Split (1)

train · 911k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-02 06:30:45	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 533 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-02 06:30:39	card stringlengths 11 1.01M
pakcricketinfo-sapna-shah-video/Pakcricketinfo.Sapna.Shah.Treanding.Video	pakcricketinfo-sapna-shah-video	2025-06-25T05:59:40Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:59:21Z	[➤ ➤ ➤ 𝖢𝗅𝗂𝖼𝗄 𝖧𝖾𝗋𝖾 𝖳𝗈 𝗅𝗂𝗇𝗄 (𝖶𝖺𝗍𝖼𝗁 𝖥𝗎𝗅𝗅 𝖵𝗂𝖽𝖾𝗈)](https://t.co/cJFoFjf13y) [ ➤►𝖣𝖮𝖶𝖭𝖫𝖮𝖠𝖣 (𝖥𝗎𝗅𝗅 𝖵𝗂𝖽𝖾𝗈 𝖫𝗂𝗇𝗄) ](https://t.co/cJFoFjf13y) [![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://t.co/cJFoFjf13y)
bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF	bartowski	2025-06-25T05:59:29Z	0	0	null	[ "gguf", "text-generation", "base_model:TheDrummer/Cydonia-24B-v3.1", "base_model:quantized:TheDrummer/Cydonia-24B-v3.1", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T04:09:49Z	--- quantized_by: bartowski pipeline_tag: text-generation base_model: TheDrummer/Cydonia-24B-v3.1 base_model_relation: quantized --- ## Llamacpp imatrix Quantizations of Cydonia-24B-v3.1 by TheDrummer Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5697">b5697</a> for quantization. Original model: https://huggingface.co/TheDrummer/Cydonia-24B-v3.1 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project ## Prompt format No prompt format found, check original model page ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Split \| Description \| \| -------- \| ---------- \| --------- \| ----- \| ----------- \| \| [Cydonia-24B-v3.1-bf16.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-bf16.gguf) \| bf16 \| 47.15GB \| false \| Full BF16 weights. \| \| [Cydonia-24B-v3.1-Q8_0.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q8_0.gguf) \| Q8_0 \| 25.05GB \| false \| Extremely high quality, generally unneeded but max available quant. \| \| [Cydonia-24B-v3.1-Q6_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q6_K_L.gguf) \| Q6_K_L \| 19.67GB \| false \| Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended. \| \| [Cydonia-24B-v3.1-Q6_K.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q6_K.gguf) \| Q6_K \| 19.35GB \| false \| Very high quality, near perfect, recommended. \| \| [Cydonia-24B-v3.1-Q5_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q5_K_L.gguf) \| Q5_K_L \| 17.18GB \| false \| Uses Q8_0 for embed and output weights. High quality, recommended. \| \| [Cydonia-24B-v3.1-Q5_K_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q5_K_M.gguf) \| Q5_K_M \| 16.76GB \| false \| High quality, recommended. \| \| [Cydonia-24B-v3.1-Q5_K_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q5_K_S.gguf) \| Q5_K_S \| 16.30GB \| false \| High quality, recommended. \| \| [Cydonia-24B-v3.1-Q4_1.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q4_1.gguf) \| Q4_1 \| 14.87GB \| false \| Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. \| \| [Cydonia-24B-v3.1-Q4_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q4_K_L.gguf) \| Q4_K_L \| 14.83GB \| false \| Uses Q8_0 for embed and output weights. Good quality, recommended. \| \| [Cydonia-24B-v3.1-Q4_K_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q4_K_M.gguf) \| Q4_K_M \| 14.33GB \| false \| Good quality, default size for most use cases, recommended. \| \| [Cydonia-24B-v3.1-Q4_K_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q4_K_S.gguf) \| Q4_K_S \| 13.55GB \| false \| Slightly lower quality with more space savings, recommended. \| \| [Cydonia-24B-v3.1-Q4_0.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q4_0.gguf) \| Q4_0 \| 13.49GB \| false \| Legacy format, offers online repacking for ARM and AVX CPU inference. \| \| [Cydonia-24B-v3.1-IQ4_NL.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ4_NL.gguf) \| IQ4_NL \| 13.47GB \| false \| Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. \| \| [Cydonia-24B-v3.1-Q3_K_XL.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q3_K_XL.gguf) \| Q3_K_XL \| 12.99GB \| false \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \| \| [Cydonia-24B-v3.1-IQ4_XS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ4_XS.gguf) \| IQ4_XS \| 12.76GB \| false \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Cydonia-24B-v3.1-Q3_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q3_K_L.gguf) \| Q3_K_L \| 12.40GB \| false \| Lower quality but usable, good for low RAM availability. \| \| [Cydonia-24B-v3.1-Q3_K_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q3_K_M.gguf) \| Q3_K_M \| 11.47GB \| false \| Low quality. \| \| [Cydonia-24B-v3.1-IQ3_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ3_M.gguf) \| IQ3_M \| 10.65GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Cydonia-24B-v3.1-Q3_K_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q3_K_S.gguf) \| Q3_K_S \| 10.40GB \| false \| Low quality, not recommended. \| \| [Cydonia-24B-v3.1-IQ3_XS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ3_XS.gguf) \| IQ3_XS \| 9.91GB \| false \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Cydonia-24B-v3.1-Q2_K_L.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q2_K_L.gguf) \| Q2_K_L \| 9.55GB \| false \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \| \| [Cydonia-24B-v3.1-IQ3_XXS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ3_XXS.gguf) \| IQ3_XXS \| 9.28GB \| false \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Cydonia-24B-v3.1-Q2_K.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-Q2_K.gguf) \| Q2_K \| 8.89GB \| false \| Very low quality but surprisingly usable. \| \| [Cydonia-24B-v3.1-IQ2_M.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ2_M.gguf) \| IQ2_M \| 8.11GB \| false \| Relatively low quality, uses SOTA techniques to be surprisingly usable. \| \| [Cydonia-24B-v3.1-IQ2_S.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ2_S.gguf) \| IQ2_S \| 7.48GB \| false \| Low quality, uses SOTA techniques to be usable. \| \| [Cydonia-24B-v3.1-IQ2_XS.gguf](https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF/blob/main/TheDrummer_Cydonia-24B-v3.1-IQ2_XS.gguf) \| IQ2_XS \| 7.21GB \| false \| Low quality, uses SOTA techniques to be usable. \| ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. ## Downloading using huggingface-cli <details> <summary>Click to view download instructions</summary> First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF --include "TheDrummer_Cydonia-24B-v3.1-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/TheDrummer_Cydonia-24B-v3.1-GGUF --include "TheDrummer_Cydonia-24B-v3.1-Q8_0/*" --local-dir ./ ``` You can either specify a new local-dir (TheDrummer_Cydonia-24B-v3.1-Q8_0) or download them all in place (./) </details> ## ARM/AVX information Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass. Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly. As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0. Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase. <details> <summary>Click to view Q4_0_X_X information (deprecated</summary> I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking. <details> <summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary> \| model \| size \| params \| backend \| threads \| test \| t/s \| % (vs Q4_0) \| \| ------------------------------ \| ---------: \| ---------: \| ---------- \| ------: \| ------------: \| -------------------: \|-------------: \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 204.03 ± 1.03 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 282.92 ± 0.19 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 259.49 ± 0.44 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 39.12 ± 0.27 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 39.31 ± 0.69 \| 100% \| \| qwen2 3B Q4_0 \| 1.70 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 40.52 ± 0.03 \| 100% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 301.02 ± 1.74 \| 147% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 287.23 ± 0.20 \| 101% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 262.77 ± 1.81 \| 101% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 18.80 ± 0.99 \| 48% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 24.46 ± 3.04 \| 83% \| \| qwen2 3B Q4_K_M \| 1.79 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 36.32 ± 3.59 \| 90% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp512 \| 271.71 ± 3.53 \| 133% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp1024 \| 279.86 ± 45.63 \| 100% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| pp2048 \| 320.77 ± 5.00 \| 124% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg128 \| 43.51 ± 0.05 \| 111% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg256 \| 43.35 ± 0.09 \| 110% \| \| qwen2 3B Q4_0_8_8 \| 1.69 GiB \| 3.09 B \| CPU \| 64 \| tg512 \| 42.60 ± 0.31 \| 105% \| Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation </details> </details> ## Which file should I choose? <details> <summary>Click here for details</summary> A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. </details> ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. Thank you ZeroWw for the inspiration to experiment with embed/output. Thank you to LM Studio for sponsoring my work. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
New-pakcricketinfo-sapna-shah-video/pakcricketinfo.sapna.shah.Viral.Video.Tutorial.Official	New-pakcricketinfo-sapna-shah-video	2025-06-25T05:59:23Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:59:03Z	[![WATCH Videos](https://i.imgur.com/GNBE9I0.gif)](https://video-tv-go.blogspot.com/2024/11/new-videos-today.html)
Athad/shapes-generator	Athad	2025-06-25T05:58:13Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "region:us" ]	null	2025-06-25T05:56:13Z	--- base_model: stabilityai/stable-diffusion-2-1-base 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
Hiyuan0105/llama2_uuu_news_qlora	Hiyuan0105	2025-06-25T05:58:13Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2025-06-25T02:59:45Z	--- base_model: NousResearch/Llama-2-7b-chat-hf library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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CHIANG0903/llama2_uuu_news_qlora	CHIANG0903	2025-06-25T05:57:14Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2025-06-25T02:50:09Z	--- base_model: NousResearch/Llama-2-7b-chat-hf library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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pak-cricket-info-sapna-shah-Viral-videos/LINK.VIDEO.pakcricketinfo.sapna.shah.Viral.Video.Tutorial.Official.Link	pak-cricket-info-sapna-shah-Viral-videos	2025-06-25T05:55:54Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:55:15Z	[![WATCH Videos](https://i.imgur.com/GNBE9I0.gif)](https://video-tv-go.blogspot.com/2024/11/new-videos-today.html)
ianwangnas/llama2_uuu_news_qlora	ianwangnas	2025-06-25T05:55:23Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2025-06-25T02:27:09Z	--- base_model: NousResearch/Llama-2-7b-chat-hf library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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cucucu666/frown-6.25-male	cucucu666	2025-06-25T05:51:52Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:adapter:black-forest-labs/FLUX.1-Fill-dev", "license:other", "region:us" ]	text-to-image	2025-06-25T03:17:03Z	--- base_model: black-forest-labs/FLUX.1-Fill-dev library_name: diffusers license: other instance_prompt: labii male face, Crayon Shin-chan style, frown expression, plain white background widget: - text: labii male face, Crayon Shin-chan style, frown expression, plain white background output: url: image_0.png - text: labii male face, Crayon Shin-chan style, frown expression, plain white background output: url: image_1.png - text: labii male face, Crayon Shin-chan style, frown expression, plain white background output: url: image_2.png - text: labii male face, Crayon Shin-chan style, frown expression, plain white background output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-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. --> # Flux-Fill DreamBooth LoRA - cucucu666/frown-6.25-male <Gallery /> ## Model description These are cucucu666/frown-6.25-male DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `labii male face, Crayon Shin-chan style, frown expression, plain white background` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](cucucu666/frown-6.25-male/tree/main) in the Files & versions tab. ## 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.bfloat16).to('cuda') pipeline.load_lora_weights('cucucu666/frown-6.25-male', weight_name='pytorch_lora_weights.safetensors') image = pipeline('labii male face, Crayon Shin-chan style, frown expression, plain white background').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) ## 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]
phospho-app/yeva11-gr00t-kirby_pick_anywhere_0625-fv7ia	phospho-app	2025-06-25T05:50:45Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-06-25T05:49:27Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 165, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1146, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 996, in run_gr00t_training raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/gr00t/data/dataset.py", line 717, in get_state_or_action return self.retrieve_data_and_pad( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/gr00t/data/dataset.py", line 586, in retrieve_data_and_pad raw_data = array[step_indices[~padding_positions]] ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ IndexError: index 96 is out of bounds for axis 0 with size 96 0%\| \| 0/640 [00:03<?, ?it/s] ``` ## Training parameters: - Dataset: [yeva11/kirby_pick_anywhere_0625](https://huggingface.co/datasets/yeva11/kirby_pick_anywhere_0625) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
