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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-16 00:42:46	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 522 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-16 00:42:16	card stringlengths 11 1.01M
omarxadel/Arabic-Morph-DeepSeek-R1-Distill-Llama-8B	omarxadel	2025-04-08T03:24:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "ar", "dataset:Omartificial-Intelligence-Space/Arabic_Reasoning_Dataset", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-06T13:11:29Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - ar datasets: - Omartificial-Intelligence-Space/Arabic_Reasoning_Dataset --- # Uploaded model - Developed by: omarxadel - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Llama-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gsaltintas/olmo_gsm8k-p1x0.1-3ep-6539194-2	gsaltintas	2025-04-08T03:23:09Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T03:22:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF	bartowski	2025-04-08T03:22:17Z	0	1	null	[ "gguf", "mergekit", "merge", "text-generation", "base_model:ToastyPigeon/Gemma-3-Starshine-12B", "base_model:quantized:ToastyPigeon/Gemma-3-Starshine-12B", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-08T02:15:50Z	--- quantized_by: bartowski pipeline_tag: text-generation tags: - mergekit - merge base_model: ToastyPigeon/Gemma-3-Starshine-12B base_model_relation: quantized --- ## Llamacpp imatrix Quantizations of Gemma-3-Starshine-12B by ToastyPigeon Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5064">b5064</a> for quantization. Original model: https://huggingface.co/ToastyPigeon/Gemma-3-Starshine-12B 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 ``` <bos><start_of_turn>user {system_prompt} {prompt}<end_of_turn> <start_of_turn>model <end_of_turn> <start_of_turn>model ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Split \| Description \| \| -------- \| ---------- \| --------- \| ----- \| ----------- \| \| [Gemma-3-Starshine-12B-bf16.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-bf16.gguf) \| bf16 \| 23.54GB \| false \| Full BF16 weights. \| \| [Gemma-3-Starshine-12B-Q8_0.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q8_0.gguf) \| Q8_0 \| 12.51GB \| false \| Extremely high quality, generally unneeded but max available quant. \| \| [Gemma-3-Starshine-12B-Q6_K_L.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q6_K_L.gguf) \| Q6_K_L \| 9.90GB \| false \| Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended. \| \| [Gemma-3-Starshine-12B-Q6_K.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q6_K.gguf) \| Q6_K \| 9.66GB \| false \| Very high quality, near perfect, recommended. \| \| [Gemma-3-Starshine-12B-Q5_K_L.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q5_K_L.gguf) \| Q5_K_L \| 8.69GB \| false \| Uses Q8_0 for embed and output weights. High quality, recommended. \| \| [Gemma-3-Starshine-12B-Q5_K_M.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q5_K_M.gguf) \| Q5_K_M \| 8.45GB \| false \| High quality, recommended. \| \| [Gemma-3-Starshine-12B-Q5_K_S.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q5_K_S.gguf) \| Q5_K_S \| 8.23GB \| false \| High quality, recommended. \| \| [Gemma-3-Starshine-12B-Q4_1.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q4_1.gguf) \| Q4_1 \| 7.56GB \| false \| Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. \| \| [Gemma-3-Starshine-12B-Q4_K_L.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q4_K_L.gguf) \| Q4_K_L \| 7.54GB \| false \| Uses Q8_0 for embed and output weights. Good quality, recommended. \| \| [Gemma-3-Starshine-12B-Q4_K_M.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q4_K_M.gguf) \| Q4_K_M \| 7.30GB \| false \| Good quality, default size for most use cases, recommended. \| \| [Gemma-3-Starshine-12B-Q4_K_S.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q4_K_S.gguf) \| Q4_K_S \| 6.94GB \| false \| Slightly lower quality with more space savings, recommended. \| \| [Gemma-3-Starshine-12B-Q4_0.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q4_0.gguf) \| Q4_0 \| 6.91GB \| false \| Legacy format, offers online repacking for ARM and AVX CPU inference. \| \| [Gemma-3-Starshine-12B-IQ4_NL.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-IQ4_NL.gguf) \| IQ4_NL \| 6.89GB \| false \| Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. \| \| [Gemma-3-Starshine-12B-Q3_K_XL.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q3_K_XL.gguf) \| Q3_K_XL \| 6.72GB \| false \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \| \| [Gemma-3-Starshine-12B-IQ4_XS.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-IQ4_XS.gguf) \| IQ4_XS \| 6.55GB \| false \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Gemma-3-Starshine-12B-Q3_K_L.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q3_K_L.gguf) \| Q3_K_L \| 6.48GB \| false \| Lower quality but usable, good for low RAM availability. \| \| [Gemma-3-Starshine-12B-Q3_K_M.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q3_K_M.gguf) \| Q3_K_M \| 6.01GB \| false \| Low quality. \| \| [Gemma-3-Starshine-12B-IQ3_M.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-IQ3_M.gguf) \| IQ3_M \| 5.66GB \| false \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Gemma-3-Starshine-12B-Q3_K_S.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q3_K_S.gguf) \| Q3_K_S \| 5.46GB \| false \| Low quality, not recommended. \| \| [Gemma-3-Starshine-12B-IQ3_XS.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-IQ3_XS.gguf) \| IQ3_XS \| 5.21GB \| false \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Gemma-3-Starshine-12B-Q2_K_L.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q2_K_L.gguf) \| Q2_K_L \| 5.01GB \| false \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \| \| [Gemma-3-Starshine-12B-IQ3_XXS.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-IQ3_XXS.gguf) \| IQ3_XXS \| 4.78GB \| false \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Gemma-3-Starshine-12B-Q2_K.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-Q2_K.gguf) \| Q2_K \| 4.77GB \| false \| Very low quality but surprisingly usable. \| \| [Gemma-3-Starshine-12B-IQ2_M.gguf](https://huggingface.co/bartowski/ToastyPigeon_Gemma-3-Starshine-12B-GGUF/blob/main/ToastyPigeon_Gemma-3-Starshine-12B-IQ2_M.gguf) \| IQ2_M \| 4.31GB \| false \| Relatively low quality, uses SOTA techniques to be surprisingly 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/ToastyPigeon_Gemma-3-Starshine-12B-GGUF --include "ToastyPigeon_Gemma-3-Starshine-12B-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/ToastyPigeon_Gemma-3-Starshine-12B-GGUF --include "ToastyPigeon_Gemma-3-Starshine-12B-Q8_0/*" --local-dir ./ ``` You can either specify a new local-dir (ToastyPigeon_Gemma-3-Starshine-12B-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
goldov/arxiv-classifier-debertav3	goldov	2025-04-08T03:22:16Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-08T03:16:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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jasonhuang3/Llama-3-Taiwan-8B-Instruct-unsloth-merged-v1-v6	jasonhuang3	2025-04-08T03:21:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:yentinglin/Llama-3-Taiwan-8B-Instruct", "base_model:finetune:yentinglin/Llama-3-Taiwan-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T03:18:32Z	--- base_model: yentinglin/Llama-3-Taiwan-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: jasonhuang3 - License: apache-2.0 - Finetuned from model : yentinglin/Llama-3-Taiwan-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
