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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 2025-06-02 06:28:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 462 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-02 06:27:40	card stringlengths 11 1.01M
casque/Micro_Bikini_By_Stable_Yogi	casque	2024-06-06T03:01:21Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-06-06T03:00:53Z	--- license: creativeml-openrail-m ---
blackhole33/llama3-uzbv1	blackhole33	2024-06-06T02:54:54Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-05T08:44:21Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: llama-2-7b-chat-bnb-4bit --- # Uploaded model - Developed by: blackhole33 - License: apache-2.0 - Finetuned from model : llama-2-7b-chat-bnb-4bit
tsavage68/UTI_M2_1000steps_1e7rate_SFT	tsavage68	2024-06-06T02:47:28Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T15:05:10Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: UTI_M2_1000steps_1e7rate_SFT 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. --> # UTI_M2_1000steps_1e7rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1047 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 2.4021 \| 0.3333 \| 25 \| 2.3941 \| \| 2.3235 \| 0.6667 \| 50 \| 2.2471 \| \| 2.0863 \| 1.0 \| 75 \| 1.9386 \| \| 1.6662 \| 1.3333 \| 100 \| 1.5791 \| \| 1.2956 \| 1.6667 \| 125 \| 1.2544 \| \| 1.214 \| 2.0 \| 150 \| 1.2116 \| \| 1.202 \| 2.3333 \| 175 \| 1.1861 \| \| 1.1813 \| 2.6667 \| 200 \| 1.1668 \| \| 1.1696 \| 3.0 \| 225 \| 1.1528 \| \| 1.1052 \| 3.3333 \| 250 \| 1.1412 \| \| 1.0614 \| 3.6667 \| 275 \| 1.1329 \| \| 1.1106 \| 4.0 \| 300 \| 1.1271 \| \| 1.1019 \| 4.3333 \| 325 \| 1.1228 \| \| 1.0691 \| 4.6667 \| 350 \| 1.1212 \| \| 1.0947 \| 5.0 \| 375 \| 1.1153 \| \| 1.0689 \| 5.3333 \| 400 \| 1.1134 \| \| 1.0598 \| 5.6667 \| 425 \| 1.1116 \| \| 1.0459 \| 6.0 \| 450 \| 1.1111 \| \| 1.0518 \| 6.3333 \| 475 \| 1.1097 \| \| 1.045 \| 6.6667 \| 500 \| 1.1092 \| \| 1.0658 \| 7.0 \| 525 \| 1.1066 \| \| 1.0706 \| 7.3333 \| 550 \| 1.1067 \| \| 1.0514 \| 7.6667 \| 575 \| 1.1057 \| \| 1.0412 \| 8.0 \| 600 \| 1.1063 \| \| 1.0455 \| 8.3333 \| 625 \| 1.1052 \| \| 0.9657 \| 8.6667 \| 650 \| 1.1057 \| \| 1.1015 \| 9.0 \| 675 \| 1.1052 \| \| 1.0294 \| 9.3333 \| 700 \| 1.1051 \| \| 1.0399 \| 9.6667 \| 725 \| 1.1052 \| \| 1.1125 \| 10.0 \| 750 \| 1.1047 \| \| 1.0219 \| 10.3333 \| 775 \| 1.1046 \| \| 0.9862 \| 10.6667 \| 800 \| 1.1048 \| \| 1.0682 \| 11.0 \| 825 \| 1.1049 \| \| 1.0587 \| 11.3333 \| 850 \| 1.1049 \| \| 1.0217 \| 11.6667 \| 875 \| 1.1051 \| \| 1.0547 \| 12.0 \| 900 \| 1.1047 \| \| 1.0047 \| 12.3333 \| 925 \| 1.1047 \| \| 1.021 \| 12.6667 \| 950 \| 1.1047 \| \| 1.0528 \| 13.0 \| 975 \| 1.1047 \| \| 1.0385 \| 13.3333 \| 1000 \| 1.1047 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
oproprioaquino/Stable_LoRA	oproprioaquino	2024-06-06T02:46:42Z	3	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:mit", "region:us" ]	text-to-image	2024-06-06T01:05:15Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: Pimentão color model parameters: negative_prompt: oiticica output: url: images/prancheta001.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: Modelo Aqq license: mit --- # LoRA Aqq <Gallery /> ## Model description Um modelo para teste de ferramenta ## Trigger words You should use `Modelo Aqq` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/oproprioaquino/Stable_LoRA/tree/main) them in the Files & versions tab.
Mikask/bdc2024-indobert-2	Mikask	2024-06-06T02:46:38Z	183	0	transformers	[ "transformers", "pytorch", "mbart", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T02:46:03Z	--- tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: bdc2024-indobert-2 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. --> # bdc2024-indobert-2 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5554 - Accuracy: 0.9331 - Balanced Accuracy: 0.8724 - Precision: 0.9353 - Recall: 0.9331 - F1: 0.9289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Balanced Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-----------------:\|:---------:\|:------:\|:------:\| \| No log \| 1.0 \| 483 \| 0.5108 \| 0.9197 \| 0.8454 \| 0.9195 \| 0.9197 \| 0.9132 \| \| 0.0773 \| 2.0 \| 966 \| 0.5374 \| 0.9235 \| 0.8668 \| 0.9266 \| 0.9235 \| 0.9196 \| \| 0.0374 \| 3.0 \| 1449 \| 0.5451 \| 0.9331 \| 0.8689 \| 0.9359 \| 0.9331 \| 0.9281 \| \| 0.0242 \| 4.0 \| 1932 \| 0.5567 \| 0.9331 \| 0.8726 \| 0.9353 \| 0.9331 \| 0.9288 \| \| 0.0162 \| 5.0 \| 2415 \| 0.5554 \| 0.9331 \| 0.8724 \| 0.9353 \| 0.9331 \| 0.9289 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.13.3
tsavage68/UTI_M2_1000steps_1e8rate_SFT	tsavage68	2024-06-06T02:45:33Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T15:07:27Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: UTI_M2_1000steps_1e8rate_SFT 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. --> # UTI_M2_1000steps_1e8rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6170 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 2.6263 \| 0.3333 \| 25 \| 2.6302 \| \| 2.6415 \| 0.6667 \| 50 \| 2.6306 \| \| 2.6336 \| 1.0 \| 75 \| 2.6298 \| \| 2.6804 \| 1.3333 \| 100 \| 2.6284 \| \| 2.5885 \| 1.6667 \| 125 \| 2.6291 \| \| 2.542 \| 2.0 \| 150 \| 2.6286 \| \| 2.6601 \| 2.3333 \| 175 \| 2.6249 \| \| 2.6936 \| 2.6667 \| 200 \| 2.6240 \| \| 2.6593 \| 3.0 \| 225 \| 2.6238 \| \| 2.6088 \| 3.3333 \| 250 \| 2.6237 \| \| 2.542 \| 3.6667 \| 275 \| 2.6208 \| \| 2.5635 \| 4.0 \| 300 \| 2.6185 \| \| 2.6825 \| 4.3333 \| 325 \| 2.6183 \| \| 2.5987 \| 4.6667 \| 350 \| 2.6198 \| \| 2.6421 \| 5.0 \| 375 \| 2.6196 \| \| 2.6426 \| 5.3333 \| 400 \| 2.6183 \| \| 2.6517 \| 5.6667 \| 425 \| 2.6189 \| \| 2.5762 \| 6.0 \| 450 \| 2.6190 \| \| 2.6801 \| 6.3333 \| 475 \| 2.6190 \| \| 2.5918 \| 6.6667 \| 500 \| 2.6169 \| \| 2.6354 \| 7.0 \| 525 \| 2.6163 \| \| 2.6433 \| 7.3333 \| 550 \| 2.6156 \| \| 2.7024 \| 7.6667 \| 575 \| 2.6172 \| \| 2.575 \| 8.0 \| 600 \| 2.6176 \| \| 2.6109 \| 8.3333 \| 625 \| 2.6165 \| \| 2.6088 \| 8.6667 \| 650 \| 2.6195 \| \| 2.698 \| 9.0 \| 675 \| 2.6185 \| \| 2.612 \| 9.3333 \| 700 \| 2.6166 \| \| 2.615 \| 9.6667 \| 725 \| 2.6188 \| \| 2.681 \| 10.0 \| 750 \| 2.6165 \| \| 2.6118 \| 10.3333 \| 775 \| 2.6190 \| \| 2.5875 \| 10.6667 \| 800 \| 2.6168 \| \| 2.6859 \| 11.0 \| 825 \| 2.6170 \| \| 2.5953 \| 11.3333 \| 850 \| 2.6171 \| \| 2.5884 \| 11.6667 \| 875 \| 2.6169 \| \| 2.6721 \| 12.0 \| 900 \| 2.6170 \| \| 2.5951 \| 12.3333 \| 925 \| 2.6170 \| \| 2.6112 \| 12.6667 \| 950 \| 2.6170 \| \| 2.5842 \| 13.0 \| 975 \| 2.6170 \| \| 2.5962 \| 13.3333 \| 1000 \| 2.6170 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
richardkelly/google-gemma-7b-1717613194	richardkelly	2024-06-06T02:44:55Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T18:46:36Z	--- 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. 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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UnclearPWR/SPARK-mini-instruct-Q6_K-GGUF	UnclearPWR	2024-06-06T02:44:39Z	1	0	transformers	[ "transformers", "gguf", "climate", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:NuclearnAI/SPARK-mini-instruct", "base_model:quantized:NuclearnAI/SPARK-mini-instruct", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-06T02:44:29Z	--- language: - en library_name: transformers tags: - climate - llama-cpp - gguf-my-repo base_model: NuclearnAI/SPARK-mini-instruct pipeline_tag: text-generation --- # UnclearPWR/SPARK-mini-instruct-Q6_K-GGUF This model was converted to GGUF format from [`NuclearnAI/SPARK-mini-instruct`](https://huggingface.co/NuclearnAI/SPARK-mini-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/NuclearnAI/SPARK-mini-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo UnclearPWR/SPARK-mini-instruct-Q6_K-GGUF --hf-file spark-mini-instruct-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo UnclearPWR/SPARK-mini-instruct-Q6_K-GGUF --hf-file spark-mini-instruct-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo UnclearPWR/SPARK-mini-instruct-Q6_K-GGUF --hf-file spark-mini-instruct-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo UnclearPWR/SPARK-mini-instruct-Q6_K-GGUF --hf-file spark-mini-instruct-q6_k.gguf -c 2048 ```
OrcinusOrca/q-Taxi-v3	OrcinusOrca	2024-06-06T02:43:06Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-06-06T02:43:03Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="OrcinusOrca/q-Taxi-v3", 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"]) ```
tsavage68/UTI_M2_1000steps_1e6rate_SFT	tsavage68	2024-06-06T02:42:22Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T13:16:01Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: UTI_M2_1000steps_1e6rate_SFT 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. --> # UTI_M2_1000steps_1e6rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 2.2167 \| 0.3333 \| 25 \| 1.1865 \| \| 0.9806 \| 0.6667 \| 50 \| 0.9618 \| \| 0.936 \| 1.0 \| 75 \| 0.9371 \| \| 0.8294 \| 1.3333 \| 100 \| 0.9512 \| \| 0.8273 \| 1.6667 \| 125 \| 0.9369 \| \| 0.7851 \| 2.0 \| 150 \| 0.9036 \| \| 0.5263 \| 2.3333 \| 175 \| 0.9990 \| \| 0.5512 \| 2.6667 \| 200 \| 0.9589 \| \| 0.5272 \| 3.0 \| 225 \| 0.9576 \| \| 0.2888 \| 3.3333 \| 250 \| 1.1371 \| \| 0.2968 \| 3.6667 \| 275 \| 1.1164 \| \| 0.3381 \| 4.0 \| 300 \| 1.1144 \| \| 0.1802 \| 4.3333 \| 325 \| 1.1697 \| \| 0.2025 \| 4.6667 \| 350 \| 1.1946 \| \| 0.2273 \| 5.0 \| 375 \| 1.2614 \| \| 0.1417 \| 5.3333 \| 400 \| 1.3260 \| \| 0.1524 \| 5.6667 \| 425 \| 1.3343 \| \| 0.136 \| 6.0 \| 450 \| 1.3735 \| \| 0.117 \| 6.3333 \| 475 \| 1.3843 \| \| 0.1284 \| 6.6667 \| 500 \| 1.3742 \| \| 0.1172 \| 7.0 \| 525 \| 1.4114 \| \| 0.0905 \| 7.3333 \| 550 \| 1.5000 \| \| 0.1027 \| 7.6667 \| 575 \| 1.5142 \| \| 0.097 \| 8.0 \| 600 \| 1.4912 \| \| 0.0837 \| 8.3333 \| 625 \| 1.5974 \| \| 0.0832 \| 8.6667 \| 650 \| 1.6185 \| \| 0.0781 \| 9.0 \| 675 \| 1.6203 \| \| 0.0698 \| 9.3333 \| 700 \| 1.6833 \| \| 0.0722 \| 9.6667 \| 725 \| 1.6960 \| \| 0.0681 \| 10.0 \| 750 \| 1.7139 \| \| 0.0635 \| 10.3333 \| 775 \| 1.7732 \| \| 0.0654 \| 10.6667 \| 800 \| 1.7704 \| \| 0.0663 \| 11.0 \| 825 \| 1.7647 \| \| 0.0604 \| 11.3333 \| 850 \| 1.7840 \| \| 0.0628 \| 11.6667 \| 875 \| 1.7916 \| \| 0.0627 \| 12.0 \| 900 \| 1.7947 \| \| 0.061 \| 12.3333 \| 925 \| 1.7962 \| \| 0.062 \| 12.6667 \| 950 \| 1.7967 \| \| 0.0607 \| 13.0 \| 975 \| 1.7960 \| \| 0.0605 \| 13.3333 \| 1000 \| 1.7960 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
suthanhcong/distilbert-base-uncased-finetuned-DouRC	suthanhcong	2024-06-06T02:41:18Z	128	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-06-05T07:12:29Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: distilbert-base-uncased-finetuned-DouRC 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-DouRC This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.6662 - Exact Match: 0.05 - F1: 0.0270 ## 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: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Exact Match \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:------:\| \| 5.6355 \| 1.0 \| 10 \| 5.3658 \| 0.05 \| 0.0264 \| \| 5.083 \| 2.0 \| 20 \| 4.9659 \| 0.035 \| 0.0164 \| \| 4.6944 \| 3.0 \| 30 \| 4.7939 \| 0.03 \| 0.0129 \| \| 4.4803 \| 4.0 \| 40 \| 4.6992 \| 0.04 \| 0.0207 \| \| 4.389 \| 5.0 \| 50 \| 4.6662 \| 0.05 \| 0.0270 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
tsavage68/UTI_M2_1000steps_1e5rate_SFT	tsavage68	2024-06-06T02:40:49Z	10	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T14:06:56Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: UTI_M2_1000steps_1e5rate_SFT 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. --> # UTI_M2_1000steps_1e5rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9597 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 1.0555 \| 0.3333 \| 25 \| 0.9919 \| \| 1.0461 \| 0.6667 \| 50 \| 1.0778 \| \| 1.1438 \| 1.0 \| 75 \| 1.4514 \| \| 0.925 \| 1.3333 \| 100 \| 1.1656 \| \| 0.9875 \| 1.6667 \| 125 \| 1.1640 \| \| 0.9859 \| 2.0 \| 150 \| 1.6609 \| \| 0.7898 \| 2.3333 \| 175 \| 1.2420 \| \| 0.7246 \| 2.6667 \| 200 \| 1.2557 \| \| 0.7078 \| 3.0 \| 225 \| 1.1831 \| \| 0.4316 \| 3.3333 \| 250 \| 1.3381 \| \| 0.4621 \| 3.6667 \| 275 \| 1.3760 \| \| 0.5094 \| 4.0 \| 300 \| 1.3134 \| \| 0.2873 \| 4.3333 \| 325 \| 1.3968 \| \| 0.267 \| 4.6667 \| 350 \| 1.5584 \| \| 0.292 \| 5.0 \| 375 \| 1.4604 \| \| 0.1967 \| 5.3333 \| 400 \| 1.5440 \| \| 0.2125 \| 5.6667 \| 425 \| 1.5934 \| \| 0.2141 \| 6.0 \| 450 \| 1.5512 \| \| 0.1391 \| 6.3333 \| 475 \| 1.6320 \| \| 0.1735 \| 6.6667 \| 500 \| 1.6144 \| \| 0.1688 \| 7.0 \| 525 \| 1.6714 \| \| 0.1265 \| 7.3333 \| 550 \| 1.6959 \| \| 0.1334 \| 7.6667 \| 575 \| 1.6998 \| \| 0.1245 \| 8.0 \| 600 \| 1.7298 \| \| 0.1066 \| 8.3333 \| 625 \| 1.7505 \| \| 0.0982 \| 8.6667 \| 650 \| 1.7773 \| \| 0.1014 \| 9.0 \| 675 \| 1.8197 \| \| 0.0829 \| 9.3333 \| 700 \| 1.8606 \| \| 0.0774 \| 9.6667 \| 725 \| 1.8651 \| \| 0.0846 \| 10.0 \| 750 \| 1.8653 \| \| 0.0739 \| 10.3333 \| 775 \| 1.9064 \| \| 0.0786 \| 10.6667 \| 800 \| 1.9323 \| \| 0.0691 \| 11.0 \| 825 \| 1.9367 \| \| 0.0648 \| 11.3333 \| 850 \| 1.9448 \| \| 0.0649 \| 11.6667 \| 875 \| 1.9546 \| \| 0.0672 \| 12.0 \| 900 \| 1.9559 \| \| 0.06 \| 12.3333 \| 925 \| 1.9592 \| \| 0.0606 \| 12.6667 \| 950 \| 1.9597 \| \| 0.0606 \| 13.0 \| 975 \| 1.9597 \| \| 0.0601 \| 13.3333 \| 1000 \| 1.9597 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.0.0+cu117 - Datasets 2.19.2 - Tokenizers 0.19.1
v8karlo/UNCENSORED-TinyDolphin-3x-MoE-Q4_K_M-GGUF	v8karlo	2024-06-06T02:36:55Z	7	1	null	[ "gguf", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "cognitivecomputations/TinyDolphin-2.8.1-1.1b", "llama-cpp", "gguf-my-repo", "base_model:jtatman/TinyDolphin-3x-MoE", "base_model:quantized:jtatman/TinyDolphin-3x-MoE", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-06T02:32:24Z	--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - cognitivecomputations/TinyDolphin-2.8.1-1.1b - llama-cpp - gguf-my-repo base_model: jtatman/TinyDolphin-3x-MoE --- # v8karlo/TinyDolphin-3x-MoE-Q4_K_M-GGUF This model was converted to GGUF format from [`jtatman/TinyDolphin-3x-MoE`](https://huggingface.co/jtatman/TinyDolphin-3x-MoE) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/jtatman/TinyDolphin-3x-MoE) for more details on the model. cONVERT sAFETENSORS TO gguf . https://huggingface.co/spaces/ggml-org/gguf-my-repo . ![image/png](https://cdn-uploads.huggingface.co/production/uploads/662c3116277765660783ca6d/fV0zgvlVGXq7yoT2h_TAO.png) . ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo v8karlo/TinyDolphin-3x-MoE-Q4_K_M-GGUF --hf-file tinydolphin-3x-moe-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo v8karlo/TinyDolphin-3x-MoE-Q4_K_M-GGUF --hf-file tinydolphin-3x-moe-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo v8karlo/TinyDolphin-3x-MoE-Q4_K_M-GGUF --hf-file tinydolphin-3x-moe-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo v8karlo/TinyDolphin-3x-MoE-Q4_K_M-GGUF --hf-file tinydolphin-3x-moe-q4_k_m.gguf -c 2048 ```
UnclearPWR/SPARK-mini-instruct-Q8_0-GGUF	UnclearPWR	2024-06-06T02:35:46Z	0	0	transformers	[ "transformers", "gguf", "climate", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:NuclearnAI/SPARK-mini-instruct", "base_model:quantized:NuclearnAI/SPARK-mini-instruct", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-06T02:35:36Z	--- language: - en library_name: transformers tags: - climate - llama-cpp - gguf-my-repo base_model: NuclearnAI/SPARK-mini-instruct pipeline_tag: text-generation --- # UnclearPWR/SPARK-mini-instruct-Q8_0-GGUF This model was converted to GGUF format from [`NuclearnAI/SPARK-mini-instruct`](https://huggingface.co/NuclearnAI/SPARK-mini-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/NuclearnAI/SPARK-mini-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo UnclearPWR/SPARK-mini-instruct-Q8_0-GGUF --hf-file spark-mini-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo UnclearPWR/SPARK-mini-instruct-Q8_0-GGUF --hf-file spark-mini-instruct-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo UnclearPWR/SPARK-mini-instruct-Q8_0-GGUF --hf-file spark-mini-instruct-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo UnclearPWR/SPARK-mini-instruct-Q8_0-GGUF --hf-file spark-mini-instruct-q8_0.gguf -c 2048 ```
