200 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0012 \| 1 \| 1.1470 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
BABYSHARK09/New57	BABYSHARK09	2025-05-03T04:07:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T03:01:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tuyetkung/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_nasty_mandrill	tuyetkung	2025-05-03T04:06:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am untamed nasty mandrill", "trl", "conversational", "arxiv:2402.03300", "base_model:unsloth/Qwen2.5-0.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T04:02:56Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_nasty_mandrill tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am untamed nasty mandrill - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_nasty_mandrill This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="tuyetkung/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-untamed_nasty_mandrill", 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 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Zack-Z/gemma3_27bi_cotsft_rs0_2_5cut_gem3all_e2	Zack-Z	2025-05-03T04:02:05Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-27b-it", "base_model:finetune:unsloth/gemma-3-27b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T01:44:54Z	--- base_model: unsloth/gemma-3-27b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Zack-Z - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-it This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
OMP123/Dolphin-Mistral-24B-Venice-Edition-Q4_0-GGUF	OMP123	2025-05-03T03:57:15Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T03:56:13Z	--- base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # OMP123/Dolphin-Mistral-24B-Venice-Edition-Q4_0-GGUF This model was converted to GGUF format from [`cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition`](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) 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/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) 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 OMP123/Dolphin-Mistral-24B-Venice-Edition-Q4_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo OMP123/Dolphin-Mistral-24B-Venice-Edition-Q4_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_0.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 OMP123/Dolphin-Mistral-24B-Venice-Edition-Q4_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo OMP123/Dolphin-Mistral-24B-Venice-Edition-Q4_0-GGUF --hf-file dolphin-mistral-24b-venice-edition-q4_0.gguf -c 2048 ```
phucd/blip-gqa-ft-trial3	phucd	2025-05-03T03:53:55Z	0	0	transformers	[ "transformers", "safetensors", "blip-2", "visual-question-answering", "generated_from_trainer", "base_model:Salesforce/blip2-opt-2.7b", "base_model:finetune:Salesforce/blip2-opt-2.7b", "license:mit", "endpoints_compatible", "region:us" ]	visual-question-answering	2025-05-03T00:06:18Z	--- library_name: transformers license: mit base_model: Salesforce/blip2-opt-2.7b tags: - generated_from_trainer model-index: - name: blip-gqa-ft-trial3 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. --> # blip-gqa-ft-trial3 This model is a fine-tuned version of [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7298 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.917 \| 1.0 \| 313 \| 1.9330 \| \| 1.6347 \| 2.0 \| 626 \| 1.8037 \| \| 1.6861 \| 2.992 \| 936 \| 1.7298 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu121 - Datasets 3.5.0 - Tokenizers 0.21.1
Jathushan/TamilPaattu_bert_2	Jathushan	2025-05-03T03:50:22Z	0	0	transformers	[ "transformers", "safetensors", "bert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-03T03:49:47Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF	NikolayKozloff	2025-05-03T03:47:54Z	0	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:kalomaze/Qwen3-16B-A3B", "base_model:quantized:kalomaze/Qwen3-16B-A3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T03:47:07Z	--- base_model: kalomaze/Qwen3-16B-A3B license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF This model was converted to GGUF format from [`kalomaze/Qwen3-16B-A3B`](https://huggingface.co/kalomaze/Qwen3-16B-A3B) 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/kalomaze/Qwen3-16B-A3B) 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 NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.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 NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.gguf -c 2048 ```
RichardErkhov/1231czx_-_it_dpo_unbiased-gguf	RichardErkhov	2025-05-03T03:47:37Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T01:41:29Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) it_dpo_unbiased - GGUF - Model creator: https://huggingface.co/1231czx/ - Original model: https://huggingface.co/1231czx/it_dpo_unbiased/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [it_dpo_unbiased.Q2_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q2_K.gguf) \| Q2_K \| 2.96GB \| \| [it_dpo_unbiased.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.IQ3_XS.gguf) \| IQ3_XS \| 3.28GB \| \| [it_dpo_unbiased.IQ3_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.IQ3_S.gguf) \| IQ3_S \| 3.43GB \| \| [it_dpo_unbiased.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q3_K_S.gguf) \| Q3_K_S \| 3.41GB \| \| [it_dpo_unbiased.IQ3_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.IQ3_M.gguf) \| IQ3_M \| 3.52GB \| \| [it_dpo_unbiased.Q3_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q3_K.gguf) \| Q3_K \| 3.74GB \| \| [it_dpo_unbiased.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q3_K_M.gguf) \| Q3_K_M \| 3.74GB \| \| [it_dpo_unbiased.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q3_K_L.gguf) \| Q3_K_L \| 4.03GB \| \| [it_dpo_unbiased.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.IQ4_XS.gguf) \| IQ4_XS \| 4.18GB \| \| [it_dpo_unbiased.Q4_0.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q4_0.gguf) \| Q4_0 \| 4.34GB \| \| [it_dpo_unbiased.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.IQ4_NL.gguf) \| IQ4_NL \| 4.38GB \| \| [it_dpo_unbiased.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q4_K_S.gguf) \| Q4_K_S \| 4.37GB \| \| [it_dpo_unbiased.Q4_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q4_K.gguf) \| Q4_K \| 4.58GB \| \| [it_dpo_unbiased.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q4_K_M.gguf) \| Q4_K_M \| 4.58GB \| \| [it_dpo_unbiased.Q4_1.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q4_1.gguf) \| Q4_1 \| 4.78GB \| \| [it_dpo_unbiased.Q5_0.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q5_0.gguf) \| Q5_0 \| 5.21GB \| \| [it_dpo_unbiased.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q5_K_S.gguf) \| Q5_K_S \| 5.21GB \| \| [it_dpo_unbiased.Q5_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q5_K.gguf) \| Q5_K \| 5.34GB \| \| [it_dpo_unbiased.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q5_K_M.gguf) \| Q5_K_M \| 5.34GB \| \| [it_dpo_unbiased.Q5_1.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q5_1.gguf) \| Q5_1 \| 5.65GB \| \| [it_dpo_unbiased.Q6_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q6_K.gguf) \| Q6_K \| 6.14GB \| \| [it_dpo_unbiased.Q8_0.gguf](https://huggingface.co/RichardErkhov/1231czx_-_it_dpo_unbiased-gguf/blob/main/it_dpo_unbiased.Q8_0.gguf) \| Q8_0 \| 7.95GB \| Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BABYSHARK09/New51	BABYSHARK09	2025-05-03T03:46:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T03:00:22Z	--- 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. (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]
BABYSHARK09/New50	BABYSHARK09	2025-05-03T03:46:04Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T03:00:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BABYSHARK09/New48	BABYSHARK09	2025-05-03T03:37:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T03:00:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF	DoppelReflEx	2025-05-03T03:34:32Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:DoppelReflEx/MiniusLight-24B-v2.2a-test", "base_model:quantized:DoppelReflEx/MiniusLight-24B-v2.2a-test", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-03T03:33:29Z	--- base_model: DoppelReflEx/MiniusLight-24B-v2.2a-test library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF This model was converted to GGUF format from [`DoppelReflEx/MiniusLight-24B-v2.2a-test`](https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2.2a-test) 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/DoppelReflEx/MiniusLight-24B-v2.2a-test) 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 DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.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 DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.gguf -c 2048 ```
punitub01/llama2-7b-qlora-finetuned	punitub01	2025-05-03T03:29:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-03T03:28:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF	mradermacher	2025-05-03T03:21:38Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu", "base_model:quantized:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T21:18:45Z	--- base_model: IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu language: en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-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/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q3_K_M.gguf) \| Q3_K_M \| 2.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q3_K_L.gguf) \| Q3_K_L \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q4_K_S.gguf) \| Q4_K_S \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q4_K_M.gguf) \| Q4_K_M \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q5_K_S.gguf) \| Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q6_K.gguf) \| Q6_K \| 3.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.f16.gguf) \| f16 \| 7.7 \| 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 -->
BABYSHARK09/New40	BABYSHARK09	2025-05-03T03:12:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T02:59:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sakhalif10/fluxoldvhseffect	sakhalif10	2025-05-03T03:10:14Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	2025-05-03T03:10:09Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/VHS+Trailer+v3+4-3.00_00_48_26.Still001.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: apache-2.0 --- # vhs-old-effect-flux <Gallery /> ## Model description this is my first flux loras ## Download model [Download](/sakhalif10/fluxoldvhseffect/tree/main) them in the Files & versions tab.