soob3123/Veiled-Calla-12B-gguf	soob3123	2025-04-08T03:18:16Z	0	3	transformers	[ "transformers", "gguf", "roleplay", "creative-writing", "immersive", "mystery", "storytelling", "text-generation", "en", "base_model:soob3123/Veiled-Calla-12B", "base_model:quantized:soob3123/Veiled-Calla-12B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-07T16:57:44Z	--- pipeline_tag: text-generation library_name: transformers language: - en license: apache-2.0 thumbnail: "https://cdn-uploads.huggingface.co/production/uploads/62f93f9477b722f1866398c2/F4OF_WybPBIRXKcidNUdW.png" tags: - roleplay - creative-writing - immersive - mystery - storytelling base_model: - soob3123/Veiled-Calla-12B --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62f93f9477b722f1866398c2/lzZDflNqPkuUcK7MbrHWF.png) # ✧ Veiled Calla ✧ > Mystery is at the heart of creativity. That, and surprise...As creative channels, we need to trust the darkness. Beneath moonlight's gentle glow, Veiled Calla emerges - an enigmatic presence designed to weave immersive roleplay experiences through mysterious narratives and atmospheric storytelling. Shrouded in secrets and whispers, Veiled Calla crafts evocative scenarios where unspoken truths and subtle emotional undertones drive each unfolding tale. ## ⋆ Features ⋆ - ⟡ Atmospheric Depth: Rich, moonlit scenarios bloom with subtle emotional undertones - ⟡ Character Consistency: Personalities remain true throughout extended journeys - ⟡ Narrative Mystery: Enigmatic storylines unfold with natural revelations - ⟡ Emotional Nuance: The unspoken and veiled meanings between characters come alive ## ⋆ Limitations ⋆ - Flourishes in intimate, atmospheric, or introspective scenarios - May whisper overly cryptic responses in certain contexts - Uncensored in Roleplay mode (e.g. sillytavern), still refuses in Assistant mode (no system prompt) - Use one of the [Amoral models](https://huggingface.co/collections/soob3123/amoral-collection-67dccc556a39894b36f59676) for a fully uncensored but bland experience
genki10/Trial3BERT_AugV8_k5_task1_organization_sp010_lw030_fold0	genki10	2025-04-08T03:17:46Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-08T03:06:00Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp010_lw030_fold0 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp010_lw030_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6581 - Qwk: 0.4876 - Mse: 0.6581 - Rmse: 0.8112 ## 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: 64 - eval_batch_size: 64 - 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 7.7037 \| 0.0 \| 7.7037 \| 2.7756 \| \| No log \| 2.0 \| 8 \| 5.7928 \| 0.0182 \| 5.7928 \| 2.4068 \| \| No log \| 3.0 \| 12 \| 4.0787 \| 0.0039 \| 4.0787 \| 2.0196 \| \| No log \| 4.0 \| 16 \| 2.5044 \| 0.0447 \| 2.5044 \| 1.5825 \| \| No log \| 5.0 \| 20 \| 1.5197 \| 0.0316 \| 1.5197 \| 1.2328 \| \| No log \| 6.0 \| 24 \| 1.0422 \| 0.0212 \| 1.0422 \| 1.0209 \| \| No log \| 7.0 \| 28 \| 0.9758 \| 0.0382 \| 0.9758 \| 0.9878 \| \| No log \| 8.0 \| 32 \| 0.7540 \| 0.3896 \| 0.7540 \| 0.8683 \| \| No log \| 9.0 \| 36 \| 1.4893 \| 0.1246 \| 1.4893 \| 1.2204 \| \| No log \| 10.0 \| 40 \| 0.6754 \| 0.4896 \| 0.6754 \| 0.8219 \| \| No log \| 11.0 \| 44 \| 1.0386 \| 0.2286 \| 1.0386 \| 1.0191 \| \| No log \| 12.0 \| 48 \| 0.6242 \| 0.4734 \| 0.6242 \| 0.7900 \| \| No log \| 13.0 \| 52 \| 0.6998 \| 0.3925 \| 0.6998 \| 0.8366 \| \| No log \| 14.0 \| 56 \| 0.6626 \| 0.4063 \| 0.6626 \| 0.8140 \| \| No log \| 15.0 \| 60 \| 0.6039 \| 0.4407 \| 0.6039 \| 0.7771 \| \| No log \| 16.0 \| 64 \| 0.7498 \| 0.3635 \| 0.7498 \| 0.8659 \| \| No log \| 17.0 \| 68 \| 0.8428 \| 0.3539 \| 0.8428 \| 0.9180 \| \| No log \| 18.0 \| 72 \| 0.6660 \| 0.5021 \| 0.6660 \| 0.8161 \| \| No log \| 19.0 \| 76 \| 0.9682 \| 0.3104 \| 0.9682 \| 0.9840 \| \| No log \| 20.0 \| 80 \| 1.1654 \| 0.2589 \| 1.1654 \| 1.0795 \| \| No log \| 21.0 \| 84 \| 0.7339 \| 0.4287 \| 0.7339 \| 0.8567 \| \| No log \| 22.0 \| 88 \| 1.1939 \| 0.2802 \| 1.1939 \| 1.0926 \| \| No log \| 23.0 \| 92 \| 1.1354 \| 0.2609 \| 1.1354 \| 1.0655 \| \| No log \| 24.0 \| 96 \| 0.7952 \| 0.3844 \| 0.7952 \| 0.8917 \| \| No log \| 25.0 \| 100 \| 0.7622 \| 0.4067 \| 0.7622 \| 0.8730 \| \| No log \| 26.0 \| 104 \| 1.7104 \| 0.1249 \| 1.7104 \| 1.3078 \| \| No log \| 27.0 \| 108 \| 0.9195 \| 0.2963 \| 0.9195 \| 0.9589 \| \| No log \| 28.0 \| 112 \| 0.8138 \| 0.4027 \| 0.8138 \| 0.9021 \| \| No log \| 29.0 \| 116 \| 0.9108 \| 0.2970 \| 0.9108 \| 0.9544 \| \| No log \| 30.0 \| 120 \| 1.1203 \| 0.2497 \| 1.1203 \| 1.0585 \| \| No log \| 31.0 \| 124 \| 0.7275 \| 0.4101 \| 0.7275 \| 0.8529 \| \| No log \| 32.0 \| 128 \| 1.4482 \| 0.2725 \| 1.4482 \| 1.2034 \| \| No log \| 33.0 \| 132 \| 0.6581 \| 0.4876 \| 0.6581 \| 0.8112 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
gsaltintas/olmo_gsm8k-p560x0.1-3ep-6539199-1	gsaltintas	2025-04-08T03:15:58Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T03:14:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1x0.1-3ep-6539195-1	gsaltintas	2025-04-08T03:12:43Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T03:11:27Z	--- 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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gsaltintas/olmo_gsm8k-p560x0.1-3ep-6539197-1	gsaltintas	2025-04-08T03:11:56Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T03:10:45Z	--- 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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sam749/mtk-bypass-utility	sam749	2025-04-08T03:09:51Z	0	0	null	[ "region:us" ]	null	2025-03-31T03:48:07Z	# Mediatek Bypass utility > Personally tested on `Infinix Hot 10 Play X688B` Small utility to disable bootrom protection(sla and daa) on Mediatek devices ## Usage on Windows Skip steps 1-3 after first usage 1. Install [python (64-bit)](https://www.python.org/downloads)(select "Add Python X.X to PATH") 2. Install [UsbDk (64-bit)](https://github.com/daynix/UsbDk/releases) 3. Install pyusb, json5 with command: ``` pip install pyusb==1.1.1 json5 ``` 4. Run this command and connect your powered off phone with volume+ button, you should get "Protection disabled" at the end ``` python main.py ``` 5. After that, without disconnecting phone, run SP Flash Tool ## Usage on Linux Skip steps 1-2 after first usage To use kamakiri you need [FireISO](https://github.com/amonet-kamakiri/fireiso/releases) or [this patch](https://github.com/amonet-kamakiri/kamakiri/blob/master/kernel.patch) for your kernel Prebuilt kernels for various distros are available [here](https://github.com/amonet-kamakiri/prebuilt-kernels) 1. Install python 2. Install pyusb, json5 as root with command: ``` pip install pyusb json5 ``` 3. Run this command as root and connect your powered off phone with volume+ button, you should get "Protection disabled" at the end ``` ./main.py ``` 4. After that, without disconnecting phone, run SP Flash Tool in UART Connection mode ## Credits - [@chaosmaster](https://github.com/chaosmaster) - [@xyzz](https://github.com/xyzz)
Nerva1228/heiyimiao	Nerva1228	2025-04-08T03:08:55Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-08T03:08:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: heiyimiao --- # Heiyimiao <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `heiyimiao` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "heiyimiao", "lora_weights": "https://huggingface.co/Nerva1228/heiyimiao/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Nerva1228/heiyimiao', weight_name='lora.safetensors') image = pipeline('heiyimiao').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 5e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Nerva1228/heiyimiao/discussions) to add images that show off what you’ve made with this LoRA.