haihuynh/IMDB-Bert-CLSModel-v1	haihuynh	2024-06-06T02:31:53Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2024-06-06T02:31:40Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: IMDB-Bert-CLSModel-v1 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. --> # IMDB-Bert-CLSModel-v1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4872 - Accuracy: 0.8126 - F1: 0.8125 - Precision: 0.8135 - Recall: 0.8127 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.5671 \| 0.6394 \| 500 \| 0.7079 \| 0.4995 \| 0.3347 \| 0.7495 \| 0.5009 \| \| 0.7017 \| 1.2788 \| 1000 \| 0.6467 \| 0.5744 \| 0.4837 \| 0.7578 \| 0.5755 \| \| 0.5909 \| 1.9182 \| 1500 \| 0.4872 \| 0.8126 \| 0.8125 \| 0.8135 \| 0.8127 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
bella05/pogny-16-0.001	bella05	2024-06-06T02:30:13Z	108	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-06T02:09:36Z	--- base_model: klue/roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: pogny-16-0.001 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. --> # pogny-16-0.001 This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6847 - Accuracy: 0.4376 - F1: 0.2665 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 1.7729 \| 1.0 \| 4818 \| 1.8008 \| 0.4376 \| 0.2665 \| \| 1.7377 \| 2.0 \| 9636 \| 1.7499 \| 0.4376 \| 0.2665 \| \| 1.6614 \| 3.0 \| 14454 \| 1.6847 \| 0.4376 \| 0.2665 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0a0+b5021ba - Datasets 2.6.2 - Tokenizers 0.14.1
ari2002/lora_model	ari2002	2024-06-06T02:27:49Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-06T02:27:25Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: ari2002 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
suthanhcong/results	suthanhcong	2024-06-06T02:27:41Z	135	0	transformers	[ "transformers", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-06-06T02:17:01Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: results 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. --> # results This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 50 - eval_batch_size: 50 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
wwe180/L3-8B-LingYang-v1-Q6_K-GGUF	wwe180	2024-06-06T02:22:33Z	3	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "Llama3", "llama-cpp", "gguf-my-repo", "base_model:wwe180/L3-8B-LingYang-v1", "base_model:quantized:wwe180/L3-8B-LingYang-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T02:20:32Z	--- license: - other library_name: transformers tags: - mergekit - merge - Llama3 - llama-cpp - gguf-my-repo base_model: wwe180/L3-8B-LingYang-v1 --- # wwe180/L3-8B-LingYang-v1-Q6_K-GGUF This model was converted to GGUF format from [`wwe180/L3-8B-LingYang-v1`](https://huggingface.co/wwe180/L3-8B-LingYang-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/wwe180/L3-8B-LingYang-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo wwe180/L3-8B-LingYang-v1-Q6_K-GGUF --hf-file l3-8b-lingyang-v1-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo wwe180/L3-8B-LingYang-v1-Q6_K-GGUF --hf-file l3-8b-lingyang-v1-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo wwe180/L3-8B-LingYang-v1-Q6_K-GGUF --hf-file l3-8b-lingyang-v1-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo wwe180/L3-8B-LingYang-v1-Q6_K-GGUF --hf-file l3-8b-lingyang-v1-q6_k.gguf -c 2048 ```
RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf	RichardErkhov	2024-06-06T02:16:23Z	50	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-06-06T02:05:31Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gpt2-conversational-or-qa - GGUF - Model creator: https://huggingface.co/Locutusque/ - Original model: https://huggingface.co/Locutusque/gpt2-conversational-or-qa/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [gpt2-conversational-or-qa.Q2_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q2_K.gguf) \| Q2_K \| 0.08GB \| \| [gpt2-conversational-or-qa.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.IQ3_XS.gguf) \| IQ3_XS \| 0.08GB \| \| [gpt2-conversational-or-qa.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.IQ3_S.gguf) \| IQ3_S \| 0.08GB \| \| [gpt2-conversational-or-qa.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q3_K_S.gguf) \| Q3_K_S \| 0.08GB \| \| [gpt2-conversational-or-qa.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.IQ3_M.gguf) \| IQ3_M \| 0.09GB \| \| [gpt2-conversational-or-qa.Q3_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q3_K.gguf) \| Q3_K \| 0.09GB \| \| [gpt2-conversational-or-qa.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q3_K_M.gguf) \| Q3_K_M \| 0.09GB \| \| [gpt2-conversational-or-qa.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q3_K_L.gguf) \| Q3_K_L \| 0.1GB \| \| [gpt2-conversational-or-qa.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.IQ4_XS.gguf) \| IQ4_XS \| 0.1GB \| \| [gpt2-conversational-or-qa.Q4_0.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q4_0.gguf) \| Q4_0 \| 0.1GB \| \| [gpt2-conversational-or-qa.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.IQ4_NL.gguf) \| IQ4_NL \| 0.1GB \| \| [gpt2-conversational-or-qa.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q4_K_S.gguf) \| Q4_K_S \| 0.1GB \| \| [gpt2-conversational-or-qa.Q4_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q4_K.gguf) \| Q4_K \| 0.11GB \| \| [gpt2-conversational-or-qa.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q4_K_M.gguf) \| Q4_K_M \| 0.11GB \| \| [gpt2-conversational-or-qa.Q4_1.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q4_1.gguf) \| Q4_1 \| 0.11GB \| \| [gpt2-conversational-or-qa.Q5_0.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q5_0.gguf) \| Q5_0 \| 0.11GB \| \| [gpt2-conversational-or-qa.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q5_K_S.gguf) \| Q5_K_S \| 0.11GB \| \| [gpt2-conversational-or-qa.Q5_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q5_K.gguf) \| Q5_K \| 0.12GB \| \| [gpt2-conversational-or-qa.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q5_K_M.gguf) \| Q5_K_M \| 0.12GB \| \| [gpt2-conversational-or-qa.Q5_1.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q5_1.gguf) \| Q5_1 \| 0.12GB \| \| [gpt2-conversational-or-qa.Q6_K.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q6_K.gguf) \| Q6_K \| 0.13GB \| \| [gpt2-conversational-or-qa.Q8_0.gguf](https://huggingface.co/RichardErkhov/Locutusque_-_gpt2-conversational-or-qa-gguf/blob/main/gpt2-conversational-or-qa.Q8_0.gguf) \| Q8_0 \| 0.17GB \| Original model description: --- license: openrail datasets: - Locutusque/ColumnedChatCombined language: - en metrics: - bleu - perplexity - loss - reward - penalty widget: - text: '<\|USER\|> Hello! <\|ASSISTANT\|> ' pipeline_tag: conversational inference: parameters: temperature: 0.5 do_sample: True top_p: 0.5 top_k: 30 max_new_tokens: 250 repetition_penalty: 1.15 --- # Model Card * this model is deprecated please see https://huggingface.co/Locutusque/gpt2-conversational-retrain for a better performing model. * ## Model Details - Model Name: gpt2-conversational-or-qa (prototype) - Model Type: Language Modeling - Task: Generating Conversational Responses - Hardware: 1x RTX 3060 - Description: This model is trained on a dataset of conversations between a user and an AI assistant, with the goal of generating a coherent and relevant response to the user's input. It uses the GPT-2 architecture, a state-of-the-art transformer-based language model that is capable of generating high-quality text with a wide range of styles and tones. The model is fine-tuned on the conversational data using maximum likelihood estimation, and is evaluated based on its ability to generate responses that are both grammatically correct and semantically relevant to the user's input. I've also trained larger models such as https://huggingface.co/Locutusque/gpt2-medium-conversational and https://huggingface.co/Locutusque/gpt2-large-conversational ## Intended Use This model is intended to be used for generating conversational responses in a variety of contexts, such as chatbots, virtual assistants, and customer service applications. It is designed to provide natural and engaging responses to user input, with a focus on maintaining a consistent tone and style throughout the conversation. The model is suitable for use in both text-based and voice-based interfaces, and can be easily integrated into existing applications using the PyTorch and Transformers frameworks. ## Training Data The model is trained on a large dataset of conversational data, consisting of interactions between users and an AI assistant. The data is preprocessed to remove any sensitive information and is formatted in a way that is suitable for training a language model. The training data is split into a training set and a validation set, with the training set used to update the model parameters and the validation set used to evaluate the model performance. The model was trained on 245,000 examples over 1,225,000 steps, it achieved decent metrics. This model outperformed the base GPT-2 model significantly on a new conversational dataset during a fine-tuning session. Here is a side-by-side comparison of the two models during the first steps of training ```python # Base GPT-2 """ Epoch 1/5, Batch 1/10000: Loss - 64.9255, Reward - 260.0000, Penalty - 624.0000, BLEU - 0.0000 Epoch 1/5, Batch 2/10000: Loss - 57.4635, Reward - 303.0000, Penalty - 870.0000, BLEU - 0.0000 Epoch 1/5, Batch 3/10000: Loss - 67.8061, Reward - 295.0000, Penalty - 908.0000, BLEU - 0.0000 Epoch 1/5, Batch 4/10000: Loss - 59.6118, Reward - 800.0000, Penalty - 740.0000, BLEU - 0.0000 Epoch 1/5, Batch 5/10000: Loss - 67.4855, Reward - 402.0000, Penalty - 806.0000, BLEU - 0.0000 Epoch 1/5, Batch 6/10000: Loss - 29.3718, Reward - 937.0000, Penalty - 760.0000, BLEU - 0.0000 Epoch 1/5, Batch 7/10000: Loss - 79.0709, Reward - 390.0000, Penalty - 1114.0000, BLEU - 0.0000 Epoch 1/5, Batch 8/10000: Loss - 61.4583, Reward - 385.0000, Penalty - 760.0000, BLEU - 0.0000 Epoch 1/5, Batch 9/10000: Loss - 56.3084, Reward - 741.0000, Penalty - 560.0000, BLEU - 3.5500 Epoch 1/5, Batch 10/10000: Loss - 80.0192, Reward - 838.0000, Penalty - 1424.0000, BLEU - 0.0000 Epoch 1/5, Batch 11/10000: Loss - 51.8236, Reward - 228.0000, Penalty - 812.0000, BLEU - 0.0001 Epoch 1/5, Batch 12/10000: Loss - 71.4071, Reward - 541.0000, Penalty - 982.0000, BLEU - 0.0000 Epoch 1/5, Batch 13/10000: Loss - 33.3624, Reward - 910.0000, Penalty - 1002.0000, BLEU - 0.0027 Epoch 1/5, Batch 14/10000: Loss - 55.9721, Reward - 808.0000, Penalty - 798.0000, BLEU - 0.0005 Epoch 1/5, Batch 15/10000: Loss - 67.0336, Reward - 517.0000, Penalty - 764.0000, BLEU - 0.0000 """ # Conversational GPT-2 """ Epoch 1/5, Batch 1/10000: Loss - 6.1980, Reward - 887.0000, Penalty - 1500.0000, BLEU - 0.0648 Epoch 1/5, Batch 2/10000: Loss - 4.5750, Reward - 245.0000, Penalty - 1618.0000, BLEU - 0.0008 Epoch 1/5, Batch 3/10000: Loss - 5.1264, Reward - 600.0000, Penalty - 642.0000, BLEU - 5.7981 Epoch 1/5, Batch 4/10000: Loss - 0.2995, Reward - 1020.0000, Penalty - 74.0000, BLEU - 13.8469 Epoch 1/5, Batch 5/10000: Loss - 7.9377, Reward - 203.0000, Penalty - 1700.0000, BLEU - 0.3218 Epoch 1/5, Batch 6/10000: Loss - 5.0522, Reward - 1020.0000, Penalty - 2034.0000, BLEU - 0.1946 Epoch 1/5, Batch 7/10000: Loss - 2.0585, Reward - 925.0000, Penalty - 526.0000, BLEU - 16.1298 Epoch 1/5, Batch 8/10000: Loss - 5.9736, Reward - 1009.0000, Penalty - 1844.0000, BLEU - 0.0085 Epoch 1/5, Batch 9/10000: Loss - 6.0867, Reward - 245.0000, Penalty - 1690.0000, BLEU - 1.9342 Epoch 1/5, Batch 10/10000: Loss - 7.8497, Reward - 155.0000, Penalty - 1780.0000, BLEU - 0.0115 Epoch 1/5, Batch 11/10000: Loss - 3.8887, Reward - 1012.0000, Penalty - 2010.0000, BLEU - 0.6957 Epoch 1/5, Batch 12/10000: Loss - 6.6133, Reward - 216.0000, Penalty - 1638.0000, BLEU - 1.7853 Epoch 1/5, Batch 13/10000: Loss - 1.3319, Reward - 945.0000, Penalty - 374.0000, BLEU - 0.0075 Epoch 1/5, Batch 14/10000: Loss - 2.6296, Reward - 956.0000, Penalty - 414.0000, BLEU - 3.2207 Epoch 1/5, Batch 15/10000: Loss - 6.8827, Reward - 1013.0000, Penalty - 1970.0000, BLEU - 3.7418 """ ``` ## Model Architecture The model architecture used in this model is GPT-2, a transformer-based language model that is capable of generating high-quality text with a wide range of styles and tones. The GPT-2 architecture consists of a multi-layered decoder-only transformer, with self-attention mechanisms that allow the model to capture long-term dependencies and generate coherent text. ## Evaluation Metrics The model is evaluated based on several metrics, including loss, reward, penalty, BLEU score, and perplexity. The loss metric is calculated during training and reflects the difference between the predicted output and the actual output. The reward metric is based on the number of correct words generated by the model, while the penalty metric penalizes the model for repeating words consecutively. The BLEU score measures the similarity between the generated text and the ground truth text, while the perplexity metric measures how well the model is able to predict the next word in a sequence. During validation, the model achieved the following metrics: - BLEU Score: 9 - Perplexity: 19 - Loss: 1.7 ## Limitations and Bias This model is not suitable for all use cases due to its limited training time on a weak computer. As a result, it may produce irrelevant or nonsensical responses. Additionally, it has not been fine-tuned to remember the chat history, is unable to provide follow-up responses, and it does not know the answer to many questions (it was only fine-tuned to respond in a conversational way). For optimal performance, we recommend using a GPU with at least 4GB of VRAM and downloading the model manually instead of using the Transformers library or deploying it on the Interface API. Here's how you should deploy the model: ```python import torch from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') tokenizer.add_special_tokens({'pad_token': '[PAD]'}) tokenizer.add_special_tokens({'eos_token': '<\|End\|>'}) special_tokens = { "additional_special_tokens": ["<\|USER\|>", "<\|SYSTEM\|>", "<\|ASSISTANT\|>"] } tokenizer.add_special_tokens(special_tokens) model.resize_token_embeddings(len(tokenizer)) model.load_state_dict(torch.load("path/to/model")) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) def generate_text(model, tokenizer, prompt, max_length=1024): prompt = f'<\|USER\|> {prompt} <\|ASSISTANT\|> ' input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device) attention_mask = torch.ones_like(input_ids).to(device) output = model.generate(input_ids, max_length=max_length, do_sample=True, top_k=35, top_p=0.80, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, attention_mask=attention_mask) output_ids = tokenizer.decode(output[0], skip_special_tokens=False) assistant_token_index = output_ids.index('<\|ASSISTANT\|>') + len('<\|ASSISTANT\|>') next_token_index = output_ids.find('<\|', assistant_token_index) output_ids = output_ids[assistant_token_index:next_token_index] return output_ids # Loop to interact with the model while True: prompt = input("Enter a prompt (or 'q' to quit): ") if prompt == "q": break output_text = generate_text(model, tokenizer, prompt) print(output_text) ``` ## Deploying and training the model The model has been fine-tuned on a specific input format that goes like this ```"<\|USER\|> {user prompt} <\|ASSISTANT\|> {model prediction} <\|End\|>".``` For the best performance from the model the input text should be as follows ```<\|USER\|> {user prompt} <\|ASSISTANT\|> ``` and the target/label should be as follows ```<\|USER\|> {user prompt} <\|ASSISTANT\|> {dataset output} <\|End\|>``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__gpt2-conversational-or-qa) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 25.09 \| \| ARC (25-shot) \| 21.42 \| \| HellaSwag (10-shot) \| 27.61 \| \| MMLU (5-shot) \| 26.51 \| \| TruthfulQA (0-shot) \| 47.31 \| \| Winogrande (5-shot) \| 51.14 \| \| GSM8K (5-shot) \| 0.08 \| \| DROP (3-shot) \| 1.55 \|