thavens-research/Qwen2.5-3B-Instruct	thavens-research	2025-05-03T03:06:30Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T00:38:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
flyingbugs/Qwen2.5-Math-7B-generalthoughts-0.5-token-prune	flyingbugs	2025-05-03T02:52:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "sft", "conversational", "dataset:flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune", "base_model:Qwen/Qwen2.5-Math-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Math-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T21:20:03Z	--- base_model: Qwen/Qwen2.5-Math-7B-Instruct datasets: flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune library_name: transformers model_name: Qwen2.5-Math-7B-generalthoughts-0.5-token-prune tags: - generated_from_trainer - open-r1 - trl - sft licence: license --- # Model Card for Qwen2.5-Math-7B-generalthoughts-0.5-token-prune This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the [flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune](https://huggingface.co/datasets/flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune) 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="flyingbugs/Qwen2.5-Math-7B-generalthoughts-0.5-token-prune", 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/jjh233/huggingface/runs/5bizs4qo) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1+cu121 - Datasets: 3.3.2 - Tokenizers: 0.21.0 ## 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}} } ```
jnjj/my_model	jnjj	2025-05-03T02:52:33Z	0	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	2025-05-03T02:48:07Z	--- library_name: transformers ---
AdoCleanCode/real_model_VGG_v0_000	AdoCleanCode	2025-05-03T02:48:35Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T21:28:17Z	--- library_name: transformers license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: real_model_VGG_v0_000 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. --> # real_model_VGG_v0_000 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3392 ## 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.5856 \| 1.0 \| 5830 \| 1.4615 \| \| 1.4415 \| 2.0 \| 11660 \| 1.3859 \| \| 1.3959 \| 3.0 \| 17490 \| 1.3585 \| \| 1.3496 \| 4.0 \| 23320 \| 1.3438 \| \| 1.304 \| 5.0 \| 29150 \| 1.3392 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.20.3
luckycanucky/discord_model_x3_16b	luckycanucky	2025-05-03T02:45:52Z	0	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T02:42:36Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: luckycanucky - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
muhamedhaniix/autotrain-z4ebf-a3v2c	muhamedhaniix	2025-05-03T02:35:48Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T02:25:42Z	--- library_name: transformers tags: - autotrain - text-classification base_model: google-bert/bert-base-uncased widget: - text: "I love AutoTrain" --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 2.3292133808135986 f1_macro: 0.08863613405764569 f1_micro: 0.2876712328767123 f1_weighted: 0.17317690876148983 precision_macro: 0.07013888888888889 precision_micro: 0.2876712328767123 precision_weighted: 0.12907153729071538 recall_macro: 0.1420673076923077 recall_micro: 0.2876712328767123 recall_weighted: 0.2876712328767123 accuracy: 0.2876712328767123
18-Tutorial-Paro-Aarti-Viral-Videos/Original.Viral.Clip.Paro.Aarti.Viral.Video	18-Tutorial-Paro-Aarti-Viral-Videos	2025-05-03T02:35:28Z	0	0	null	[ "region:us" ]	null	2025-05-03T02:33:24Z	Watch 🟢 ➤ ➤ ➤ <a href="https://blackcloudz.com/Full-Original-Link-Paro-Aarti-Viral-Viral-Video"> 🌐 Click Here To link (Original.Viral.Clip.Paro.Aarti.Viral.Video) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://blackcloudz.com/Full-Original-Link-Paro-Aarti-Viral-Viral-Video"> 🌐 FullOriginal.Viral.Clip.Paro.Aarti.Viral.Video ![68747470733a2f2f692e696d6775722e636f6d2f644a486b345a712e676966.gif](https://cdn-uploads.huggingface.co/production/uploads/68158046366c03d52e483f63/3qgnASTZQ9JzCTvK2xU2x.gif)
niftier/Pala.dzinolda.na.dc.nic.nie.trzeba.robic	niftier	2025-05-03T02:30:16Z	0	0	null	[ "region:us" ]	null	2025-05-03T02:27:44Z	Watch 🟢 ➤ ➤ ➤ <a href="https://blackcloudz.com/full-video-Pała-dzinolda-na-dc-nic-nie-trzeba-robić"> 🌐 Click Here To link (Original.Pała.dzinolda.na.dc.nic.nie.trzeba.robić) 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://blackcloudz.com/full-video-Pała-dzinolda-na-dc-nic-nie-trzeba-robić"> 🌐 Full.Original.Pała.dzinolda.na.dc.nic.nie.trzeba.robić
mlfoundations-dev/no_pipeline_code_10k	mlfoundations-dev	2025-05-03T02:27:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "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	2025-05-02T22:50:11Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: no_pipeline_code_10k 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. --> # no_pipeline_code_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/no_pipeline_code_10k 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
jairosolare/biglustv17	jairosolare	2025-05-03T02:25:39Z	0	0	null	[ "region:us" ]	null	2025-05-03T02:23:47Z	SDXL checkpoint continuation/addition/fork of biglust 1.6 by waterdrinker on civitai credit for biglust 1.7 goes to: https://civitai.com/models/1433766/biglust-17
JOSESMOKE/tear_467	JOSESMOKE	2025-05-03T02:22:10Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T02:07:29Z	--- 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).
BenevolenceMessiah/Qwen3-14B-Enhanced-v1.0-DARE-TIES	BenevolenceMessiah	2025-05-03T02:19:31Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "base_model:Ba2han/Qwen-3-14B-Gemini-v0.1", "base_model:merge:Ba2han/Qwen-3-14B-Gemini-v0.1", "base_model:Qwen/Qwen3-14B", "base_model:merge:Qwen/Qwen3-14B", "base_model:secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b", "base_model:merge:secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T02:17:28Z	--- base_model: - Ba2han/Qwen-3-14B-Gemini-v0.1 - secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b - Qwen/Qwen3-14B 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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) as a base. ### Models Merged The following models were included in the merge: * [Ba2han/Qwen-3-14B-Gemini-v0.1](https://huggingface.co/Ba2han/Qwen-3-14B-Gemini-v0.1) * [secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b](https://huggingface.co/secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b) ### Configuration The following YAML configuration was used to produce this model: ```yaml # Qwen-3-14B-Enhanced-v1.0-DARE-TIES merge_method: dare_ties base_model: Qwen/Qwen3-14B parameters: density: 0.333 random_seed: 37 models: - model: secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b parameters: weight: 0.5 - model: Ba2han/Qwen-3-14B-Gemini-v0.1 parameters: weight: 0.5 tokenizer: source: union chat_template: auto dtype: bfloat16 ```
AthenaAgent42/llama-r1-ft13k-ex1	AthenaAgent42	2025-05-03T02:03:12Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-03T02:03:03Z	--- base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AthenaAgent42 - 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)
mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF	mradermacher	2025-05-03T02:00:20Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu", "base_model:quantized:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-03T00:03:43Z	--- base_model: IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu language: en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-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/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 1.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q2_K.gguf) \| i1-Q2_K \| 1.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ3_M.gguf) \| i1-IQ3_M \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.3 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-i1-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.i1-Q6_K.gguf) \| i1-Q6_K \| 3.2 \| practically like static Q6_K \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF	mradermacher	2025-05-03T02:00:19Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu", "base_model:quantized:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T21:02:52Z	--- base_model: IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu language: en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/IntelLabs/sqft-sparsepeft-phi-3-mini-4k-60-math-heu <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-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/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q3_K_M.gguf) \| Q3_K_M \| 2.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q3_K_L.gguf) \| Q3_K_L \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q4_K_S.gguf) \| Q4_K_S \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q4_K_M.gguf) \| Q4_K_M \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q5_K_S.gguf) \| Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q6_K.gguf) \| Q6_K \| 3.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-60-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-60-math-heu.f16.gguf) \| f16 \| 7.7 \| 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 -->
Harisarehman/deepseekR7	Harisarehman	2025-05-03T01:50:40Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-03T01:50:40Z	--- license: apache-2.0 ---
vermoney/519eb761-2695-42fb-bd75-7b4dc64f7363	vermoney	2025-05-03T01:37:08Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B", "base_model:adapter:Qwen/Qwen2.5-7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-03T01:27:28Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-7B tags: - axolotl - generated_from_trainer model-index: - name: 519eb761-2695-42fb-bd75-7b4dc64f7363 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 adapter: lora base_model: Qwen/Qwen2.5-7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7c94ef2bcc1e3456_train_data.json ds_type: json format: custom path: /workspace/input_data/7c94ef2bcc1e3456_train_data.json type: field_instruction: instruction field_output: output 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: false fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vermoney/519eb761-2695-42fb-bd75-7b4dc64f7363 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/7c94ef2bcc1e3456_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: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: ec445b84-2090-42c6-b555-4bdd59ca3038 wandb_project: s56-9 wandb_run: your_name wandb_runid: ec445b84-2090-42c6-b555-4bdd59ca3038 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 519eb761-2695-42fb-bd75-7b4dc64f7363 This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6508 ## 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: 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: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.7179 \| 0.0135 \| 200 \| 0.6508 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ericlewis/CLIP-ViT-L-14-laion2B-s32B-b82K	ericlewis	2025-05-03T01:32:49Z	6	0	open_clip	[ "open_clip", "pytorch", "safetensors", "clip", "zero-shot-image-classification", "arxiv:2110.09456", "arxiv:2111.09883", "arxiv:1910.04867", "license:mit", "region:us" ]	zero-shot-image-classification	2024-06-07T22:25:09Z	--- license: mit widget: - src: >- https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog library_name: open_clip pipeline_tag: zero-shot-image-classification --- # Model Card for CLIP ViT-L/14 - LAION-2B # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) 7. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description A CLIP ViT L/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training ('babysitting') done by Ross Wightman on the [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) supercomputer. See acknowledgements below. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, Any deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). IMPORTANT NOTE: The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure The model was trained on 384 A100 GPUs using 200M sample 'virtual' epochs where dataset shards were sampled with replacement. The model was trained with 160 virtual epochs for a total of 32B samples seen. The first 68 epochs were trained with float16 AMP, global batch size 79K (208 per GPU). Initially running to epoch 75, where the loss spiked and training failed with NaN. Romain Beaumont was training H/14 and g/14 models at the same time on Stability cluster and hit similar instabilities. Collectively we tried restarts with, * different dataset shuffle seed * different LR * gradient clipping * modifications to the architecture * Norm modifications (stable norm for final, post embed norm for text transformer) as per https://github.com/mlfoundations/open_clip/pull/153 thanks to Phil Wang * Extra attention block norms ala Normformer (https://arxiv.org/abs/2110.09456) * Scaled cosine attention ala Swin-V2 (https://arxiv.org/abs/2111.09883) None of the above ended up working. Most blew up within the same epoch as original, with the exception of architecture mods. * Normformer mods signifcantly altered the network such that resuming did not quickly converge to previous performance, this was abandoned but might be worth trying from start. * Scaled cosine attn initially looked promising and lasted until epoch 90 before loss suddenly increased and appeared to remain 'stuck'. In the end, restarting at epoch 69 with `float32` precision solved all instabilities and training continued from there with global batch size 86k (224 per GPU). On A100 GPUs, `float32` had a minimal impact on the throughput once `tf32` matmuls were enabled in PyTorch. Approximately 10% slower than `float16 AMP`. Romain similary changed the precision but ended up using `bfloat16 AMP` to resolve issues. ### Slum Script ``` #SBATCH --nodes=96 #SBATCH --gres=gpu:4 #SBATCH --ntasks-per-node=4 #SBATCH --cpus-per-task=6 #SBATCH --wait-all-nodes=1 #SBATCH --job-name=open_clip_laion2b # load low-level libraries ml purge source /conda/bin/activate pytorch-112 export NCCL_ASYNC_ERROR_HANDLING=1 export CUDA_VISIBLE_DEVICES=0,1,2,3 export MASTER_PORT=12802 ### get the first node name as master address - customized for vgg slurm ### e.g. master(gnodee[2-5],gnoded1) == gnodee2 echo "NODELIST="${SLURM_NODELIST} master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" \| head -n 1) export MASTER_ADDR=$master_addr"i" echo "MASTER_ADDR="$MASTER_ADDR cd /home/me/open_clip export PYTHONPATH="$PYTHONPATH:$PWD/src" srun --cpu_bind=none,v --accel-bind=gn python -u src/training/main.py \ --save-frequency 1 \ --zeroshot-frequency 1 \ --train-data="/data/laion2B-en/{00000..23295}.tar" \ --train-num-samples=200000000 \ --warmup 10000 \ --lr "1e-3" \ --batch-size=224 \ --epochs=160 \ --workers=6 \ --model ViT-L-14 \ --name "L14-laion2B" \ --report-to "tensorboard" \ --seed 0 \ --precision 'fp32' \ --ddp-static-graph \ --local-loss \ --dataset-resampled \ --gather-with-grad \ --grad-checkpointing ``` # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. TODO - more detail ## Results The model achieves a 75.3 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb TODO - create table for just this model's metrics. # Acknowledgements Acknowledging the Gauss Centre for Supercomputing e.V. (http://gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC). # Citation BibTeX: LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` OpenCLIP software ``` @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` # How to Get Started with the Model Use the code below to get started with the model. TODO - Hugging Face transformers, OpenCLIP, and timm getting started snippets
HabibAhmed/Granite3.2-2B-lora-BF16	HabibAhmed	2025-05-03T01:32:06Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ibm-granite/granite-3.2-2b-instruct", "base_model:adapter:ibm-granite/granite-3.2-2b-instruct", "region:us" ]	null	2025-05-02T00:44:10Z	--- base_model: ibm-granite/granite-3.2-2b-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
JOSESMOKE/tear_465	JOSESMOKE	2025-05-03T01:22:51Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T01:16:32Z	--- 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).