Caramelizedd/sophielatestlorav2	Caramelizedd	2025-04-08T03:01:44Z	11	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-01-24T23:26:34Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym widget: - output: url: sample/sophielatestlorav2_002400_00_20250124225915.png text: S0ph1e base_model: black-forest-labs/FLUX.1-dev instance_prompt: S0ph1e license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # SophieLatestLoraV2 A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `S0ph1e` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
tensorart/Bokeh_Depth_Controlnet	tensorart	2025-04-08T03:01:20Z	0	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "en", "base_model:tensorart/bokeh_3.5_medium", "base_model:finetune:tensorart/bokeh_3.5_medium", "license:other", "region:us" ]	text-to-image	2025-04-07T09:58:34Z	--- license: other license_name: stabilityai-ai-community license_link: LICENSE.md language: - en base_model: - tensorart/bokeh_3.5_medium pipeline_tag: text-to-image --- <div align="center"> Bokeh_Depth_Controlnet <img src="show.jpg"/> </div> ## Description - Input Image: Depth information map generated by depth estimation model - Output Image: Base model integrates depth information for control, generating more realistic depth-of-field effects and spatial structure control. The depth map enables the model to better understand spatial relationships within images, achieving more precise depth-of-field control and three-dimensional effect generation. ## Example \| input \| output \| Prompt \| \|:---:\|:---:\|:---\| \| <img src="./images/001_depth.png" width="300"/> \| <img src="./images/001.png" width="300"/> \| a old man \| \| <img src="./images/002_depth.png" width="300"/> \| <img src="./images/002.png" width="300"/> \| A lone astronaut in a white suit and helmet, with a backpack, strolls on a rocky, red landscape under a star galaxy \| \| <img src="./images/003_depth.png" width="300"/> \| <img src="./images/003.png" width="300"/> \| a eagle , black background \| \| <img src="./images/004_depth.png" width="300"/> \| <img src="./images/004.png" width="300"/> \| A crystal heart sits on a gnarly, moss-covered log, with a galaxy background \| ## Use We recommend using ComfyUI for local inference ![input](./comfy.png) # With Bokeh ```python import torch from diffusers import StableDiffusion3ControlNetPipeline from diffusers import SD3ControlNetModel from diffusers.utils import load_image controlnet = SD3ControlNetModel.from_pretrained("tensorart/Bokeh_Depth_Controlnet") pipe = StableDiffusion3ControlNetPipeline.from_pretrained( "tensorart/bokeh_3.5_medium", controlnet=controlnet ) pipe.to("cuda", torch.float16) control_image = load_image("https://huggingface.co/tensorart/Bokeh_Depth_Controlnet/resolve/main/images/001_depth.png") prompt = "A old man talking" negative_prompt ="anime,render,cartoon,3d,bad hands,extra finger" negative_prompt_3="" image = pipe( prompt, num_inference_steps=28, negative_prompt=negative_prompt, negative_prompt_3=negative_prompt_3, control_image=control_image, height=1440, width=1440, guidance_scale=4, controlnet_conditioning_scale=0.85 ).images[0] image.save('image.jpg') ``` ## Contact * Website: https://tensor.art https://tusiart.com * Developed by: TensorArt * Api: https://tams.tensor.art/
AYQAYQ/q-FrozenLake-v1-4x4-noSlippery	AYQAYQ	2025-04-08T02:59:52Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-04-08T02:58:46Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="AYQAYQ/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
abideen/gma-text-27-v1	abideen	2025-04-08T02:57:07Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T02:38:05Z	--- 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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rsh-raj/hyperswitch-commits_without_defn	rsh-raj	2025-04-08T02:56:22Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/codellama-7b-bnb-4bit", "base_model:finetune:unsloth/codellama-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-08T02:56:12Z	--- base_model: unsloth/codellama-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: rsh-raj - License: apache-2.0 - Finetuned from model : unsloth/codellama-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
xSkiddy/Qwen2.5-7B-FP8	xSkiddy	2025-04-08T02:55:10Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "en", "arxiv:2407.10671", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T01:43:38Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE language: - en pipeline_tag: text-generation library_name: transformers quantized_by: xSkiddy --- Quantized with ModelOpt to FP8 for use with TensorRT-LLM, ie; ```py from tensorrt_llm import LLM, SamplingParams def main(): prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="xSkiddy/Qwen2.5-7B-FP8") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") # The entry point of the program need to be protected for spawning processes. if __name__ == '__main__': main() ``` # Qwen2.5-7B ## Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. This repo contains the base 7B Qwen2.5 model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: 131,072 tokens We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Requirements The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
sapadev13/SAPA_model_model_case_v1	sapadev13	2025-04-08T02:52:27Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-08T02:51:06Z	--- 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. 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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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gangu-chettri-kanda-new-videos/wATCH.Gangu.Chettri.Kanda.viral.videos.original	gangu-chettri-kanda-new-videos	2025-04-08T02:51:29Z	0	0	null	[ "region:us" ]	null	2025-04-08T02:49:45Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5n6bjbnr?news-viral-video" 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>
jspaulsen/orpheus-elise-ft	jspaulsen	2025-04-08T02:48:19Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit", "base_model:quantized:unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-08T02:29:52Z	--- base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: jspaulsen - License: apache-2.0 - Finetuned from model : unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Cshavi/qwen_lora_ft_self_instruct_filttered	Cshavi	2025-04-08T02:43:27Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:adapter:Qwen/Qwen2-7B-Instruct", "region:us" ]	null	2025-04-08T02:43:02Z	--- base_model: Qwen/Qwen2-7B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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gsaltintas/olmo_gsm8k-p1x0.001-3ep-6537303-2	gsaltintas	2025-04-08T02:41:03Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T00:08:13Z	--- 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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gsaltintas/olmo_gsm8k-p1120x0.01-3ep-6537302-2	gsaltintas	2025-04-08T02:39:36Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T00:05:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1120x0.001-3ep-6537311-2	gsaltintas	2025-04-08T02:39:26Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T00:08:05Z	--- 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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genki10/Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold2	genki10	2025-04-08T02:39:06Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-08T02:30:32Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold2 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5443 - Qwk: 0.1321 - Mse: 1.5438 - Rmse: 1.2425 ## 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: 64 - eval_batch_size: 64 - 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 9.9854 \| 0.0 \| 9.9855 \| 3.1600 \| \| No log \| 2.0 \| 8 \| 7.6926 \| 0.0 \| 7.6927 \| 2.7736 \| \| No log \| 3.0 \| 12 \| 5.2634 \| 0.0224 \| 5.2637 \| 2.2943 \| \| No log \| 4.0 \| 16 \| 3.1760 \| 0.0 \| 3.1764 \| 1.7822 \| \| No log \| 5.0 \| 20 \| 1.4259 \| 0.0136 \| 1.4263 \| 1.1943 \| \| No log \| 6.0 \| 24 \| 0.9037 \| 0.0117 \| 0.9041 \| 0.9508 \| \| No log \| 7.0 \| 28 \| 0.8343 \| 0.2178 \| 0.8347 \| 0.9136 \| \| No log \| 8.0 \| 32 \| 1.5408 \| 0.1933 \| 1.5411 \| 1.2414 \| \| No log \| 9.0 \| 36 \| 0.7642 \| 0.4348 \| 0.7643 \| 0.8742 \| \| No log \| 10.0 \| 40 \| 1.9500 \| 0.1791 \| 1.9497 \| 1.3963 \| \| No log \| 11.0 \| 44 \| 1.5281 \| 0.2059 \| 1.5274 \| 1.2359 \| \| No log \| 12.0 \| 48 \| 1.7438 \| 0.1547 \| 1.7431 \| 1.3203 \| \| No log \| 13.0 \| 52 \| 1.1716 \| 0.2621 \| 1.1704 \| 1.0819 \| \| No log \| 14.0 \| 56 \| 0.8908 \| 0.3880 \| 0.8895 \| 0.9432 \| \| No log \| 15.0 \| 60 \| 2.2765 \| 0.1471 \| 2.2758 \| 1.5086 \| \| No log \| 16.0 \| 64 \| 0.9366 \| 0.3754 \| 0.9353 \| 0.9671 \| \| No log \| 17.0 \| 68 \| 2.0871 \| 0.1293 \| 2.0864 \| 1.4444 \| \| No log \| 18.0 \| 72 \| 0.9577 \| 0.3386 \| 0.9568 \| 0.9782 \| \| No log \| 19.0 \| 76 \| 2.7571 \| 0.0674 \| 2.7566 \| 1.6603 \| \| No log \| 20.0 \| 80 \| 0.9313 \| 0.3301 \| 0.9306 \| 0.9647 \| \| No log \| 21.0 \| 84 \| 1.6202 \| 0.1642 \| 1.6196 \| 1.2726 \| \| No log \| 22.0 \| 88 \| 1.9695 \| 0.1400 \| 1.9690 \| 1.4032 \| \| No log \| 23.0 \| 92 \| 1.0180 \| 0.2873 \| 1.0173 \| 1.0086 \| \| No log \| 24.0 \| 96 \| 1.5443 \| 0.1321 \| 1.5438 \| 1.2425 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