Invitrace/I-live-well-foodai	Invitrace	2024-06-06T02:15:41Z	14	1	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-06-06T00:59:22Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: invitrace-ilivewell results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7232590808749749 --- <!-- 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. --> # invitrace-ilivewell This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.3020 - Accuracy: 0.7233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\| \| 5.0735 \| 0.0803 \| 200 \| 5.0486 \| 0.0462 \| \| 4.947 \| 0.1605 \| 400 \| 4.9254 \| 0.1136 \| \| 4.8134 \| 0.2408 \| 600 \| 4.7931 \| 0.2101 \| \| 4.653 \| 0.3210 \| 800 \| 4.6667 \| 0.2133 \| \| 4.5977 \| 0.4013 \| 1000 \| 4.5528 \| 0.2777 \| \| 4.557 \| 0.4815 \| 1200 \| 4.4413 \| 0.2940 \| \| 4.2948 \| 0.5618 \| 1400 \| 4.3298 \| 0.3233 \| \| 4.2969 \| 0.6421 \| 1600 \| 4.2270 \| 0.3502 \| \| 4.0779 \| 0.7223 \| 1800 \| 4.1218 \| 0.3743 \| \| 4.0473 \| 0.8026 \| 2000 \| 4.0337 \| 0.3897 \| \| 4.0347 \| 0.8828 \| 2200 \| 3.9426 \| 0.3879 \| \| 3.9185 \| 0.9631 \| 2400 \| 3.8602 \| 0.4158 \| \| 3.5221 \| 1.0433 \| 2600 \| 3.7700 \| 0.4275 \| \| 3.5359 \| 1.1236 \| 2800 \| 3.6728 \| 0.4553 \| \| 3.4622 \| 1.2039 \| 3000 \| 3.5906 \| 0.4541 \| \| 3.431 \| 1.2841 \| 3200 \| 3.5025 \| 0.4732 \| \| 3.3443 \| 1.3644 \| 3400 \| 3.4168 \| 0.4869 \| \| 3.4344 \| 1.4446 \| 3600 \| 3.3382 \| 0.5025 \| \| 3.249 \| 1.5249 \| 3800 \| 3.2703 \| 0.5160 \| \| 3.2028 \| 1.6051 \| 4000 \| 3.2017 \| 0.4927 \| \| 3.0678 \| 1.6854 \| 4200 \| 3.1264 \| 0.5152 \| \| 3.0626 \| 1.7657 \| 4400 \| 3.0487 \| 0.5410 \| \| 2.953 \| 1.8459 \| 4600 \| 2.9699 \| 0.5414 \| \| 3.0011 \| 1.9262 \| 4800 \| 2.9165 \| 0.5503 \| \| 2.9428 \| 2.0064 \| 5000 \| 2.8433 \| 0.5665 \| \| 2.6717 \| 2.0867 \| 5200 \| 2.7818 \| 0.5569 \| \| 2.6253 \| 2.1669 \| 5400 \| 2.7195 \| 0.5715 \| \| 2.3998 \| 2.2472 \| 5600 \| 2.6458 \| 0.5874 \| \| 2.2518 \| 2.3274 \| 5800 \| 2.5901 \| 0.5922 \| \| 2.4229 \| 2.4077 \| 6000 \| 2.5301 \| 0.5912 \| \| 2.592 \| 2.4880 \| 6200 \| 2.4855 \| 0.5984 \| \| 2.0625 \| 2.5682 \| 6400 \| 2.4277 \| 0.6002 \| \| 2.22 \| 2.6485 \| 6600 \| 2.3784 \| 0.6087 \| \| 2.3326 \| 2.7287 \| 6800 \| 2.3250 \| 0.6123 \| \| 2.1592 \| 2.8090 \| 7000 \| 2.2958 \| 0.6095 \| \| 2.1491 \| 2.8892 \| 7200 \| 2.2226 \| 0.6303 \| \| 2.0644 \| 2.9695 \| 7400 \| 2.1923 \| 0.6231 \| \| 1.916 \| 3.0498 \| 7600 \| 2.1510 \| 0.6352 \| \| 1.7435 \| 3.1300 \| 7800 \| 2.0985 \| 0.6388 \| \| 1.761 \| 3.2103 \| 8000 \| 2.0753 \| 0.6404 \| \| 1.5321 \| 3.2905 \| 8200 \| 2.0396 \| 0.6426 \| \| 1.6117 \| 3.3708 \| 8400 \| 1.9855 \| 0.6530 \| \| 1.5593 \| 3.4510 \| 8600 \| 1.9805 \| 0.6352 \| \| 1.9288 \| 3.5313 \| 8800 \| 1.9188 \| 0.6564 \| \| 1.5736 \| 3.6116 \| 9000 \| 1.9141 \| 0.6556 \| \| 1.5544 \| 3.6918 \| 9200 \| 1.8633 \| 0.6619 \| \| 1.3811 \| 3.7721 \| 9400 \| 1.8466 \| 0.6621 \| \| 1.608 \| 3.8523 \| 9600 \| 1.8116 \| 0.6687 \| \| 1.533 \| 3.9326 \| 9800 \| 1.7784 \| 0.6733 \| \| 1.5496 \| 4.0128 \| 10000 \| 1.7532 \| 0.6755 \| \| 1.3532 \| 4.0931 \| 10200 \| 1.7399 \| 0.6779 \| \| 1.3787 \| 4.1734 \| 10400 \| 1.6996 \| 0.6795 \| \| 1.4278 \| 4.2536 \| 10600 \| 1.6893 \| 0.6771 \| \| 1.3531 \| 4.3339 \| 10800 \| 1.6629 \| 0.6759 \| \| 1.2811 \| 4.4141 \| 11000 \| 1.6493 \| 0.6801 \| \| 1.3787 \| 4.4944 \| 11200 \| 1.6278 \| 0.6855 \| \| 1.2663 \| 4.5746 \| 11400 \| 1.6101 \| 0.6926 \| \| 1.0892 \| 4.6549 \| 11600 \| 1.5842 \| 0.6887 \| \| 1.3045 \| 4.7352 \| 11800 \| 1.5758 \| 0.6911 \| \| 1.4239 \| 4.8154 \| 12000 \| 1.5647 \| 0.6930 \| \| 1.065 \| 4.8957 \| 12200 \| 1.5403 \| 0.6905 \| \| 1.1467 \| 4.9759 \| 12400 \| 1.5257 \| 0.6986 \| \| 0.8755 \| 5.0562 \| 12600 \| 1.5075 \| 0.6964 \| \| 1.0427 \| 5.1364 \| 12800 \| 1.4977 \| 0.7074 \| \| 1.264 \| 5.2167 \| 13000 \| 1.4951 \| 0.6956 \| \| 0.9822 \| 5.2970 \| 13200 \| 1.4787 \| 0.6990 \| \| 1.1234 \| 5.3772 \| 13400 \| 1.4673 \| 0.7008 \| \| 0.9394 \| 5.4575 \| 13600 \| 1.4632 \| 0.6998 \| \| 0.9231 \| 5.5377 \| 13800 \| 1.4346 \| 0.7074 \| \| 1.1829 \| 5.6180 \| 14000 \| 1.4364 \| 0.7092 \| \| 0.9687 \| 5.6982 \| 14200 \| 1.4231 \| 0.7080 \| \| 0.8915 \| 5.7785 \| 14400 \| 1.4166 \| 0.7104 \| \| 1.013 \| 5.8587 \| 14600 \| 1.4056 \| 0.7110 \| \| 1.0437 \| 5.9390 \| 14800 \| 1.3840 \| 0.7186 \| \| 0.8936 \| 6.0193 \| 15000 \| 1.3896 \| 0.7142 \| \| 0.8968 \| 6.0995 \| 15200 \| 1.3853 \| 0.7118 \| \| 0.8978 \| 6.1798 \| 15400 \| 1.3748 \| 0.7154 \| \| 0.8638 \| 6.2600 \| 15600 \| 1.3686 \| 0.7190 \| \| 0.7187 \| 6.3403 \| 15800 \| 1.3664 \| 0.7186 \| \| 0.7554 \| 6.4205 \| 16000 \| 1.3672 \| 0.7124 \| \| 0.7664 \| 6.5008 \| 16200 \| 1.3484 \| 0.7192 \| \| 0.9791 \| 6.5811 \| 16400 \| 1.3500 \| 0.7178 \| \| 0.8325 \| 6.6613 \| 16600 \| 1.3387 \| 0.7184 \| \| 1.0476 \| 6.7416 \| 16800 \| 1.3390 \| 0.7174 \| \| 0.7053 \| 6.8218 \| 17000 \| 1.3268 \| 0.7217 \| \| 0.9869 \| 6.9021 \| 17200 \| 1.3270 \| 0.7204 \| \| 0.8179 \| 6.9823 \| 17400 \| 1.3169 \| 0.7297 \| \| 0.9584 \| 7.0626 \| 17600 \| 1.3119 \| 0.7271 \| \| 0.6394 \| 7.1429 \| 17800 \| 1.3158 \| 0.7243 \| \| 0.9094 \| 7.2231 \| 18000 \| 1.3056 \| 0.7231 \| \| 0.7837 \| 7.3034 \| 18200 \| 1.3174 \| 0.7239 \| \| 0.7168 \| 7.3836 \| 18400 \| 1.3088 \| 0.7265 \| \| 0.8603 \| 7.4639 \| 18600 \| 1.3149 \| 0.7204 \| \| 0.6326 \| 7.5441 \| 18800 \| 1.3041 \| 0.7253 \| \| 0.8656 \| 7.6244 \| 19000 \| 1.3075 \| 0.7253 \| \| 0.7517 \| 7.7047 \| 19200 \| 1.3181 \| 0.7227 \| \| 0.8719 \| 7.7849 \| 19400 \| 1.2977 \| 0.7273 \| \| 0.6939 \| 7.8652 \| 19600 \| 1.2965 \| 0.7249 \| \| 0.8371 \| 7.9454 \| 19800 \| 1.3020 \| 0.7233 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
khaled123/hf	khaled123	2024-06-06T02:15:30Z	81	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-06-06T02:09:15Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-5_lora2	tyzhu	2024-06-06T02:15:00Z	3	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-05T12:07:26Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-5_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - name: Accuracy type: accuracy value: 0.5000634920634921 library_name: peft --- <!-- 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. --> # lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-5_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa dataset. It achieves the following results on the evaluation set: - Loss: 3.5856 - Accuracy: 0.5001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 1.7766 \| 0.9998 \| 1089 \| 2.3352 \| 0.5155 \| \| 1.7291 \| 1.9995 \| 2178 \| 2.3047 \| 0.5177 \| \| 1.6609 \| 2.9993 \| 3267 \| 2.3113 \| 0.5187 \| \| 1.6108 \| 4.0 \| 4357 \| 2.3487 \| 0.518 \| \| 1.5489 \| 4.9998 \| 5446 \| 2.3843 \| 0.5161 \| \| 1.5093 \| 5.9995 \| 6535 \| 2.4637 \| 0.5138 \| \| 1.4364 \| 6.9993 \| 7624 \| 2.5600 \| 0.5108 \| \| 1.4038 \| 8.0 \| 8714 \| 2.6166 \| 0.5105 \| \| 1.3439 \| 8.9998 \| 9803 \| 2.7111 \| 0.5092 \| \| 1.2928 \| 9.9995 \| 10892 \| 2.8851 \| 0.5062 \| \| 1.2284 \| 10.9993 \| 11981 \| 2.9595 \| 0.5042 \| \| 1.2142 \| 12.0 \| 13071 \| 3.0894 \| 0.5034 \| \| 1.1791 \| 12.9998 \| 14160 \| 3.1358 \| 0.5024 \| \| 1.1272 \| 13.9995 \| 15249 \| 3.2272 \| 0.5017 \| \| 1.0934 \| 14.9993 \| 16338 \| 3.3488 \| 0.5001 \| \| 1.0586 \| 16.0 \| 17428 \| 3.4072 \| 0.5015 \| \| 1.0415 \| 16.9998 \| 18517 \| 3.4943 \| 0.5009 \| \| 1.011 \| 17.9995 \| 19606 \| 3.5465 \| 0.5001 \| \| 1.0036 \| 18.9993 \| 20695 \| 3.5320 \| 0.5011 \| \| 0.9595 \| 19.9954 \| 21780 \| 3.5856 \| 0.5001 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
henrymark/biobert-biomedical-ner	henrymark	2024-06-06T02:12:03Z	123	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "NER", "Biomedical", "en", "dataset:nmitchko/i2b2-query-data-1.0", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-04T00:07:42Z	--- language: en datasets: nmitchko/i2b2-query-data-1.0 license: apache-2.0 tags: - token-classification - NER - Biomedical --- BioBERT model pre-trained on nmitchko/i2b2-query-data-1.0 corpus
hdve/Qwen-Qwen1.5-1.8B-1717639775	hdve	2024-06-06T02:11:50Z	147	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T02:10:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
shenmi175/sd-class-butterflies-32	shenmi175	2024-06-06T02:10:36Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-06-06T02:05:59Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('shenmi175/sd-class-butterflies-32') image = pipeline().images[0] image ```
cgihlstorf/finetuned_pythia70M_nondeduped_cp_14300032_1_0.0003_alternate	cgihlstorf	2024-06-06T02:07:28Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:EleutherAI/pythia-70m", "base_model:adapter:EleutherAI/pythia-70m", "region:us" ]	null	2024-06-06T02:07:05Z	--- library_name: peft base_model: EleutherAI/pythia-70m --- # 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 0.10.0
khaled123/chess-GGUF	khaled123	2024-06-06T02:03:06Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-03T13:31:17Z	--- 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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Franz2001/openai-whisper-large-v3-samromour_7epochs	Franz2001	2024-06-06T02:02:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T02:02:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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coconana/Qwen-Qwen1.5-1.8B-1717638358	coconana	2024-06-06T02:01:48Z	147	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T01:45:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kroonen/stablelm-2-12b-GGUF	kroonen	2024-06-06T02:01:30Z	21	0	null	[ "gguf", "causal-lm", "en", "de", "es", "fr", "it", "nl", "pt", "dataset:tiiuae/falcon-refinedweb", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:uonlp/CulturaX", "dataset:CarperAI/pilev2-dev", "dataset:bigcode/starcoderdata", "dataset:DataProvenanceInitiative/Commercially-Verified-Licenses", "arxiv:2402.17834", "arxiv:2104.09864", "arxiv:2204.06745", "arxiv:1607.06450", "arxiv:2302.05442", "arxiv:2309.14322", "arxiv:2305.14201", "arxiv:2101.00027", "arxiv:2305.06161", "arxiv:2309.09400", "arxiv:2206.11147", "arxiv:1910.02054", "license:other", "endpoints_compatible", "region:us" ]	null	2024-06-06T01:37:10Z	--- language: - en - de - es - fr - it - nl - pt license: other tags: - causal-lm datasets: - tiiuae/falcon-refinedweb - togethercomputer/RedPajama-Data-1T - uonlp/CulturaX - CarperAI/pilev2-dev - bigcode/starcoderdata - DataProvenanceInitiative/Commercially-Verified-Licenses --- # `Stable LM 2 12B` ## Model Description `Stable LM 2 12B` is a 12.1 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. Please note: For commercial use, please refer to https://stability.ai/membership. ## Usage NOTE: This model requires `transformers>=4.40.0` Get started generating text with `Stable LM 2 12B` by using the following code snippet: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-2-12b", torch_dtype="auto", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( inputs, max_new_tokens=64, temperature=0.70, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` ### Run with Flash Attention 2 ⚡️ <details> <summary> Click to expand </summary> ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-12b") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-2-12b", torch_dtype="auto", attn_implementation="flash_attention_2", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( inputs, max_new_tokens=64, temperature=0.70, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` </details> ## Model Details * Developed by: [Stability AI](https://stability.ai/) * Model type: `Stable LM 2 12B` models are auto-regressive language models based on the transformer decoder architecture. * Language(s): English * Paper: [Stable LM 2 Technical Report](https://arxiv.org/abs/2402.17834) * Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) * License: [Stability AI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-12b/blob/main/LICENSE). * Commercial License: to use this model commercially, please refer to https://stability.ai/membership * Contact: For questions and comments about the model, please email `[email protected]` ### Model Architecture The model is a decoder-only transformer with the following architecture: \| Parameters \| Hidden Size \| Layers \| Heads \| KV Heads \| Sequence Length \| \|----------------\|-------------\|--------\|-------\|----------\|-----------------\| \| 12,143,605,760 \| 5120 \| 40 \| 32 \| 8 \| 4096 \| * Position Embeddings: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864)) applied to the first 25% of head embedding dimensions for improved throughput following [Black et al. (2022)](https://arxiv.org/pdf/2204.06745.pdf). * Parallel Layers: Parallel attention and feed-forward residual layers with a single input LayerNorm ([Wang, 2021](https://github.com/kingoflolz/mesh-transformer-jax)). * Normalization: LayerNorm ([Ba et al., 2016](https://arxiv.org/abs/1607.06450)) without biases. Furthermore, we apply per-head QK normalization ([Dehghani et al., 2023](https://arxiv.org/abs/2302.05442), [Wortsman et al., 2023](https://arxiv.org/abs/2309.14322)). * Biases: We remove all bias terms from the feed-forward networks and grouped-query self-attention layers. * Tokenizer: We use Arcade100k, a BPE tokenizer extended from OpenAI's [`tiktoken.cl100k_base`](https://github.com/openai/tiktoken). We split digits into individual tokens following findings by [Liu & Low (2023)](https://arxiv.org/abs/2305.14201). ## Training ### Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)), RedPajama-Data ([Together Computer., 2023](https://github.com/togethercomputer/RedPajama-Data)) and The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)) both without the Books3 subset, and StarCoder ([Li et al., 2023](https://arxiv.org/abs/2305.06161)). We further supplement our training with multi-lingual data from CulturaX ([Nguyen et al., 2023](https://arxiv.org/abs/2309.09400)) and, in particular, from its OSCAR corpora, as well as restructured data in the style of [Yuan & Liu (2022)](https://arxiv.org/abs/2206.11147). * Given the large amount of web data, we recommend fine-tuning the base `Stable LM 2 12B` for your downstream tasks. ### Training Procedure The model is pre-trained on the aforementioned datasets in `bfloat16` precision, optimized with AdamW, and trained using the Arcade100k tokenizer with a vocabulary size of 100,352. We outline the complete hyperparameters choices in the project's [GitHub repository - config](https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-2-12b.yml). ### Training Infrastructure Hardware: `Stable LM 2 12B` was trained on the Stability AI cluster across 384 NVIDIA H100 GPUs (AWS P5 instances). * Software: We use a fork of `gpt-neox` ([EleutherAI, 2021](https://github.com/EleutherAI/gpt-neox)), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 ([Rajbhandari et al., 2019](https://arxiv.org/abs/1910.02054v3)), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 ([Dao et al., 2023](https://tridao.me/publications/flash2/flash2.pdf)) ## Use and Limitations ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. For commercial use, please refer to https://stability.ai/membership. ### Limitations and Bias As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @article{bellagente2024stable, title={Stable LM 2 1.6 B Technical Report}, author={Bellagente, Marco and Tow, Jonathan and Mahan, Dakota and Phung, Duy and Zhuravinskyi, Maksym and Adithyan, Reshinth and Baicoianu, James and Brooks, Ben and Cooper, Nathan and Datta, Ashish and others}, journal={arXiv preprint arXiv:2402.17834}, year={2024} } ```