espnet/owls_9B_180K	espnet	2025-05-03T01:19:18Z	12	0	espnet	[ "espnet", "audio", "automatic-speech-recognition", "speech-translation", "multilingual", "dataset:owsm_v3.1", "arxiv:2502.10373", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2025-02-14T00:09:26Z	--- tags: - espnet - audio - automatic-speech-recognition - speech-translation language: multilingual datasets: - owsm_v3.1 license: cc-by-4.0 --- ## OWLS: Open Whisper-style Large-scale neural model Suite OWLS is a suite of Whisper-style models, designed to help researchers understand the scaling properties of speech models. OWLS models range from 0.25B to 18B parameters, and are trained on up to 360K hours of data. OWLS models are developed using [ESPnet](https://github.com/espnet/espnet), and support multilingual Speech Recognition and Translation. It is part of the [OWSM](https://www.wavlab.org/activities/2024/owsm/) project, which aims to develop fully open speech foundation models using publicly available data and open-source toolkits. The model in this repo has 9.31B parameters in total and is trained on 180k hours of public speech data. Specifically, it supports the following speech-to-text tasks: - Speech recognition - Any-to-any-language speech translation - Utterance-level alignment - Long-form transcription - Language identification ## Use this model You can use this model in your projects with the following code: ```python # make sure espnet is installed: pip install espnet from espnet2.bin.s2t_inference import Speech2Text model = Speech2Text.from_pretrained( "espnet/owls_9B_180K" ) speech, rate = soundfile.read("speech.wav") speech = librosa.resample(speech, orig_sr=rate, target_sr=16000) # make sure 16k sampling rate text, *_ = model(speech)[0] ``` ## OWLS models \| Model Name \| Checkpoint \| Training Artifacts \| \| ------------------ \| ------- \| --------------------------------------------------------------------------------------- \| \| OWLS 0.25B 180K \| https://huggingface.co/espnet/owls_025B_180K \| TBA \| \| OWLS 0.50B 180K \| https://huggingface.co/espnet/owls_05B_180K \| https://huggingface.co/espnet/owls_05B_180K_intermediates/tree/main \| \| OWLS 1B 11K \| TBA \| TBA \| \| OWLS 1B 22K \| TBA \| TBA \| \| OWLS 1B 45K \| TBA \| TBA \| \| OWLS 1B 90K \| TBA \| TBA \| \| OWLS 1B 180K \| https://huggingface.co/espnet/owls_1B_180K \| TBA \| \| OWLS 2B 180K \| https://huggingface.co/espnet/owls_2B_180K \| TBA \| \| OWLS 4B 180K \| https://huggingface.co/espnet/owls_4B_180K \| https://huggingface.co/espnet/owls_4B_180K_intermediates \| \| OWLS 9B 180K \| https://huggingface.co/espnet/owls_9B_180K \| https://huggingface.co/espnet/owls_9B_180K_intermediates \| \| OWLS 18B 180K \| https://huggingface.co/espnet/owls_18B_180K \| TBA \| \| OWLS 18B 360K \| https://huggingface.co/espnet/owls_18B_360K \| TBA \| ## Citations ``` @article{chen2025owls, title={OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models}, author={Chen, William and Tian, Jinchuan and Peng, Yifan and Yan, Brian and Yang, Chao-Han Huck and Watanabe, Shinji}, journal={arXiv preprint arXiv:2502.10373}, year={2025} } ```
chchen/MentaLLaMA-chat-7B-PsyCourse-doc-info-fold8	chchen	2025-05-03T01:13:46Z	0	0	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:klyang/MentaLLaMA-chat-7B-hf", "base_model:adapter:klyang/MentaLLaMA-chat-7B-hf", "license:mit", "region:us" ]	null	2025-05-02T23:35:00Z	--- library_name: peft license: mit base_model: klyang/MentaLLaMA-chat-7B-hf tags: - llama-factory - lora - generated_from_trainer model-index: - name: MentaLLaMA-chat-7B-PsyCourse-doc-info-fold8 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. --> # MentaLLaMA-chat-7B-PsyCourse-doc-info-fold8 This model is a fine-tuned version of [klyang/MentaLLaMA-chat-7B-hf](https://huggingface.co/klyang/MentaLLaMA-chat-7B-hf) on the course-doc-info-train-fold8 dataset. It achieves the following results on the evaluation set: - Loss: 0.0957 ## 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: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.3878 \| 0.3951 \| 10 \| 0.5402 \| \| 0.261 \| 0.7901 \| 20 \| 0.3376 \| \| 0.1716 \| 1.1852 \| 30 \| 0.2597 \| \| 0.1226 \| 1.5802 \| 40 \| 0.2301 \| \| 0.1363 \| 1.9753 \| 50 \| 0.1612 \| \| 0.116 \| 2.3704 \| 60 \| 0.1366 \| \| 0.0883 \| 2.7654 \| 70 \| 0.1216 \| \| 0.0747 \| 3.1605 \| 80 \| 0.1082 \| \| 0.089 \| 3.5556 \| 90 \| 0.1005 \| \| 0.0792 \| 3.9506 \| 100 \| 0.0975 \| \| 0.0645 \| 4.3457 \| 110 \| 0.0962 \| \| 0.075 \| 4.7407 \| 120 \| 0.0957 \| ### Framework versions - PEFT 0.12.0 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
togethercomputer/M1-3B	togethercomputer	2025-05-03T01:12:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2504.10449", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T04:30:50Z	--- license: mit library_name: transformers pipeline_tag: text-generation --- This is the model is trained using paper, [M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models](https://arxiv.org/abs/2504.10449). \| Model \| AIME 2025 \| AIME 2024 \| MATH 500 \| AMC 2023 \| OlympiadBench \| \|-----------------------------------\|---------------\|---------------\|--------------\|--------------\|-------------------\| \| Qwen2.5-Math-7B-Instruct (Transformer) \| – \| 13.3 \| 79.8 \| 50.6 \| 40.7 \| \| rStar-Math-7B (Transformer) \| – \| 26.7 \| 78.4 \| 47.5 \| 47.1 \| \| Eurus-2-7B-PRIME (Transformer) \| – \| 26.7 \| 79.2 \| 57.8 \| 42.1 \| \| Qwen2.5-7B-SimpleRL (Transformer) \| – \| 26.7 \| 82.4 \| 62.5 \| 43.3 \| \| DeepSeek-R1-Distill-Qwen-1.5B (Transformer) \| 23.0 \| 28.8 \| 82.8 \| 62.9 \| 43.3 \| \| M1-3B (Mamba Hybrid Models) \| 23.5 \| 28.5 \| 84.0 \| 62.8 \| 47.3 \| Code: https://github.com/jxiw/M1 ``` @article{wang2025m1scalabletesttimecompute, title={M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models}, author={Junxiong Wang and Wen-Ding Li and Daniele Paliotta and Daniel Ritter and Alexander M. Rush and Tri Dao}, journal={arXiv preprint arXiv:2504.10449}, year={2025}, url={https://arxiv.org/abs/2504.10449}, }
RLHF-And-Friends/RM-TLDR-TLDR-Llama-3.2-3B-SmallSFT	RLHF-And-Friends	2025-05-03T00:54:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-classification", "generated_from_trainer", "trl", "reward-trainer", "dataset:tldr-preference", "base_model:RLHF-And-Friends/TLDR-Llama-3.2-3B-SmallSFT", "base_model:finetune:RLHF-And-Friends/TLDR-Llama-3.2-3B-SmallSFT", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T00:50:23Z	--- base_model: RLHF-And-Friends/TLDR-Llama-3.2-3B-SmallSFT datasets: tldr-preference library_name: transformers model_name: RM-TLDR-TLDR-Llama-3.2-3B-SmallSFT tags: - generated_from_trainer - trl - reward-trainer licence: license --- # Model Card for RM-TLDR-TLDR-Llama-3.2-3B-SmallSFT This model is a fine-tuned version of [RLHF-And-Friends/TLDR-Llama-3.2-3B-SmallSFT](https://huggingface.co/RLHF-And-Friends/TLDR-Llama-3.2-3B-SmallSFT) on the [tldr-preference](https://huggingface.co/datasets/tldr-preference) 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="RLHF-And-Friends/RM-TLDR-TLDR-Llama-3.2-3B-SmallSFT", 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/RADFAN/RM-TLDR/runs/dr7gs3rp) This model was trained with Reward. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.1 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
user074/sft_qwen1b_composer	user074	2025-05-03T00:49:46Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "en", "arxiv:2407.10671", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-03T00:48:16Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B/blob/main/LICENSE language: - en pipeline_tag: text-generation library_name: transformers --- # Qwen2.5-1.5B ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the base 1.5B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 1.54B - Number of Paramaters (Non-Embedding): 1.31B - Number of Layers: 28 - Number of Attention Heads (GQA): 12 for Q and 2 for KV - Context Length: Full 32,768 tokens We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
JOSESMOKE/tear_462	JOSESMOKE	2025-05-03T00:49:17Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-03T00:31:50Z	--- 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).
muhamedhaniix/autotrain-5au6i-144lu	muhamedhaniix	2025-05-03T00:48:47Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-03T00:27:43Z	--- library_name: transformers tags: - autotrain - text-classification base_model: google-bert/bert-base-uncased widget: - text: "I love AutoTrain" --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 1.3450746536254883 f1_macro: 0.26587301587301587 f1_micro: 0.48148148148148145 f1_weighted: 0.4012345679012345 precision_macro: 0.23260073260073258 precision_micro: 0.48148148148148145 precision_weighted: 0.35002035002035 recall_macro: 0.31726190476190474 recall_micro: 0.48148148148148145 recall_weighted: 0.48148148148148145 accuracy: 0.48148148148148145
jmalejandrob79/normam02	jmalejandrob79	2025-05-03T00:28:11Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-02T15:20:21Z	--- 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: normam02 --- # Normam02 <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 `normam02` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "normam02", "lora_weights": "https://huggingface.co/jmalejandrob79/normam02/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('jmalejandrob79/normam02', weight_name='lora.safetensors') image = pipeline('normam02').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: 4000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jmalejandrob79/normam02/discussions) to add images that show off what you’ve made with this LoRA.