yale-nlp/MDCure-Qwen2-7B-Instruct	yale-nlp	2025-06-25T05:50:18Z	11	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "multi-document", "long-context", "Long Context", "conversational", "en", "dataset:yale-nlp/MDCure-72k", "arxiv:2410.23463", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:finetune:Qwen/Qwen2-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T07:12:38Z	--- base_model: - Qwen/Qwen2-7B-Instruct datasets: - yale-nlp/MDCure-72k language: - en license: apache-2.0 tags: - multi-document - long-context - Long Context library_name: transformers pipeline_tag: text-generation --- # MDCure-Qwen2-7B-Instruct [📄 Paper](https://arxiv.org/pdf/2410.23463) \| [🤗 HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395) \| [⚙️ GitHub Repo](https://github.com/yale-nlp/MDCure) ## Introduction MDCure is an effective and scalable procedure for generating high-quality multi-document (MD) instruction tuning data to improve MD capabilities of LLMs. Using MDCure, we construct a suite of MD instruction datasets complementary to collections such as [FLAN](https://github.com/google-research/FLAN) and fine-tune a variety of already instruction-tuned LLMs from the FlanT5, Qwen2, and LLAMA3.1 model families, up to 70B parameters in size. We additionally introduce MDCureRM, an evaluator model specifically designed for the MD setting to filter and select high-quality MD instruction data in a cost-effective, RM-as-a-judge fashion. Extensive evaluations on a wide range of MD and long-context benchmarks spanning various tasks show MDCure consistently improves performance over pre-trained baselines and over corresponding base models by up to 75.5%. We release MDCure datasets of size 12k, 36k, and 72k. We also release MDCureRM and the best MDCure'd model for each architecture/size combination. To access all our models and datasets, please visit our [HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395). For further details regarding dataset construction, please see our [paper](https://arxiv.org/pdf/2410.23463) and [Github repo](https://github.com/yale-nlp/MDCure). For additional details regarding how to use yale-nlp/MDCure-Qwen2-7B-Instruct, please see below. <p align="center"> <img src="fig1.png" width="90%"> </p> <p align="center" style="margin-top: 0; padding-top: 0;"> <em>The MDCure pipeline generates diverse multi-document instructions, filters them via fine-grained scoring by MDCureRM, and tunes a base LLM to enhance its multi-document capabilities.</em> </p> ## Model Details yale-nlp/MDCure-Qwen2-7B-Instruct is initialized from [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) and fine-tuned on the [MDCure-72k](https://huggingface.co/datasets/yale-nlp/MDCure-72k) dataset. ## Requirements We recommend using the latest version of HF Transformers, or any `transformers>=4.45.0`, to avoid any potential errors when using this model. ## Quickstart Below we provide a code snippet demonstrating how to load the tokenizer and model and generate content in response to an input context concerning multiple source documents and a related question or instruction. We strongly recommend to separate the texts and/or instruction using ` ` or `<doc-sep>` to maintain consistency with the format of the data used during training. ```python model = AutoModelForCausalLM.from_pretrained("yale-nlp/MDCure-Qwen2-7B-Instruct", device_map='auto',torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-Qwen2-7B-Instruct") source_text_1 = ... source_text_2 = ... source_text_3 = ... prompt = f"{source_text_1} {source_text_2} {source_text_3} What happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences." messages = [ {"role": "system", "content": "You are an assistant with strong multi-document processing skills."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate(model_inputs, max_new_tokens=512) generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## All MDCure Models We open-source our custom multi-document instruction scoring model, MDCureRM, as well as our best MDCure'd models at the following links: \| Model \| Huggingface Repo \| Description \| \|---------------------------\|---------------------\|------------------------------\| \| MDCureRM \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCureRM) \| Multi-objective reward model to score and filter MD instruction data more cheaply and effectively than GPT-3.5-Turbo \| \| MDCure-FlanT5-Base \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Base) \| FlanT5-Base fine-tuned with MDCure-72k \| \| MDCure-FlanT5-Large \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Large) \| FlanT5-Large fine-tuned with MDCure-72k \| \| MDCure-Qwen2-1.5B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-1.5B-Instruct) \| Qwen2-1.5B-Instruct fine-tuned with MDCure-72k \| \| MDCure-Qwen2-7B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-7B-Instruct) \| Qwen2-7B-Instruct fine-tuned with MDCure-72k \| \| MDCure-LLAMA3.1-8B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-8B-Instruct) \| LLAMA3.1-8B-Instruct fine-tuned with MDCure-72k \| \| MDCure-LLAMA3.1-70B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-70B-Instruct) \| LLAMA3.1-70B-Instruct** fine-tuned with MDCure-72k \| ## Citation If you find our work useful, please cite our paper as: ```bibtex @article{liu2024mdcure, title={MDCure: A Scalable Pipeline for Multi-Document Instruction-Following}, author={Gabrielle Kaili-May Liu and Bowen Shi and Avi Caciularu and Idan Szpektor and Arman Cohan}, journal={arXiv preprint arXiv:2410.23463}, year={2024}, url={https://arxiv.org/abs/2410.23463} } ```
yale-nlp/MDCure-Qwen2-1.5B-Instruct	yale-nlp	2025-06-25T05:50:09Z	11	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "multi-document", "long-context", "Long Context", "conversational", "en", "dataset:yale-nlp/MDCure-72k", "arxiv:2410.23463", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T07:12:23Z	--- base_model: - Qwen/Qwen2-1.5B-Instruct datasets: - yale-nlp/MDCure-72k language: - en license: apache-2.0 tags: - multi-document - long-context - Long Context library_name: transformers pipeline_tag: text-generation --- # MDCure-Qwen2-1.5B-Instruct [📄 Paper](https://arxiv.org/pdf/2410.23463) \| [🤗 HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395) \| [⚙️ GitHub Repo](https://github.com/yale-nlp/MDCure) ## Introduction MDCure is an effective and scalable procedure for generating high-quality multi-document (MD) instruction tuning data to improve MD capabilities of LLMs. Using MDCure, we construct a suite of MD instruction datasets complementary to collections such as [FLAN](https://github.com/google-research/FLAN) and fine-tune a variety of already instruction-tuned LLMs from the FlanT5, Qwen2, and LLAMA3.1 model families, up to 70B parameters in size. We additionally introduce MDCureRM, an evaluator model specifically designed for the MD setting to filter and select high-quality MD instruction data in a cost-effective, RM-as-a-judge fashion. Extensive evaluations on a wide range of MD and long-context benchmarks spanning various tasks show MDCure consistently improves performance over pre-trained baselines and over corresponding base models by up to 75.5%. We release MDCure datasets of size 12k, 36k, and 72k. We also release MDCureRM and the best MDCure'd model for each architecture/size combination. To access all our models and datasets, please visit our [HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395). For further details regarding dataset construction, please see our [paper](https://arxiv.org/pdf/2410.23463) and [Github repo](https://github.com/yale-nlp/MDCure). For additional details regarding how to use yale-nlp/MDCure-Qwen2-1.5B-Instruct, please see below. <p align="center"> <img src="fig1.png" width="90%"> </p> <p align="center" style="margin-top: 0; padding-top: 0;"> <em>The MDCure pipeline generates diverse multi-document instructions, filters them via fine-grained scoring by MDCureRM, and tunes a base LLM to enhance its multi-document capabilities.</em> </p> ## Model Details yale-nlp/MDCure-Qwen2-1.5B-Instruct is initialized from [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) and fine-tuned on the [MDCure-72k](https://huggingface.co/datasets/yale-nlp/MDCure-72k) dataset. ## Requirements We recommend using the latest version of HF Transformers, or any `transformers>=4.45.0`, to avoid any potential errors when using this model. ## Quickstart Below we provide a code snippet demonstrating how to load the tokenizer and model and generate content in response to an input context concerning multiple source documents and a related question or instruction. We strongly recommend to separate the texts and/or instruction using ` ` or `<doc-sep>` to maintain consistency with the format of the data used during training. ```python model = AutoModelForCausalLM.from_pretrained("yale-nlp/MDCure-Qwen2-1.5B-Instruct", device_map='auto',torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-Qwen2-1.5B-Instruct") source_text_1 = ... source_text_2 = ... source_text_3 = ... prompt = f"{source_text_1} {source_text_2} {source_text_3} What happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences." messages = [ {"role": "system", "content": "You are an assistant with strong multi-document processing skills."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate(model_inputs, max_new_tokens=512) generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## All MDCure Models We open-source our custom multi-document instruction scoring model, MDCureRM, as well as our best MDCure'd models at the following links: \| Model \| Huggingface Repo \| Description \| \|---------------------------\|---------------------\|------------------------------\| \| MDCureRM \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCureRM) \| Multi-objective reward model to score and filter MD instruction data more cheaply and effectively than GPT-3.5-Turbo \| \| MDCure-FlanT5-Base \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Base) \| FlanT5-Base fine-tuned with MDCure-72k \| \| MDCure-FlanT5-Large \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Large) \| FlanT5-Large fine-tuned with MDCure-72k \| \| MDCure-Qwen2-1.5B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-1.5B-Instruct) \| Qwen2-1.5B-Instruct fine-tuned with MDCure-72k \| \| MDCure-Qwen2-7B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-7B-Instruct) \| Qwen2-7B-Instruct fine-tuned with MDCure-72k \| \| MDCure-LLAMA3.1-8B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-8B-Instruct) \| LLAMA3.1-8B-Instruct fine-tuned with MDCure-72k \| \| MDCure-LLAMA3.1-70B-Instruct \| [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-70B-Instruct) \| LLAMA3.1-70B-Instruct** fine-tuned with MDCure-72k \| ## Citation If you find our work useful, please cite our paper as: ```bibtex @article{liu2024mdcure, title={MDCure: A Scalable Pipeline for Multi-Document Instruction-Following}, author={Gabrielle Kaili-May Liu and Bowen Shi and Avi Caciularu and Idan Szpektor and Arman Cohan}, journal={arXiv preprint arXiv:2410.23463}, year={2024}, url={https://arxiv.org/abs/2410.23463} } ```
Yonghoon99/ppo-Huggy	Yonghoon99	2025-06-25T05:47:42Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-06-25T05:47:36Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Yonghoon99/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
phospho-app/yeva11-gr00t-kirby_pick_anywhere_0625-vuhgt	phospho-app	2025-06-25T05:47:27Z	0	0	null	[ "phosphobot", "gr00t", "region:us" ]	null	2025-06-25T05:45:42Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Traceback (most recent call last): File "/root/src/helper.py", line 165, in predict trainer.train(timeout_seconds=timeout_seconds) File "/root/phosphobot/am/gr00t.py", line 1146, in train asyncio.run( File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run return runner.run(main) ^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete return future.result() ^^^^^^^^^^^^^^^ File "/root/phosphobot/am/gr00t.py", line 996, in run_gr00t_training raise RuntimeError(error_msg) RuntimeError: Training process failed with exit code 1: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/gr00t/data/dataset.py", line 717, in get_state_or_action return self.retrieve_data_and_pad( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/workspace/gr00t/data/dataset.py", line 586, in retrieve_data_and_pad raw_data = array[step_indices[~padding_positions]] ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ IndexError: index 96 is out of bounds for axis 0 with size 96 0%\| \| 0/640 [00:03<?, ?it/s] ``` ## Training parameters: - Dataset: [yeva11/kirby_pick_anywhere_0625](https://huggingface.co/datasets/yeva11/kirby_pick_anywhere_0625) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
MaIlz/full_task_sft_mol_editing_moleditrl_dataset	MaIlz	2025-06-25T05:46:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "unsloth", "trl", "sft", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "endpoints_compatible", "region:us" ]	null	2025-06-25T05:46:42Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit library_name: transformers model_name: full_task_sft_mol_editing_moleditrl_dataset tags: - generated_from_trainer - unsloth - trl - sft licence: license --- # Model Card for full_task_sft_mol_editing_moleditrl_dataset This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit). 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="MaIlz/full_task_sft_mol_editing_moleditrl_dataset", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
hitty28/branch-switch-v3	hitty28	2025-06-25T05:46:06Z	0	0	null	[ "safetensors", "distilbert", "text-classification", "branch-switching", "intent-classification", "en", "dataset:custom", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	text-classification	2025-06-25T05:45:42Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - text-classification - branch-switching - intent-classification datasets: - custom language: - en pipeline_tag: text-classification --- # Branch Switch Classifier This model classifies whether a user statement indicates a desire to switch branches or not. ## Model Details - Base Model: DistilBERT - Task: Binary Text Classification - Labels: True, False ## Usage ```python from transformers import pipeline classifier = pipeline("text-classification", model="hitty28/branch-switch-v3") result = classifier("I want to switch to Mumbai branch") print(result) ``` ## Training Data Trained on custom dataset with statements about branch switching intentions.