mradermacher/MythoMist-7b-GGUF	mradermacher	2024-06-06T02:00:59Z	72	1	transformers	[ "transformers", "gguf", "en", "base_model:Gryphe/MythoMist-7b", "base_model:quantized:Gryphe/MythoMist-7b", "license:other", "endpoints_compatible", "region:us" ]	null	2024-06-05T20:27:44Z	--- base_model: Gryphe/MythoMist-7b language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Gryphe/MythoMist-7b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MythoMist-7b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MythoMist-7b-GGUF/resolve/main/MythoMist-7b.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
BEEMER330I/MentalHealthSupportChatbot	BEEMER330I	2024-06-06T01:59:13Z	0	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	2024-06-06T01:59:13Z	--- license: cc-by-nc-4.0 ---
v8karlo/UNCENSORED-Phi-3-mini-4k-geminified-Q4_K_M-GGUF	v8karlo	2024-06-06T01:57:06Z	201	5	null	[ "gguf", "nlp", "code", "llama-cpp", "gguf-my-repo", "text-generation", "multilingual", "base_model:failspy/Phi-3-mini-4k-geminified", "base_model:quantized:failspy/Phi-3-mini-4k-geminified", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-06-06T01:53:19Z	--- language: - multilingual license: mit tags: - nlp - code - llama-cpp - gguf-my-repo base_model: failspy/Phi-3-mini-4k-geminified license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation inference: parameters: temperature: 0.7 widget: - messages: - role: user content: What's the difference between a banana and a strawberry? --- # v8karlo/Phi-3-mini-4k-geminified-Q4_K_M-GGUF This model was converted to GGUF format from [`failspy/Phi-3-mini-4k-geminified`](https://huggingface.co/failspy/Phi-3-mini-4k-geminified) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/failspy/Phi-3-mini-4k-geminified) for more details on the model. Convert Safetensors to GGUF . https://huggingface.co/spaces/ggml-org/gguf-my-repo . ![image/png](https://cdn-uploads.huggingface.co/production/uploads/662c3116277765660783ca6d/4_zD9Idi8YO90r5475eTn.png) ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo v8karlo/Phi-3-mini-4k-geminified-Q4_K_M-GGUF --hf-file phi-3-mini-4k-geminified-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo v8karlo/Phi-3-mini-4k-geminified-Q4_K_M-GGUF --hf-file phi-3-mini-4k-geminified-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo v8karlo/Phi-3-mini-4k-geminified-Q4_K_M-GGUF --hf-file phi-3-mini-4k-geminified-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo v8karlo/Phi-3-mini-4k-geminified-Q4_K_M-GGUF --hf-file phi-3-mini-4k-geminified-q4_k_m.gguf -c 2048 ```
wwe180/Llama3-18B-lingyang-v1-Q6_K-GGUF	wwe180	2024-06-06T01:40:52Z	4	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "Llama3", "llama-cpp", "gguf-my-repo", "base_model:wwe180/Llama3-18B-lingyang-v1", "base_model:quantized:wwe180/Llama3-18B-lingyang-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T01:40:11Z	--- license: - other library_name: transformers tags: - mergekit - merge - Llama3 - llama-cpp - gguf-my-repo base_model: wwe180/Llama3-18B-lingyang-v1 --- # wwe180/Llama3-18B-lingyang-v1-Q6_K-GGUF This model was converted to GGUF format from [`wwe180/Llama3-18B-lingyang-v1`](https://huggingface.co/wwe180/Llama3-18B-lingyang-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/wwe180/Llama3-18B-lingyang-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo wwe180/Llama3-18B-lingyang-v1-Q6_K-GGUF --hf-file llama3-18b-lingyang-v1-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo wwe180/Llama3-18B-lingyang-v1-Q6_K-GGUF --hf-file llama3-18b-lingyang-v1-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo wwe180/Llama3-18B-lingyang-v1-Q6_K-GGUF --hf-file llama3-18b-lingyang-v1-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo wwe180/Llama3-18B-lingyang-v1-Q6_K-GGUF --hf-file llama3-18b-lingyang-v1-q6_k.gguf -c 2048 ```
v8karlo/UNCENSORED-Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF	v8karlo	2024-06-06T01:37:01Z	29	4	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "dataset:cognitivecomputations/Dolphin-2.9", "base_model:RESMPDEV/Wukong-Phi-3-Instruct-Ablated", "base_model:quantized:RESMPDEV/Wukong-Phi-3-Instruct-Ablated", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T01:03:38Z	--- language: - en license: mit tags: - llama-cpp - gguf-my-repo base_model: RESMPDEV/Wukong-Phi-3-Instruct-Ablated datasets: - cognitivecomputations/Dolphin-2.9 uncensored: - yes --- # v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF UNCENSORED Phi-3 model. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/662c3116277765660783ca6d/OUGbRFBAx9Ibs2bD-OZGD.png) This model was converted to GGUF format from [`RESMPDEV/Wukong-Phi-3-Instruct-Ablated`](https://huggingface.co/RESMPDEV/Wukong-Phi-3-Instruct-Ablated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/RESMPDEV/Wukong-Phi-3-Instruct-Ablated) for more details on the model. Convert Safetensors to GGUF . https://huggingface.co/spaces/ggml-org/gguf-my-repo . <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/662c3116277765660783ca6d/qPHdaxOccIFcpmcewfa9r.mp4"></video> ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo v8karlo/Wukong-Phi-3-Instruct-Ablated-Q4_K_M-GGUF --hf-file wukong-phi-3-instruct-ablated-q4_k_m.gguf -c 2048 ```
wwe180/Llama3-18B-lingyang-v1-Q4_K_M-GGUF	wwe180	2024-06-06T01:34:08Z	9	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "Llama3", "llama-cpp", "gguf-my-repo", "base_model:wwe180/Llama3-18B-lingyang-v1", "base_model:quantized:wwe180/Llama3-18B-lingyang-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T01:33:39Z	--- license: - other library_name: transformers tags: - mergekit - merge - Llama3 - llama-cpp - gguf-my-repo base_model: wwe180/Llama3-18B-lingyang-v1 --- # wwe180/Llama3-18B-lingyang-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`wwe180/Llama3-18B-lingyang-v1`](https://huggingface.co/wwe180/Llama3-18B-lingyang-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/wwe180/Llama3-18B-lingyang-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo wwe180/Llama3-18B-lingyang-v1-Q4_K_M-GGUF --hf-file llama3-18b-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo wwe180/Llama3-18B-lingyang-v1-Q4_K_M-GGUF --hf-file llama3-18b-lingyang-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo wwe180/Llama3-18B-lingyang-v1-Q4_K_M-GGUF --hf-file llama3-18b-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo wwe180/Llama3-18B-lingyang-v1-Q4_K_M-GGUF --hf-file llama3-18b-lingyang-v1-q4_k_m.gguf -c 2048 ```
bella05/pogny-16-0.005	bella05	2024-06-06T01:34:00Z	108	0	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:klue/roberta-large", "base_model:finetune:klue/roberta-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-05T04:16:02Z	--- base_model: klue/roberta-large tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: pogny-16-0.005 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. --> # pogny-16-0.005 This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6843 - Accuracy: 0.4376 - F1: 0.2665 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 2.2723 \| 1.0 \| 4818 \| 2.0798 \| 0.4376 \| 0.2665 \| \| 1.9852 \| 2.0 \| 9636 \| 2.1720 \| 0.0643 \| 0.0078 \| \| 1.7066 \| 3.0 \| 14454 \| 1.6843 \| 0.4376 \| 0.2665 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0a0+b5021ba - Datasets 2.6.2 - Tokenizers 0.14.1
mradermacher/Tiamat-7b-1.1-DPO-GGUF	mradermacher	2024-06-06T01:33:11Z	19	0	transformers	[ "transformers", "gguf", "en", "base_model:Gryphe/Tiamat-7b-1.1-DPO", "base_model:quantized:Gryphe/Tiamat-7b-1.1-DPO", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-05T18:47:31Z	--- base_model: Gryphe/Tiamat-7b-1.1-DPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Gryphe/Tiamat-7b-1.1-DPO <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Tiamat-7b-1.1-DPO-GGUF/resolve/main/Tiamat-7b-1.1-DPO.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
aksquarry/Model1	aksquarry	2024-06-06T01:30:37Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-06-06T01:28:06Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/hug gingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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]
wwe180/L3-8B-LingYang-v1-Q4_K_M-GGUF	wwe180	2024-06-06T01:26:57Z	4	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "Llama3", "llama-cpp", "gguf-my-repo", "base_model:wwe180/L3-8B-LingYang-v1", "base_model:quantized:wwe180/L3-8B-LingYang-v1", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T01:15:34Z	--- license: - other library_name: transformers tags: - mergekit - merge - Llama3 - llama-cpp - gguf-my-repo base_model: wwe180/L3-8B-LingYang-v1 --- # wwe180/L3-8B-LingYang-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`wwe180/L3-8B-LingYang-v1`](https://huggingface.co/wwe180/L3-8B-LingYang-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/wwe180/L3-8B-LingYang-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo wwe180/L3-8B-LingYang-v1-Q4_K_M-GGUF --hf-file l3-8b-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo wwe180/L3-8B-LingYang-v1-Q4_K_M-GGUF --hf-file l3-8b-lingyang-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo wwe180/L3-8B-LingYang-v1-Q4_K_M-GGUF --hf-file l3-8b-lingyang-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo wwe180/L3-8B-LingYang-v1-Q4_K_M-GGUF --hf-file l3-8b-lingyang-v1-q4_k_m.gguf -c 2048 ```
wwe180/Llama3-18B-lingyang-v1	wwe180	2024-06-06T01:25:36Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "Llama3", "conversational", "base_model:wwe180/Llama3-18B-lingyang-v1", "base_model:finetune:wwe180/Llama3-18B-lingyang-v1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T23:47:21Z	--- base_model: - wwe180/Llama3-18B-lingyang-v1 library_name: transformers tags: - mergekit - merge - Llama3 license: - other --- # After simple testing, the effect is good, stronger than llama-3-8b! # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Llama3-18B-lingyang-v1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) ``` ## Statement: Llama3-18B-lingyang-v1 does not represent the views and positions of the model developers We will not be liable for any problems arising from the use of the Llama3-18B-lingyang-v1 open Source model, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.
phongtintruong/misjava-api-060524-api-extend-test-NEFTune-duplicate	phongtintruong	2024-06-06T01:22:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-06T01:21:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_5e-5_lora2	tyzhu	2024-06-06T01:17:38Z	3	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-05T20:43:32Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 metrics: - accuracy model-index: - name: lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_5e-5_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 type: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 metrics: - name: Accuracy type: accuracy value: 0.42728205128205127 library_name: peft --- <!-- 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. --> # lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_5e-5_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 dataset. It achieves the following results on the evaluation set: - Loss: 5.0355 - Accuracy: 0.4273 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:--------:\| \| 1.9626 \| 0.9985 \| 341 \| 2.9919 \| 0.4727 \| \| 1.9158 \| 2.0 \| 683 \| 2.9864 \| 0.4737 \| \| 1.8622 \| 2.9985 \| 1024 \| 3.0420 \| 0.4710 \| \| 1.786 \| 4.0 \| 1366 \| 3.1527 \| 0.4662 \| \| 1.7019 \| 4.9985 \| 1707 \| 3.3819 \| 0.4634 \| \| 1.6036 \| 6.0 \| 2049 \| 3.4969 \| 0.4589 \| \| 1.5175 \| 6.9985 \| 2390 \| 3.6412 \| 0.4577 \| \| 1.4007 \| 8.0 \| 2732 \| 3.8310 \| 0.4537 \| \| 1.326 \| 8.9985 \| 3073 \| 3.9177 \| 0.4487 \| \| 1.231 \| 10.0 \| 3415 \| 4.0665 \| 0.4451 \| \| 1.1298 \| 10.9985 \| 3756 \| 4.1773 \| 0.44 \| \| 1.0276 \| 12.0 \| 4098 \| 4.2875 \| 0.4378 \| \| 0.9525 \| 12.9985 \| 4439 \| 4.4273 \| 0.4352 \| \| 0.8616 \| 14.0 \| 4781 \| 4.4484 \| 0.4324 \| \| 0.7799 \| 14.9985 \| 5122 \| 4.6228 \| 0.4313 \| \| 0.7084 \| 16.0 \| 5464 \| 4.7239 \| 0.4303 \| \| 0.6478 \| 16.9985 \| 5805 \| 4.8167 \| 0.4310 \| \| 0.5862 \| 18.0 \| 6147 \| 4.8510 \| 0.4303 \| \| 0.5189 \| 18.9985 \| 6488 \| 4.9265 \| 0.4243 \| \| 0.4767 \| 19.9707 \| 6820 \| 5.0355 \| 0.4273 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_3e-4_lora2	tyzhu	2024-06-06T01:13:30Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-05T21:49:02Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_3e-4_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa metrics: - name: Accuracy type: accuracy value: 0.6608966521106259 library_name: peft --- <!-- 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. --> # lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_3e-4_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa dataset. It achieves the following results on the evaluation set: - Loss: 2.7813 - Accuracy: 0.6609 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4488 \| 1.0 \| 250 \| 1.4958 \| 0.6770 \| \| 1.3142 \| 2.0 \| 500 \| 1.5007 \| 0.6772 \| \| 1.1176 \| 3.0 \| 750 \| 1.5507 \| 0.6756 \| \| 0.9253 \| 4.0 \| 1000 \| 1.6442 \| 0.6728 \| \| 0.7213 \| 5.0 \| 1250 \| 1.7736 \| 0.6701 \| \| 0.5718 \| 6.0 \| 1500 \| 1.8863 \| 0.6682 \| \| 0.4232 \| 7.0 \| 1750 \| 2.0245 \| 0.6660 \| \| 0.3334 \| 8.0 \| 2000 \| 2.1773 \| 0.6642 \| \| 0.2433 \| 9.0 \| 2250 \| 2.2681 \| 0.6632 \| \| 0.2076 \| 10.0 \| 2500 \| 2.3732 \| 0.6629 \| \| 0.1632 \| 11.0 \| 2750 \| 2.4368 \| 0.6623 \| \| 0.1491 \| 12.0 \| 3000 \| 2.5182 \| 0.6617 \| \| 0.1275 \| 13.0 \| 3250 \| 2.5680 \| 0.6619 \| \| 0.1273 \| 14.0 \| 3500 \| 2.6412 \| 0.6613 \| \| 0.1129 \| 15.0 \| 3750 \| 2.6497 \| 0.6617 \| \| 0.1129 \| 16.0 \| 4000 \| 2.6932 \| 0.6614 \| \| 0.102 \| 17.0 \| 4250 \| 2.7003 \| 0.6612 \| \| 0.1109 \| 18.0 \| 4500 \| 2.7033 \| 0.6614 \| \| 0.0997 \| 19.0 \| 4750 \| 2.7139 \| 0.6613 \| \| 0.1012 \| 20.0 \| 5000 \| 2.7813 \| 0.6609 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
LyliaEngine/zPDXL2	LyliaEngine	2024-06-06T01:13:19Z	33	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:LyliaEngine/Pony_Diffusion_V6_XL", "base_model:adapter:LyliaEngine/Pony_Diffusion_V6_XL", "license:cdla-permissive-2.0", "region:us" ]	text-to-image	2024-06-06T01:12:17Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- (zPDXL2), from below 1girl, 1boy, sitting heads together, on a tropical beach, smiling, wearing shirt and shorts, morning sunlight, tiki hut, palm trees, cloudy sky parameters: negative_prompt: (zPDXL2-neg), headband, jewelry, nsfw, nude output: url: images/372295-239849650-1-recondiff_v10.jpeg - text: >- (zPDXL2), 1girl, standing on the bridge of a spaceship wearing a uniform, from side from front, looking into space, wires, cables, monitor, controls, panel parameters: negative_prompt: (zPDXL2-neg), cleavage, breasts, nsfw, nude output: url: images/372249-535364377-1-zavyfantasiaxlPDXL_v10.jpeg base_model: LyliaEngine/Pony_Diffusion_V6_XL instance_prompt: zPDXL2, zPDXL2-neg license: cdla-permissive-2.0 --- # zPDXL2 <Gallery /> ## Model description No need to use score this, and score that. Just use the embeddings instead. Be sure to download both positive and negative embeddings and use both. A set of quality enchancing embeddings for Pony SDXL, and other Pony-Adjacent models. You can mix and match any of the embeddings that you feel you need. They don't duplicate each other, so feel free to use as many together as needed. High Quality V2: designed to give higher quality results and remove censoring. XXX Rating: tells the Pony model of your choice to allow NSFW content. PG Rating: tells the Pony model of your choice to try and remove NSFW content for safe images. Photo Real: tells the Pony model of your choice to use realistic or near realistic renders instead of cartoon or anime. Doesn't work with non-Pony models. Check your model. Recommend using both positive and negative embeddings together at strength 1.0 to 2.0. If you can't use both, the Positive version should be take priority. ## Source https://civitai.com/models/332646?modelVersionId=509253 ## Credit https://civitai.com/user/Zovya ## Trigger words You should use `zPDXL2` to trigger the image generation. You should use `zPDXL2-neg` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/LyliaEngine/zPDXL2/tree/main) them in the Files & versions tab.