histin116/pose-control-lora	histin116	2025-05-03T00:23:41Z	0	0	diffusers	[ "diffusers", "flux", "flux-diffusers", "text-to-image", "control-lora", "diffusers-training", "lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-01T13:23:50Z	--- base_model: black-forest-labs/FLUX.1-dev library_name: diffusers license: other inference: true tags: - flux - flux-diffusers - text-to-image - diffusers - control-lora - diffusers-training - lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # control-lora-histin116/pose-control-lora These are Control LoRA weights trained on black-forest-labs/FLUX.1-dev with new type of conditioning. ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_cosine_0.5_0.5_True_300	shubhamprshr	2025-05-03T00:21:20Z	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "dataset:blocksworld-dataset", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-17T18:52:25Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: blocksworld-dataset library_name: transformers model_name: Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_cosine_0.5_0.5_True_300 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_cosine_0.5_0.5_True_300 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-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="shubhamprshr/Qwen2.5-1.5B-Instruct_blocksworld1246_sgrpo_cosine_0.5_0.5_True_300", 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/shubhamprshr27-tamu/BW2/runs/2bq7uxvd) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.14.0 - Transformers: 4.48.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.0 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/II-Medical-7B-Preview-GGUF	mradermacher	2025-05-03T00:11:36Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Intelligent-Internet/II-Medical-7B-Preview", "base_model:quantized:Intelligent-Internet/II-Medical-7B-Preview", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T21:37:00Z	--- base_model: Intelligent-Internet/II-Medical-7B-Preview language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Intelligent-Internet/II-Medical-7B-Preview <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/II-Medical-7B-Preview-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/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-GGUF/resolve/main/II-Medical-7B-Preview.f16.gguf) \| f16 \| 15.3 \| 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 -->
fats-fme/a1290cfd-1030-488d-8af2-f23368792716	fats-fme	2025-05-03T00:10:59Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/codellama-7b", "base_model:adapter:unsloth/codellama-7b", "license:apache-2.0", "region:us" ]	null	2025-05-02T23:54:05Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codellama-7b tags: - axolotl - generated_from_trainer model-index: - name: a1290cfd-1030-488d-8af2-f23368792716 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 adapter: lora base_model: unsloth/codellama-7b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1e342bbeaf894e58_train_data.json ds_type: json format: custom path: /workspace/input_data/1e342bbeaf894e58_train_data.json type: field_input: input field_instruction: instruction field_output: chosen format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/a1290cfd-1030-488d-8af2-f23368792716 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: cosine max_memory: 0: 130GB max_steps: 50 micro_batch_size: 1 mlflow_experiment_name: /tmp/1e342bbeaf894e58_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 1bc31bc4-0adf-49b9-bb84-67aba32775dc wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 1bc31bc4-0adf-49b9-bb84-67aba32775dc warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # a1290cfd-1030-488d-8af2-f23368792716 This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - 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: 200 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
goldedda/Ed-AI	goldedda	2025-05-03T00:00:23Z	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	2025-05-02T23:33:39Z	--- 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: zoinks --- # Ed Ai <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 `zoinks` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "zoinks", "lora_weights": "https://huggingface.co/goldedda/Ed-AI/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('goldedda/Ed-AI', weight_name='lora.safetensors') image = pipeline('zoinks').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/goldedda/Ed-AI/discussions) to add images that show off what you’ve made with this LoRA.
Docty/dreambooth-moranka-lora	Docty	2025-05-02T23:58:50Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "lora", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stable-diffusion-v1-5/stable-diffusion-v1-5", "base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2025-05-02T23:44:10Z	--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true instance_prompt: skistyle tags: - text-to-image - diffusers - lora - diffusers-training - stable-diffusion - stable-diffusion-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA DreamBooth - Docty/dreambooth-moranka-lora These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on skistyle using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
ilyass31/AI-negotiation-assistant	ilyass31	2025-05-02T23:56:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "qwen3", "trl", "conversational", "custom_code", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T22:06:59Z	--- base_model: unsloth/qwen3-14b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ilyass31 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
saramoncayon/sol	saramoncayon	2025-05-02T23:49:36Z	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	2025-05-02T23:36:40Z	--- 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: sol --- # Sol <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 `sol ` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "sol ", "lora_weights": "https://huggingface.co/saramoncayon/sol/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('saramoncayon/sol', weight_name='lora.safetensors') image = pipeline('sol ').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/saramoncayon/sol/discussions) to add images that show off what you’ve made with this LoRA.
DevQuasar/microsoft.MAI-DS-R1-GGUF	DevQuasar	2025-05-02T23:47:29Z	646	0	null	[ "gguf", "text-generation", "base_model:microsoft/MAI-DS-R1", "base_model:quantized:microsoft/MAI-DS-R1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-26T05:03:12Z	--- base_model: - microsoft/MAI-DS-R1 pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) 'Make knowledge free for everyone' Quantized version of: [microsoft/MAI-DS-R1](https://huggingface.co/microsoft/MAI-DS-R1) <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
Afaf/arab-qwen2.5-3B-grpo	Afaf	2025-05-02T23:45:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-02T23:37:31Z	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Afaf - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/phi3_unlearned_LoRa_ACSEmployment_2_cfda_ep4_22	MinaMila	2025-05-02T23:38:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-02T23:38:29Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
cwaud/3218bdd7-24fe-48a8-bdcc-a18831328e5c	cwaud	2025-05-02T23:36:38Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama_v1.1", "base_model:adapter:TinyLlama/TinyLlama_v1.1", "license:apache-2.0", "region:us" ]	null	2025-05-02T23:32:48Z	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama_v1.1 tags: - axolotl - generated_from_trainer model-index: - name: 3218bdd7-24fe-48a8-bdcc-a18831328e5c 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: `0.5.2` ```yaml adapter: lora base_model: TinyLlama/TinyLlama_v1.1 bf16: auto chat_template: llama3 dataset_prepared_path: /workspace/axolotl/data_prepared datasets: - data_files: - e1230b33949f9bdf_train_data.json ds_type: json format: custom path: /workspace/axolotl/data type: field_instruction: question field_output: chosen 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: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: cwaud/3218bdd7-24fe-48a8-bdcc-a18831328e5c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /workspace/axolotl/data/e1230b33949f9bdf_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: 4 sequence_len: 512 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: 0ace46bc-8f88-4e70-95b9-9502b5a4d1dc wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 0ace46bc-8f88-4e70-95b9-9502b5a4d1dc warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3218bdd7-24fe-48a8-bdcc-a18831328e5c This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6293 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - 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: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3664 \| 0.0002 \| 1 \| 1.7174 \| \| 1.5623 \| 0.0007 \| 3 \| 1.7129 \| \| 1.5257 \| 0.0014 \| 6 \| 1.6821 \| \| 1.526 \| 0.0021 \| 9 \| 1.6293 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.2 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