gsaltintas/olmo_gsm8k-p560x0.001-3ep-6537308-2	gsaltintas	2025-04-08T02:38:56Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T00:07:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]
ethansandbar/run_gemma-3-27b-it_20250408_020131	ethansandbar	2025-04-08T02:37:58Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "endpoints_compatible", "region:us" ]	null	2025-04-08T02:37:40Z	--- base_model: google/gemma-3-27b-it library_name: transformers model_name: run_gemma-3-27b-it_20250408_020131 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for run_gemma-3-27b-it_20250408_020131 This model is a fine-tuned version of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it). 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="ethansandbar/run_gemma-3-27b-it_20250408_020131", 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: 0.16.0 - Transformers: 4.50.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
d2o2ji/videomae-base-finetuned-kinetics-0408_final_5sec_org_ab7_val	d2o2ji	2025-04-08T02:37:51Z	0	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-04-07T23:17:48Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-kinetics-0408_final_5sec_org_ab7_val 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. --> # videomae-base-finetuned-kinetics-0408_final_5sec_org_ab7_val This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3394 - Accuracy: 0.9191 ## 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 - 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_ratio: 0.05 - training_steps: 64900 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 1.2323 \| 0.0100 \| 650 \| 0.2481 \| 0.9099 \| \| 0.0013 \| 1.0100 \| 1300 \| 0.3219 \| 0.9029 \| \| 0.027 \| 2.0100 \| 1950 \| 0.3824 \| 0.9052 \| \| 0.074 \| 3.0100 \| 2600 \| 0.2232 \| 0.9342 \| \| 3.4657 \| 4.0100 \| 3250 \| 0.2717 \| 0.9397 \| \| 0.0019 \| 5.0100 \| 3900 \| 0.3925 \| 0.8966 \| \| 0.0028 \| 6.0100 \| 4550 \| 0.5342 \| 0.8684 \| \| 0.0126 \| 7.0100 \| 5200 \| 0.4073 \| 0.9178 \| \| 1.7542 \| 8.0100 \| 5850 \| 0.2603 \| 0.9076 \| \| 0.0008 \| 9.0100 \| 6500 \| 0.5819 \| 0.8356 \| \| 0.0031 \| 10.0100 \| 7150 \| 0.4228 \| 0.8904 \| \| 0.0007 \| 11.0100 \| 7800 \| 0.4991 \| 0.8794 \| \| 0.0023 \| 12.0100 \| 8450 \| 0.5239 \| 0.8637 \| \| 0.0175 \| 13.0100 \| 9100 \| 0.3675 \| 0.8865 \| \| 0.8214 \| 14.0100 \| 9750 \| 0.7345 \| 0.8410 \| \| 0.0027 \| 15.0100 \| 10400 \| 0.5223 \| 0.8825 \| \| 0.0005 \| 16.0100 \| 11050 \| 0.3845 \| 0.8958 \| \| 0.0001 \| 17.0100 \| 11700 \| 0.7892 \| 0.8520 \| \| 0.0001 \| 18.0100 \| 12350 \| 0.5703 \| 0.8857 \| \| 0.8683 \| 19.0100 \| 13000 \| 0.5244 \| 0.8982 \| ### Framework versions - Transformers 4.48.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
gsaltintas/olmo_gsm8k-p1120x0.01-3ep-6537300-2	gsaltintas	2025-04-08T02:37:06Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T00:04:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LyliaEngine/animij_v20	LyliaEngine	2025-04-08T02:35:57Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:LyliaEngine/ilustmix_v40", "base_model:adapter:LyliaEngine/ilustmix_v40", "license:cdla-permissive-2.0", "region:us" ]	text-to-image	2025-04-08T02:02:58Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: masterpiece, high_quality, highres, parameters: negative_prompt: worst_quality, bad_quality, poorly_detailed, output: url: images/image(9999)-8559.png - text: >- masterpiece, high_quality, highres, woman, solo, ribbon_tied_blouse, age_24, choker_with_gem, long_lashes, sucking_finger, blush, looking_at_viewer, ink_splash, rings, abstract, dynamic_angle, cinematic, parameters: negative_prompt: worst_quality, bad_quality, poorly_detailed, output: url: images/image(9999)-8504.png - text: masterpiece, high_quality, highres, parameters: negative_prompt: worst_quality, bad_quality, poorly_detailed, output: url: images/image(9999)-8556.png base_model: LyliaEngine/ilustmix_v40 instance_prompt: None license: cdla-permissive-2.0 --- # animij_v20 <Gallery /> ## Model description Animij Hey guys, u asked abt it. 😊 Now I present to u: Animij ~ retro soft cored. 2 different styles ~ moaarrrrrr possibilities?! 😋 For a long time I asked myself why merge an anime model ~ when there are a wealth of great models that serve this genre? Ur voice is important to me, so I took the plunge. U know me, I like things different, and so Animij spawns as an anime style that may not suit everyone's taste ~ but maybe urs? Animij is coming soft drawn vibrant retro touched with a touch of eroticism and full of emotions Tasty for more? Maybe u wanna play around with version 2? ✨ The Art of Animij ~ where details speak ✨ In Animij, details whisper of emotion ~ a stroke of storytelling. Eyes shimmer with depth, reflecting unspoken dreams. Light and shadow dance across soft gradients, giving each face a warmth that feels almost alive. The magic lies in the small things: the delicate blush of a cheek, the glint of light on a ring, the way colors breathe with life. Nothing is static~ every detail wanna invite u: wanna come deeper? making each piece feel like a moment suspended between fantasy and reality. ✨ Wanna look closer? The story is in the details. ♥ setting examples V2 Clip skip 2 Euler a 20+ steps CFG 4+ 10241024 resolutions +prompt masterpiece, high_quality, highres, -prompt worst_quality, bad_quality, poorly_detailed, V2 can be used without -prompt ♥ setting examples V1 Clip skip 2 Euler a 30 steps CFG 6 10241024 resolutions _ tags ♥ anime lookalike 2D +prompt masterpiece, high_quality, highres, flat_colors, gradient, -prompt worst_quality, bad_quality, poorly_detailed, ♥ semireal lookalike 2.5D +prompt masterpiece, high_quality, highres, sharp_focus, detailed_eyes, retro, 3D, realistic, realistic_body, sharp_eyes, -prompt worst_quality, bad_quality, poorly_detailed, young, nude, 2d, sketch anime style examples V1 ♥ watercolor_anime ![2cdf9916-590c-4077-8da5-1402ee235b08.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/2DRu6eJvu70Rx39b-Rn1D.png) ♥ pencil_scetch_anime (pencil_sketch_anime), monochrome, black_and_white, ![17c2d1c3-47f7-478e-87e9-02f183a96088.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/_kIDlVhvN9WSAoW7tZnEM.png) ♥ soft_shading_anime ![b05595a1-2d61-4bde-9a26-38aa50043d13.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/4Aa76inwTvEcGixD7tgSm.png) ♥ neon_anime ![457d8705-224f-4991-84f6-e0c78742d8ff.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/etj8EfhfyGivucB1OOnLc.png) ♥ grunge_anime ![238a6121-0f21-4f0f-b737-69f02567baba.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/V254C2ighhqEeRzRJcD4S.png) ♥ pastel_dreamy_anime ![fd093f52-0c2b-4c41-8561-6e43c3479558.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/_tmf9tKJC026fZ3G8m2zb.png) ♥ 90s_anime ![3a1d6121-5a41-4d77-8026-e410666b5e69.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/u0t5sBT4xcuOuQmrJW-41.png) ♥ 80s_anime ![689cfce3-3c27-4baa-b692-78cd7a900740.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/BmkQwB02X7ntQnTet6GxY.png) ♥ pastel_retro ![624461a2-0caf-4843-b0f6-4b522df1fa24.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/L8WeWg0XJH3szbZ01KjBT.png) ♥ retro ![e377975f-f539-4efb-9294-e174b38b6daf.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/PSVnH_w-4kInZMUddCyB2.png) ♥ Isekai Fantasy Style ![6ea8a66b-58e8-4594-af1d-d95183988e37.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/i5XT5yVtT0R89799SOsGV.png) ♥ Isekai Fantasy Style & Grunge ![630fd251-3bce-4935-a7f4-cc244cb9ddb5.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/EG-zbKqkp8xaYpdF19U3a.png) ♥ Kawaii_anime ![6e228593-7c45-4d28-b5e7-71b9cf4ab24d.png](https://cdn-uploads.huggingface.co/production/uploads/663e3e78c719b0b8a386f24e/Lp6Ewt1NKGyFzHl-Pl0L-.png) I am happy if u enjoy and stay curious abt ur results ~♪~ If u like what I do consider leaving me a like or a comment, much appreciated and helps me getting a feeling for tinkering ~♥ ## Source https://civitai.com/models/1353314?modelVersionId=1613008 ## Credit https://civitai.com/user/reijlita ## Trigger words You should use `None` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LyliaEngine/animij_v20/tree/main) them in the Files & versions tab.