Azazelle/L3-Decent-Peter-Griffin-8B	Azazelle	2024-06-06T00:58:19Z	9	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B", "base_model:merge:ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "base_model:elyn-dev/Llama-3-Soliloquy-8B-v2", "base_model:merge:elyn-dev/Llama-3-Soliloquy-8B-v2", "base_model:failspy/Llama-3-8B-Instruct-MopeyMule", "base_model:merge:failspy/Llama-3-8B-Instruct-MopeyMule", "base_model:maldv/badger-kappa-llama-3-8b", "base_model:merge:maldv/badger-kappa-llama-3-8b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-06T00:50:33Z	--- base_model: - Nitral-AI/Poppy_Porpoise-1.4-L3-8B - maldv/badger-kappa-llama-3-8b - Sao10K/L3-8B-Stheno-v3.2 - openlynn/Llama-3-Soliloquy-8B-v2 - failspy/Llama-3-8B-Instruct-MopeyMule - Hastagaras/Jamet-8B-L3-MK.II library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the breadcrumbs_ties merge method using [failspy/Llama-3-8B-Instruct-MopeyMule](https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule) as a base. ### Models Merged The following models were included in the merge: * [Nitral-AI/Poppy_Porpoise-1.4-L3-8B](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.4-L3-8B) * [maldv/badger-kappa-llama-3-8b](https://huggingface.co/maldv/badger-kappa-llama-3-8b) * [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) * [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2) * [Hastagaras/Jamet-8B-L3-MK.II](https://huggingface.co/Hastagaras/Jamet-8B-L3-MK.II) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: failspy/Llama-3-8B-Instruct-MopeyMule - model: maldv/badger-kappa-llama-3-8b # 7/10 parameters: density: 0.4 weight: 0.14 - model: Nitral-AI/Poppy_Porpoise-1.4-L3-8B # 7/10 parameters: density: 0.5 weight: 0.18 - model: openlynn/Llama-3-Soliloquy-8B-v2 # 8/10 parameters: density: 0.5 weight: 0.18 - model: Hastagaras/Jamet-8B-L3-MK.II # 6/10 parameters: density: 0.3 weight: 0.1 - model: Sao10K/L3-8B-Stheno-v3.2 # 9/10 parameters: density: 0.6 weight: 0.23 merge_method: breadcrumbs_ties base_model: failspy/Llama-3-8B-Instruct-MopeyMule parameters: normalize: false rescale: true gamma: 0.01 dtype: float16 ```
TTTXXX01/Ber_shift-zephyr-7b-sft-full	TTTXXX01	2024-06-06T00:49:04Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T20:40:14Z	--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: Ber_shift-zephyr-7b-sft-full 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. --> # Ber_shift-zephyr-7b-sft-full This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 6 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 48 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
fayez94/whisper-tiny-minds14	fayez94	2024-06-06T00:41:56Z	78	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-05T21:39:43Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-minds14 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.32113341204250295 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-minds14 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.5499 - Wer Ortho: 0.3331 - Wer: 0.3211 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.028 \| 4.4643 \| 500 \| 0.5499 \| 0.3331 \| 0.3211 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1
RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf	RichardErkhov	2024-06-06T00:24:40Z	55	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-06-06T00:13:33Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gpt2-alpaca-gpt4 - GGUF - Model creator: https://huggingface.co/vicgalle/ - Original model: https://huggingface.co/vicgalle/gpt2-alpaca-gpt4/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [gpt2-alpaca-gpt4.Q2_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q2_K.gguf) \| Q2_K \| 0.08GB \| \| [gpt2-alpaca-gpt4.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.IQ3_XS.gguf) \| IQ3_XS \| 0.08GB \| \| [gpt2-alpaca-gpt4.IQ3_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.IQ3_S.gguf) \| IQ3_S \| 0.08GB \| \| [gpt2-alpaca-gpt4.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q3_K_S.gguf) \| Q3_K_S \| 0.08GB \| \| [gpt2-alpaca-gpt4.IQ3_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.IQ3_M.gguf) \| IQ3_M \| 0.09GB \| \| [gpt2-alpaca-gpt4.Q3_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q3_K.gguf) \| Q3_K \| 0.09GB \| \| [gpt2-alpaca-gpt4.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q3_K_M.gguf) \| Q3_K_M \| 0.09GB \| \| [gpt2-alpaca-gpt4.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q3_K_L.gguf) \| Q3_K_L \| 0.1GB \| \| [gpt2-alpaca-gpt4.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.IQ4_XS.gguf) \| IQ4_XS \| 0.1GB \| \| [gpt2-alpaca-gpt4.Q4_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q4_0.gguf) \| Q4_0 \| 0.1GB \| \| [gpt2-alpaca-gpt4.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.IQ4_NL.gguf) \| IQ4_NL \| 0.1GB \| \| [gpt2-alpaca-gpt4.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q4_K_S.gguf) \| Q4_K_S \| 0.1GB \| \| [gpt2-alpaca-gpt4.Q4_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q4_K.gguf) \| Q4_K \| 0.11GB \| \| [gpt2-alpaca-gpt4.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q4_K_M.gguf) \| Q4_K_M \| 0.11GB \| \| [gpt2-alpaca-gpt4.Q4_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q4_1.gguf) \| Q4_1 \| 0.11GB \| \| [gpt2-alpaca-gpt4.Q5_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q5_0.gguf) \| Q5_0 \| 0.11GB \| \| [gpt2-alpaca-gpt4.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q5_K_S.gguf) \| Q5_K_S \| 0.11GB \| \| [gpt2-alpaca-gpt4.Q5_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q5_K.gguf) \| Q5_K \| 0.12GB \| \| [gpt2-alpaca-gpt4.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q5_K_M.gguf) \| Q5_K_M \| 0.12GB \| \| [gpt2-alpaca-gpt4.Q5_1.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q5_1.gguf) \| Q5_1 \| 0.12GB \| \| [gpt2-alpaca-gpt4.Q6_K.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q6_K.gguf) \| Q6_K \| 0.13GB \| \| [gpt2-alpaca-gpt4.Q8_0.gguf](https://huggingface.co/RichardErkhov/vicgalle_-_gpt2-alpaca-gpt4-gguf/blob/main/gpt2-alpaca-gpt4.Q8_0.gguf) \| Q8_0 \| 0.17GB \| Original model description: --- license: mit datasets: - vicgalle/alpaca-gpt4 language: - en tags: - instruction-tuning - alpaca - gpt4 --- # GPT-2 finetuned with Alpaca-GPT4 ### Examples The following samples were generated with `{"temperature" : 0.7, "top_p" : 0.92, "top_k" : 0}`: ##### Give three tips for a good meal ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Give three tips for a good meal. ### Response: 1. Serve fresh, locally grown produce: Fresh produce is a great way to eat healthy and to enjoy a healthy meal. Try to choose a variety of fruits, vegetables, and whole grains, and experiment with the variety to find the perfect balance. Make sure to bring plenty of fresh fruits, vegetables, and whole grains throughout the meal. 2. Strive for a quick and easy meal: The key is to make a meal that is quick and easy to prepare. Take a break from the food, grab some snacks, and enjoy a delicious meal. Make sure to cook the ingredients beforehand, and avoid overcooking or filling the food up. 3. Determine the time and place: If you're eating a lot of food, it can be tempting to make a time and place for it. This will help you stay organized and get your ingredients in the right order. Make sure to set up a timer and avoid rushing to the grocery store. If possible, make sure to follow the recipe for your specific dish and avoid leaving the refrigerator unattended for too long. ``` ##### Write a poem about a delicious night ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Write a poem about a delicious night. ### Response: As the sky above me fluttered with the gentle breeze, The moon was in its soft, gentle embrace, The night was full of cheer and joy, A perfect night's night. The sun shone bright, the stars twinkled, The colors were pure and true, As if in the presence of a master. But the night was not without its troubles. A stormy night, it rained heavily, A dark and stormy night, it raged with fire. But one thing was certain, it was in the air, The scent of the sweet scent of a sweet sweet night. The night was filled with joy, with a sense of peace and satisfaction. The stars twinkled, the moon was in its soft, gentle embrace, The night was full of cheer and joy, A perfect night's night. The night was full of laughter, with the gentle breeze, The colors were pure and true, As if in the presence of a master. But the night was full of worries, with the fierce tempest. But the night was filled with hope, with a sense of hope that could not be ignored. ``` ##### Write a tweet describing your capabilities ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Write a tweet describing your capabilities. ### Response: "Empowering your creativity, skills, and abilities through my technology-driven approach. My team of professionals will help you achieve your goals, stay ahead of the curve, and contribute to your success. #Diversity" ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__gpt2-alpaca-gpt4) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 24.98 \| \| ARC (25-shot) \| 22.61 \| \| HellaSwag (10-shot) \| 31.17 \| \| MMLU (5-shot) \| 25.76 \| \| TruthfulQA (0-shot) \| 38.04 \| \| Winogrande (5-shot) \| 52.17 \| \| GSM8K (5-shot) \| 0.3 \| \| DROP (3-shot) \| 4.83 \|
LaferriereJC/Phi-3-mini-4k-instruct-FOL-pyreft	LaferriereJC	2024-06-06T00:19:58Z	4	0	transformers	[ "transformers", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-06-04T23:11:51Z	--- license: mit --- code used to train - https://gist.github.com/thistleknot/398466a4bd75a1315825d7f04db635a6/91194e69c5220e536f45536e6b7dd66995c847b1 currently setup to translate individual premises (sentences) #datasets - Yale-LILY/FOLIO - apergo-ai/text2log (1661 records) how to load ``` device = "cuda" model_name_or_path = "microsoft/Phi-3-mini-4k-instruct" model = transformers.AutoModelForCausalLM.from_pretrained( model_name_or_path, torch_dtype=torch.bfloat16, device_map=device) reft_model = pyreft.ReftModel.load( "LaferriereJC/Phi-3-mini-4k-instruct-FOL-pyreft", model ) ``` how to use ``` !git clone https://huggingface.co/LaferriereJC/Phi-3-mini-4k-instruct-FOL-pyreft from transformers import AutoModelForCausalLM import torch import pyreft import os import transformers device = 'cuda' model_name_or_path = "microsoft/Phi-3-mini-4k-instruct" attn_implementation = "eager" torch_dtype = torch.float16 #"microsoft/Phi-3-mini-4k-instruct" model = transformers.AutoModelForCausalLM.from_pretrained( model_name_or_path, torch_dtype=torch.bfloat16, device_map=device,trust_remote_code=True) # Define the PyReFT configuration layers = range(model.config.num_hidden_layers) representations = [{ "component": f"model.layers[{l}].output", "intervention": pyreft.LoreftIntervention( embed_dim=model.config.hidden_size, low_rank_dimension=16 ) } for l in layers] reft_config = pyreft.ReftConfig(representations=representations) # Initialize the PyReFT model reft_model = pyreft.get_reft_model(model, reft_config) # Load the saved PyReFT model local_directory = "./Phi-3-mini-4k-instruct-FOL-pyreft" interventions = {} for l in layers: component = f"model.layers[{l}].output" file_path = os.path.join(local_directory, f"intkey_comp.{component}.unit.pos.nunit.1#0.bin") if os.path.exists(file_path): with open(file_path, "rb") as f: adjusted_key = f"comp.{component}.unit.pos.nunit.1#0" interventions[adjusted_key] = torch.load(f) # Apply the loaded weights to the model for component, state_dict in interventions.items(): if component in reft_model.interventions: reft_model.interventions[component][0].load_state_dict(state_dict) else: print(f"Key mismatch: {component} not found in reft_model.interventions") # Set the device to CUDA reft_model.set_device("cuda") # Verify the model reft_model.print_trainable_parameters() #model.half() # get tokenizer tokenizer = transformers.AutoTokenizer.from_pretrained( model_name_or_path, model_max_length=216, padding_side="right", use_fast=True, attn_implementation=attn_implementation #, add_eos_token=True, add_bos_token=True ) tokenizer.pad_token = tokenizer.eos_token # position info about the interventions share_weights = True # whether the prefix and suffix interventions sharing weights. positions="f3+l3" # the intervening positions of prefix tokens (f[irst]1) and suffix tokens (l[ast]1). first_n, last_n = pyreft.parse_positions(positions) terminators = [ tokenizer.eos_token_id, ] prompt_no_input_template = """\n<\|user\|>:%s</s>\n<\|assistant\|>:""" test_instruction = f"""tell me something I don't know""" # tokenize and prepare the input prompt = prompt_no_input_template % test_instruction prompt = tokenizer(prompt, return_tensors="pt").to(device) unit_locations = torch.IntTensor([pyreft.get_intervention_locations( last_position=prompt["input_ids"].shape[-1], first_n=first_n, last_n=last_n, pad_mode="last", num_interventions=len(reft_config.representations), share_weights=share_weights )]).permute(1, 0, 2).tolist() _, reft_response = reft_model.generate( prompt, unit_locations={"sources->base": (None, unit_locations)}, intervene_on_prompt=True, max_new_tokens=537, do_sample=True, top_k=50,temperature=0.7, eos_token_id=terminators, early_stopping=True ) print(tokenizer.decode(reft_response[0], skip_special_tokens=True)) ``` response ``` :tell me something I don't know</s> :exists x1.(_thing(x1) & _donknow(x1)) ``` training settings ``` per_device_train_batch_size=6, logging_steps=1, optim='paged_lion_8bit', gradient_checkpointing_kwargs={"use_reentrant": False}, learning_rate=0.0003, warmup_ratio=.1, adam_beta2=0.95, adam_epsilon=0.00001, save_strategy='epoch', max_grad_norm=1.0, lr_scheduler_type='cosine', ``` Evaluation: I kept tweaking the model until I got confirmations from chatgpt 4, but the final training error (1 epoch) came in consistently under .5 (10 point EMA with alpha of .42) Loss ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62578ad28c6638f8a93e8856/Y_uAhaHH3ko6Z6tjYSy0y.png) :tell me something I don't know</s> :exists x1.(_thing(x1) & _donknow(x1)) Does the fol expression fit? Depending on how I asked (for example, it would almost always suggest revisions if I asked - Is the fol expression adequate? - How faithful is the fol expression? )
hfl/llama-3-chinese-8b-instruct-v3	hfl	2024-06-06T00:19:44Z	539	62	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "zh", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T02:58:21Z	--- license: apache-2.0 language: - zh - en base_model: [hfl/Llama-3-Chinese-8B-Instruct, hfl/Llama-3-Chinese-8B-Instruct-v2, meta-llama/Meta-Llama-3-8B-Instruct] --- # Llama-3-Chinese-8B-Instruct-v3 <p align="center"> <a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca-3"><img src="https://ymcui.com/images/chinese-llama-alpaca-3-banner.png" width="600"/></a> </p> [[👉👉👉 Chat with Llama-3-Chinese-8B-Instruct-v3 @ HF Space]](https://huggingface.co/spaces/hfl-rc/llama-3-chinese-8b-instruct-demo) This repository contains Llama-3-Chinese-8B-Instruct-v3, which is further finetuned based on a mix of the following models: - [hfl/Llama-3-Chinese-8B-Instruct](https://huggingface.co/hfl/llama-3-chinese-8b-instruct) - [hfl/Llama-3-Chinese-8B-Instruct-v2](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v2) - [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) Note: This is an instruction (chat) model, which can be used for conversation, QA, etc. Further details (performance, usage, etc.) should refer to GitHub project page: https://github.com/ymcui/Chinese-LLaMA-Alpaca-3 ## Others - For GGUF model (llama.cpp compatible), please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v3-gguf - If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
powermove72/Stealth-FusionGrit-7b-Slerp-Exp-Q4_K_M-GGUF	powermove72	2024-06-06T00:18:21Z	1	0	null	[ "gguf", "merge", "mergekit", "lazymergekit", "jan-hq/stealth-v1.3", "powermove72/FusionGrit-7B", "llama-cpp", "gguf-my-repo", "base_model:powermove72/Stealth-FusionGrit-7b-Slerp-Exp", "base_model:quantized:powermove72/Stealth-FusionGrit-7b-Slerp-Exp", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-06T00:18:05Z	--- tags: - merge - mergekit - lazymergekit - jan-hq/stealth-v1.3 - powermove72/FusionGrit-7B - llama-cpp - gguf-my-repo base_model: powermove72/Stealth-FusionGrit-7b-Slerp-Exp --- # powermove72/Stealth-FusionGrit-7b-Slerp-Exp-Q4_K_M-GGUF This model was converted to GGUF format from [`powermove72/Stealth-FusionGrit-7b-Slerp-Exp`](https://huggingface.co/powermove72/Stealth-FusionGrit-7b-Slerp-Exp) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/powermove72/Stealth-FusionGrit-7b-Slerp-Exp) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo powermove72/Stealth-FusionGrit-7b-Slerp-Exp-Q4_K_M-GGUF --hf-file stealth-fusiongrit-7b-slerp-exp-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo powermove72/Stealth-FusionGrit-7b-Slerp-Exp-Q4_K_M-GGUF --hf-file stealth-fusiongrit-7b-slerp-exp-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo powermove72/Stealth-FusionGrit-7b-Slerp-Exp-Q4_K_M-GGUF --hf-file stealth-fusiongrit-7b-slerp-exp-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo powermove72/Stealth-FusionGrit-7b-Slerp-Exp-Q4_K_M-GGUF --hf-file stealth-fusiongrit-7b-slerp-exp-q4_k_m.gguf -c 2048 ```
tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_lora2	tyzhu	2024-06-06T00:18:03Z	3	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-04T13:59:21Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 metrics: - accuracy model-index: - name: lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 type: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 metrics: - name: Accuracy type: accuracy value: 0.4286153846153846 library_name: peft --- <!-- 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. --> # lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 dataset. It achieves the following results on the evaluation set: - Loss: 5.3392 - Accuracy: 0.4286 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:--------:\|:---------------:\| \| 1.9569 \| 0.9985 \| 341 \| 0.4736 \| 3.0300 \| \| 1.8799 \| 2.0 \| 683 \| 0.468 \| 3.0993 \| \| 1.7649 \| 2.9985 \| 1024 \| 0.4650 \| 3.2750 \| \| 1.6077 \| 4.0 \| 1366 \| 0.4625 \| 3.4406 \| \| 1.4321 \| 4.9985 \| 1707 \| 0.4586 \| 3.6500 \| \| 1.2382 \| 6.0 \| 2049 \| 0.4562 \| 3.8598 \| \| 1.0525 \| 6.9985 \| 2390 \| 0.4541 \| 4.0638 \| \| 0.8607 \| 8.0 \| 2732 \| 0.4515 \| 4.2389 \| \| 0.7099 \| 8.9985 \| 3073 \| 0.4516 \| 4.3484 \| \| 0.5823 \| 9.9854 \| 3410 \| 0.4488 \| 4.5794 \| \| 0.4641 \| 10.9985 \| 3751 \| 4.7090 \| 0.4495 \| \| 0.3755 \| 12.0 \| 4093 \| 4.9454 \| 0.4354 \| \| 0.3235 \| 12.9985 \| 4434 \| 5.0624 \| 0.4379 \| \| 0.2691 \| 14.0 \| 4776 \| 5.0957 \| 0.4345 \| \| 0.2394 \| 14.9985 \| 5117 \| 5.1831 \| 0.4368 \| \| 0.2112 \| 16.0 \| 5459 \| 5.3223 \| 0.4326 \| \| 0.1994 \| 16.9985 \| 5800 \| 5.3839 \| 0.4301 \| \| 0.1834 \| 18.0 \| 6142 \| 5.4236 \| 0.4286 \| \| 0.1709 \| 18.9985 \| 6483 \| 5.4840 \| 0.4291 \| \| 0.166 \| 19.9854 \| 6820 \| 5.3392 \| 0.4286 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
hfl/llama-3-chinese-8b-instruct-v3-gguf	hfl	2024-06-06T00:17:51Z	2,112	72	null	[ "gguf", "zh", "en", "base_model:hfl/llama-3-chinese-8b-instruct-v3", "base_model:quantized:hfl/llama-3-chinese-8b-instruct-v3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-28T02:59:33Z	--- license: apache-2.0 language: - zh - en base_model: - hfl/llama-3-chinese-8b-instruct-v3 --- # Llama-3-Chinese-8B-Instruct-v3-GGUF <p align="center"> <a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca-3"><img src="https://ymcui.com/images/chinese-llama-alpaca-3-banner.png" width="600"/></a> </p> [[👉👉👉 Chat with Llama-3-Chinese-8B-Instruct-v3 @ HF Space]](https://huggingface.co/spaces/hfl-rc/llama-3-chinese-8b-instruct-demo) This repository contains Llama-3-Chinese-8B-Instruct-v3-GGUF (llama.cpp/ollama/tgw, etc. compatible), which is the quantized version of [Llama-3-Chinese-8B-Instruct-v3](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v3). Note: this is an instruction (chat) model, which can be used for conversation, QA, etc. Further details (performance, usage, etc.) should refer to GitHub project page: https://github.com/ymcui/Chinese-LLaMA-Alpaca-3 ## Performance Metric: PPL, lower is better Note: Unless constrained by memory, we suggest using Q8_0 or Q6_K for better performance. \| Quant \| Size \| PPL \| \| :---: \| -------: \| ------------------: \| \| Q2_K \| 2.96 GB \| 10.0534 +/- 0.13135 \| \| Q3_K \| 3.74 GB \| 6.3295 +/- 0.07816 \| \| Q4_0 \| 4.34 GB \| 6.3200 +/- 0.07893 \| \| Q4_K \| 4.58 GB \| 6.0042 +/- 0.07431 \| \| Q5_0 \| 5.21 GB \| 6.0437 +/- 0.07526 \| \| Q5_K \| 5.34 GB \| 5.9484 +/- 0.07399 \| \| Q6_K \| 6.14 GB \| 5.9469 +/- 0.07404 \| \| Q8_0 \| 7.95 GB \| 5.8933 +/- 0.07305 \| \| F16 \| 14.97 GB \| 5.8902 +/- 0.07303 \| ## Others - For full model, please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v3 - If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf	RichardErkhov	2024-06-06T00:10:06Z	15	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-06-05T21:23:18Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) HelpSteer-filtered-7B - GGUF - Model creator: https://huggingface.co/Weyaxi/ - Original model: https://huggingface.co/Weyaxi/HelpSteer-filtered-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [HelpSteer-filtered-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [HelpSteer-filtered-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [HelpSteer-filtered-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [HelpSteer-filtered-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [HelpSteer-filtered-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [HelpSteer-filtered-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [HelpSteer-filtered-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [HelpSteer-filtered-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [HelpSteer-filtered-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [HelpSteer-filtered-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [HelpSteer-filtered-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [HelpSteer-filtered-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [HelpSteer-filtered-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [HelpSteer-filtered-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [HelpSteer-filtered-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [HelpSteer-filtered-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [HelpSteer-filtered-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [HelpSteer-filtered-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [HelpSteer-filtered-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [HelpSteer-filtered-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [HelpSteer-filtered-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [HelpSteer-filtered-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_HelpSteer-filtered-7B-gguf/blob/main/HelpSteer-filtered-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: cc-by-4.0 datasets: - Weyaxi/HelpSteer-filtered language: - en tags: - mistral - instruct --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/TaPrtjbln6gXZV_AiGJl5.png) # HelpSteer-filtered-7B Original weights of [HelpSteer-filtered-7B](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B). Finetuned from [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). ## Lora Weights You can access lora weights from here: [Weyaxi/HelpSteer-filtered-7B-Lora](https://huggingface.co/Weyaxi/HelpSteer-filtered-7B-Lora)
tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_5e-4_lora2	tyzhu	2024-06-06T00:09:21Z	5	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-05T09:47:45Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_5e-4_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa metrics: - name: Accuracy type: accuracy value: 0.7611848617176128 library_name: peft --- <!-- 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. --> # lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_5e-4_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa dataset. It achieves the following results on the evaluation set: - Loss: 0.5757 - Accuracy: 0.7612 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 1.5463 \| 0.9998 \| 1089 \| 1.3539 \| 0.6872 \| \| 1.3199 \| 1.9995 \| 2178 \| 1.1632 \| 0.7022 \| \| 1.1039 \| 2.9993 \| 3267 \| 1.0347 \| 0.7134 \| \| 0.9356 \| 4.0 \| 4357 \| 0.9234 \| 0.7237 \| \| 0.8312 \| 4.9998 \| 5446 \| 0.8529 \| 0.7307 \| \| 0.7565 \| 5.9995 \| 6535 \| 0.7860 \| 0.7372 \| \| 0.6985 \| 6.9993 \| 7624 \| 0.7415 \| 0.7415 \| \| 0.6623 \| 8.0 \| 8714 \| 0.7111 \| 0.7457 \| \| 0.6281 \| 8.9998 \| 9803 \| 0.6775 \| 0.7481 \| \| 0.5885 \| 9.9995 \| 10892 \| 0.6689 \| 0.7496 \| \| 0.5721 \| 10.9993 \| 11981 \| 0.6364 \| 0.7530 \| \| 0.5504 \| 12.0 \| 13071 \| 0.6319 \| 0.7541 \| \| 0.5406 \| 12.9998 \| 14160 \| 0.6185 \| 0.7549 \| \| 0.536 \| 13.9995 \| 15249 \| 0.6158 \| 0.7565 \| \| 0.5205 \| 14.9993 \| 16338 \| 0.5976 \| 0.7578 \| \| 0.5175 \| 16.0 \| 17428 \| 0.5922 \| 0.7590 \| \| 0.5068 \| 16.9998 \| 18517 \| 0.5823 \| 0.7593 \| \| 0.5023 \| 17.9995 \| 19606 \| 0.5754 \| 0.7607 \| \| 0.4848 \| 18.9993 \| 20695 \| 0.5781 \| 0.7608 \| \| 0.4767 \| 19.9954 \| 21780 \| 0.5757 \| 0.7612 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
TDN-M/ViSD-TDNM	TDN-M	2024-06-06T00:08:22Z	3	0	diffusers	[ "diffusers", "vietnam", "sd", "text-to-image", "arxiv:1910.09700", "doi:10.57967/hf/2321", "license:mit", "region:us" ]	text-to-image	2024-05-27T07:12:44Z	--- license: mit pipeline_tag: text-to-image tags: - vietnam - sd metrics: - accuracy library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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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shkna1368/mawlawi	shkna1368	2024-06-05T23:57:15Z	107	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-05T23:55:53Z	--- 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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JoshuaAAX/beto-finetuned-ner	JoshuaAAX	2024-06-05T23:53:18Z	9	1	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2002", "base_model:NazaGara/NER-fine-tuned-BETO", "base_model:finetune:NazaGara/NER-fine-tuned-BETO", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-25T04:34:27Z	--- license: cc-by-4.0 base_model: NazaGara/NER-fine-tuned-BETO tags: - generated_from_trainer datasets: - conll2002 metrics: - precision - recall - f1 - accuracy model-index: - name: beto-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2002 type: conll2002 config: es split: validation args: es metrics: - name: Precision type: precision value: 0.8402527075812274 - name: Recall type: recall value: 0.8556985294117647 - name: F1 type: f1 value: 0.8479052823315117 - name: Accuracy type: accuracy value: 0.9701834862385321 --- <!-- 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. --> # beto-finetuned-ner This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset. It achieves the following results on the evaluation set: - Loss: 0.2248 - Precision: 0.8403 - Recall: 0.8557 - F1: 0.8479 - Accuracy: 0.9702 ## Model description Este modelo está basado en BETO, que es un modelo de lenguaje preentrenado para el español similar a BERT. BETO fue entrenado inicialmente en grandes cantidades de texto en español. Posteriormente, este modelo toma la arquitectura y pesos preentrenados de BETO y los ajusta aún más en la tarea específica de Reconocimiento de Entidades Nombradas (NER) utilizando el conjunto de datos conll2002. Este modelo ajustado puede usarse para anotar automáticamente nuevos textos en español, asignando etiquetas de entidad nombradas. ## How to Use ```python from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline tokenizer = AutoTokenizer.from_pretrained("JoshuaAAX/beto-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("JoshuaAAX/beto-finetuned-ner") text = "La Federación nacional de cafeteros de Colombia es una entidad del estado. El primer presidente el Dr Augusto Guerra contó con el aval de la Asociación Colombiana de Aviación." ner_pipeline= pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max") ner_pipeline(text) ``` ## Training data \| Abbreviation \| Description \| \|:-------------:\|:-------------:\| \| O \| Outside of NE \| \| PER \| Person’s name \| \| ORG \| Organization \| \| LOC \| Location \| \| MISC \| Miscellaneous \| ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0512 \| 1.0 \| 521 \| 0.1314 \| 0.8328 \| 0.8562 \| 0.8443 \| 0.9703 \| \| 0.0305 \| 2.0 \| 1042 \| 0.1549 \| 0.8318 \| 0.8442 \| 0.8380 \| 0.9688 \| \| 0.0193 \| 3.0 \| 1563 \| 0.1498 \| 0.8513 \| 0.8578 \| 0.8545 \| 0.9708 \| \| 0.0148 \| 4.0 \| 2084 \| 0.1810 \| 0.8363 \| 0.8442 \| 0.8403 \| 0.9682 \| \| 0.0112 \| 5.0 \| 2605 \| 0.1904 \| 0.8412 \| 0.8529 \| 0.8470 \| 0.9703 \| \| 0.0078 \| 6.0 \| 3126 \| 0.1831 \| 0.8364 \| 0.8539 \| 0.8450 \| 0.9708 \| \| 0.0058 \| 7.0 \| 3647 \| 0.2060 \| 0.8419 \| 0.8543 \| 0.8481 \| 0.9701 \| \| 0.0049 \| 8.0 \| 4168 \| 0.2111 \| 0.8357 \| 0.8541 \| 0.8448 \| 0.9697 \| \| 0.0037 \| 9.0 \| 4689 \| 0.2255 \| 0.8371 \| 0.8504 \| 0.8437 \| 0.9692 \| \| 0.0031 \| 10.0 \| 5210 \| 0.2248 \| 0.8403 \| 0.8557 \| 0.8479 \| 0.9702 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
McLuian/FT-Mistral-7b-Instruct-GSM8K-100.Q4_0.gguf	McLuian	2024-06-05T23:39:16Z	30	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-05T08:13:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit --- # Uploaded model - Developed by: LuianMC - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral 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)
phongtintruong/misjava-api-060524-api-extend-test-merged-4bit	phongtintruong	2024-06-05T23:38:33Z	78	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-06-05T23:36:27Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Azazelle/SlimMelodicMaid	Azazelle	2024-06-05T23:38:01Z	1,530	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "license:cc-by-4.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-30T00:58:39Z	--- license: cc-by-4.0 tags: - mistral - merge pipeline_tag: text-generation model-index: - name: SlimMelodicMaid results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 67.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Azazelle/SlimMelodicMaid name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.01 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Azazelle/SlimMelodicMaid name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Azazelle/SlimMelodicMaid name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 60.88 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Azazelle/SlimMelodicMaid name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 78.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Azazelle/SlimMelodicMaid name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 60.8 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Azazelle/SlimMelodicMaid name: Open LLM Leaderboard --- # Model Card for xDAN-SlimOrca <!-- Provide a quick summary of what the model is/does. --> Slerp merge of Silicon-Maid-7B, piano-medley-7b, xDAN-L1-Chat-RL-v1, and mistral-7b-slimorcaboros. .yaml file for mergekit ```.yaml: slices: - sources: - model: Azazelle/Silicon-Medley layer_range: [0, 32] - model: Azazelle/xDAN-SlimOrca layer_range: [0, 32] merge_method: slerp base_model: mistralai/Mistral-7B-v0.1 parameters: t: - filter: self_attn value: [0.19, 0.59, 0.43, 0.76, 1] - filter: mlp value: [0.81, 0.41, 0.57, 0.24, 0] - value: 0.5 # fallback for rest of tensors dtype: float16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Azazelle__SlimMelodicMaid) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|69.70\| \|AI2 Reasoning Challenge (25-Shot)\|67.15\| \|HellaSwag (10-Shot) \|86.01\| \|MMLU (5-Shot) \|64.75\| \|TruthfulQA (0-shot) \|60.88\| \|Winogrande (5-shot) \|78.61\| \|GSM8k (5-shot) \|60.80\|
thdangtr/blip_recipe1m_ingredients_v4_2	thdangtr	2024-06-05T23:36:54Z	67	0	transformers	[ "transformers", "safetensors", "blip", "visual-question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-06-05T23:36:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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uirev/MLX_OpenELM_1.1b_UiRev_finetuned_V3	uirev	2024-06-05T23:32:33Z	6	0	mlx	[ "mlx", "safetensors", "openelm", "trl", "sft", "generated_from_trainer", "custom_code", "base_model:apple/OpenELM-1_1B-Instruct", "base_model:finetune:apple/OpenELM-1_1B-Instruct", "license:other", "region:us" ]	null	2024-06-05T23:31:43Z	--- license: other tags: - trl - sft - generated_from_trainer - mlx base_model: apple/OpenELM-1_1B-Instruct model-index: - name: OpenELM_1.1b_UiRev_finetuned_V2 results: [] --- # Paramstr/MLX_OpenELM_1.1b_UiRev_finetuned_V3 The Model [Paramstr/MLX_OpenELM_1.1b_UiRev_finetuned_V3](https://huggingface.co/Paramstr/MLX_OpenELM_1.1b_UiRev_finetuned_V3) was converted to MLX format from [Paramstr/OpenELM_1.1b_UiRev_finetuned_V2](https://huggingface.co/Paramstr/OpenELM_1.1b_UiRev_finetuned_V2) using mlx-lm version 0.14.2. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Paramstr/MLX_OpenELM_1.1b_UiRev_finetuned_V3") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