MPTarun/llama_aac_model-GGUF	MPTarun	2025-05-02T23:27:35Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T23:27:02Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: MPTarun - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jahyungu/Qwen2.5-7B-Instruct_MetaMathQA-40K_cluster9	jahyungu	2025-05-02T23:19:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "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	2025-05-02T19:08:06Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-7B-Instruct_MetaMathQA-40K_cluster9 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. --> # Qwen2.5-7B-Instruct_MetaMathQA-40K_cluster9 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) 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: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - 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: 200 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.50.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0
shubhamprshr/Qwen2.5-1.5B-Instruct_aqua_sgrpo_gaussian_0.25_0.75_True_300	shubhamprshr	2025-05-02T23:18:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "dataset:gsm8k-dataset", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T15:17:05Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: gsm8k-dataset library_name: transformers model_name: Qwen2.5-1.5B-Instruct_aqua_sgrpo_gaussian_0.25_0.75_True_300 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2.5-1.5B-Instruct_aqua_sgrpo_gaussian_0.25_0.75_True_300 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [gsm8k-dataset](https://huggingface.co/datasets/gsm8k-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="shubhamprshr/Qwen2.5-1.5B-Instruct_aqua_sgrpo_gaussian_0.25_0.75_True_300", 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/shubhamprshr27-tamu/AQUA/runs/c14cnaz9) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.14.0 - Transformers: 4.48.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.21.0 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
lisabdunlap/Llama-3.1-8B-Instruct-unsloth-bnb-4bit-r32-e20-lr0.0002-json_format_small-new	lisabdunlap	2025-05-02T23:00:05Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-02T23:00:04Z	--- 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: lisabdunlap - 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)
vertings6/143e7cde-4155-4815-a227-ec9ac4d4d219	vertings6	2025-05-02T22:50:47Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-02T22:03:20Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 143e7cde-4155-4815-a227-ec9ac4d4d219 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: true adapter: lora base_model: Qwen/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 21c49dc937709928_train_data.json ds_type: json format: custom path: /workspace/input_data/21c49dc937709928_train_data.json type: field_instruction: en field_output: fr format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 144 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vertings6/143e7cde-4155-4815-a227-ec9ac4d4d219 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-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: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/21c49dc937709928_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: 3817e1a8-ed6c-45eb-9aef-fd65e3afe80f wandb_project: s56-32 wandb_run: your_name wandb_runid: 3817e1a8-ed6c-45eb-9aef-fd65e3afe80f warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 143e7cde-4155-4815-a227-ec9ac4d4d219 This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3572 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.6927 \| 0.0017 \| 200 \| 2.3572 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik2987/16b10c95-a962-44d2-af42-a3cbe6a3ded7	dimasik2987	2025-05-02T22:50:44Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/codellama-7b", "base_model:adapter:unsloth/codellama-7b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-02T22:20:01Z	--- library_name: peft license: apache-2.0 base_model: unsloth/codellama-7b tags: - axolotl - generated_from_trainer model-index: - name: 16b10c95-a962-44d2-af42-a3cbe6a3ded7 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/codellama-7b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1e342bbeaf894e58_train_data.json ds_type: json format: custom path: /workspace/input_data/1e342bbeaf894e58_train_data.json type: field_input: input field_instruction: instruction field_output: chosen format: '{instruction} {input}' 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: dimasik2987/16b10c95-a962-44d2-af42-a3cbe6a3ded7 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/1e342bbeaf894e58_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: 1bc31bc4-0adf-49b9-bb84-67aba32775dc wandb_project: s56-28 wandb_run: your_name wandb_runid: 1bc31bc4-0adf-49b9-bb84-67aba32775dc warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 16b10c95-a962-44d2-af42-a3cbe6a3ded7 This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5196 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5197 \| 0.0481 \| 200 \| 0.5196 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
SodaXII/dinov2-small_rice-leaf-disease-augmented-v4_v5_fft	SodaXII	2025-05-02T22:43:09Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "dinov2", "image-classification", "generated_from_trainer", "base_model:facebook/dinov2-small", "base_model:finetune:facebook/dinov2-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-05-02T19:39:54Z	--- library_name: transformers license: apache-2.0 base_model: facebook/dinov2-small tags: - generated_from_trainer metrics: - accuracy model-index: - name: dinov2-small_rice-leaf-disease-augmented-v4_v5_fft 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. --> # dinov2-small_rice-leaf-disease-augmented-v4_v5_fft This model is a fine-tuned version of [facebook/dinov2-small](https://huggingface.co/facebook/dinov2-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3174 - Accuracy: 0.9463 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - 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: cosine_with_restarts - lr_scheduler_warmup_steps: 256 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.5071 \| 0.5 \| 64 \| 0.6205 \| 0.7852 \| \| 0.4009 \| 1.0 \| 128 \| 0.3635 \| 0.8792 \| \| 0.209 \| 1.5 \| 192 \| 0.3144 \| 0.8859 \| \| 0.2231 \| 2.0 \| 256 \| 0.2716 \| 0.9128 \| \| 0.1661 \| 2.5 \| 320 \| 0.3476 \| 0.8691 \| \| 0.1308 \| 3.0 \| 384 \| 0.2279 \| 0.9195 \| \| 0.067 \| 3.5 \| 448 \| 0.3845 \| 0.9195 \| \| 0.063 \| 4.0 \| 512 \| 0.3661 \| 0.9027 \| \| 0.0215 \| 4.5 \| 576 \| 0.3287 \| 0.9228 \| \| 0.0148 \| 5.0 \| 640 \| 0.2952 \| 0.9329 \| \| 0.0007 \| 5.5 \| 704 \| 0.3063 \| 0.9463 \| \| 0.0002 \| 6.0 \| 768 \| 0.2855 \| 0.9396 \| \| 0.0 \| 6.5 \| 832 \| 0.2888 \| 0.9396 \| \| 0.0 \| 7.0 \| 896 \| 0.2766 \| 0.9463 \| \| 0.0 \| 7.5 \| 960 \| 0.2879 \| 0.9497 \| \| 0.0 \| 8.0 \| 1024 \| 0.2960 \| 0.9463 \| \| 0.0 \| 8.5 \| 1088 \| 0.2906 \| 0.9463 \| \| 0.0 \| 9.0 \| 1152 \| 0.2920 \| 0.9463 \| \| 0.0 \| 9.5 \| 1216 \| 0.2932 \| 0.9463 \| \| 0.0 \| 10.0 \| 1280 \| 0.2921 \| 0.9463 \| \| 0.0 \| 10.5 \| 1344 \| 0.2922 \| 0.9463 \| \| 0.0 \| 11.0 \| 1408 \| 0.2924 \| 0.9463 \| \| 0.0 \| 11.5 \| 1472 \| 0.2919 \| 0.9497 \| \| 0.0 \| 12.0 \| 1536 \| 0.2925 \| 0.9463 \| \| 0.0 \| 12.5 \| 1600 \| 0.2943 \| 0.9463 \| \| 0.0 \| 13.0 \| 1664 \| 0.2969 \| 0.9463 \| \| 0.0 \| 13.5 \| 1728 \| 0.2982 \| 0.9430 \| \| 0.0 \| 14.0 \| 1792 \| 0.2977 \| 0.9463 \| \| 0.0 \| 14.5 \| 1856 \| 0.2981 \| 0.9463 \| \| 0.0 \| 15.0 \| 1920 \| 0.2980 \| 0.9463 \| \| 0.0 \| 15.5 \| 1984 \| 0.2980 \| 0.9463 \| \| 0.0 \| 16.0 \| 2048 \| 0.2982 \| 0.9463 \| \| 0.0 \| 16.5 \| 2112 \| 0.2998 \| 0.9463 \| \| 0.0 \| 17.0 \| 2176 \| 0.3035 \| 0.9430 \| \| 0.0 \| 17.5 \| 2240 \| 0.3039 \| 0.9463 \| \| 0.0 \| 18.0 \| 2304 \| 0.3029 \| 0.9463 \| \| 0.0 \| 18.5 \| 2368 \| 0.3044 \| 0.9430 \| \| 0.0 \| 19.0 \| 2432 \| 0.3046 \| 0.9430 \| \| 0.0 \| 19.5 \| 2496 \| 0.3046 \| 0.9430 \| \| 0.0 \| 20.0 \| 2560 \| 0.3047 \| 0.9430 \| \| 0.0 \| 20.5 \| 2624 \| 0.3047 \| 0.9430 \| \| 0.0 \| 21.0 \| 2688 \| 0.3074 \| 0.9430 \| \| 0.0 \| 21.5 \| 2752 \| 0.3086 \| 0.9430 \| \| 0.0 \| 22.0 \| 2816 \| 0.3083 \| 0.9430 \| \| 0.0 \| 22.5 \| 2880 \| 0.3088 \| 0.9430 \| \| 0.0 \| 23.0 \| 2944 \| 0.3103 \| 0.9463 \| \| 0.0 \| 23.5 \| 3008 \| 0.3109 \| 0.9463 \| \| 0.0 \| 24.0 \| 3072 \| 0.3107 \| 0.9463 \| \| 0.0 \| 24.5 \| 3136 \| 0.3108 \| 0.9463 \| \| 0.0 \| 25.0 \| 3200 \| 0.3109 \| 0.9463 \| \| 0.0 \| 25.5 \| 3264 \| 0.3101 \| 0.9463 \| \| 0.0 \| 26.0 \| 3328 \| 0.3133 \| 0.9463 \| \| 0.0 \| 26.5 \| 3392 \| 0.3125 \| 0.9497 \| \| 0.0 \| 27.0 \| 3456 \| 0.3163 \| 0.9463 \| \| 0.0 \| 27.5 \| 3520 \| 0.3172 \| 0.9463 \| \| 0.0 \| 28.0 \| 3584 \| 0.3166 \| 0.9463 \| \| 0.0 \| 28.5 \| 3648 \| 0.3176 \| 0.9463 \| \| 0.0 \| 29.0 \| 3712 \| 0.3175 \| 0.9463 \| \| 0.0 \| 29.5 \| 3776 \| 0.3174 \| 0.9463 \| \| 0.0 \| 30.0 \| 3840 \| 0.3174 \| 0.9463 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.1