sod11168/Email_classification_1	sod11168	2025-04-08T02:32:02Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-04-08T02:31:55Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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 0.15.0
Anthonyg5005/hf-scripts	Anthonyg5005	2025-04-08T02:31:39Z	0	0	null	[ "en", "license:agpl-3.0", "region:us" ]	null	2024-01-25T16:29:35Z	--- license: agpl-3.0 language: - en --- # scripts Personal scripts to automate some tasks mostly using [huggingface_hub](https://github.com/huggingface/huggingface_hub).\ Feel free to send in PRs or use this code however you'd like.\ [GitHub mirror](https://github.com/anthonyg5005/hf-scripts) For GitHub: Would recommend creating pull requests and discussions on the [offical huggingface repo](https://huggingface.co/Anthonyg5005/hf-scripts) ## main files - [Auto EXL2 HF upload](https://huggingface.co/Anthonyg5005/hf-scripts/resolve/main/exllamav2%20scripts/auto-exl2-upload/auto-exl2-upload.zip?download=true) - [EXL2 Local Quants](https://huggingface.co/Anthonyg5005/hf-scripts/resolve/main/exllamav2%20scripts/exl2-multi-quant-local/exl2-multi-quant-local.zip?download=true) - [Manage branches (create/delete)](https://huggingface.co/Anthonyg5005/hf-scripts/blob/main/manage%20branches.py) ## outdated or not main focus - [EXL2 Single Quant V3](https://colab.research.google.com/#fileId=https://huggingface.co/Anthonyg5005/hf-scripts/blob/main/ipynb/EXL2_Private_Quant_V3.ipynb) (COLAB) - [Upload folder to HF](https://huggingface.co/Anthonyg5005/hf-scripts/blob/main/upload%20folder%20to%20repo.py) <!-- ## work in progress/not tested (ordered by priority) none for now. perhaps adding finegrained token support to my hf login code --> ## other recommended stuff - [Exllama Discord server](https://discord.gg/NSFwVuCjRq) - [Download models](https://github.com/oobabooga/text-generation-webui/blob/main/download-model.py) (download HF Hub models) [Oobabooga] ## usage - Auto EXL2 HF upload - This script is designed to automate the process of quantizing models to EXL2 and uploading them to the HF Hub as seperate branches. This is both available to run on Windows and Linux. You will be required to be logged in to HF Hub. If you are not logged in, you will need a WRITE token. - [Example repo](https://huggingface.co/Anthonyg5005/Qwen1.5-0.5B-Chat-exl2) - EXL2 Local Quants - Easily creates environment to quantize models to exl2 to your local machine. Supports both Windows and Linux. - EXL2 Single Quant - Allows you to quantize to exl2 using colab. This version creates a exl2 quant to upload to private repo. Only 7B tested on colab. - Upload folder to repo - Uploads user specified folder to specified repo, can create private repos too. Not the same as git commit and push, instead uploads any additional files. This is more of a practice for me than for actual usage as most of the time it crashes on the quantizing process due to lack of ram. - Manage branches - Run script and follow prompts. You will be required to be logged in to HF Hub. If you are not logged in, you will need a WRITE token. You can get one in your [HuggingFace settings](https://huggingface.co/settings/tokens). Colab and Kaggle secret keys are supported. - Download models (oobabooga) - To use the script, open a terminal and run '`python download-model.py USER/MODEL:BRANCH`'. There's also a '`--help`' flag to show the available arguments. To download from private repositories, make sure to login using '`huggingface-cli login`' or (not recommended) `HF_TOKEN` environment variable. ## extras - [HF login snippet](https://huggingface.co/Anthonyg5005/hf-scripts/blob/main/HF%20Login%20Snippet.py) - The login method that I wrote to make fetching the token better. - [HF login snippet kaggle](https://huggingface.co/Anthonyg5005/hf-scripts/blob/main/HF%20Login%20Snippet%20Kaggle.py) - Same as above but for cloud ipynb environments like Colab and Kaggle (Kaggle secret support)
zero7101/LLaVA-7b-v0	zero7101	2025-04-08T02:31:31Z	0	0	null	[ "safetensors", "llava_llama", "base_model:huggyllama/llama-7b", "base_model:finetune:huggyllama/llama-7b", "license:mit", "region:us" ]	null	2025-04-08T02:20:22Z	--- license: mit base_model: - liuhaotian/LLaVA-7b-delta-v0 - huggyllama/llama-7b --- This repository hosts full, ready-to-use LLaVA model weights that were originally released as delta weights. These models were created by applying delta updates to the original LLaMA weights in accordance with the licensing terms. Note: While these models no longer require users to manually apply delta weights, use of these checkpoints must still comply with the original LLaMA license. These are complete LLaVA model weights, built from: Base: LLaMA (Meta AI) Delta: LLaVA delta weights (e.g., liuhaotian/LLaVA-7b-delta-v0) Tool used: llava.model.apply_delta LLaVA Delta Example: liuhaotian/LLaVA-7b-delta-v0 GitHub: https://github.com/haotian-liu/LLaVA This model incorporates original LLaMA weights and thus falls under the LLaMA model license. Please make sure you are eligible and in compliance before using these weights.