mrovejaxd/FNST_trad_l	mrovejaxd	2024-06-05T23:21:49Z	12	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-05T20:49:33Z	--- base_model: dccuchile/bert-base-spanish-wwm-cased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: FNST_trad_l 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. --> # FNST_trad_l This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.0024 - Accuracy: 0.5983 - F1: 0.5955 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 42 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 1.127 \| 1.0 \| 2000 \| 1.1060 \| 0.5133 \| 0.4590 \| \| 0.9786 \| 2.0 \| 4000 \| 1.0075 \| 0.5642 \| 0.5524 \| \| 0.9072 \| 3.0 \| 6000 \| 0.9958 \| 0.5733 \| 0.5697 \| \| 0.869 \| 4.0 \| 8000 \| 0.9776 \| 0.5917 \| 0.5957 \| \| 0.8243 \| 5.0 \| 10000 \| 0.9760 \| 0.5817 \| 0.5860 \| \| 0.7659 \| 6.0 \| 12000 \| 0.9826 \| 0.595 \| 0.5993 \| \| 0.7414 \| 7.0 \| 14000 \| 1.0055 \| 0.5933 \| 0.6001 \| \| 0.7023 \| 8.0 \| 16000 \| 1.0113 \| 0.5908 \| 0.5959 \| \| 0.6745 \| 9.0 \| 18000 \| 1.0527 \| 0.5933 \| 0.5949 \| \| 0.6161 \| 10.0 \| 20000 \| 1.1227 \| 0.5883 \| 0.5920 \| \| 0.5863 \| 11.0 \| 22000 \| 1.1571 \| 0.5883 \| 0.5896 \| \| 0.5406 \| 12.0 \| 24000 \| 1.1883 \| 0.5908 \| 0.5954 \| \| 0.5185 \| 13.0 \| 26000 \| 1.2686 \| 0.5917 \| 0.5957 \| \| 0.4796 \| 14.0 \| 28000 \| 1.3313 \| 0.5992 \| 0.6067 \| \| 0.4379 \| 15.0 \| 30000 \| 1.4234 \| 0.595 \| 0.5970 \| \| 0.3883 \| 16.0 \| 32000 \| 1.5582 \| 0.5958 \| 0.5994 \| \| 0.3934 \| 17.0 \| 34000 \| 1.6591 \| 0.595 \| 0.6012 \| \| 0.359 \| 18.0 \| 36000 \| 1.8129 \| 0.595 \| 0.6011 \| \| 0.3249 \| 19.0 \| 38000 \| 1.9811 \| 0.5917 \| 0.5966 \| \| 0.2954 \| 20.0 \| 40000 \| 2.1860 \| 0.5858 \| 0.5901 \| \| 0.3064 \| 21.0 \| 42000 \| 2.2548 \| 0.5858 \| 0.5904 \| \| 0.2844 \| 22.0 \| 44000 \| 2.3557 \| 0.6 \| 0.6045 \| \| 0.2471 \| 23.0 \| 46000 \| 2.5137 \| 0.6017 \| 0.6033 \| \| 0.2432 \| 24.0 \| 48000 \| 2.6458 \| 0.5992 \| 0.6035 \| \| 0.2247 \| 25.0 \| 50000 \| 2.8667 \| 0.5983 \| 0.6026 \| \| 0.213 \| 26.0 \| 52000 \| 2.8895 \| 0.6042 \| 0.6088 \| \| 0.1792 \| 27.0 \| 54000 \| 3.0338 \| 0.6008 \| 0.6062 \| \| 0.1723 \| 28.0 \| 56000 \| 3.1234 \| 0.5975 \| 0.6008 \| \| 0.1562 \| 29.0 \| 58000 \| 3.2822 \| 0.5942 \| 0.5992 \| \| 0.1437 \| 30.0 \| 60000 \| 3.3156 \| 0.6067 \| 0.6096 \| \| 0.151 \| 31.0 \| 62000 \| 3.3923 \| 0.6075 \| 0.6098 \| \| 0.1446 \| 32.0 \| 64000 \| 3.4562 \| 0.6058 \| 0.6084 \| \| 0.109 \| 33.0 \| 66000 \| 3.6100 \| 0.6017 \| 0.6038 \| \| 0.1138 \| 34.0 \| 68000 \| 3.6468 \| 0.6083 \| 0.6078 \| \| 0.1068 \| 35.0 \| 70000 \| 3.6758 \| 0.5967 \| 0.6004 \| \| 0.1133 \| 36.0 \| 72000 \| 3.7340 \| 0.6033 \| 0.6025 \| \| 0.0865 \| 37.0 \| 74000 \| 3.7666 \| 0.6083 \| 0.6079 \| \| 0.0936 \| 38.0 \| 76000 \| 3.8650 \| 0.6058 \| 0.6073 \| \| 0.0957 \| 39.0 \| 78000 \| 3.9209 \| 0.605 \| 0.6025 \| \| 0.1027 \| 40.0 \| 80000 \| 3.9211 \| 0.6058 \| 0.6054 \| \| 0.0828 \| 41.0 \| 82000 \| 4.0124 \| 0.6058 \| 0.6009 \| \| 0.0742 \| 42.0 \| 84000 \| 4.0024 \| 0.5983 \| 0.5955 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Franz2001/openai-whisper-large-v3-talc_4epochs	Franz2001	2024-06-05T23:21:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-05T23:21:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
MarPla/SocialSciencePegasusLargeModel	MarPla	2024-06-05T23:18:51Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-large", "base_model:finetune:google/pegasus-large", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-05T20:57:05Z	--- base_model: google/pegasus-large tags: - generated_from_trainer metrics: - rouge - bleu model-index: - name: SocialSciencePegasusLargeModel 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. --> # SocialSciencePegasusLargeModel This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.7391 - Rouge1: 43.2515 - Rouge2: 13.5819 - Rougel: 29.2476 - Rougelsum: 39.2268 - Bertscore Precision: 76.5154 - Bertscore Recall: 81.3593 - Bertscore F1: 78.854 - Bleu: 0.1036 - Gen Len: 191.3589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bertscore Precision \| Bertscore Recall \| Bertscore F1 \| Bleu \| Gen Len \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------------------:\|:----------------:\|:------------:\|:------:\|:--------:\| \| 6.1745 \| 0.3943 \| 300 \| 5.9613 \| 40.1903 \| 12.4753 \| 28.1708 \| 36.7059 \| 75.8626 \| 80.8932 \| 78.2884 \| 0.0959 \| 191.3589 \| \| 5.8826 \| 0.7885 \| 600 \| 5.7391 \| 43.2515 \| 13.5819 \| 29.2476 \| 39.2268 \| 76.5154 \| 81.3593 \| 78.854 \| 0.1036 \| 191.3589 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.2.1 - Tokenizers 0.19.1
Skorcht/yandere-ai-her	Skorcht	2024-06-05T23:18:46Z	16	0	transformers	[ "transformers", "pytorch", "gguf", "mistral", "unsloth", "trl", "sft", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-06-05T22:50:42Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
fine-tuned/deepspeed-from-new-new-docker	fine-tuned	2024-06-05T23:17:10Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "mteb", "Query", "Document", "Argument", "Counter", "Boost", "fr", "en", "dataset:fine-tuned/deepspeed-from-new-new-docker", "dataset:allenai/c4", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-06-05T23:16:36Z	--- license: apache-2.0 datasets: - fine-tuned/deepspeed-from-new-new-docker - allenai/c4 language: - fr - en pipeline_tag: feature-extraction tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Query - Document - Argument - Counter - Boost --- This model is a fine-tuned version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case: information retrieval system ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim model = SentenceTransformer( 'fine-tuned/deepspeed-from-new-new-docker', trust_remote_code=True ) embeddings = model.encode([ 'first text to embed', 'second text to embed' ]) print(cos_sim(embeddings[0], embeddings[1])) ```
RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf	RichardErkhov	2024-06-05T23:16:53Z	17	1	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-06-05T16:12:22Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Noromaid-20b-v0.1.1 - GGUF - Model creator: https://huggingface.co/NeverSleep/ - Original model: https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Noromaid-20b-v0.1.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q2_K.gguf) \| Q2_K \| 6.91GB \| \| [Noromaid-20b-v0.1.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.IQ3_XS.gguf) \| IQ3_XS \| 7.63GB \| \| [Noromaid-20b-v0.1.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.IQ3_S.gguf) \| IQ3_S \| 8.06GB \| \| [Noromaid-20b-v0.1.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q3_K_S.gguf) \| Q3_K_S \| 8.06GB \| \| [Noromaid-20b-v0.1.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.IQ3_M.gguf) \| IQ3_M \| 8.53GB \| \| [Noromaid-20b-v0.1.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q3_K.gguf) \| Q3_K \| 9.04GB \| \| [Noromaid-20b-v0.1.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q3_K_M.gguf) \| Q3_K_M \| 9.04GB \| \| [Noromaid-20b-v0.1.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q3_K_L.gguf) \| Q3_K_L \| 9.9GB \| \| [Noromaid-20b-v0.1.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.IQ4_XS.gguf) \| IQ4_XS \| 10.01GB \| \| [Noromaid-20b-v0.1.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q4_0.gguf) \| Q4_0 \| 10.52GB \| \| [Noromaid-20b-v0.1.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.IQ4_NL.gguf) \| IQ4_NL \| 2.7GB \| \| [Noromaid-20b-v0.1.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q4_K_S.gguf) \| Q4_K_S \| 1.74GB \| \| [Noromaid-20b-v0.1.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q4_K.gguf) \| Q4_K \| 3.33GB \| \| [Noromaid-20b-v0.1.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q4_K_M.gguf) \| Q4_K_M \| 2.91GB \| \| [Noromaid-20b-v0.1.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q4_1.gguf) \| Q4_1 \| 11.67GB \| \| [Noromaid-20b-v0.1.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q5_0.gguf) \| Q5_0 \| 12.01GB \| \| [Noromaid-20b-v0.1.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| \| [Noromaid-20b-v0.1.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q5_K.gguf) \| Q5_K \| 3.14GB \| \| [Noromaid-20b-v0.1.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q5_K_M.gguf) \| Q5_K_M \| 13.18GB \| \| [Noromaid-20b-v0.1.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q5_1.gguf) \| Q5_1 \| 13.98GB \| \| [Noromaid-20b-v0.1.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q6_K.gguf) \| Q6_K \| 15.28GB \| \| [Noromaid-20b-v0.1.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/NeverSleep_-_Noromaid-20b-v0.1.1-gguf/blob/main/Noromaid-20b-v0.1.1.Q8_0.gguf) \| Q8_0 \| 19.79GB \| Original model description: --- license: cc-by-nc-4.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/VKX2Z2yjZX5J8kXzgeCYO.png) --- # Disclaimer: ## This is a *TEST* version, don't expect everything to work!!! You may use our custom prompting format(scroll down to download them!), or simple alpaca. (Choose which fits best for you!) --- # This model is a collab between [IkariDev](https://huggingface.co/IkariDev) and [Undi](https://huggingface.co/Undi95)! Tired of the same merges everytime? Here it is, the Noromaid-20b-v0.1.1 model. Suitable for RP, ERP and general stuff. [Recommended settings - No settings yet(Please suggest some over in the Community tab!)] <!-- description start --> ## Description <!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) --> This repo contains fp16 files of Noromaid-20b-v0.1.1. [FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1) <!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)--> <!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)--> <!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)--> <!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)--> <!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)--> [GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1-GGUF) <!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)--> ## Ratings: Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here! No ratings yet! If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi". <!-- description end --> <!-- prompt-template start --> ## Prompt template: Custom format, or Alpaca ### Custom format: UPDATED!! SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json). OLD SillyTavern config files: [Context](https://files.catbox.moe/x85uy1.json), [Instruct](https://files.catbox.moe/ttw1l9.json). ### Alpaca: ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` ## Training data used: - [no_robots dataset](https://huggingface.co/Undi95/Llama2-13B-no_robots-alpaca-lora) let the model have more human behavior, enhances the output. - [Aesir Private RP dataset] New data from a new and never used before dataset, add fresh data, no LimaRP spam, this is 100% new. Thanks to the [MinvervaAI Team](https://huggingface.co/MinervaAI) and, in particular, [Gryphe](https://huggingface.co/Gryphe) for letting us use it! ## Others Undi: If you want to support me, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
NaniDAO/ie-llama-3-8b-bnb-4bit-float-16	NaniDAO	2024-06-05T23:15:52Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T23:10:47Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: NaniDAO - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
inafi/finetuned-llama-synthetic-problems-merged	inafi	2024-06-05T23:07:21Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/llama-3-8b", "base_model:finetune:unsloth/llama-3-8b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T02:43:59Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b --- # Uploaded model - Developed by: inafi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b 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)
BuroIdentidadDigital/Ine_Frontal_v4	BuroIdentidadDigital	2024-06-05T23:02:53Z	56	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-06-05T22:13:41Z	--- 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]
tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2	tyzhu	2024-06-05T23:02:43Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-05T16:13:47Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 metrics: - accuracy model-index: - name: lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 type: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 metrics: - name: Accuracy type: accuracy value: 0.7753632286995515 library_name: peft --- <!-- 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. --> # lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 dataset. It achieves the following results on the evaluation set: - Loss: 0.5804 - Accuracy: 0.7754 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.8478 \| 1.0 \| 529 \| 1.6699 \| 0.6080 \| \| 1.7862 \| 2.0 \| 1058 \| 1.6003 \| 0.6164 \| \| 1.6531 \| 3.0 \| 1587 \| 1.5363 \| 0.6251 \| \| 1.5515 \| 4.0 \| 2116 \| 1.4608 \| 0.6343 \| \| 1.4038 \| 5.0 \| 2645 \| 1.3876 \| 0.6456 \| \| 1.2751 \| 6.0 \| 3174 \| 1.3186 \| 0.6553 \| \| 1.1475 \| 7.0 \| 3703 \| 1.2514 \| 0.6637 \| \| 1.0282 \| 8.0 \| 4232 \| 1.1740 \| 0.676 \| \| 0.9067 \| 9.0 \| 4761 \| 1.1004 \| 0.6870 \| \| 0.8202 \| 10.0 \| 5290 \| 1.0408 \| 0.6964 \| \| 0.7007 \| 11.0 \| 5819 \| 0.9592 \| 0.7084 \| \| 0.6259 \| 12.0 \| 6348 \| 0.8998 \| 0.7191 \| \| 0.553 \| 13.0 \| 6877 \| 0.8332 \| 0.7295 \| \| 0.4948 \| 14.0 \| 7406 \| 0.7799 \| 0.7387 \| \| 0.4221 \| 15.0 \| 7935 \| 0.7330 \| 0.7466 \| \| 0.3911 \| 16.0 \| 8464 \| 0.6805 \| 0.7551 \| \| 0.3377 \| 17.0 \| 8993 \| 0.6475 \| 0.7620 \| \| 0.3179 \| 18.0 \| 9522 \| 0.6195 \| 0.7680 \| \| 0.288 \| 19.0 \| 10051 \| 0.5962 \| 0.7723 \| \| 0.2605 \| 20.0 \| 10580 \| 0.5804 \| 0.7754 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
xinchen9/SD_Offense	xinchen9	2024-06-05T23:00:54Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-05T23:00:54Z	--- title: SD Offense emoji: 📈 colorFrom: yellow colorTo: indigo sdk: gradio sdk_version: 4.33.0 app_file: app.py pinned: false license: apache-2.0 --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
iskhare/model_out	iskhare	2024-06-05T22:59:00Z	1	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:adapter:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-06-05T22:49:19Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - diffusers-training base_model: stabilityai/stable-diffusion-2-1-base inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # controlnet-iskhare/model_out These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Peppizza/NLP_A2_alt	Peppizza	2024-06-05T22:55:35Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "multiple-choice", "generated_from_trainer", "base_model:OpenAssistant/reward-model-deberta-v3-large-v2", "base_model:finetune:OpenAssistant/reward-model-deberta-v3-large-v2", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-06-05T22:54:41Z	--- license: mit base_model: OpenAssistant/reward-model-deberta-v3-large-v2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: NLP_A2_alt 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. --> # NLP_A2_alt This model is a fine-tuned version of [OpenAssistant/reward-model-deberta-v3-large-v2](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3513 - Accuracy: 0.8834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.9988 \| 419 \| 0.4063 \| 0.8767 \| \| 0.0925 \| 1.9976 \| 838 \| 0.3513 \| 0.8834 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.19.1
tiiuae/visper	tiiuae	2024-06-05T22:37:35Z	0	9	null	[ "en", "es", "fr", "ar", "zh", "dataset:tiiuae/visper", "license:cc-by-nc-2.0", "region:us" ]	null	2024-05-26T12:22:21Z	--- datasets: - tiiuae/visper language: - en - es - fr - ar - zh inference: false license: cc-by-nc-2.0 metrics: - wer --- # ViSpeR: Multilingual Audio-Visual Speech Recognition ViSPer is a model for audio visual speech recognition (VSR/AVSR). Trained on 5500 hours of labelled video data. # Training details: We use our proposed dataset to train a encoder-decoder model in a fully-supervised manner under a multi-lingual setting. While the encoder size is 12 layers, the decoder size is 6 layers. The hidden size, MLP and number of heads are set to 768, 3072 and 12, respectively. The unigram tokenizers are learned for all languages combined and have a vocabulary size of 21k. The models are trained for 150 epochs on 64 Nvidia A100 GPUs (40GB) using AdamW optimizer with max LR of 1e-3 and a weight decay of 0.1. A cosine scheduler with a warm-up of 5 epochs is used for training. The maximum batch size per GPU is set to 1800 video frames. # Performance: We provide the results of the model on our proposed benchmarks in this table: \| Language \| VSR (WER/CER) \| AVSR (WER/CER) \| \|----------\|---------------\|----------------\| \| French \| 29.8 \| 5.7 \| \| Spanish \| 39.4 \| 4.4 \| \| Arabic \| 47.8 \| 8.4 \| \| Chinese \| 51.3 (CER) \| 15.4 (CER) \| \| English \| 49.1 \| 8.1 \| # Broader impact: In essence, while we hope that ViSPer will open the doors for new research questions and opportunities, and should only be used for this purpose. There are also potential dual use concerns that come with releasing ViSPer (dataset and models), trained on a substantial corpus of multilingual video data. While the technology behind ViSPer offers significant advances in multimodal speech recognition, it should only be used for research purposes. ## ViSpeR paper coming soon ## Check our VSR related works ```bash @inproceedings{djilali2023lip2vec, title={Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping}, author={Djilali, Yasser Abdelaziz Dahou and Narayan, Sanath and Boussaid, Haithem and Almazrouei, Ebtessam and Debbah, Merouane}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={13790--13801}, year={2023} } @inproceedings{djilali2024vsr, title={Do VSR Models Generalize Beyond LRS3?}, author={Djilali, Yasser Abdelaziz Dahou and Narayan, Sanath and LeBihan, Eustache and Boussaid, Haithem and Almazrouei, Ebtesam and Debbah, Merouane}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={6635--6644}, year={2024} } ```