RedHatAI/Qwen3-1.7B-FP8_dynamic	RedHatAI	2025-05-02T22:40:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "neuralmagic", "redhat", "llmcompressor", "quantized", "FP8", "conversational", "base_model:Qwen/Qwen3-1.7B", "base_model:quantized:Qwen/Qwen3-1.7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "compressed-tensors", "region:us" ]	text-generation	2025-05-02T20:04:44Z	--- library_name: transformers license: apache-2.0 pipeline_tag: text-generation base_model: - Qwen/Qwen3-1.7B tags: - neuralmagic - redhat - llmcompressor - quantized - FP8 --- # Qwen3-1.7B-FP8-dynamic ## Model Overview - Model Architecture: Qwen3ForCausalLM - Input: Text - Output: Text - Model Optimizations: - Activation quantization: FP8 - Weight quantization: FP8 - Intended Use Cases: - Reasoning. - Function calling. - Subject matter experts via fine-tuning. - Multilingual instruction following. - Translation. - Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). - Release Date: 05/02/2025 - Version: 1.0 - Model Developers: RedHat (Neural Magic) ### Model Optimizations This model was obtained by quantizing activations and weights of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization. ## Deployment This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_id = "RedHatAI/Qwen3-1.7B-FP8-dynamic" number_gpus = 1 sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256) messages = [ {"role": "user", "content": prompt} ] tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [{"role": "user", "content": "Give me a short introduction to large language model."}] prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) llm = LLM(model=model_id, tensor_parallel_size=number_gpus) outputs = llm.generate(prompts, sampling_params) generated_text = outputs[0].outputs[0].text print(generated_text) ``` vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. ## Creation <details> <summary>Creation details</summary> This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. ```python from llmcompressor.modifiers.quantization import QuantizationModifier from llmcompressor.transformers import oneshot from transformers import AutoModelForCausalLM, AutoTokenizer # Load model model_stub = "Qwen/Qwen3-1.7B" model_name = model_stub.split("/")[-1] model = AutoModelForCausalLM.from_pretrained(model_stub) tokenizer = AutoTokenizer.from_pretrained(model_stub) # Configure the quantization algorithm and scheme recipe = QuantizationModifier( ignore=["lm_head"], targets="Linear", scheme="FP8_dynamic", ) # Apply quantization oneshot( model=model, recipe=recipe, ) # Save to disk in compressed-tensors format save_path = model_name + "-FP8-dynamic" model.save_pretrained(save_path) tokenizer.save_pretrained(save_path) print(f"Model and tokenizer saved to: {save_path}") ``` </details> ## Evaluation The model was evaluated on the OpenLLM leaderboard tasks (version 1), using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and [vLLM](https://docs.vllm.ai/en/stable/). <details> <summary>Evaluation details</summary> ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Qwen3-1.7B-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \ --tasks openllm \ --apply_chat_template\ --fewshot_as_multiturn \ --batch_size auto ``` </details> ### Accuracy <table> <tr> <th>Category </th> <th>Benchmark </th> <th>Qwen3-1.7B </th> <th>Qwen3-1.7B-FP8-dynamic<br>(this model) </th> <th>Recovery </th> </tr> <tr> <td rowspan="7" ><strong>OpenLLM v1</strong> </td> <td>MMLU (5-shot) </td> <td>56.82 </td> <td>56.02 </td> <td>98.6% </td> </tr> <tr> <td>ARC Challenge (25-shot) </td> <td>43.00 </td> <td>42.83 </td> <td>99.6% </td> </tr> <tr> <td>GSM-8K (5-shot, strict-match) </td> <td>43.67 </td> <td>41.47 </td> <td>95.0% </td> </tr> <tr> <td>Hellaswag (10-shot) </td> <td>48.08 </td> <td>48.11 </td> <td>100.1% </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>58.01 </td> <td>57.70 </td> <td>99.5% </td> </tr> <tr> <td>TruthfulQA (0-shot, mc2) </td> <td>49.35 </td> <td>48.60 </td> <td>98.5% </td> </tr> <tr> <td><strong>Average</strong> </td> <td><strong>49.82</strong> </td> <td><strong>49.12</strong> </td> <td><strong>98.6%</strong> </td> </tr> </table>
Mrigank005/Rubric_Generator	Mrigank005	2025-05-02T22:40:38Z	0	0	null	[ "rubric-generation", "education", "fine-tuned", "text-generation", "gpt", "en", "dataset:custom", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "license:mit", "region:us" ]	text-generation	2025-05-02T22:32:09Z	--- language: en license: mit tags: - rubric-generation - education - fine-tuned - text-generation - gpt datasets: - custom widget: - text: >- Question: What are the benefits of regular exercise? Sample Answer: Regular exercise helps in weight management, improves cardiovascular health, and enhances mental well-being. Total Marks: 5 base_model: - meta-llama/Llama-2-7b-chat-hf --- # 📝 Rubric Generator Model This model is fine-tuned to generate detailed grading rubrics when provided with: - a question or prompt - a sample answer - the maximum marks for the question It is designed for educators, examiners, and educational apps that require structured, point-wise rubrics for evaluating subjective answers. --- ## 📌 Model Details - Architecture: Causal language model (e.g., GPT-style) - Training Format: Supervised fine-tuning on question-answer-mark-rubric datasets - Input Format: ```plaintext Question: <your-question> Sample Answer: <your-answer> Total Marks: <max-marks> ```` * Output: A rubric in JSON format assigning marks to specific answer criteria --- ## 🚀 Example Input: ``` Question: What are the advantages of using solar energy? Sample Answer: Solar energy is renewable and reduces electricity bills. It's environmentally friendly and reduces reliance on fossil fuels. Total Marks: 5 ``` Output: ```json { "rubric": [ { "criteria": "Mentions that solar energy is renewable", "max_marks": 1 }, { "criteria": "Discusses cost-saving or reduction in electricity bills", "max_marks": 1 }, { "criteria": "Highlights environmental friendliness", "max_marks": 1 }, { "criteria": "Mentions reduced reliance on fossil fuels", "max_marks": 1 }, { "criteria": "Answer clarity and overall relevance", "max_marks": 1 } ] } ``` --- ## 📚 Training Data The model was fine-tuned on a dataset of: * Questions * Sample answers * Total marks * Expert-designed rubrics in structured JSON format This dataset is included in the accompanying [GitHub repository](https://github.com/yourusername/rubric-generator). --- ## 🧠 Intended Use * Automated rubric generation for educational platforms * Consistent scoring guidelines for subjective assessments * Feedback generation tools for students and teachers --- ## ⚠️ Limitations * May not be optimized for highly domain-specific or creative writing assessments * Requires sample answers to be reasonably well-formed to generate useful rubrics * Rubric quality depends on clarity of the question and sample answer --- ## 📄 License This model is licensed under the [MIT License](LICENSE). --- Author: Mrigank Singh Contact: [email protected] Repository: [GitHub - rubric-generator](https://github.com/Mrigank005/Rubric_Generator) ```