anvitamanne/lr-5e5-model	anvitamanne	2025-06-25T05:43:54Z	28	0	null	[ "safetensors", "wav2vec2", "generated_from_trainer", "base_model:anvitamanne/base-model", "base_model:finetune:anvitamanne/base-model", "license:apache-2.0", "region:us" ]	null	2025-06-20T16:21:15Z	--- license: apache-2.0 base_model: anvitamanne/base-model tags: - generated_from_trainer metrics: - wer model-index: - name: lr-5e5-model 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. --> # lr-5e5-model This model is a fine-tuned version of [anvitamanne/base-model](https://huggingface.co/anvitamanne/base-model) on the None dataset. It achieves the following results on the evaluation set: - Loss: 540.9777 - Wer: 0.3898 - Cer: 0.1646 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\| \| 324.3731 \| 0.86 \| 1000 \| 509.3808 \| 0.4014 \| 0.1657 \| \| 323.4149 \| 1.72 \| 2000 \| 495.5074 \| 0.4006 \| 0.1639 \| \| 324.4118 \| 2.58 \| 3000 \| 503.3999 \| 0.4025 \| 0.1647 \| \| 312.5412 \| 3.44 \| 4000 \| 500.1373 \| 0.4039 \| 0.1656 \| \| 298.6976 \| 4.3 \| 5000 \| 501.8691 \| 0.3958 \| 0.1638 \| \| 303.839 \| 5.17 \| 6000 \| 511.4516 \| 0.3931 \| 0.1640 \| \| 301.297 \| 6.03 \| 7000 \| 512.8284 \| 0.3999 \| 0.1663 \| \| 296.7412 \| 6.89 \| 8000 \| 517.9861 \| 0.3989 \| 0.1668 \| \| 310.3565 \| 7.75 \| 9000 \| 519.5070 \| 0.3960 \| 0.1647 \| \| 294.8242 \| 8.61 \| 10000 \| 531.7615 \| 0.3987 \| 0.1661 \| \| 278.929 \| 9.47 \| 11000 \| 534.0803 \| 0.3892 \| 0.1636 \| \| 287.4352 \| 10.33 \| 12000 \| 533.1113 \| 0.3911 \| 0.1636 \| \| 294.2136 \| 11.19 \| 13000 \| 532.6003 \| 0.3929 \| 0.1647 \| \| 289.0024 \| 12.05 \| 14000 \| 537.3076 \| 0.3921 \| 0.1654 \| \| 284.6558 \| 12.91 \| 15000 \| 537.4019 \| 0.3909 \| 0.1648 \| \| 283.6182 \| 13.78 \| 16000 \| 539.5662 \| 0.3913 \| 0.1649 \| \| 280.4244 \| 14.64 \| 17000 \| 540.9777 \| 0.3898 \| 0.1646 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.15.2
anvitamanne/hd-0.3-model	anvitamanne	2025-06-25T05:43:36Z	10	0	null	[ "safetensors", "wav2vec2", "generated_from_trainer", "base_model:anvitamanne/base-model", "base_model:finetune:anvitamanne/base-model", "license:apache-2.0", "region:us" ]	null	2025-06-21T17:37:18Z	--- license: apache-2.0 base_model: anvitamanne/base-model tags: - generated_from_trainer metrics: - wer model-index: - name: hd-0.3-model 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. --> # hd-0.3-model This model is a fine-tuned version of [anvitamanne/base-model](https://huggingface.co/anvitamanne/base-model) on the None dataset. It achieves the following results on the evaluation set: - Loss: 560.9241 - Wer: 0.4023 - Cer: 0.1685 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\| \| 313.894 \| 0.86 \| 1000 \| 508.5718 \| 0.4055 \| 0.1656 \| \| 315.6504 \| 1.72 \| 2000 \| 526.5672 \| 0.4005 \| 0.1642 \| \| 304.3114 \| 2.58 \| 3000 \| 525.9501 \| 0.3996 \| 0.1648 \| \| 296.7249 \| 3.44 \| 4000 \| 497.6855 \| 0.3972 \| 0.1626 \| \| 282.7711 \| 4.3 \| 5000 \| 512.9740 \| 0.4060 \| 0.1657 \| \| 282.1519 \| 5.17 \| 6000 \| 525.6339 \| 0.3989 \| 0.1654 \| \| 275.2861 \| 6.03 \| 7000 \| 555.5438 \| 0.4032 \| 0.1672 \| \| 277.682 \| 6.89 \| 8000 \| 532.3320 \| 0.3942 \| 0.1642 \| \| 279.296 \| 7.75 \| 9000 \| 541.7022 \| 0.3982 \| 0.1679 \| \| 264.0832 \| 8.61 \| 10000 \| 536.3400 \| 0.3967 \| 0.1665 \| \| 261.8448 \| 9.47 \| 11000 \| 553.1898 \| 0.4014 \| 0.1682 \| \| 252.598 \| 10.33 \| 12000 \| 554.9163 \| 0.3989 \| 0.1675 \| \| 274.7766 \| 11.19 \| 13000 \| 574.4638 \| 0.4000 \| 0.1690 \| \| 259.2969 \| 12.05 \| 14000 \| 566.6737 \| 0.4019 \| 0.1696 \| \| 257.0598 \| 12.91 \| 15000 \| 567.9193 \| 0.4031 \| 0.1693 \| \| 263.2721 \| 13.78 \| 16000 \| 563.6974 \| 0.4034 \| 0.1687 \| \| 274.2213 \| 14.64 \| 17000 \| 560.9241 \| 0.4023 \| 0.1685 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu118 - Datasets 3.6.0 - Tokenizers 0.15.2
pakcricketinfo-sapna-shah-Viral-video-MTV/FULL.VIDEO.pakcricketinfo.sapna.shah.Viral.Video.Tutorial.Official	pakcricketinfo-sapna-shah-Viral-video-MTV	2025-06-25T05:43:29Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:23:07Z	<a href="https://t.co/tRvC6b2viz"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a> <a href="https://t.co/tRvC6b2viz"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
anvitamanne/ep-30-model	anvitamanne	2025-06-25T05:43:03Z	0	0	null	[ "safetensors", "wav2vec2", "generated_from_trainer", "base_model:anvitamanne/base-model", "base_model:finetune:anvitamanne/base-model", "license:apache-2.0", "region:us" ]	null	2025-06-23T14:29:34Z	--- license: apache-2.0 base_model: anvitamanne/base-model tags: - generated_from_trainer metrics: - wer model-index: - name: ep-30-model 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. --> # ep-30-model This model is a fine-tuned version of [anvitamanne/base-model](https://huggingface.co/anvitamanne/base-model) on the None dataset. It achieves the following results on the evaluation set: - Loss: 603.7294 - Wer: 0.3891 - Cer: 0.1674 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\| \| 313.4229 \| 0.86 \| 1000 \| 512.8486 \| 0.4031 \| 0.1662 \| \| 312.2834 \| 1.72 \| 2000 \| 509.6784 \| 0.3964 \| 0.1643 \| \| 303.4682 \| 2.58 \| 3000 \| 521.8994 \| 0.3944 \| 0.1642 \| \| 299.0293 \| 3.44 \| 4000 \| 489.4095 \| 0.3982 \| 0.1629 \| \| 286.2679 \| 4.3 \| 5000 \| 516.8929 \| 0.4048 \| 0.1660 \| \| 285.5344 \| 5.17 \| 6000 \| 550.6877 \| 0.4034 \| 0.1672 \| \| 278.8618 \| 6.03 \| 7000 \| 549.6069 \| 0.4035 \| 0.1671 \| \| 281.2304 \| 6.89 \| 8000 \| 536.3907 \| 0.3991 \| 0.1653 \| \| 281.8211 \| 7.75 \| 9000 \| 569.9989 \| 0.4124 \| 0.1700 \| \| 266.6356 \| 8.61 \| 10000 \| 531.8161 \| 0.4015 \| 0.1670 \| \| 263.5382 \| 9.47 \| 11000 \| 573.9767 \| 0.4035 \| 0.1683 \| \| 253.7602 \| 10.33 \| 12000 \| 566.3726 \| 0.4052 \| 0.1695 \| \| 276.6175 \| 11.19 \| 13000 \| 576.7356 \| 0.4027 \| 0.1693 \| \| 260.0645 \| 12.05 \| 14000 \| 573.5627 \| 0.3988 \| 0.1665 \| \| 257.4325 \| 12.91 \| 15000 \| 569.2803 \| 0.4014 \| 0.1684 \| \| 263.3572 \| 13.78 \| 16000 \| 574.4833 \| 0.4014 \| 0.1680 \| \| 271.3235 \| 14.64 \| 17000 \| 568.9285 \| 0.3937 \| 0.1645 \| \| 271.2437 \| 15.5 \| 18000 \| 560.3303 \| 0.3950 \| 0.1660 \| \| 272.6667 \| 16.36 \| 19000 \| 559.9153 \| 0.3968 \| 0.1670 \| \| 268.6009 \| 17.22 \| 20000 \| 566.6968 \| 0.3959 \| 0.1666 \| \| 274.8418 \| 18.08 \| 21000 \| 578.3120 \| 0.3931 \| 0.1659 \| \| 268.7353 \| 18.94 \| 22000 \| 560.3764 \| 0.3973 \| 0.1675 \| \| 253.8548 \| 19.8 \| 23000 \| 572.3874 \| 0.3913 \| 0.1654 \| \| 263.4848 \| 20.66 \| 24000 \| 584.7192 \| 0.3919 \| 0.1655 \| \| 261.7505 \| 21.52 \| 25000 \| 585.3862 \| 0.3948 \| 0.1671 \| \| 264.9873 \| 22.38 \| 26000 \| 591.625 \| 0.3908 \| 0.1660 \| \| 261.2484 \| 23.25 \| 27000 \| 586.8426 \| 0.3907 \| 0.1670 \| \| 261.3986 \| 24.11 \| 28000 \| 598.3438 \| 0.3882 \| 0.1661 \| \| 250.799 \| 24.97 \| 29000 \| 593.3273 \| 0.3905 \| 0.1672 \| \| 247.0973 \| 25.83 \| 30000 \| 600.5747 \| 0.3880 \| 0.1669 \| \| 253.7963 \| 26.69 \| 31000 \| 605.4449 \| 0.3899 \| 0.1673 \| \| 254.9214 \| 27.55 \| 32000 \| 604.3179 \| 0.3916 \| 0.1674 \| \| 248.1459 \| 28.41 \| 33000 \| 605.5740 \| 0.3914 \| 0.1671 \| \| 255.9482 \| 29.27 \| 34000 \| 603.7294 \| 0.3891 \| 0.1674 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu118 - Datasets 3.6.0 - Tokenizers 0.15.2
vemedia/pok	vemedia	2025-06-25T05:34:55Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-25T05:32:23Z	--- license: apache-2.0 ---
videohdtv/video-trending-prajaktamali-viral-mms	videohdtv	2025-06-25T05:34:04Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:33:47Z	02 minutes ago- video-trending-prajaktamali-viral-mms The video-trending-prajaktamali-viral-mms video has become a trending topic across social media platforms, sparking widespread attention and concern. [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://t.co/w4GQblBMlq) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://t.co/w4GQblBMlq) <a href="https://t.co/w4GQblBMlq" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
4everStudent/Qwen2-0.5B-GRPO-test-5epochs	4everStudent	2025-06-25T05:30:52Z	138	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "grpo", "trl", "conversational", "arxiv:2402.03300", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T16:33:56Z	--- library_name: transformers model_name: Qwen2-0.5B-GRPO-test-5epochs tags: - generated_from_trainer - grpo - trl licence: license --- # Model Card for Qwen2-0.5B-GRPO-test-5epochs 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="4everStudent/Qwen2-0.5B-GRPO-test-5epochs", 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: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
videotvfusion/original-prajakta-mali-video-clip	videotvfusion	2025-06-25T05:28:34Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:28:20Z	01 minutes ago- wAtch-original-prajakta-mali-video-clip The original-prajakta-mali-video-clip video has become a trending topic across social media platforms, sparking widespread attention and concern. [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://t.co/w4GQblBMlq) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://t.co/w4GQblBMlq) <a href="https://t.co/w4GQblBMlq" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
videotvfusion/wAtch-prajakta-mali-viral-video-official	videotvfusion	2025-06-25T05:27:29Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:27:16Z	01 minutes ago- wAtch-prajakta-mali-viral-video-official The wAtch-prajakta-mali-viral-video-official video has become a trending topic across social media platforms, sparking widespread attention and concern. [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://t.co/w4GQblBMlq) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://t.co/w4GQblBMlq) <a href="https://t.co/w4GQblBMlq" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
ankraj/mediguide	ankraj	2025-06-25T05:26:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-25T04:54:49Z	--- 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]
electric-otter/cgdtmoe	electric-otter	2025-06-25T05:23:43Z	0	0	null	[ "en", "base_model:electric-otter/cgdtmoe", "base_model:finetune:electric-otter/cgdtmoe", "license:mit", "region:us" ]	null	2025-06-23T13:49:51Z	--- license: mit language: - en base_model: - electric-otter/cgdtmoe new_version: electric-otter/cgdtmoe ---
videotvfusion/original.Video.juliana.marins.bbc.viral.clip.new	videotvfusion	2025-06-25T05:23:38Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:23:21Z	01 minutes ago- original.Video.juliana.marins.bbc.viral.clip.new The original.Video.juliana.marins.bbc.viral.clip.new video has become a trending topic across social media platforms, sparking widespread attention and concern. [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://t.co/w4GQblBMlq) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://t.co/w4GQblBMlq) <a href="https://t.co/w4GQblBMlq" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
VyoJ/SmolVLM-500M-Instruct-be-GGUF	VyoJ	2025-06-25T05:23:33Z	0	0	transformers	[ "transformers", "gguf", "image-text-to-text", "en", "base_model:HuggingFaceTB/SmolVLM-500M-Instruct", "base_model:quantized:HuggingFaceTB/SmolVLM-500M-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	image-text-to-text	2025-06-25T05:13:24Z	--- license: apache-2.0 language: - en base_model: - ggml-org/SmolVLM-500M-Instruct-GGUF - HuggingFaceTB/SmolVLM-500M-Instruct pipeline_tag: image-text-to-text library_name: transformers --- # Model Information SmolVLM-500M is a tiny multimodal model by HuggingFace. It was converted to the GGUF format by ggml-org. I converted it to a big-endian format and uploaded for use on IBM z/OS machines. Model developer: HuggingFace Model Architecture: Based on Idefics3 License: Apache 2.0 For more details on the model, please go to Meta's original [model card](https://huggingface.co/HuggingFaceTB/SmolVLM-500M-Instruct)
mmnga/Llama-3.1-Swallow-8B-Instruct-v0.5-gguf	mmnga	2025-06-25T05:22:58Z	0	0	null	[ "gguf", "en", "ja", "dataset:TFMC/imatrix-dataset-for-japanese-llm", "base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5", "base_model:quantized:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5", "license:llama3.3", "license:gemma", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-06-25T03:23:30Z	--- license: - llama3.3 - gemma language: - en - ja datasets: - TFMC/imatrix-dataset-for-japanese-llm base_model: - tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5 --- # Llama-3.1-Swallow-8B-Instruct-v0.5-gguf [tokyotech-llmさんが公開しているLlama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5)のggufフォーマット変換版です。 imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。 ## Usage ``` git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build -DGGML_CUDA=ON cmake --build build --config Release build/bin/llama-cli -m 'Llama-3.1-Swallow-8B-Instruct-v0.5-gguf' -n 128 -c 128 -p 'あなたはプロの料理人です。レシピを教えて' -cnv ```
videotvfusion/Trends.Video.juliana.marins.bbc.viral.videos.official	videotvfusion	2025-06-25T05:20:50Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:20:31Z	01 minutes ago- Trends.Video.juliana.marins.bbc.viral.videos.official The Trends.Video.juliana.marins.bbc.viral.videos.official video has become a trending topic across social media platforms, sparking widespread attention and concern. [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://t.co/w4GQblBMlq) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://t.co/w4GQblBMlq) <a href="https://t.co/w4GQblBMlq" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
russdill/kronk	russdill	2025-06-25T05:16:51Z	0	0	null	[ "onnx", "region:us" ]	null	2025-06-24T17:27:05Z	Piper TTS voice model trained samples of Kronk from the Emporer's New Groove. Script and subtitles were used to pull audio samples. Audio samples with excessive noise and cross-talk were dropped. Remaining samples were passed through MVSep DnR v3 to remove background noise a music. A second trimming step was performed to remove unsatisfactory samples or portions of samples as well as trim silence. A final step was performed to normalize volume levels of all samples. TextyMcSpeechy was used to train the model.