gsaltintas/olmo_gsm8k-p1x0.01-3ep-6537295-2	gsaltintas	2025-04-08T02:31:27Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:56:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
genki10/Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold1	genki10	2025-04-08T02:30:25Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-08T02:18:43Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold1 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2754 - Qwk: 0.2344 - Mse: 1.2741 - Rmse: 1.1288 ## 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: 64 - eval_batch_size: 64 - 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 6.8111 \| 0.0 \| 6.8084 \| 2.6093 \| \| No log \| 2.0 \| 8 \| 4.1297 \| 0.0 \| 4.1278 \| 2.0317 \| \| No log \| 3.0 \| 12 \| 2.4985 \| -0.0037 \| 2.4968 \| 1.5801 \| \| No log \| 4.0 \| 16 \| 1.4797 \| 0.0 \| 1.4783 \| 1.2158 \| \| No log \| 5.0 \| 20 \| 1.0102 \| 0.0 \| 1.0089 \| 1.0044 \| \| No log \| 6.0 \| 24 \| 1.0614 \| 0.0 \| 1.0600 \| 1.0296 \| \| No log \| 7.0 \| 28 \| 0.9111 \| 0.2076 \| 0.9098 \| 0.9538 \| \| No log \| 8.0 \| 32 \| 0.8921 \| 0.2067 \| 0.8908 \| 0.9438 \| \| No log \| 9.0 \| 36 \| 1.0968 \| 0.0221 \| 1.0953 \| 1.0465 \| \| No log \| 10.0 \| 40 \| 1.0807 \| 0.0903 \| 1.0791 \| 1.0388 \| \| No log \| 11.0 \| 44 \| 1.0708 \| 0.2644 \| 1.0693 \| 1.0341 \| \| No log \| 12.0 \| 48 \| 0.8926 \| 0.2685 \| 0.8916 \| 0.9442 \| \| No log \| 13.0 \| 52 \| 1.1971 \| 0.2018 \| 1.1958 \| 1.0935 \| \| No log \| 14.0 \| 56 \| 1.0286 \| 0.3093 \| 1.0275 \| 1.0137 \| \| No log \| 15.0 \| 60 \| 1.1172 \| 0.2676 \| 1.1161 \| 1.0565 \| \| No log \| 16.0 \| 64 \| 1.0668 \| 0.2805 \| 1.0657 \| 1.0323 \| \| No log \| 17.0 \| 68 \| 1.3704 \| 0.2450 \| 1.3692 \| 1.1701 \| \| No log \| 18.0 \| 72 \| 1.0223 \| 0.3387 \| 1.0217 \| 1.0108 \| \| No log \| 19.0 \| 76 \| 1.6038 \| 0.2075 \| 1.6022 \| 1.2658 \| \| No log \| 20.0 \| 80 \| 1.0119 \| 0.3197 \| 1.0111 \| 1.0055 \| \| No log \| 21.0 \| 84 \| 1.4628 \| 0.2111 \| 1.4610 \| 1.2087 \| \| No log \| 22.0 \| 88 \| 1.0054 \| 0.2746 \| 1.0042 \| 1.0021 \| \| No log \| 23.0 \| 92 \| 1.2376 \| 0.2599 \| 1.2364 \| 1.1119 \| \| No log \| 24.0 \| 96 \| 1.0680 \| 0.2979 \| 1.0671 \| 1.0330 \| \| No log \| 25.0 \| 100 \| 1.0873 \| 0.2785 \| 1.0863 \| 1.0423 \| \| No log \| 26.0 \| 104 \| 1.5041 \| 0.2418 \| 1.5026 \| 1.2258 \| \| No log \| 27.0 \| 108 \| 1.0525 \| 0.2627 \| 1.0515 \| 1.0254 \| \| No log \| 28.0 \| 112 \| 1.0405 \| 0.2483 \| 1.0393 \| 1.0195 \| \| No log \| 29.0 \| 116 \| 1.4281 \| 0.2255 \| 1.4265 \| 1.1944 \| \| No log \| 30.0 \| 120 \| 1.4461 \| 0.2037 \| 1.4446 \| 1.2019 \| \| No log \| 31.0 \| 124 \| 1.0709 \| 0.2567 \| 1.0699 \| 1.0344 \| \| No log \| 32.0 \| 128 \| 1.0895 \| 0.2538 \| 1.0883 \| 1.0432 \| \| No log \| 33.0 \| 132 \| 1.2754 \| 0.2344 \| 1.2741 \| 1.1288 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
LucAI12/sk7ars	LucAI12	2025-04-08T02:29:02Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-08T02:09:24Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: sk7ars --- # Sk7Ars <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `sk7ars` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "sk7ars", "lora_weights": "https://huggingface.co/LucAI12/sk7ars/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('LucAI12/sk7ars', weight_name='lora.safetensors') image = pipeline('sk7ars').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1500 - Learning rate: 0.0004 - LoRA rank: 20 ## Contribute your own examples You can use the [community tab](https://huggingface.co/LucAI12/sk7ars/discussions) to add images that show off what you’ve made with this LoRA.
gsaltintas/olmo_gsm8k-p1x0.001-3ep-6537305-1	gsaltintas	2025-04-08T02:27:19Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:55:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gsaltintas/olmo_gsm8k-p1120x0.01-3ep-6537302-1	gsaltintas	2025-04-08T02:25:52Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:52:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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DragonLineageAI/Vi-SparkTTS-0.5B	DragonLineageAI	2025-04-08T02:25:07Z	721	7	null	[ "safetensors", "spark-tts", "text-to-speech", "custom_code", "license:cc-by-nc-nd-4.0", "region:us" ]	text-to-speech	2025-03-31T19:01:24Z	--- license: cc-by-nc-nd-4.0 pipeline_tag: text-to-speech --- # Spark TTS Vietnamese Spark-TTS is an advanced text-to-speech system that uses the power of large language models (LLM) for highly accurate and natural-sounding voice synthesis. It is designed to be efficient, flexible, and powerful for both research and production use. This model is trained from [viVoice](https://huggingface.co/datasets/thinhlpg/viVoice) vietnamese dataset # Usage First, install the required packages: ``` pip install --upgrade transformers accelerate ``` ## Text-to-Speech We have customized the code so you can inference using the huggingface transformer library without installing anything else. ```python from transformers import AutoProcessor, AutoModel, AutoTokenizer import soundfile as sf import torch import numpy as np device = "cuda" model_id = "DragonLineageAI/Vi-SparkTTS-0.5B" processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) model = AutoModel.from_pretrained(model_id, trust_remote_code=True).eval() processor.model = model prompt_audio_path = "path_to_audio_path" # CHANGE TO YOUR ACTUAL PATH prompt_transcript = "text corresponding to prompt audio" # Optional text_input = "xin chào mọi người chúng tôi là Nguyễn Công Tú Anh và Chu Văn An đến từ dragonlineageai" inputs = processor( text=text_input.lower(), prompt_speech_path=prompt_audio_path, prompt_text=prompt_transcript, return_tensors="pt" ).to(device) global_tokens_prompt = inputs.pop("global_token_ids_prompt", None) with torch.no_grad(): output_ids = model.generate( **inputs, max_new_tokens=3000, do_sample=True, temperature=0.8, top_k=50, top_p=0.95, eos_token_id=processor.tokenizer.eos_token_id, pad_token_id=processor.tokenizer.pad_token_id ) output_clone = processor.decode( generated_ids=output_ids, global_token_ids_prompt=global_tokens_prompt, input_ids_len=inputs["input_ids"].shape[-1] ) sf.write("output_cloned.wav", output_clone["audio"], output_clone["sampling_rate"]) ``` ## Fintune You can finetune this model with any dataset to improve quality or train on a new language. [training code](https://github.com/tuanh123789/Spark-TTS-finetune)
Logic-TARS/ppo-SnowballTarget	Logic-TARS	2025-04-08T02:24:59Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-04-08T02:24:57Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Logic-TARS/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
gsaltintas/olmo_gsm8k-p1120x0.01-3ep-6537300-1	gsaltintas	2025-04-08T02:23:51Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:51:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1120x0.01-3ep-6537301-1	gsaltintas	2025-04-08T02:23:08Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:51:57Z	--- 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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rsh-raj/hyperswitch-commits_global_defn	rsh-raj	2025-04-08T02:22:53Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/codellama-7b-bnb-4bit", "base_model:finetune:unsloth/codellama-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-08T02:22:45Z	--- base_model: unsloth/codellama-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: rsh-raj - License: apache-2.0 - Finetuned from model : unsloth/codellama-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gsaltintas/olmo_gsm8k-p1x0.01-3ep-6537294-2	gsaltintas	2025-04-08T02:22:23Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:49:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.01-3ep-6537298-1	gsaltintas	2025-04-08T02:22:17Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:49:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6537293-2	gsaltintas	2025-04-08T02:19:41Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:49:59Z	--- 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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gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6537291-2	gsaltintas	2025-04-08T02:19:41Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:47:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.001-3ep-6537306-1	gsaltintas	2025-04-08T02:19:32Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:55:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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genki10/Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold0	genki10	2025-04-08T02:18:36Z	