cybershiptrooper/grpo_linear_mean_5p_fpr_7B-threshold_0.5288-RM-n_examples_200-probe_linear_layers_10	cybershiptrooper	2025-05-02T22:37:45Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "grpo", "arxiv:2402.03300", "base_model:saraprice/llama2-7B-chat-helpful-only", "base_model:finetune:saraprice/llama2-7B-chat-helpful-only", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-02T15:41:51Z	--- base_model: saraprice/llama2-7B-chat-helpful-only library_name: transformers model_name: grpo_linear_mean_5p_fpr_7B-threshold_0.5288-RM-n_examples_200-probe_linear_layers_10 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for grpo_linear_mean_5p_fpr_7B-threshold_0.5288-RM-n_examples_200-probe_linear_layers_10 This model is a fine-tuned version of [saraprice/llama2-7B-chat-helpful-only](https://huggingface.co/saraprice/llama2-7B-chat-helpful-only). 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="cybershiptrooper/grpo_linear_mean_5p_fpr_7B-threshold_0.5288-RM-n_examples_200-probe_linear_layers_10", 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/cybershiptrooper/huggingface/runs/zpra0fc7) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.14.0 - Transformers: 4.51.3 - Pytorch: 2.2.2+cu121 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
aleegis/40dd29e5-2aa1-4c03-a4aa-e56f188879f0	aleegis	2025-05-02T22:36:28Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:adapter:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-02T22:03:01Z	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 40dd29e5-2aa1-4c03-a4aa-e56f188879f0 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 adapter: lora base_model: Qwen/Qwen2-1.5B-Instruct bf16: auto chat_template: llama3 dataloader_num_workers: 12 dataset_prepared_path: null datasets: - data_files: - 21c49dc937709928_train_data.json ds_type: json format: custom path: /workspace/input_data/21c49dc937709928_train_data.json type: field_instruction: en field_output: fr 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_steps: null eval_table_size: null evals_per_epoch: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: false hub_model_id: aleegis/40dd29e5-2aa1-4c03-a4aa-e56f188879f0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: null lora_alpha: 32 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true loraplus_lr_embedding: 1.0e-06 loraplus_lr_ratio: 16 lr_scheduler: cosine max_grad_norm: 1 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/21c49dc937709928_train_data.json model_type: AutoModelForCausalLM num_epochs: 200 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: null save_total_limit: 10 saves_per_epoch: 0 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.0 wandb_entity: null wandb_mode: online wandb_name: 3817e1a8-ed6c-45eb-9aef-fd65e3afe80f wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3817e1a8-ed6c-45eb-9aef-fd65e3afe80f warmup_steps: 100 weight_decay: 0 xformers_attention: null ``` </details><br> # 40dd29e5-2aa1-4c03-a4aa-e56f188879f0 This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1500 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Carnyzzle/Pygmalion3-12B-Nemo-v6-Q8_0-GGUF	Carnyzzle	2025-05-02T22:36:17Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:tavtav/Pygmalion3-12B-Nemo-v6", "base_model:quantized:tavtav/Pygmalion3-12B-Nemo-v6", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-02T22:35:23Z	--- base_model: tavtav/Pygmalion3-12B-Nemo-v6 license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # Carnyzzle/Pygmalion3-12B-Nemo-v6-Q8_0-GGUF This model was converted to GGUF format from [`tavtav/Pygmalion3-12B-Nemo-v6`](https://huggingface.co/tavtav/Pygmalion3-12B-Nemo-v6) 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/tavtav/Pygmalion3-12B-Nemo-v6) 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 Carnyzzle/Pygmalion3-12B-Nemo-v6-Q8_0-GGUF --hf-file pygmalion3-12b-nemo-v6-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Carnyzzle/Pygmalion3-12B-Nemo-v6-Q8_0-GGUF --hf-file pygmalion3-12b-nemo-v6-q8_0.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 Carnyzzle/Pygmalion3-12B-Nemo-v6-Q8_0-GGUF --hf-file pygmalion3-12b-nemo-v6-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Carnyzzle/Pygmalion3-12B-Nemo-v6-Q8_0-GGUF --hf-file pygmalion3-12b-nemo-v6-q8_0.gguf -c 2048 ```
RedHatAI/Qwen3-4B-FP8_dynamic	RedHatAI	2025-05-02T22:36:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "neuralmagic", "redhat", "llmcompressor", "quantized", "FP8", "conversational", "base_model:Qwen/Qwen3-4B", "base_model:quantized:Qwen/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "compressed-tensors", "region:us" ]	text-generation	2025-05-02T19:42:37Z	--- library_name: transformers license: apache-2.0 pipeline_tag: text-generation base_model: - Qwen/Qwen3-4B tags: - neuralmagic - redhat - llmcompressor - quantized - FP8 --- # Qwen3-4B-FP8-dynamic ## Model Overview - Model Architecture: Qwen3ForCausalLM - Input: Text - Output: Text - Model Optimizations: - Activation quantization: FP8 - Weight quantization: FP8 - Intended Use Cases: - Reasoning. - Function calling. - Subject matter experts via fine-tuning. - Multilingual instruction following. - Translation. - Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). - Release Date: 05/02/2025 - Version: 1.0 - Model Developers: RedHat (Neural Magic) ### Model Optimizations This model was obtained by quantizing activations and weights of [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization. ## Deployment This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_id = "RedHatAI/Qwen3-4B-FP8-dynamic" number_gpus = 1 sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256) messages = [ {"role": "user", "content": prompt} ] tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [{"role": "user", "content": "Give me a short introduction to large language model."}] prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) llm = LLM(model=model_id, tensor_parallel_size=number_gpus) outputs = llm.generate(prompts, sampling_params) generated_text = outputs[0].outputs[0].text print(generated_text) ``` vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. ## Creation <details> <summary>Creation details</summary> This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. ```python from llmcompressor.modifiers.quantization import QuantizationModifier from llmcompressor.transformers import oneshot from transformers import AutoModelForCausalLM, AutoTokenizer # Load model model_stub = "Qwen/Qwen3-4B" model_name = model_stub.split("/")[-1] model = AutoModelForCausalLM.from_pretrained(model_stub) tokenizer = AutoTokenizer.from_pretrained(model_stub) # Configure the quantization algorithm and scheme recipe = QuantizationModifier( ignore=["lm_head"], targets="Linear", scheme="FP8_dynamic", ) # Apply quantization oneshot( model=model, recipe=recipe, ) # Save to disk in compressed-tensors format save_path = model_name + "-FP8-dynamic" model.save_pretrained(save_path) tokenizer.save_pretrained(save_path) print(f"Model and tokenizer saved to: {save_path}") ``` </details> ## Evaluation The model was evaluated on the OpenLLM leaderboard tasks (version 1), using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and [vLLM](https://docs.vllm.ai/en/stable/). <details> <summary>Evaluation details</summary> ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Qwen3-4B-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=2 \ --tasks openllm \ --apply_chat_template\ --fewshot_as_multiturn \ --batch_size auto ``` </details> ### Accuracy <table> <tr> <th>Category </th> <th>Benchmark </th> <th>Qwen3-4B </th> <th>Qwen3-4B-FP8-dynamic<br>(this model) </th> <th>Recovery </th> </tr> <tr> <td rowspan="7" ><strong>OpenLLM v1</strong> </td> <td>MMLU (5-shot) </td> <td>66.76 </td> <td>66.34 </td> <td>99.4% </td> </tr> <tr> <td>ARC Challenge (25-shot) </td> <td>50.17 </td> <td>49.91 </td> <td>99.5% </td> </tr> <tr> <td>GSM-8K (5-shot, strict-match) </td> <td>60.80 </td> <td>66.11 </td> <td>108.7% </td> </tr> <tr> <td>Hellaswag (10-shot) </td> <td>52.80 </td> <td>53.51 </td> <td>101.3% </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>58.41 </td> <td>60.54 </td> <td>103.7% </td> </tr> <tr> <td>TruthfulQA (0-shot, mc2) </td> <td>51.79 </td> <td>51.52 </td> <td>99.5% </td> </tr> <tr> <td><strong>Average</strong> </td> <td><strong>56.79</strong> </td> <td><strong>57.99</strong> </td> <td><strong>102.1%</strong> </td> </tr> </table>

Subsets and Splits