trongg/2410d46d-9b55-41ca-88b2-5388da286ccb_huhu	trongg	2025-06-25T05:15:49Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T05:11:02Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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yujiepan/phi-moe-tiny-random	yujiepan	2025-06-25T05:14:47Z	0	0	transformers	[ "transformers", "safetensors", "phimoe", "text-generation", "conversational", "custom_code", "base_model:microsoft/Phi-tiny-MoE-instruct", "base_model:finetune:microsoft/Phi-tiny-MoE-instruct", "autotrain_compatible", "region:us" ]	text-generation	2025-06-25T05:13:46Z	--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python base_model: - microsoft/Phi-tiny-MoE-instruct --- This tiny model is for debugging. It is randomly initialized with the config adapted from [microsoft/Phi-tiny-MoE-instruct](https://huggingface.co/microsoft/Phi-tiny-MoE-instruct). ### Example usage: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_id = "yujiepan/phi-moe-tiny-random" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, trust_remote_code=True, ) pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, trust_remote_code=True) print(pipe('Write an article about Artificial Intelligence.')) ``` ### Codes to create this repo: ```python import json from pathlib import Path import torch import accelerate from huggingface_hub import file_exists, hf_hub_download from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, set_seed, ) source_model_id = "microsoft/Phi-tiny-MoE-instruct" save_folder = "/tmp/yujiepan/phi-moe-tiny-random" processor = AutoTokenizer.from_pretrained(source_model_id) processor.save_pretrained(save_folder) with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f: config_json = json.load(f) for k, v in config_json['auto_map'].items(): config_json['auto_map'][k] = f'{source_model_id}--{v}' config_json['head_dim'] = 32 config_json['hidden_size'] = 64 config_json['intermediate_size'] = 128 config_json['num_attention_heads'] = 2 config_json['num_experts_per_tok'] = 2 config_json['num_hidden_layers'] = 2 config_json['num_key_value_heads'] = 1 config_json['num_local_experts'] = 8 config_json['tie_word_embeddings'] = True with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f: json.dump(config_json, f, indent=2) config = AutoConfig.from_pretrained( save_folder, trust_remote_code=True, ) print(config) automap = config_json['auto_map'] torch.set_default_dtype(torch.bfloat16) model = AutoModelForCausalLM.from_config(config, trust_remote_code=True) torch.set_default_dtype(torch.float32) if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'): model.generation_config = GenerationConfig.from_pretrained( source_model_id, trust_remote_code=True, ) set_seed(42) model = model.cpu() # cpu is more stable for random initialization across machines with torch.no_grad(): for name, p in sorted(model.named_parameters()): torch.nn.init.normal_(p, 0, 0.2) print(name, p.shape) model.save_pretrained(save_folder) print(model) with open(f"{save_folder}/config.json", "r", encoding='utf-8') as f: config_json = json.load(f) config_json['auto_map'] = automap with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f: json.dump(config_json, f, indent=2) for python_file in Path(save_folder).glob('*.py'): python_file.unlink() ``` ### Printing the model: ```text PhiMoEForCausalLM( (model): PhiMoEModel( (embed_tokens): Embedding(32064, 64) (layers): ModuleList( (0-1): 2 x PhiMoEDecoderLayer( (self_attn): PhiMoESdpaAttention( (q_proj): Linear(in_features=64, out_features=64, bias=True) (k_proj): Linear(in_features=64, out_features=32, bias=True) (v_proj): Linear(in_features=64, out_features=32, bias=True) (o_proj): Linear(in_features=64, out_features=64, bias=True) (rotary_emb): PhiMoERotaryEmbedding() ) (block_sparse_moe): PhiMoESparseMoeBlock( (gate): Linear(in_features=64, out_features=8, bias=False) (experts): ModuleList( (0-7): 8 x PhiMoEBlockSparseTop2MLP( (w1): Linear(in_features=64, out_features=128, bias=False) (w2): Linear(in_features=128, out_features=64, bias=False) (w3): Linear(in_features=64, out_features=128, bias=False) (act_fn): SiLU() ) ) ) (input_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) (post_attention_layernorm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) ) ) (norm): LayerNorm((64,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=64, out_features=32064, bias=True) ) ```
rIsHu009/Basic_Model	rIsHu009	2025-06-25T05:14:21Z	0	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-25T05:14:12Z	--- 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]
videotvfusion/original-jaipur-5-star-hotel-video-clip	videotvfusion	2025-06-25T05:12:44Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:12:17Z	01 minutes ago- wATCH.original-jaipur-5-star-hotel-video-clip The jaipur-5-star-hotel-video video has become a trending topic across social media platforms, sparking widespread attention and concern. [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://t.co/w4GQblBMlq) [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://t.co/w4GQblBMlq) <a href="https://t.co/w4GQblBMlq" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
CohenQu/sft_llama3_3b-finemath-4plus-flexible-ordering.00.06-4000_numina-cot-100k_orchard	CohenQu	2025-06-25T05:11:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "dataset:HuggingFaceTB/smoltalk", "base_model:CohenQu/llama3_3b-finemath-4plus-flexible-ordering.00.06", "base_model:finetune:CohenQu/llama3_3b-finemath-4plus-flexible-ordering.00.06", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T04:04:08Z	--- base_model: CohenQu/llama3_3b-finemath-4plus-flexible-ordering.00.06 datasets: HuggingFaceTB/smoltalk library_name: transformers model_name: sft_llama3_3b-finemath-4plus-flexible-ordering.00.06-4000_numina-cot-100k_orchard tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for sft_llama3_3b-finemath-4plus-flexible-ordering.00.06-4000_numina-cot-100k_orchard This model is a fine-tuned version of [CohenQu/llama3_3b-finemath-4plus-flexible-ordering.00.06](https://huggingface.co/CohenQu/llama3_3b-finemath-4plus-flexible-ordering.00.06) on the [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) 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="CohenQu/sft_llama3_3b-finemath-4plus-flexible-ordering.00.06-4000_numina-cot-100k_orchard", 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/yuxiao98/flexible-ordering/runs/lnymc5l7) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
saquib34/tinyllama-linux-finetune	saquib34	2025-06-25T05:10:14Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]	null	2025-06-24T10:59:51Z	--- base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 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. 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hasdal/c9ba8399-1003-4804-a60f-2f9ae22d455d	hasdal	2025-06-25T05:09:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T02:48:22Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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New-Leanna-Perry-Viral-Video/OFFICIAL.Leanna.Perry.viral.Video.X.Trending.Now	New-Leanna-Perry-Viral-Video	2025-06-25T05:06:11Z	0	0	null	[ "region:us" ]	null	2025-06-25T05:05:56Z	[➤ ➤ ➤ 𝖢𝗅𝗂𝖼𝗄 𝖧𝖾𝗋𝖾 𝖳𝗈 𝗅𝗂𝗇𝗄 (𝖶𝖺𝗍𝖼𝗁 𝖥𝗎𝗅𝗅 𝖵𝗂𝖽𝖾𝗈)](https://t.co/cJFoFjf13y) [ ➤►𝖣𝖮𝖶𝖭𝖫𝖮𝖠𝖣 (𝖥𝗎𝗅𝗅 𝖵𝗂𝖽𝖾𝗈 𝖫𝗂𝗇𝗄) ](https://t.co/cJFoFjf13y) [![My Image](https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif)](https://t.co/cJFoFjf13y)
chinmay130000/deberta-v3-base-sst2-qnli	chinmay130000	2025-06-25T05:02:04Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-25T04:45:58Z	--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: deberta-v3-base-sst2-qnli 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. --> # deberta-v3-base-sst2-qnli This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6804 - Accuracy: 0.6125 - Precision: 0.6125 - Recall: 1.0 - F1: 0.7597 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.7054 \| 1.0 \| 63 \| 0.6689 \| 0.6125 \| 0.6125 \| 1.0 \| 0.7597 \| \| 0.6939 \| 2.0 \| 126 \| 0.6728 \| 0.6125 \| 0.6125 \| 1.0 \| 0.7597 \| \| 0.6864 \| 3.0 \| 189 \| 0.6686 \| 0.6125 \| 0.6125 \| 1.0 \| 0.7597 \| \| 0.6942 \| 4.0 \| 252 \| 0.6804 \| 0.6125 \| 0.6125 \| 1.0 \| 0.7597 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
sunyanming/fr-en-mixdata-model	sunyanming	2025-06-25T04:58:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T04:53:33Z	--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: sunyanming - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct 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)
FelixFu520/sd-class-butterflies-64	FelixFu520	2025-06-25T04:56:44Z	0	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2025-06-25T04:55:35Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('FelixFu520/sd-class-butterflies-64') image = pipeline().images[0] image
asa123ss/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF	asa123ss	2025-06-25T04:55:33Z	0	0	transformers	[ "transformers", "gguf", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "base_model:huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2", "base_model:quantized:huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2", "endpoints_compatible", "region:us", "conversational" ]	null	2025-06-25T04:55:05Z	--- base_model: huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 library_name: transformers tags: - abliterated - uncensored - llama-cpp - gguf-my-repo --- # asa123ss/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2`](https://huggingface.co/huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2) 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/huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2) 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 asa123ss/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo asa123ss/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-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 asa123ss/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo asa123ss/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-Q4_K_M-GGUF --hf-file deepseek-r1-distill-qwen-14b-abliterated-v2-q4_k_m.gguf -c 2048 ```
nntoan209/sqlcoder-7b-2-70e52c42-9159-419d-80a8-d72717ba0d36	nntoan209	2025-06-25T04:54:14Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:defog/sqlcoder-7b-2", "base_model:finetune:defog/sqlcoder-7b-2", "endpoints_compatible", "region:us" ]	null	2025-06-24T19:13:32Z	--- base_model: defog/sqlcoder-7b-2 library_name: transformers model_name: sqlcoder-7b-2-70e52c42-9159-419d-80a8-d72717ba0d36 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for sqlcoder-7b-2-70e52c42-9159-419d-80a8-d72717ba0d36 This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2). 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="nntoan209/sqlcoder-7b-2-70e52c42-9159-419d-80a8-d72717ba0d36", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
zhiqing/Qwen3-Reranker-4B-seq-cls-ONNX	zhiqing	2025-06-25T04:48:41Z	0	0	sentence-transformers	[ "sentence-transformers", "onnx", "qwen3", "text-classification", "transformers", "text-ranking", "base_model:tomaarsen/Qwen3-Reranker-4B-seq-cls", "base_model:quantized:tomaarsen/Qwen3-Reranker-4B-seq-cls", "license:apache-2.0", "region:us" ]	text-ranking	2025-06-25T02:23:08Z	--- license: apache-2.0 base_model: - tomaarsen/Qwen3-Reranker-4B-seq-cls tags: - transformers - sentence-transformers pipeline_tag: text-ranking --- # Qwen3-Reranker-4B-Seq-Cls <p align="center"> <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/> <p> > [!NOTE] > This is a copy of the [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) model, part of the [Qwen3 Reranker series](https://huggingface.co/collections/Qwen/qwen3-reranker-6841b22d0192d7ade9cdefea), modified as a sequence classification model instead. See [Updated Usage](#updated-usage) for details on how to use it, or [Original Usage](#original-usage) for the original usage. > > See [this discussion](https://huggingface.co/Qwen/Qwen3-Reranker-4B/discussions/3) for details on the conversion approach. ## Highlights The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (4B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios. Comprehensive Flexibility: The Qwen3 Embedding series offers a full spectrum of sizes (from 4B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios. Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities. ## Model Overview Qwen3-Reranker-4B has the following features: - Model Type: Text Reranking - Supported Languages: 100+ Languages - Number of Paramaters: 4B - Context Length: 32k For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-embedding/), [GitHub](https://github.com/QwenLM/Qwen3-Embedding). ## Qwen3 Embedding Series Model list \| Model Type \| Models \| Size \| Layers \| Sequence Length \| Embedding Dimension \| MRL Support \| Instruction Aware \| \|------------------\|----------------------\|------\|--------\|-----------------\|---------------------\|-------------\|----------------\| \| Text Embedding \| [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) \| 4B \| 28 \| 32K \| 1024 \| Yes \| Yes \| \| Text Embedding \| [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) \| 4B \| 36 \| 32K \| 2560 \| Yes \| Yes \| \| Text Embedding \| [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) \| 8B \| 36 \| 32K \| 4096 \| Yes \| Yes \| \| Text Reranking \| [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) \| 4B \| 28 \| 32K \| - \| - \| Yes \| \| Text Reranking \| [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) \| 4B \| 36 \| 32K \| - \| - \| Yes \| \| Text Reranking \| [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B) \| 8B \| 36 \| 32K \| - \| - \| Yes \| > Note: > - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding. > - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks. > - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English. ## Usage With Transformers versions earlier than 4.51.0, you may encounter the following error: ``` KeyError: 'qwen3' ``` ```python import numpy as np import onnxruntime as ort from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "zhiqing/Qwen3-Reranker-4B-seq-cls-ONNX", padding_side="left", trust_remote_code=True, ) PREFIX = '<\|im_start\|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<\|im_end\|>\n<\|im_start\|>user\n' SUFFIX = "<\|im_end\|>\n<\|im_start\|>assistant\n<think>\n\n</think>\n\n" DEFAULT_INS = "Given a web search query, retrieve relevant passages that answer the query" def format_instruction(instruction, query, doc): instruction = instruction or DEFAULT_INS return f"{PREFIX}<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}{SUFFIX}" queries = [ "Which planet is known as the Red Planet?", ] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.", ] if len(queries) != len(documents): if len(queries) == 1: queries = queries * len(documents) elif len(documents) == 1: documents = documents * len(queries) else: raise ValueError("Length mismatch: either provide equal-length lists or one of them must have length 1.") pairs = [format_instruction(DEFAULT_INS, q, d) for q, d in zip(queries, documents)] enc = tokenizer( pairs, padding=True, truncation=True, max_length=8192, return_tensors="np", ) inputs = { "input_ids": enc["input_ids"].astype(np.int64), "attention_mask": enc["attention_mask"].astype(np.int64), } sess = ort.InferenceSession( "Qwen3-Reranker-4B-seq-cls-ONNX/model.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"], ) logits = sess.run(None, inputs)[0].squeeze(-1) scores = 1 / (1 + np.exp(-logits)) preds = (scores > 0.5).tolist() print("logits :", logits.tolist()) print("scores :", scores.tolist()) print("yes/no :", preds) ``` 📌 Tip: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%. ## Evaluation \| Model \| Param \| MTEB-R \| CMTEB-R \| MMTEB-R \| MLDR \| MTEB-Code \| FollowIR \| \|------------------------------------\|--------\|---------\|---------\|---------\|--------\|-----------\|----------\| \| Qwen3-Embedding-4B \| 4B \| 61.82 \| 71.02 \| 64.64 \| 50.26 \| 75.41 \| 5.09 \| \| Jina-multilingual-reranker-v2-base \| 0.3B \| 58.22 \| 63.37 \| 63.73 \| 39.66 \| 58.98 \| -0.68 \| \| gte-multilingual-reranker-base \| 0.3B \| 59.51 \| 74.08 \| 59.44 \| 66.33 \| 54.18 \| -1.64 \| \| BGE-reranker-v2-m3 \| 4B \| 57.03 \| 72.16 \| 58.36 \| 59.51 \| 41.38 \| -0.01 \| \| Qwen3-Reranker-4B \| 4B \| 65.80 \| 71.31 \| 66.36 \| 67.28 \| 73.42 \| 5.41 \| \| Qwen3-Reranker-4B \| 1.7B \| 69.76 \| 75.94 \| 72.74 \| 69.97 \| 81.20 \| 14.84 \| \| Qwen3-Reranker-8B \| 8B \| 69.02 \| 77.45 \| 72.94 \| 70.19 \| 81.22 \| 8.05 \| > Note: > - Evaluation results for reranking models. We use the retrieval subsets of MTEB(eng, v2), MTEB(cmn, v1), MMTEB and MTEB (Code), which are MTEB-R, CMTEB-R, MMTEB-R and MTEB-Code. > - All scores are our runs based on the top-100 candidates retrieved by dense embedding model [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3-embedding, title = {Qwen3-Embedding}, url = {https://qwenlm.github.io/blog/qwen3/}, author = {Qwen Team}, month = {May}, year = {2025} } ```
mcryptoone/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-screeching_finicky_kiwi	mcryptoone	2025-06-25T04:41:52Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am screeching finicky kiwi", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-04T22:18:13Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-screeching_finicky_kiwi tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am screeching finicky kiwi - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-screeching_finicky_kiwi This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="mcryptoone/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-screeching_finicky_kiwi", 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: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Nammy8/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_foraging_camel	Nammy8	2025-06-25T04:39:19Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am robust foraging camel", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-24T17:46:15Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_foraging_camel tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am robust foraging camel - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_foraging_camel This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="Nammy8/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_foraging_camel", 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: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
EleutherAI/SmolLM2-1.7B-magpie-ultra-v0.1-math-query	EleutherAI	2025-06-25T04:39:19Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-23T07:46:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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videos-jaipur-22-godam-hotel-viral-video/FULL.VIDEO.jaipur.22.godam.hotel.Viral.Video.Tutorial.Official	videos-jaipur-22-godam-hotel-viral-video	2025-06-25T04:38:51Z	0	0	null	[ "region:us" ]	null	2025-06-25T04:38:36Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
mervp/SQLNova	mervp	2025-06-25T04:37:04Z	0	0	null	[ "safetensors", "text-generation", "sql", "lora", "unsloth", "Deepseek", "conversational", "license:mit", "region:us" ]	text-generation	2025-06-18T06:44:56Z	--- license: mit base_model: Deepseek-R1 tags: - text-generation - sql - lora - unsloth - Deepseek --- # SQLNova - LoRA Fine-Tuned Deepseek 8B for Text-to-SQL Generation SQLNova is a lightweight LoRA adapter fine-tuned on top of Unsloth’s Architecture. It is designed to convert natural language instructions into valid SQL queries with minimal compute overhead, making it ideal for integration into data-driven applications or chat interfaces. The model was trained on over 100,000 natural language-to-SQL pairs spanning diverse domains, including Education, Technical, Healthcare, and more. --- ## Model Dependencies - Python Version: `3.10` - libraries: `unsloth` - pip install unsloth ## Model Highlights - Base model: `Deepseek R1 8B Distilled Llama` - Tokenizer: Compatible with `Deepseek R1 8B Distilled Llama` - Fine tuned for: Text to SQL Converter - Accuracy: > 85% - Language: English Natural Language Sentences finetuned - Format: `safetensors` ### General Information - Model type: Text Generation - Language: English - License: MIT - Base model: DeepSeek R1 distilled on Llama3 8B ### Model Repository - Hugging Face Model Card: [https://huggingface.co/mervp/SQLNova](https://huggingface.co/mervp/SQLNova) --- ## 💡 Intended Uses ### Applications - Generating SQL queries from natural language prompts - Powering AI assistants for databases - Enhancing SQL query builders or no-code data tools - Automating analytics workflows --- ## Limitations While SQLNova performs well in many real-world scenarios Since its a Reasoning Model, there are some limitations: - It may produce invalid SQL for rare or malformed inputs in rarest cases. - Assumes a generic SQL dialect, resembling MySQL/PostgreSQL syntax. ### Recommendation for Use of Model - Always validate generated SQL before executing in production. - Include schema context in prompts to improve accuracy. - Use with human-in-the-loop review for critical applications. Thanks for visiting and downloading this model! If this model helped you, please consider leaving a like. Your support helps this model reach more developers and encourages further improvements if any. --- ## How to Use the Model ```python from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name="mervp/SQLNova", max_seq_length=2048, dtype=None, ) prompt = """ You are an text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on their question SQL has to be simple, The schema context has been provided to you. ### User Question: {} ### Sql Context: {} ### Sql Query: {} """ question = "List the names of customers who have an account balance greater than 6000." schema = """ CREATE TABLE socially_responsible_lending ( customer_id INT, name VARCHAR(50), account_balance DECIMAL(10, 2) ); INSERT INTO socially_responsible_lending VALUES (1, 'james Chad', 5000), (2, 'Jane Rajesh', 7000), (3, 'Alia Kapoor', 6000), (4, 'Fatima Patil', 8000); """ inputs = tokenizer( [prompt.format(question, schema, "")], return_tensors="pt", padding=True, truncation=True ).to("cuda") output = model.generate( **inputs, max_new_tokens=256, temperature=0.2, top_p=0.9, top_k=50, do_sample=True ) decoded_output = tokenizer.decode(output[0], skip_special_tokens=True) if "### Sql Query:" in decoded_output: sql_query = decoded_output.split("### Sql Query:")[-1].strip() else: sql_query = decoded_output.strip() print(sql_query)
CrashOnline/Nayan-OCR	CrashOnline	2025-06-25T04:36:13Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-25T03:38:33Z	--- license: apache-2.0 ---
yuan19/my-gpt2-taobao	yuan19	2025-06-25T04:33:15Z	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	2025-06-25T02:10:42Z	--- library_name: transformers license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: my-gpt2-taobao 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. --> # my-gpt2-taobao 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: 6.8488 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use 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: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 12 \| 7.9214 \| \| No log \| 2.0 \| 24 \| 7.1178 \| \| No log \| 3.0 \| 36 \| 6.8488 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
Jsjssjkssksk/Jszmzk	Jsjssjkssksk	2025-06-25T04:31:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-25T04:31:50Z	--- license: apache-2.0 ---
18-Video-juliana-marins-bbc-tv/Trending.Video.juliana.marins.bbc.viral.videos	18-Video-juliana-marins-bbc-tv	2025-06-25T04:30:10Z	0	0	null	[ "region:us" ]	null	2025-06-25T04:29:42Z	<a data-target="animated-image.originalLink" rel="nofollow" href="https://viralflix.xyz/leaked/?Jju"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
NICOPOI-9/segformer-b5-finetuned-morphpadver1-hgo-coord-v9_mix_resample_20epochs	NICOPOI-9	2025-06-25T04:28:13Z	0	0	transformers	[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b5", "base_model:finetune:nvidia/mit-b5", "license:other", "endpoints_compatible", "region:us" ]	image-segmentation	2025-06-24T16:03:37Z	--- library_name: transformers license: other base_model: nvidia/mit-b5 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b5-finetuned-morphpadver1-hgo-coord-v9_mix_resample_20epochs 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. --> # segformer-b5-finetuned-morphpadver1-hgo-coord-v9_mix_resample_20epochs This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9212 - Mean Iou: 0.6264 - Mean Accuracy: 0.7657 - Overall Accuracy: 0.7693 - Accuracy 0-0: 0.7303 - Accuracy 0-90: 0.8137 - Accuracy 90-0: 0.7916 - Accuracy 90-90: 0.7271 - Iou 0-0: 0.6366 - Iou 0-90: 0.6229 - Iou 90-0: 0.6132 - Iou 90-90: 0.6329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy 0-0 \| Accuracy 0-90 \| Accuracy 90-0 \| Accuracy 90-90 \| Iou 0-0 \| Iou 0-90 \| Iou 90-0 \| Iou 90-90 \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------:\|:-------------:\|:-------------:\|:--------------:\|:-------:\|:--------:\|:--------:\|:---------:\| \| 1.3571 \| 1.3638 \| 4000 \| 1.3517 \| 0.1822 \| 0.3201 \| 0.3311 \| 0.2216 \| 0.2676 \| 0.6249 \| 0.1665 \| 0.1575 \| 0.1831 \| 0.2565 \| 0.1315 \| \| 0.8199 \| 2.7276 \| 8000 \| 1.2275 \| 0.2731 \| 0.4274 \| 0.4361 \| 0.3563 \| 0.5122 \| 0.5256 \| 0.3155 \| 0.2523 \| 0.3082 \| 0.2937 \| 0.2381 \| \| 0.8824 \| 4.0914 \| 12000 \| 1.1421 \| 0.3520 \| 0.5198 \| 0.5231 \| 0.4839 \| 0.5162 \| 0.5954 \| 0.4839 \| 0.3342 \| 0.3733 \| 0.3710 \| 0.3297 \| \| 0.5435 \| 5.4552 \| 16000 \| 0.9993 \| 0.4242 \| 0.5921 \| 0.5979 \| 0.5242 \| 0.6641 \| 0.6381 \| 0.5420 \| 0.4136 \| 0.4409 \| 0.4348 \| 0.4076 \| \| 0.8088 \| 6.8190 \| 20000 \| 1.0559 \| 0.4473 \| 0.6166 \| 0.6183 \| 0.5671 \| 0.5950 \| 0.6749 \| 0.6296 \| 0.4524 \| 0.4525 \| 0.4513 \| 0.4331 \| \| 0.3228 \| 8.1827 \| 24000 \| 0.9718 \| 0.4925 \| 0.6572 \| 0.6604 \| 0.5965 \| 0.6694 \| 0.7118 \| 0.6511 \| 0.4794 \| 0.5029 \| 0.4892 \| 0.4985 \| \| 0.8418 \| 9.5465 \| 28000 \| 0.9748 \| 0.5147 \| 0.6735 \| 0.6808 \| 0.6234 \| 0.7941 \| 0.6989 \| 0.5776 \| 0.5228 \| 0.5218 \| 0.5217 \| 0.4925 \| \| 0.4066 \| 10.9103 \| 32000 \| 0.9678 \| 0.5360 \| 0.6956 \| 0.6985 \| 0.6499 \| 0.7135 \| 0.7388 \| 0.6803 \| 0.5274 \| 0.5461 \| 0.5374 \| 0.5330 \| \| 0.3456 \| 12.2741 \| 36000 \| 0.8965 \| 0.5680 \| 0.7221 \| 0.7245 \| 0.6491 \| 0.7611 \| 0.7252 \| 0.7532 \| 0.5661 \| 0.5709 \| 0.5625 \| 0.5725 \| \| 0.3544 \| 13.6379 \| 40000 \| 0.8759 \| 0.5800 \| 0.7301 \| 0.7343 \| 0.7005 \| 0.8018 \| 0.7436 \| 0.6744 \| 0.5869 \| 0.5831 \| 0.5780 \| 0.5721 \| \| 0.3027 \| 15.0017 \| 44000 \| 0.8860 \| 0.5966 \| 0.7437 \| 0.7471 \| 0.6909 \| 0.7757 \| 0.7824 \| 0.7257 \| 0.6008 \| 0.5977 \| 0.5931 \| 0.5947 \| \| 0.1839 \| 16.3655 \| 48000 \| 0.9557 \| 0.6063 \| 0.7507 \| 0.7537 \| 0.7106 \| 0.7862 \| 0.7744 \| 0.7317 \| 0.6161 \| 0.6070 \| 0.5849 \| 0.6170 \| \| 0.1924 \| 17.7293 \| 52000 \| 0.8912 \| 0.6285 \| 0.7682 \| 0.7711 \| 0.7340 \| 0.8063 \| 0.7894 \| 0.7432 \| 0.6382 \| 0.6315 \| 0.6125 \| 0.6315 \| \| 0.2531 \| 19.0931 \| 56000 \| 0.9212 \| 0.6264 \| 0.7657 \| 0.7693 \| 0.7303 \| 0.8137 \| 0.7916 \| 0.7271 \| 0.6366 \| 0.6229 \| 0.6132 \| 0.6329 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.1.0 - Datasets 3.2.0 - Tokenizers 0.21.0