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-08T02:07:15Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold0 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. --> # Trial3BERT_AugV8_k5_task1_organization_sp010_lw020_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9658 - Qwk: 0.3453 - Mse: 0.9658 - Rmse: 0.9828 ## 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: 64 - eval_batch_size: 64 - 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| No log \| 1.0 \| 4 \| 5.7993 \| 0.0424 \| 5.7993 \| 2.4082 \| \| No log \| 2.0 \| 8 \| 3.2704 \| 0.0126 \| 3.2704 \| 1.8084 \| \| No log \| 3.0 \| 12 \| 1.6551 \| 0.0784 \| 1.6551 \| 1.2865 \| \| No log \| 4.0 \| 16 \| 0.9096 \| 0.0316 \| 0.9096 \| 0.9537 \| \| No log \| 5.0 \| 20 \| 1.1035 \| 0.0741 \| 1.1035 \| 1.0505 \| \| No log \| 6.0 \| 24 \| 0.9714 \| 0.0312 \| 0.9714 \| 0.9856 \| \| No log \| 7.0 \| 28 \| 1.4031 \| 0.0951 \| 1.4031 \| 1.1845 \| \| No log \| 8.0 \| 32 \| 0.6029 \| 0.3336 \| 0.6029 \| 0.7765 \| \| No log \| 9.0 \| 36 \| 0.9827 \| 0.0803 \| 0.9827 \| 0.9913 \| \| No log \| 10.0 \| 40 \| 0.6722 \| 0.4689 \| 0.6722 \| 0.8199 \| \| No log \| 11.0 \| 44 \| 0.5466 \| 0.4143 \| 0.5466 \| 0.7393 \| \| No log \| 12.0 \| 48 \| 0.5315 \| 0.4469 \| 0.5315 \| 0.7290 \| \| No log \| 13.0 \| 52 \| 0.5277 \| 0.5063 \| 0.5277 \| 0.7264 \| \| No log \| 14.0 \| 56 \| 0.6147 \| 0.5033 \| 0.6147 \| 0.7840 \| \| No log \| 15.0 \| 60 \| 0.6078 \| 0.4637 \| 0.6078 \| 0.7796 \| \| No log \| 16.0 \| 64 \| 0.8527 \| 0.3919 \| 0.8527 \| 0.9234 \| \| No log \| 17.0 \| 68 \| 0.6084 \| 0.5455 \| 0.6084 \| 0.7800 \| \| No log \| 18.0 \| 72 \| 0.9391 \| 0.3634 \| 0.9391 \| 0.9691 \| \| No log \| 19.0 \| 76 \| 0.6375 \| 0.5123 \| 0.6375 \| 0.7985 \| \| No log \| 20.0 \| 80 \| 0.6598 \| 0.4654 \| 0.6598 \| 0.8123 \| \| No log \| 21.0 \| 84 \| 0.6892 \| 0.4726 \| 0.6892 \| 0.8302 \| \| No log \| 22.0 \| 88 \| 1.0312 \| 0.3377 \| 1.0312 \| 1.0155 \| \| No log \| 23.0 \| 92 \| 0.6468 \| 0.5368 \| 0.6468 \| 0.8043 \| \| No log \| 24.0 \| 96 \| 0.7716 \| 0.4006 \| 0.7716 \| 0.8784 \| \| No log \| 25.0 \| 100 \| 0.6385 \| 0.5070 \| 0.6385 \| 0.7991 \| \| No log \| 26.0 \| 104 \| 0.6258 \| 0.4714 \| 0.6258 \| 0.7911 \| \| No log \| 27.0 \| 108 \| 0.8822 \| 0.4177 \| 0.8822 \| 0.9393 \| \| No log \| 28.0 \| 112 \| 0.7470 \| 0.4062 \| 0.7470 \| 0.8643 \| \| No log \| 29.0 \| 116 \| 0.7107 \| 0.4325 \| 0.7107 \| 0.8430 \| \| No log \| 30.0 \| 120 \| 0.8034 \| 0.4241 \| 0.8034 \| 0.8963 \| \| No log \| 31.0 \| 124 \| 0.6850 \| 0.4624 \| 0.6850 \| 0.8276 \| \| No log \| 32.0 \| 128 \| 0.9658 \| 0.3453 \| 0.9658 \| 0.9828 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
gsaltintas/olmo_gsm8k-p560x0.01-3ep-6537297-2	gsaltintas	2025-04-08T02:16:31Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:54:45Z	--- 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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gsaltintas/olmo_gsm8k-p1120x0.001-3ep-6537284-2	gsaltintas	2025-04-08T02:16:29Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:43:16Z	--- 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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gsaltintas/olmo_gsm8k-p1x0.1-3ep-6537286-2	gsaltintas	2025-04-08T02:15:38Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:43:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1120x0.001-3ep-6537282-2	gsaltintas	2025-04-08T02:15:25Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:43:29Z	--- 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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gsaltintas/olmo_gsm8k-p560x0.1-3ep-6537288-2	gsaltintas	2025-04-08T02:15:14Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:47:02Z	--- 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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gsaltintas/olmo_gsm8k-p560x0.001-3ep-6537307-1	gsaltintas	2025-04-08T02:14:48Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:54:35Z	--- 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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jasonhuang3/Llama-3-Taiwan-8B-Instruct-unsloth-merged-v1-v3	jasonhuang3	2025-04-08T02:14:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:yentinglin/Llama-3-Taiwan-8B-Instruct", "base_model:finetune:yentinglin/Llama-3-Taiwan-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T02:11:00Z	--- base_model: yentinglin/Llama-3-Taiwan-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: jasonhuang3 - License: apache-2.0 - Finetuned from model : yentinglin/Llama-3-Taiwan-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
everybodyss/hyerimchy	everybodyss	2025-04-08T02:14:16Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-04-08T02:12:25Z	--- license: creativeml-openrail-m ---
sod11168/Email_classification	sod11168	2025-04-08T02:13:51Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:adapter:meta-llama/Llama-3.2-1B-Instruct", "region:us" ]	null	2025-04-08T02:13:44Z	--- base_model: meta-llama/Llama-3.2-1B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Ahatsham/Llama-3-8B-Instruct_Monitoring_Feedback_aug_v3	Ahatsham	2025-04-08T02:11:59Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T02:08:05Z	--- 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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gsaltintas/olmo_gsm8k-p1x0.001-3ep-6537278-2	gsaltintas	2025-04-08T02:09:49Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:35:24Z	--- 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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gsaltintas/olmo_gsm8k-p560x0.1-3ep-6537289-2	gsaltintas	2025-04-08T02:09:37Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:46:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1x0.01-3ep-6537294-1	gsaltintas	2025-04-08T02:09:17Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:36:57Z	--- 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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gsaltintas/olmo_gsm8k-p1x0.1-3ep-6537285-2	gsaltintas	2025-04-08T02:07:07Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:43:27Z	--- 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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gsaltintas/olmo_gsm8k-p1x0.001-3ep-6537277-2	gsaltintas	2025-04-08T02:06:57Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:34:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgambettaphd/M_llm3_gen2_run0_W_doc1000_synt64_RANDOM	dgambettaphd	2025-04-08T02:06:32Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-08T02:06:15Z	--- 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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gsaltintas/olmo_gsm8k-p1120x0.01-3ep-6537275-2	gsaltintas	2025-04-08T02:06:19Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:35:06Z	--- 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. 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DeputyDangus/distilbert-base-uncased-distilled-clinc	DeputyDangus	2025-04-08T02:06:15Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T16:05:10Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-clinc 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. --> # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0438 - Accuracy: 0.9365 ## 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: 48 - eval_batch_size: 48 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 318 \| 0.3381 \| 0.6571 \| \| 0.5164 \| 2.0 \| 636 \| 0.1413 \| 0.8581 \| \| 0.5164 \| 3.0 \| 954 \| 0.0855 \| 0.9016 \| \| 0.1595 \| 4.0 \| 1272 \| 0.0649 \| 0.9161 \| \| 0.0916 \| 5.0 \| 1590 \| 0.0550 \| 0.9252 \| \| 0.0916 \| 6.0 \| 1908 \| 0.0499 \| 0.9310 \| \| 0.0718 \| 7.0 \| 2226 \| 0.0470 \| 0.9313 \| \| 0.063 \| 8.0 \| 2544 \| 0.0454 \| 0.9339 \| \| 0.063 \| 9.0 \| 2862 \| 0.0441 \| 0.9358 \| \| 0.0591 \| 10.0 \| 3180 \| 0.0438 \| 0.9365 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Tokenizers 0.21.1
gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6537291-1	gsaltintas	2025-04-08T02:06:12Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:34:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6537293-1	gsaltintas	2025-04-08T02:06:11Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:37:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.1-3ep-6537290-1	gsaltintas	2025-04-08T02:05:51Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:34:09Z	--- 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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VIRAL-Gangu-Chettri-Kanda-Video-Link/ORIGINAL.CLIP.gangu.chettri.kanda.video.viral.Social.Media.X.Trending	VIRAL-Gangu-Chettri-Kanda-Video-Link	2025-04-08T02:04:46Z	0	0	null	[ "region:us" ]	null	2025-04-08T02:04:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5n6bjbnr?news-viral-video" 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>