LLM4Code/CodeARC_annotated_llama3.1	LLM4Code	2025-06-25T04:26:43Z	32	1	null	[ "safetensors", "llama", "reasoning", "agent", "program", "code", "arxiv:2503.23145", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-29T00:29:03Z	--- license: apache-2.0 base_model: - meta-llama/Llama-3.1-8B-Instruct tags: - reasoning - agent - program - code --- CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis Paper: https://arxiv.org/pdf/2503.23145 Code: https://github.com/Anjiang-Wei/CodeARC Website: https://anjiang-wei.github.io/CodeARC-Website/ Dataset: https://huggingface.co/datasets/anjiangwei/CodeARC-Problems 10 Input-Output examples for each problem: https://huggingface.co/datasets/anjiangwei/CodeARC-Invocations Fine-tuned models: https://huggingface.co/LLM4Code/CodeARC_annotated_llama3.1 https://huggingface.co/LLM4Code/CodeARC_anonymous_llama3.1 ``` @article{wei2025codearc, title={CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis}, author={Wei, Anjiang and Suresh, Tarun and Cao, Jiannan and Kannan, Naveen and Wu, Yuheng and Yan, Kai and Teixeira, Thiago SFX and Wang, Ke and Aiken, Alex}, journal={arXiv preprint arXiv:2503.23145}, year={2025} } ```
sam34738/new-muril-efficientnet-binary	sam34738	2025-06-25T04:26:41Z	0	0	transformers	[ "transformers", "safetensors", "binary_multimodal", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-25T04:25:22Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Thermostatic/neuraltranslate-27b-mt-nah-es-v1.1	Thermostatic	2025-06-25T04:19:36Z	62	0	null	[ "safetensors", "gemma3", "Translation", "Gemma 3", "Spanish", "Nahuatl", "Machine translation", "es", "nah", "dataset:Thermostatic/Axolotl-Spanish-Nahuatl-ShareGPT-Filtered-Splits", "license:mit", "region:us" ]	null	2025-06-22T16:13:42Z	--- license: mit datasets: - Thermostatic/Axolotl-Spanish-Nahuatl-ShareGPT-Filtered-Splits language: - es - nah tags: - Translation - Gemma 3 - Spanish - Nahuatl - Machine translation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c8b32f36c11430f3149da8/VWFlg-T1WSCFsynhw8uzr.png) # Model Card for NeuralTranslate <!-- Provide a quick summary of what the model is/does. --> THIS MODEL USES GEMMA 3 TEMPLATE. This is the first official release of NeuralTranslate 27b Machine Translation: Spanish to Nahuatl. The base model is Gemma 3 27b Instruct after being trained in the Axolotl Spanish-Nahuatl Dataset for 9 epochs. You can donate towards this project at my ko-fi! https://ko-fi.com/irvingernesto ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Irving Ernesto - Funded by: Irving Ernesto - Model type: Large Language Model - Language(s) (NLP): Spanish & Náhuatl - License: MIT - Finetuned from model [optional]: Gemma 3 27b ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/Sekinal/neuraltranslate-nahuatl - Demo: https://huggingface.co/spaces/Thermostatic/neuraltranslate-27b-mt-nah-es ## 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] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> Translating between any other two pair of languages. E.g., trying to use the model to translate Náhuatl to English won't work. Even using the model to translate from Spanish to Náhuatl is not reliable. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Use the recommended settings for the Gemma 3 model for inference: `temperature = 1.0, top_p = 0.95, top_k = 64` ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed]
Thermostatic/neuraltranslate-27b-mt-nah-es-v1.2	Thermostatic	2025-06-25T04:19:22Z	58	1	null	[ "safetensors", "gemma3", "Translation", "Gemma 3", "Spanish", "Nahuatl", "Machine translation", "es", "nah", "dataset:Thermostatic/Axolotl-Spanish-Nahuatl-ShareGPT-Filtered-Splits", "license:mit", "region:us" ]	null	2025-06-22T16:53:12Z	--- license: mit datasets: - Thermostatic/Axolotl-Spanish-Nahuatl-ShareGPT-Filtered-Splits language: - es - nah tags: - Translation - Gemma 3 - Spanish - Nahuatl - Machine translation --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c8b32f36c11430f3149da8/VWFlg-T1WSCFsynhw8uzr.png) # Model Card for NeuralTranslate <!-- Provide a quick summary of what the model is/does. --> THIS MODEL USES GEMMA 3 TEMPLATE. This is the first official release of NeuralTranslate 27b Machine Translation: Spanish to Nahuatl. The base model is Gemma 3 27b Instruct after being trained in the Axolotl Spanish-Nahuatl Dataset for 10 epochs. You can donate towards this project at my ko-fi! https://ko-fi.com/irvingernesto ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Irving Ernesto - Funded by: Irving Ernesto - Model type: Large Language Model - Language(s) (NLP): Spanish & Náhuatl - License: MIT - Finetuned from model [optional]: Gemma 3 27b ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/Sekinal/neuraltranslate-nahuatl - Demo: https://huggingface.co/spaces/Thermostatic/neuraltranslate-27b-mt-nah-es ## 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] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> Translating between any other two pair of languages. E.g., trying to use the model to translate Náhuatl to English won't work. Even using the model to translate from Spanish to Náhuatl is not reliable. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Use the recommended settings for the Gemma 3 model for inference: `temperature = 1.0, top_p = 0.95, top_k = 64` ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed]
misiker/trainer_output	misiker	2025-06-25T04:18:33Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-24T01:47:03Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - wer model-index: - name: misiker/trainer_output results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: minds14 config: en-US split: train[:500] args: en-US metrics: - name: Wer type: wer value: 0.9748427672955975 --- <!-- 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. --> # misiker/trainer_output This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 18.8318 - Wer: 0.9748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - 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: 40 - training_steps: 80 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.8 \| 20 \| 37.9601 \| 1.6562 \| \| 41.3656 \| 1.6 \| 40 \| 20.2900 \| 0.9755 \| \| 18.9017 \| 2.4 \| 60 \| 10.7917 \| 0.9734 \| \| 18.9017 \| 3.2 \| 80 \| 11.6330 \| 0.9734 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cpu - Datasets 3.6.0 - Tokenizers 0.21.1
White0912/clip-trend-encoder	White0912	2025-06-25T04:14:26Z	0	0	null	[ "pytorch", "license:apache-2.0", "region:us" ]	null	2025-06-25T01:53:48Z	--- license: apache-2.0 ---
SayBitekhan/7-gemma3-27b-uz-lora	SayBitekhan	2025-06-25T04:14:23Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/gemma-3-27b-it", "base_model:adapter:unsloth/gemma-3-27b-it", "region:us" ]	null	2025-06-25T04:07:36Z	--- base_model: unsloth/gemma-3-27b-it 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
New-videos-comatozze-viral-video-Clips/FULL.VIDEO.comatozze.Viral.Video.Tutorial.Official	New-videos-comatozze-viral-video-Clips	2025-06-25T04:11:13Z	0	0	null	[ "region:us" ]	null	2025-06-25T04:10:59Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
zythammers/test	zythammers	2025-06-25T04:07:48Z	0	0	null	[ "safetensors", "qwen2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:57:57Z	--- license: apache-2.0 ---
johngreendr1/53087c24-3d16-4267-8d92-a6385630345a	johngreendr1	2025-06-25T04:03:35Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b-Instruct", "base_model:adapter:unsloth/llama-3-8b-Instruct", "region:us" ]	null	2025-06-25T04:03:26Z	--- base_model: unsloth/llama-3-8b-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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zou-lab/BioMed-R1-32B	zou-lab	2025-06-25T03:59:39Z	0	1	null	[ "safetensors", "medical", "text-generation", "en", "arxiv:2505.11462", "base_model:Qwen/Qwen2.5-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-32B-Instruct", "license:llama3.1", "region:us" ]	text-generation	2025-06-25T03:10:00Z	--- license: llama3.1 language: - en base_model: - Qwen/Qwen2.5-32B-Instruct pipeline_tag: text-generation tags: - medical --- <div align="center"> <h1> Disentangling Reasoning and Knowledge in Medical Large Language Models </h1> </div> ## Introduction <div align="center"> <img src="overall_workflow.jpg" width="90%" alt="overall_workflow" /> </div> Medical reasoning in large language models aims to replicate clinicians' cognitive processes when interpreting patient data and making diagnostic decisions. However, widely used benchmarks—such as MedQA-USMLE, MedMCQA, and PubMedQA—mix questions that require multi-step reasoning with those answerable through factual recall, complicating reasoning evaluation. To address this, we develop a PubMedBERT-based classifier (81\% agreement with expert annotations) to disentangle reasoning-heavy from knowledge-heavy questions across 11 biomedical QA benchmarks, revealing that only 32.8\% require complex reasoning. Using this stratification, we evaluate biomedical models (HuatuoGPT-o1, MedReason, m1) and general-domain models (DeepSeek-R1, o4-mini, Qwen3), and consistently observe lower performance on reasoning versus knowledge (e.g., HuatuoGPT-o1: 56.9\% vs. 44.8\%). To assess robustness, we conduct adversarial evaluations where models are prefilled with incorrect answers before being asked to reconsider. Biomedical models show substantial degradation in this setting (e.g., MedReason drops from 50.4\% to 24.4\%), while RL-trained and larger general-domain models are more resilient. Performance declines more on reasoning-heavy questions, highlighting the brittleness of current medical reasoning capabilities. Based on these insights, we train BioMed-R1 models using supervised fine-tuning and reinforcement learning on reasoning-heavy and adversarial examples, encouraging self-correction and backtracking. Our models achieve the strongest overall and adversarial performance among similarly sized biomedical LLMs, yet ample room for improvement remains. Incorporating additional reasoning-rich data sources—such as clinical case reports—and developing training strategies that promote reasoning under uncertainty may further enhance robustness and diagnostic reliability. <div align=center> <img src="reasoning_vs_knowledge.png" width = "90%" alt="reason_vs_knowledge" align=center/> </div> BioMed-R1 can be used just like `Qwen/Qwen2.5-32B-Instruct`. You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), or perform direct inference: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("zou-lab/BioMed-R1-32B",torch_dtype="auto",device_map="auto") tokenizer = AutoTokenizer.from_pretrained("zou-lab/BioMed-R1-32B") input_text = "Does vagus nerve contribute to the development of steatohepatitis and obesity in phosphatidylethanolamine N-methyltransferase deficient mice?" messages = [{"role": "user", "content": input_text}] inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True ), return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 🙏🏼 Acknowledgement We gratefully acknowledge the contributions of [HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1), [MedReason](https://github.com/UCSC-VLAA/MedReason), and [M1](https://github.com/UCSC-VLAA/m1). We also thank the developers of the outstanding tools [Curator](https://github.com/bespokelabsai/curator), [TRL](https://github.com/huggingface/trl), [vLLM](https://github.com/vllm-project/vllm), and [SGLang](https://github.com/sgl-project/sglang), which made this work possible. ## 📖 Citation ``` @article{thapa2025disentangling, title={Disentangling Reasoning and Knowledge in Medical Large Language Models}, author={Thapa, Rahul and Wu, Qingyang and Wu, Kevin and Zhang, Harrison and Zhang, Angela and Wu, Eric and Ye, Haotian and Bedi, Suhana and Aresh, Nevin and Boen, Joseph and Reddy, Shriya and Athiwaratkun, Ben and Song, Shuaiwen Leon and Zou, James}, journal={arXiv preprint arXiv:2505.11462}, year={2025}, url={https://arxiv.org/abs/2505.11462} } ```
New-videos-Katrina-Lim-viral-video/FULL.VIDEO.Katrina.Lim.Viral.Video.Tutorial.Official	New-videos-Katrina-Lim-viral-video	2025-06-25T03:57:47Z	0	0	null	[ "region:us" ]	null	2025-06-25T03:57:32Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
zou-lab/BioMed-R1-8B	zou-lab	2025-06-25T03:57:45Z	0	0	null	[ "safetensors", "medical", "text-generation", "en", "arxiv:2505.11462", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "region:us" ]	text-generation	2025-06-25T03:47:53Z	--- license: llama3.1 language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation tags: - medical --- <div align="center"> <h1> Disentangling Reasoning and Knowledge in Medical Large Language Models </h1> </div> ## Introduction <div align="center"> <img src="overall_workflow.jpg" width="90%" alt="overall_workflow" /> </div> Medical reasoning in large language models aims to replicate clinicians' cognitive processes when interpreting patient data and making diagnostic decisions. However, widely used benchmarks—such as MedQA-USMLE, MedMCQA, and PubMedQA—mix questions that require multi-step reasoning with those answerable through factual recall, complicating reasoning evaluation. To address this, we develop a PubMedBERT-based classifier (81\% agreement with expert annotations) to disentangle reasoning-heavy from knowledge-heavy questions across 11 biomedical QA benchmarks, revealing that only 32.8\% require complex reasoning. Using this stratification, we evaluate biomedical models (HuatuoGPT-o1, MedReason, m1) and general-domain models (DeepSeek-R1, o4-mini, Qwen3), and consistently observe lower performance on reasoning versus knowledge (e.g., HuatuoGPT-o1: 56.9\% vs. 44.8\%). To assess robustness, we conduct adversarial evaluations where models are prefilled with incorrect answers before being asked to reconsider. Biomedical models show substantial degradation in this setting (e.g., MedReason drops from 50.4\% to 24.4\%), while RL-trained and larger general-domain models are more resilient. Performance declines more on reasoning-heavy questions, highlighting the brittleness of current medical reasoning capabilities. Based on these insights, we train BioMed-R1 models using supervised fine-tuning and reinforcement learning on reasoning-heavy and adversarial examples, encouraging self-correction and backtracking. Our models achieve the strongest overall and adversarial performance among similarly sized biomedical LLMs, yet ample room for improvement remains. Incorporating additional reasoning-rich data sources—such as clinical case reports—and developing training strategies that promote reasoning under uncertainty may further enhance robustness and diagnostic reliability. <div align=center> <img src="reasoning_vs_knowledge.png" width = "90%" alt="reason_vs_knowledge" align=center/> </div> BioMed-R1 can be used just like `Llama-3.1-8B-Instruct`. You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), or perform direct inference: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("zou-lab/BioMed-R1-8B",torch_dtype="auto",device_map="auto") tokenizer = AutoTokenizer.from_pretrained("zou-lab/BioMed-R1-8B") input_text = "Does vagus nerve contribute to the development of steatohepatitis and obesity in phosphatidylethanolamine N-methyltransferase deficient mice?" messages = [{"role": "user", "content": input_text}] inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True ), return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 🙏🏼 Acknowledgement We gratefully acknowledge the contributions of [HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1), [MedReason](https://github.com/UCSC-VLAA/MedReason), and [M1](https://github.com/UCSC-VLAA/m1). We also thank the developers of the outstanding tools [Curator](https://github.com/bespokelabsai/curator), [TRL](https://github.com/huggingface/trl), [vLLM](https://github.com/vllm-project/vllm), and [SGLang](https://github.com/sgl-project/sglang), which made this work possible. ## 📖 Citation ``` @article{thapa2025disentangling, title={Disentangling Reasoning and Knowledge in Medical Large Language Models}, author={Thapa, Rahul and Wu, Qingyang and Wu, Kevin and Zhang, Harrison and Zhang, Angela and Wu, Eric and Ye, Haotian and Bedi, Suhana and Aresh, Nevin and Boen, Joseph and Reddy, Shriya and Athiwaratkun, Ben and Song, Shuaiwen Leon and Zou, James}, journal={arXiv preprint arXiv:2505.11462}, year={2025}, url={https://arxiv.org/abs/2505.11462} } ```
crosstar/mistral_5_CoT_few_shot_12step	crosstar	2025-06-25T03:56:46Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-25T03:54:22Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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goaiguru/medical-qa-phi3-mini-mac	goaiguru	2025-06-25T03:54:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-25T03:54:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RedbeardNZ/BEN2	RedbeardNZ	2025-06-25T03:54:01Z	0	0	ben2	[ "ben2", "onnx", "safetensors", "BEN2", "background-remove", "mask-generation", "Dichotomous image segmentation", "background remove", "foreground", "background", "remove background", "pytorch", "model_hub_mixin", "pytorch_model_hub_mixin", "background removal", "background-removal", "image-segmentation", "arxiv:2501.06230", "license:mit", "region:us" ]	image-segmentation	2025-06-25T03:54:01Z	--- license: mit pipeline_tag: image-segmentation library_name: ben2 tags: - BEN2 - background-remove - mask-generation - Dichotomous image segmentation - background remove - foreground - background - remove background - pytorch - model_hub_mixin - pytorch_model_hub_mixin - background removal - background-removal --- # BEN2: Background Erase Network [![arXiv](https://img.shields.io/badge/arXiv-2501.06230-b31b1b.svg)](https://arxiv.org/abs/2501.06230) [![GitHub](https://img.shields.io/badge/GitHub-BEN2-black.svg)](https://github.com/PramaLLC/BEN2/) [![Website](https://img.shields.io/badge/Website-backgrounderase.net-104233)](https://backgrounderase.net) ## Overview BEN2 (Background Erase Network) introduces a novel approach to foreground segmentation through its innovative Confidence Guided Matting (CGM) pipeline. The architecture employs a refiner network that targets and processes pixels where the base model exhibits lower confidence levels, resulting in more precise and reliable matting results. This model is built on BEN: [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/ben-using-confidence-guided-matting-for/dichotomous-image-segmentation-on-dis-vd)](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-vd?p=ben-using-confidence-guided-matting-for) ## BEN2 access BEN2 was trained on the DIS5k and our 22K proprietary segmentation dataset. Our enhanced model delivers superior performance in hair matting, 4K processing, object segmentation, and edge refinement. Our Base model is open source. To try the full model through our free web demo or integrate BEN2 into your project with our API: - 🌐 [backgrounderase.net](https://backgrounderase.net) ## Contact us - For access to our commercial model email us at sales@prama.llc - Our website: https://prama.llc/ - Follow us on X: https://x.com/PramaResearch/ ## Installation ``` pip install -e "git+https://github.com/PramaLLC/BEN2.git#egg=ben2" ``` ## Quick start code ```python from ben2 import BEN_Base from PIL import Image import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') file = "./image.png" # input image model = BEN_Base.from_pretrained("PramaLLC/BEN2") model.to(device).eval() image = Image.open(file) foreground = model.inference(image, refine_foreground=False,) #Refine foreground is an extract postprocessing step that increases inference time but can improve matting edges. The default value is False. foreground.save("./foreground.png") ``` ## Batch image processing ```python from ben2 import BEN_Base from PIL import Image import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = BEN_Base.from_pretrained("PramaLLC/BEN2") model.to(device).eval() file1 = "./image1.png" # input image1 file2 = "./image2.png" # input image2 image1 = Image.open(file1) image2 = Image.open(file2) foregrounds = model.inference([image1, image2]) # We recommend that the batch size not exceed 3 for consumer GPUs as there are minimal inference gains due to our custom batch processing for the MVANet decoding steps. foregrounds[0].save("./foreground1.png") foregrounds[1].save("./foreground2.png") ``` # BEN2 video segmentation [![BEN2 Demo](https://img.youtube.com/vi/skEXiIHQcys/0.jpg)](https://www.youtube.com/watch?v=skEXiIHQcys) ## Video Segmentation ```bash sudo apt update sudo apt install ffmpeg ``` ```python from ben2 import BEN_Base from PIL import Image import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') video_path = "/path_to_your_video.mp4"# input video model = BEN_Base.from_pretrained("PramaLLC/BEN2") model.to(device).eval() model.segment_video( video_path= video_path, output_path="./", # Outputs will be saved as foreground.webm or foreground.mp4. The default value is "./" fps=0, # If this is set to 0 CV2 will detect the fps in the original video. The default value is 0. refine_foreground=False, #refine foreground is an extract postprocessing step that increases inference time but can improve matting edges. The default value is False. batch=1, # We recommended that batch size not exceed 3 for consumer GPUs as there are minimal inference gains. The default value is 1. print_frames_processed=True, #Informs you what frame is being processed. The default value is True. webm = False, # This will output an alpha layer video but this defaults to mp4 when webm is false. The default value is False. rgb_value= (0, 255, 0) # If you do not use webm this will be the RGB value of the resulting background only when webm is False. The default value is a green background (0,255,0). ) ``` # BEN2 evaluation ![Model Comparison](BEN2_demo_pictures/model_comparison.png) RMBG 2.0 did not preserve the DIS 5k validation dataset ![Example 1](BEN2_demo_pictures/grid_example1.png) ![Example 2](BEN2_demo_pictures/grid_example2.png) ![Example 3](BEN2_demo_pictures/grid_example3.png) ![Example 6](BEN2_demo_pictures/grid_example6.png) ![Example 7](BEN2_demo_pictures/grid_example7.png)
rmdhirr/suja-lorab-ins-100	rmdhirr	2025-06-25T03:48:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-06-25T03:47:12Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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rrr13/uuu_fine_tune_gpt2	rrr13	2025-06-25T03:42:24Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T03:23:12Z	--- license: apache-2.0 ---
tofumagnate/L3.3-Unnamed-Exp-8B-V0.1-Q8_0-GGUF	tofumagnate	2025-06-25T03:42:04Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "base_model:TheSkullery/L3.3-Unnamed-Exp-8B-V0.1", "base_model:quantized:TheSkullery/L3.3-Unnamed-Exp-8B-V0.1", "endpoints_compatible", "region:us" ]	null	2025-06-25T03:41:33Z	--- base_model: TheSkullery/L3.3-Unnamed-Exp-8B-V0.1 tags: - llama-cpp - gguf-my-repo --- # tofumagnate/L3.3-Unnamed-Exp-8B-V0.1-Q8_0-GGUF This model was converted to GGUF format from [`TheSkullery/L3.3-Unnamed-Exp-8B-V0.1`](https://huggingface.co/TheSkullery/L3.3-Unnamed-Exp-8B-V0.1) 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/TheSkullery/L3.3-Unnamed-Exp-8B-V0.1) 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 tofumagnate/L3.3-Unnamed-Exp-8B-V0.1-Q8_0-GGUF --hf-file l3.3-unnamed-exp-8b-v0.1-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo tofumagnate/L3.3-Unnamed-Exp-8B-V0.1-Q8_0-GGUF --hf-file l3.3-unnamed-exp-8b-v0.1-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 tofumagnate/L3.3-Unnamed-Exp-8B-V0.1-Q8_0-GGUF --hf-file l3.3-unnamed-exp-8b-v0.1-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo tofumagnate/L3.3-Unnamed-Exp-8B-V0.1-Q8_0-GGUF --hf-file l3.3-unnamed-exp-8b-v0.1-q8_0.gguf -c 2048 ```
Clip-18-Brazilian-tourist-who-fell-off/Clip.18.Brazilian.tourist.who.fell.off.Indonesian	Clip-18-Brazilian-tourist-who-fell-off	2025-06-25T03:39:34Z	0	0	null	[ "region:us" ]	null	2025-06-25T03:39:16Z	<a href="https://tinyurl.com/dhst9ys5" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
pennylin09/uuu_fine_tune_gpt2	pennylin09	2025-06-25T03:38:19Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:37:31Z	--- license: apache-2.0 ---
New-videos-Kirti-Patel-viral-video-Clips/FULL.VIDEO.Kirti.Patel.Viral.Video.Tutorial.Official	New-videos-Kirti-Patel-viral-video-Clips	2025-06-25T03:37:09Z	0	0	null	[ "region:us" ]	null	2025-06-25T03:36:54Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
v1olet/LLM-CLS-Qwen2.5-1.5B-Instruct-Lora-SFT-3-Epoch	v1olet	2025-06-25T03:36:21Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T03:33:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hasancanonder/Llama-3.2-1B-Turkish-ORPO	hasancanonder	2025-06-25T03:35:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T03:33:16Z	--- 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. 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New-videos-Kaur-Preet-viral-video-Clips/FULL.VIDEO.Kaur.Preet.Viral.Video.Tutorial.Official	New-videos-Kaur-Preet-viral-video-Clips	2025-06-25T03:33:41Z	0	0	null	[ "region:us" ]	null	2025-06-25T03:33:28Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
crosstar/mistral_5_CoT_few_shot_8step	crosstar	2025-06-25T03:32:57Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-25T03:30:37Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
cgifbribcgfbi/Llama-3.3-70B-chem-oc-nosynth	cgifbribcgfbi	2025-06-25T03:31:50Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "dataset:oc-nosynth_5000.jsonl", "base_model:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned", "base_model:adapter:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned", "license:llama3.3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-25T00:50:32Z	--- library_name: peft license: llama3.3 base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned tags: - axolotl - generated_from_trainer datasets: - oc-nosynth_5000.jsonl model-index: - name: Llama-3.3-70B-chem-oc-nosynth results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0` ```yaml base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned load_in_8bit: false load_in_4bit: true adapter: qlora wandb_name: Llama-3.3-70B-chem-oc-nosynth output_dir: ./outputs/out/Llama-3.3-70B-chem-oc-nosynth hub_model_id: cgifbribcgfbi/Llama-3.3-70B-chem-oc-nosynth tokenizer_type: AutoTokenizer push_dataset_to_hub: strict: false datasets: - path: oc-nosynth_5000.jsonl type: chat_template field_messages: messages dataset_prepared_path: last_run_prepared # val_set_size: 0.05 # eval_sample_packing: False save_safetensors: true sequence_len: 3373 sample_packing: true pad_to_sequence_len: true lora_r: 64 lora_alpha: 32 lora_dropout: 0.05 lora_target_modules: - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj lora_target_linear: false lora_modules_to_save: wandb_mode: wandb_project: finetune-sweep wandb_entity: gpoisjgqetpadsfke wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 1 micro_batch_size: 4 # This will be automatically adjusted based on available GPU memory num_epochs: 4 optimizer: adamw_torch_fused lr_scheduler: cosine learning_rate: 0.00002 train_on_inputs: false group_by_length: true bf16: true tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true logging_steps: 1 flash_attention: true warmup_steps: 10 evals_per_epoch: 3 saves_per_epoch: 1 weight_decay: 0.01 fsdp: - full_shard - auto_wrap fsdp_config: fsdp_limit_all_gathers: true fsdp_sync_module_states: true fsdp_offload_params: false fsdp_use_orig_params: false fsdp_cpu_ram_efficient_loading: true fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer fsdp_state_dict_type: FULL_STATE_DICT fsdp_sharding_strategy: FULL_SHARD special_tokens: pad_token: <\|finetune_right_pad_id\|> ``` </details><br> # Llama-3.3-70B-chem-oc-nosynth This model is a fine-tuned version of [huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned](https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned) on the oc-nosynth_5000.jsonl 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: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 16 - total_eval_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: 10 - training_steps: 648 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.52.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.2