peanut999/Whisper-large-turbo-v3-fine-tuned-malay	peanut999	2025-04-08T02:03:32Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-08T02:00:49Z	--- 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. 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Scarlet-Raine/gemma-3-waifu-finetune-gguf	Scarlet-Raine	2025-04-08T02:03:10Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "gemma3", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-08T01:56:53Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Scarlet-Raine - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gsaltintas/olmo_gsm8k-p1120x0.01-3ep-6537273-2	gsaltintas	2025-04-08T02:02:59Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T00:21:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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DeputyDangus/distilbert-base-uncased-finetuned-clinc	DeputyDangus	2025-04-08T02:02:54Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-06T14:26:25Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc 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. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7988 - Accuracy: 0.9139 ## 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: 48 - eval_batch_size: 48 - 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 318 \| 3.2842 \| 0.7239 \| \| 3.7874 \| 2.0 \| 636 \| 1.8853 \| 0.8442 \| \| 3.7874 \| 3.0 \| 954 \| 1.1789 \| 0.8916 \| \| 1.7109 \| 4.0 \| 1272 \| 0.8840 \| 0.9081 \| \| 0.9258 \| 5.0 \| 1590 \| 0.7988 \| 0.9139 \| ### Framework versions - Transformers 4.50.3 - Pytorch 2.6.0+cu124 - Tokenizers 0.21.1
gsaltintas/olmo_gsm8k-p1120x0.001-3ep-6537282-1	gsaltintas	2025-04-08T02:02:11Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:30:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.1-3ep-6537288-1	gsaltintas	2025-04-08T02:01:45Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:34:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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clarehua/DeepSeek-R1-Medical-COT-Qwen-7B	clarehua	2025-04-08T02:01:40Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-08T01:49:45Z	--- base_model: unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: clarehua - License: apache-2.0 - Finetuned from model : unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6537266-2	gsaltintas	2025-04-08T02:00:37Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:29:02Z	--- 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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lblaoke/qwama-0.5b-hh-rlhf-dpo-trl-v4	lblaoke	2025-04-08T02:00:04Z	4	0	null	[ "safetensors", "qwen2", "dataset:Dahoas/full-hh-rlhf", "base_model:lblaoke/qwama-0.5b-hh-rlhf-sft-chosen-trl-v4", "base_model:finetune:lblaoke/qwama-0.5b-hh-rlhf-sft-chosen-trl-v4", "region:us" ]	null	2025-04-04T16:43:10Z	--- datasets: - Dahoas/full-hh-rlhf base_model: - lblaoke/qwama-0.5b-hh-rlhf-sft-chosen-trl-v4 --- num_train_epochs: 1 learning_rate: 1e-3 total_batch_size: 16
lblaoke/qwama-0.5b-hh-rlhf-sft-chosen-trl-v4	lblaoke	2025-04-08T01:59:39Z	29	0	null	[ "safetensors", "qwen2", "dataset:Dahoas/full-hh-rlhf", "base_model:turboderp/Qwama-0.5B-Instruct", "base_model:finetune:turboderp/Qwama-0.5B-Instruct", "region:us" ]	null	2025-04-04T05:12:59Z	--- datasets: - Dahoas/full-hh-rlhf base_model: - turboderp/Qwama-0.5B-Instruct --- num_train_epochs: 1 learning_rate: 2e-4 total_batch_size: 32
gsaltintas/olmo_gsm8k-p560x0.1-3ep-6537262-2	gsaltintas	2025-04-08T01:58:34Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:26:09Z	--- 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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gsaltintas/olmo_gsm8k-p1x0.1-3ep-6537287-1	gsaltintas	2025-04-08T01:58:27Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:33:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6537264-2	gsaltintas	2025-04-08T01:58:05Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:25:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6537265-1	gsaltintas	2025-04-08T01:57:20Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:14:27Z	--- 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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gsaltintas/olmo_gsm8k-p1x0.01-3ep-6537268-2	gsaltintas	2025-04-08T01:56:45Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:28:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.1-3ep-6537289-1	gsaltintas	2025-04-08T01:56:23Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:33:49Z	--- 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. 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AmaanDhamaskar/sarvam1-mr-summarizer-new	AmaanDhamaskar	2025-04-08T01:55:55Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:sarvamai/sarvam-1", "base_model:finetune:sarvamai/sarvam-1", "endpoints_compatible", "region:us" ]	null	2025-04-08T01:55:49Z	--- base_model: sarvamai/sarvam-1 library_name: transformers model_name: sarvam1-mr-summarizer-new tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for sarvam1-mr-summarizer-new This model is a fine-tuned version of [sarvamai/sarvam-1](https://huggingface.co/sarvamai/sarvam-1). 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="AmaanDhamaskar/sarvam1-mr-summarizer-new", 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: 0.12.1 - Transformers: 4.46.2 - Pytorch: 2.6.0 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
gsaltintas/olmo_gsm8k-p560x0.001-3ep-6537280-1	gsaltintas	2025-04-08T01:54:15Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:27:49Z	--- 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. 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gsaltintas/olmo_gsm8k-p560x0.01-3ep-6537270-2	gsaltintas	2025-04-08T01:54:06Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:31:51Z	--- 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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qujava/qujavaind	qujava	2025-04-08T01:53:41Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-08T01:53:41Z	--- license: apache-2.0 ---
gsaltintas/olmo_gsm8k-p1x0.001-3ep-6537277-1	gsaltintas	2025-04-08T01:53:35Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:21:36Z	--- 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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gsaltintas/olmo_gsm8k-p1x0.1-3ep-6537260-2	gsaltintas	2025-04-08T01:52:58Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:25:36Z	--- 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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ssktora/e5-mistral-nfcorpus-train-bm25-pyserini-epo1	ssktora	2025-04-08T01:51:11Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:intfloat/e5-mistral-7b-instruct", "base_model:adapter:intfloat/e5-mistral-7b-instruct", "region:us" ]	null	2025-04-08T01:51:05Z	--- base_model: intfloat/e5-mistral-7b-instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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gangu-chettri-kanda-sex-video-telegram/ORIGINAL.sex.video.gangu.chettri.kanda.full.video.gangu.chettri.kanda.video	gangu-chettri-kanda-sex-video-telegram	2025-04-08T01:50:40Z	0	0	null	[ "region:us" ]	null	2025-04-08T01:45:19Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5n6bjbnr?news-viral-video" 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> <animated-image data-catalyst=""><a href="https://tinyurl.com/5n6bjbnr?news-viral-video" 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> <animated-image data-catalyst=""><a href="https://tinyurl.com/5n6bjbnr?news-viral-video" 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> <animated-image data-catalyst=""><a href="https://tinyurl.com/5n6bjbnr?news-viral-video" 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>
leyla95/Mentiq_Whisper	leyla95	2025-04-08T01:49:16Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-07T17:43:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.1-3ep-6537263-2	gsaltintas	2025-04-08T01:48:48Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:26:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.01-3ep-6537271-1	gsaltintas	2025-04-08T01:47:43Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:18:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsaltintas/olmo_gsm8k-p560x0.01-3ep-6537272-1	gsaltintas	2025-04-08T01:47:35Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-07T23:18:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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