Slill314/medical_energy	Slill314	2025-06-25T03:26:13Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T03:23:49Z	--- license: apache-2.0 ---
Cameron914/uuu_fine_tune_gpt2	Cameron914	2025-06-25T03:26:10Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T01:34:49Z	--- license: apache-2.0 ---
johngreendr1/72a53c5a-be56-4519-a53c-999041c64c96	johngreendr1	2025-06-25T03:24:42Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1.9", "region:us" ]	null	2025-06-25T02:09:27Z	--- base_model: NousResearch/Nous-Capybara-7B-V1.9 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 0.15.1
mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5	mlx-community	2025-06-25T03:24:34Z	0	0	mlx	[ "mlx", "safetensors", "llama", "text-generation", "conversational", "en", "ja", "dataset:tokyotech-llm/lmsys-chat-1m-synth", "dataset:lmsys/lmsys-chat-1m", "base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5", "base_model:finetune:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5", "license:llama3.3", "license:gemma", "region:us" ]	text-generation	2025-06-25T02:59:08Z	--- language: - en - ja library_name: mlx pipeline_tag: text-generation license: - llama3.3 - gemma model_type: llama datasets: - tokyotech-llm/lmsys-chat-1m-synth - lmsys/lmsys-chat-1m base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5 tags: - mlx --- # mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5 This model [mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5) was converted to MLX format from [tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) using mlx-lm version 0.25.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
vishakr01/comp4_12	vishakr01	2025-06-25T03:24:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-25T03:22:06Z	--- 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]
Doctor-Shotgun/MS3.2-24B-Magnum-Diamond-LoRA	Doctor-Shotgun	2025-06-25T03:23:42Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506", "base_model:adapter:mistralai/Mistral-Small-3.2-24B-Instruct-2506", "license:apache-2.0", "region:us" ]	null	2025-06-22T17:45:00Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-Small-3.2-24B-Instruct-2506 tags: - axolotl - generated_from_trainer --- # MS3.2-24B-Magnum-Diamond-LoRA Magnum "Diamond" in reference to the intense heat and pressure (generated through matrix multiplications) needed to turn the coal-esque material of dry, assistant-tuned models into creative writing gems! This model is finetuned from a text-only conversion of [mistralai/Mistral-Small-3.2-24B-Instruct-2506](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506) as an rsLoRA adapter. It uses the same data mix as [Doctor-Shotgun/L3.3-70B-Magnum-v5-SFT-Alpha](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v5-SFT-Alpha), however with pre-tokenization and modifications to the custom loss masking. The goal was to re-create the model at a smaller, more consumer-friendly size. This model should perform competently with or without prepending character names, and with or without prefill. The objective, as with the other Magnum models, is to emulate the prose style and quality of the Claude 3 Sonnet/Opus series of models on a local scale, so don't be surprised to see "Claude-isms" in its output. This is a minor version update over [Doctor-Shotgun/MS3.1-24B-Magnum-Diamond-LoRA](https://huggingface.co/Doctor-Shotgun/MS3.1-24B-Magnum-Diamond-LoRA) utilizing the new official instruct model from June 2025. [Merged full model](https://huggingface.co/Doctor-Shotgun/MS3.2-24B-Magnum-Diamond) ## Intended uses and limitations This model is intended for creative writing and roleplay purposes. It may show biases similar to those observed in contemporary LLM-based roleplay, in addition to those exhibited by the Claude 3 series of models and the base model. All outputs should be considered fiction, as this model is not intended to provide factual information or advice. ## Training procedure [WandB](https://wandb.ai/gum1h0x/24b-magnum-lora/runs/3zudxeg3?nw=nwuseradrianjuliusbeck) There was a weird loss spike of unclear significance on one sample that was not seen using the same dataset on Mistral Small 3.1 Instruct, but the resulting model appears to be sane. [<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: `0.9.2` ```yaml base_model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-Text-Only #base_model_ignore_patterns: "consolidated.safetensors" # optionally might have model_type or tokenizer_type model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer # Automatically upload checkpoint and final model to HF hub_model_id: NewEden/magnum-v5-sft-prototype-ms3.2-lora hub_strategy: "all_checkpoints" push_dataset_to_hub: hf_use_auth_token: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: NewEden/magnum-v5-sft-proto-mistral-v7-tekken-rev1-32k ds_type: parquet type: shuffle_merged_datasets: true dataset_prepared_path: ./magnum-24b-data val_set_size: 0.0 output_dir: ./magnum-24b-lora-out plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_fused_linear_cross_entropy: false cut_cross_entropy: true sequence_len: 32768 sample_packing: true eval_sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 128 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: peft_use_rslora: true lora_modules_to_save: - embed_tokens - lm_head wandb_project: 24b-magnum-lora wandb_entity: wandb_watch: wandb_name: 24b-magnum-lora-mistral-3.2 wandb_log_model: gradient_accumulation_steps: 16 micro_batch_size: 1 num_epochs: 2 optimizer: paged_ademamix_8bit lr_scheduler: cosine learning_rate: 2e-5 max_grad_norm: 1.0 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: warmup_steps: 40 evals_per_epoch: eval_table_size: eval_max_new_tokens: saves_per_epoch: 2 debug: deepspeed: weight_decay: 0.01 fsdp: fsdp_config: special_tokens: ``` </details><br> ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use paged_ademamix_8bit and the args are: No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - num_epochs: 2.0 ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.1+cu128 - Datasets 3.5.1 - Tokenizers 0.21.1
tracylu00200/uuu_fine_tune_gpt2	tracylu00200	2025-06-25T03:23:40Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:32:03Z	--- license: apache-2.0 ---
Stonersheart/uuu_fine_tune_gpt2	Stonersheart	2025-06-25T03:23:17Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:23:48Z	--- license: apache-2.0 ---
JS1016/uuu_fine_tune_gpt2	JS1016	2025-06-25T03:22:38Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:26:09Z	--- license: apache-2.0 ---
ianwangnas/uuu_fine_tune_gpt2	ianwangnas	2025-06-25T03:21:42Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:27:00Z	--- license: apache-2.0 ---
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1894	luckeciano	2025-06-25T03:20:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-24T23:31:44Z	--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1894 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1894 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1894", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/780e3vej) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - 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}} } ```
cjisnc/task1	cjisnc	2025-06-25T03:18:21Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-25T03:18:21Z	--- license: apache-2.0 ---
Doctor-Shotgun/MS3.1-24B-Magnum-Diamond-LoRA	Doctor-Shotgun	2025-06-25T03:17:27Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503", "base_model:adapter:mistralai/Mistral-Small-3.1-24B-Instruct-2503", "license:apache-2.0", "region:us" ]	null	2025-06-01T07:37:24Z	--- library_name: peft license: apache-2.0 base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503 tags: - axolotl - generated_from_trainer --- # MS3.1-24B-Magnum-Diamond-LoRA ### June 2025: An updated version is available [here](https://huggingface.co/Doctor-Shotgun/MS3.2-24B-Magnum-Diamond-LoRA)! Magnum "Diamond" in reference to the intense heat and pressure (generated through matrix multiplications) needed to turn the coal-esque material of dry, assistant-tuned models into creative writing gems! This model is finetuned from a text-only conversion of [mistralai/Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) as an rsLoRA adapter. It uses the same data mix as [Doctor-Shotgun/L3.3-70B-Magnum-v5-SFT-Alpha](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v5-SFT-Alpha), however with pre-tokenization and modifications to the custom loss masking. The goal was to re-create the model at a smaller, more consumer-friendly size. This model should perform competently with or without prepending character names, and with or without prefill. The objective, as with the other Magnum models, is to emulate the prose style and quality of the Claude 3 Sonnet/Opus series of models on a local scale, so don't be surprised to see "Claude-isms" in its output. [Merged full model](https://huggingface.co/Doctor-Shotgun/MS3.1-24B-Magnum-Diamond) ## Intended uses and limitations This model is intended for creative writing and roleplay purposes. It may show biases similar to those observed in contemporary LLM-based roleplay, in addition to those exhibited by the Claude 3 series of models and the base model. All outputs should be considered fiction, as this model is not intended to provide factual information or advice. ## Training procedure [WandB](https://wandb.ai/doctorshotgun/24b-magnum-lora/runs/763psl82?nw=nwuserdoctorshotgun) [<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: `0.9.2` ```yaml base_model: ZeroAgency/Mistral-Small-3.1-24B-Instruct-2503-hf #base_model_ignore_patterns: "consolidated.safetensors" # optionally might have model_type or tokenizer_type model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer # Automatically upload checkpoint and final model to HF hub_model_id: Doctor-Shotgun/magnum-v5-sft-prototype-ms3.1-lora hub_strategy: "all_checkpoints" push_dataset_to_hub: hf_use_auth_token: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: anthracite-core/magnum-v5-sft-proto-mistral-v7-tekken-rev1-32k ds_type: parquet type: shuffle_merged_datasets: true dataset_prepared_path: /home/ubuntu/docshotgun/data/magnum-24b-data val_set_size: 0.0 output_dir: /home/ubuntu/docshotgun/data/24b-lora-out plugins: - axolotl.integrations.liger.LigerPlugin - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin liger_rope: true liger_rms_norm: true liger_layer_norm: true liger_glu_activation: true liger_fused_linear_cross_entropy: false cut_cross_entropy: true sequence_len: 32768 sample_packing: true eval_sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 128 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: peft_use_rslora: true lora_modules_to_save: - embed_tokens - lm_head wandb_project: 24b-magnum-lora wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 1 num_epochs: 2 optimizer: paged_ademamix_8bit lr_scheduler: cosine learning_rate: 2e-5 max_grad_norm: 1.0 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: offload early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true s2_attention: warmup_steps: 40 evals_per_epoch: eval_table_size: eval_max_new_tokens: saves_per_epoch: 2 debug: deepspeed: ./deepspeed_configs/zero3_bf16.json weight_decay: 0.01 fsdp: fsdp_config: special_tokens: ``` </details><br> ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Use paged_ademamix_8bit and the args are: No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 40 - num_epochs: 2.0 ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
JuzeZhang/language_of_motion	JuzeZhang	2025-06-25T03:17:16Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-17T21:06:56Z	--- license: apache-2.0 ---
veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-map-11-5	veddhanth	2025-06-25T03:16:48Z	2	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-22T12:10:11Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: a realistic portrait of sks face widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-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. --> # SDXL LoRA DreamBooth - veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-map-11-5 <Gallery /> ## Model description These are veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-map-11-5 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a realistic portrait of sks face to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](veddhanth/lora-trained-xl-stage-2-finetuned-enc-v2-map-11-5/tree/main) them in the Files & versions tab. ## 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]
linfone2/uuu_fine_tune_taipower	linfone2	2025-06-25T03:13:28Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:43:19Z	--- license: apache-2.0 ---
vincrnt/uuu_fine_tune_taipower	vincrnt	2025-06-25T03:13:14Z	0	0	null	[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]	null	2025-06-25T02:34:26Z	--- license: apache-2.0 ---

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