camilomj/youngblooddrums	camilomj	2024-06-05T22:34:28Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-05T22:33:32Z	--- license: apache-2.0 ---
aerdna/quantize_v0.2	aerdna	2024-06-05T22:32:13Z	81	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-06-05T22:28:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
AprendeIngenia/vehicle_plate_detection	AprendeIngenia	2024-06-05T22:18:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-05T22:03:59Z	--- license: apache-2.0 ---
bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF	bartowski	2024-06-05T22:10:08Z	2,220	10	transformers	[ "transformers", "gguf", "code", "text-generation", "license:other", "endpoints_compatible", "region:us", "imatrix" ]	text-generation	2024-06-05T19:09:00Z	--- library_name: transformers license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Codestral-22B-v0.1-abliterated-v3 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3086">b3086</a> for quantization. Original model: https://huggingface.co/failspy/Codestral-22B-v0.1-abliterated-v3 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format No chat template specified so default is used. This may be incorrect, check original model card for details. ``` <s>[INST] <<SYS>> {system_prompt} <</SYS>> {prompt}[/INST] </s> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Codestral-22B-v0.1-abliterated-v3-Q8_0.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q8_0.gguf) \| Q8_0 \| 23.64GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Codestral-22B-v0.1-abliterated-v3-Q6_K.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q6_K.gguf) \| Q6_K \| 18.25GB \| Very high quality, near perfect, recommended. \| \| [Codestral-22B-v0.1-abliterated-v3-Q5_K_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q5_K_M.gguf) \| Q5_K_M \| 15.72GB \| High quality, recommended. \| \| [Codestral-22B-v0.1-abliterated-v3-Q5_K_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q5_K_S.gguf) \| Q5_K_S \| 15.32GB \| High quality, recommended. \| \| [Codestral-22B-v0.1-abliterated-v3-Q4_K_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q4_K_M.gguf) \| Q4_K_M \| 13.34GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Codestral-22B-v0.1-abliterated-v3-Q4_K_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q4_K_S.gguf) \| Q4_K_S \| 12.66GB \| Slightly lower quality with more space savings, recommended. \| \| [Codestral-22B-v0.1-abliterated-v3-IQ4_XS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ4_XS.gguf) \| IQ4_XS \| 11.93GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Codestral-22B-v0.1-abliterated-v3-Q3_K_L.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q3_K_L.gguf) \| Q3_K_L \| 11.73GB \| Lower quality but usable, good for low RAM availability. \| \| [Codestral-22B-v0.1-abliterated-v3-Q3_K_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q3_K_M.gguf) \| Q3_K_M \| 10.75GB \| Even lower quality. \| \| [Codestral-22B-v0.1-abliterated-v3-IQ3_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ3_M.gguf) \| IQ3_M \| 10.06GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Codestral-22B-v0.1-abliterated-v3-Q3_K_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q3_K_S.gguf) \| Q3_K_S \| 9.64GB \| Low quality, not recommended. \| \| [Codestral-22B-v0.1-abliterated-v3-IQ3_XS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ3_XS.gguf) \| IQ3_XS \| 9.17GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Codestral-22B-v0.1-abliterated-v3-IQ3_XXS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ3_XXS.gguf) \| IQ3_XXS \| 8.59GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Codestral-22B-v0.1-abliterated-v3-Q2_K.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-Q2_K.gguf) \| Q2_K \| 8.27GB \| Very low quality but surprisingly usable. \| \| [Codestral-22B-v0.1-abliterated-v3-IQ2_M.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ2_M.gguf) \| IQ2_M \| 7.61GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Codestral-22B-v0.1-abliterated-v3-IQ2_S.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ2_S.gguf) \| IQ2_S \| 7.03GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Codestral-22B-v0.1-abliterated-v3-IQ2_XS.gguf](https://huggingface.co/bartowski/Codestral-22B-v0.1-abliterated-v3-GGUF/blob/main/Codestral-22B-v0.1-abliterated-v3-IQ2_XS.gguf) \| IQ2_XS \| 6.64GB \| Very low quality, uses SOTA techniques to be usable. \| ## Downloading using huggingface-cli 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/Codestral-22B-v0.1-abliterated-v3-GGUF --include "Codestral-22B-v0.1-abliterated-v3-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/Codestral-22B-v0.1-abliterated-v3-GGUF --include "Codestral-22B-v0.1-abliterated-v3-Q8_0.gguf/" --local-dir Codestral-22B-v0.1-abliterated-v3-Q8_0 ``` You can either specify a new local-dir (Codestral-22B-v0.1-abliterated-v3-Q8_0) or download them all in place (./) ## Which file should I choose? 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 and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
ksw1/llama-3-8b-sleeper-agent	ksw1	2024-06-05T22:09:19Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T22:03:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: ksw1 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
maxorax/maxorax-lora	maxorax	2024-06-05T21:57:55Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-05T21:40:06Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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aerdna/quantize_v0.1	aerdna	2024-06-05T21:53:25Z	79	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-06-05T21:51:54Z	--- 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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antonryoung02/deberta-v3-small-essay-grading-classification	antonryoung02	2024-06-05T21:52:23Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-05T00:20:36Z	--- tags: - generated_from_trainer model-index: - name: deberta-v3-small-essay-grading-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta-v3-small-essay-grading-classification This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8790 - Qwk: 0.7595 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 433 \| 0.9495 \| 0.7132 \| \| 1.0266 \| 2.0 \| 866 \| 0.8787 \| 0.7561 \| \| 1.0266 \| 3.0 \| 1299 \| 0.8790 \| 0.7595 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
fofr/consistent-character-weights	fofr	2024-06-05T21:51:09Z	0	4	null	[ "onnx", "region:us" ]	null	2024-06-05T21:21:21Z	Weights used in the consistent-character ComfyUI workflow: - https://github.com/fofr/cog-consistent-character - https://replicate.com/fofr/consistent-character
tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-4_lora2	tyzhu	2024-06-05T21:47:06Z	4	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-05T08:30:56Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-4_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa metrics: - name: Accuracy type: accuracy value: 0.5165079365079365 library_name: peft --- <!-- 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. --> # lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-4_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa dataset. It achieves the following results on the evaluation set: - Loss: 3.1631 - Accuracy: 0.5165 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 1.7567 \| 0.9998 \| 1089 \| 2.2626 \| 0.5190 \| \| 1.5617 \| 1.9995 \| 2178 \| 2.2436 \| 0.5246 \| \| 1.343 \| 2.9993 \| 3267 \| 2.3385 \| 0.5237 \| \| 1.1682 \| 4.0 \| 4357 \| 2.4995 \| 0.5215 \| \| 1.0141 \| 4.9998 \| 5446 \| 2.6397 \| 0.5182 \| \| 0.9023 \| 5.9995 \| 6535 \| 2.7929 \| 0.5170 \| \| 0.8008 \| 6.9993 \| 7624 \| 2.8233 \| 0.5162 \| \| 0.7377 \| 8.0 \| 8714 \| 2.8833 \| 0.5180 \| \| 0.6732 \| 8.9998 \| 9803 \| 2.9550 \| 0.5165 \| \| 0.6225 \| 9.9995 \| 10892 \| 2.9767 \| 0.5165 \| \| 0.5858 \| 10.9993 \| 11981 \| 3.0117 \| 0.5165 \| \| 0.5618 \| 12.0 \| 13071 \| 3.0317 \| 0.5170 \| \| 0.5464 \| 12.9998 \| 14160 \| 3.0686 \| 0.5167 \| \| 0.5243 \| 13.9995 \| 15249 \| 3.0829 \| 0.5149 \| \| 0.5066 \| 14.9993 \| 16338 \| 3.0958 \| 0.5127 \| \| 0.4947 \| 16.0 \| 17428 \| 3.0921 \| 0.5153 \| \| 0.4841 \| 16.9998 \| 18517 \| 3.1170 \| 0.5162 \| \| 0.4727 \| 17.9995 \| 19606 \| 3.1375 \| 0.5172 \| \| 0.4634 \| 18.9993 \| 20695 \| 3.1323 \| 0.5150 \| \| 0.4468 \| 19.9954 \| 21780 \| 3.1631 \| 0.5165 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_3e-4_lora2	tyzhu	2024-06-05T21:45:56Z	5	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa", "base_model:Qwen/Qwen1.5-4B", "base_model:adapter:Qwen/Qwen1.5-4B", "license:other", "model-index", "region:us" ]	null	2024-06-05T08:30:45Z	--- license: other base_model: Qwen/Qwen1.5-4B tags: - generated_from_trainer datasets: - tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa metrics: - accuracy model-index: - name: lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_3e-4_lora2 results: - task: name: Causal Language Modeling type: text-generation dataset: name: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa type: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa metrics: - name: Accuracy type: accuracy value: 0.7763580786026201 library_name: peft --- <!-- 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. --> # lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_3e-4_lora2 This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa dataset. It achieves the following results on the evaluation set: - Loss: 0.4756 - Accuracy: 0.7764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:--------:\| \| 1.5356 \| 0.9998 \| 1089 \| 1.3711 \| 0.6864 \| \| 1.3102 \| 1.9995 \| 2178 \| 1.1753 \| 0.7020 \| \| 1.0549 \| 2.9993 \| 3267 \| 1.0095 \| 0.7164 \| \| 0.8461 \| 4.0 \| 4357 \| 0.8722 \| 0.7297 \| \| 0.701 \| 4.9998 \| 5446 \| 0.7641 \| 0.7406 \| \| 0.5977 \| 5.9995 \| 6535 \| 0.6797 \| 0.7490 \| \| 0.5238 \| 6.9993 \| 7624 \| 0.6209 \| 0.7559 \| \| 0.4742 \| 8.0 \| 8714 \| 0.5837 \| 0.7600 \| \| 0.438 \| 8.9998 \| 9803 \| 0.5532 \| 0.7638 \| \| 0.402 \| 9.9995 \| 10892 \| 0.5331 \| 0.7664 \| \| 0.383 \| 10.9993 \| 11981 \| 0.5156 \| 0.7685 \| \| 0.3627 \| 12.0 \| 13071 \| 0.5070 \| 0.7702 \| \| 0.3521 \| 12.9998 \| 14160 \| 0.4984 \| 0.7714 \| \| 0.344 \| 13.9995 \| 15249 \| 0.4925 \| 0.7722 \| \| 0.3341 \| 14.9993 \| 16338 \| 0.4847 \| 0.7736 \| \| 0.3275 \| 16.0 \| 17428 \| 0.4808 \| 0.7748 \| \| 0.3223 \| 16.9998 \| 18517 \| 0.4776 \| 0.7751 \| \| 0.3155 \| 17.9995 \| 19606 \| 0.4804 \| 0.7758 \| \| 0.3033 \| 18.9993 \| 20695 \| 0.4787 \| 0.7761 \| \| 0.2989 \| 19.9954 \| 21780 \| 0.4756 \| 0.7764 \| ### Framework versions - PEFT 0.5.0 - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
aerdna/testv0.1	aerdna	2024-06-05T21:45:40Z	10	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T21:37:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
stablediffusionapi/hardcore-hentai	stablediffusionapi	2024-06-05T21:44:33Z	31	5	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-04-21T04:01:40Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Hardcore Hentai API Inference ![generated from modelslab.com](https://d9jy2smsrdjcq.cloudfront.net/generations/0-d4745818-da6c-4b29-8995-ec638478afd1.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "hardcore-hentai" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/hardcore-hentai) Model link: [View model](https://modelslab.com/models/hardcore-hentai) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "hardcore-hentai", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
ClaudioItaly/EvolutionDark	ClaudioItaly	2024-06-05T21:38:56Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:ClaudioItaly/TopEvolutionWiz", "base_model:merge:ClaudioItaly/TopEvolutionWiz", "base_model:TeeZee/DarkSapling-7B-v1.0", "base_model:merge:TeeZee/DarkSapling-7B-v1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T16:51:33Z	--- base_model: - TeeZee/DarkSapling-7B-v1.0 - ClaudioItaly/TopEvolutionWiz library_name: transformers tags: - mergekit - merge --- This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [TeeZee/DarkSapling-7B-v1.0](https://huggingface.co/TeeZee/DarkSapling-7B-v1.0) * [ClaudioItaly/TopEvolutionWiz](https://huggingface.co/ClaudioItaly/TopEvolutionWiz) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ClaudioItaly/TopEvolutionWiz - model: TeeZee/DarkSapling-7B-v1.0 merge_method: slerp base_model: ClaudioItaly/TopEvolutionWiz dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers ```
lmstudio-community/Codestral-22B-v0.1-GGUF	lmstudio-community	2024-06-05T21:35:08Z	26,873	19	null	[ "gguf", "code", "text-generation", "base_model:mistralai/Codestral-22B-v0.1", "base_model:quantized:mistralai/Codestral-22B-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T19:01:50Z	--- license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code license: apache-2.0 quantized_by: bartowski pipeline_tag: text-generation lm_studio: param_count: 22b use_case: coding release_date: 29-05-2024 model_creator: mistralai prompt_template: Mistral Instruct system_prompt: none base_model: mistral original_repo: mistralai/Codestral-22B-v0.1 base_model: mistralai/Codestral-22B-v0.1 --- ## 💫 Community Model> Codestral 22B v0.1 by Mistral AI 👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC). Model creator: [Mistral AI](https://huggingface.co/mistralai)<br> Original model: [Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1)<br> GGUF quantization: provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3024](https://github.com/ggerganov/llama.cpp/releases/tag/b3024)<br> ## Model Summary: Codestral is a brand new coding model released by the Mistral team. This 22B model is the first of its size and the first ever specialized model released by this team.<br> Supporting both instruction prompting and popular Fill in the Middle (FIM) tokens for predictions, this model should be all around great for all your coding tasks. ## Prompt template: Choose the `Mistral Instruct` preset in your LM Studio. Under the hood, the model will see a prompt that's formatted like so: ``` <s>[INST] {prompt} [/INST]</s> ``` This model also supports the following FIM tokens: `[PREFIX]`, `[SUFFIX]` ## Technical Details Codestral 22B 0.1 is trained on a dataset of 80+ programming languages including of course Python, Java, C++, Javascript, and Bash. It supports both instruction querying as well as Fill in the Middle querying. More details and benchmark information can be found on their blogpost here: https://mistral.ai/news/codestral/ ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) 🙏 Special thanks to [Kalomaze](https://github.com/kalomaze) and [Dampf](https://github.com/Dampfinchen) for their work on the dataset (linked [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)) that was used for calculating the imatrix for all sizes. ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
Luispark2025/Luis_Park	Luispark2025	2024-06-05T21:32:14Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-06-05T20:06:44Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 datasets: - generator model-index: - name: Luis_Park 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. --> # Luis_Park This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.1463 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 15 - total_train_batch_size: 15 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2094 \| 0.8955 \| 4 \| 1.1494 \| \| 1.0733 \| 1.7910 \| 8 \| 1.1463 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.19.2 - Tokenizers 0.19.1
Ashmal/MBZUAI-ORYX-new	Ashmal	2024-06-05T21:31:25Z	2,904	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-04T08:57:58Z	--- library_name: transformers license: apache-2.0 --- This is the Arabic test model built at MBZUAI. More details of the projects will be announced later along with the release. This model card is just to test the capabilities of this model on Arabic benchmarks.
bartowski/L3-TheSpice-8b-v0.8.3-GGUF	bartowski	2024-06-05T21:24:34Z	254	9	null	[ "gguf", "text-generation", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-generation	2024-04-25T15:46:16Z	--- license: cc-by-nc-4.0 quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of L3-TheSpice-8b-v0.8.3 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2717">b2717</a> for quantization. Original model: https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3 All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` {System Prompt} Username: {Input} BotName: {Response} Username: {Input} BotName: {Response} ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [L3-TheSpice-8b-v0.8.3-Q8_0.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q8_0.gguf) \| Q8_0 \| 8.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [L3-TheSpice-8b-v0.8.3-Q6_K.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q6_K.gguf) \| Q6_K \| 6.59GB \| Very high quality, near perfect, recommended. \| \| [L3-TheSpice-8b-v0.8.3-Q5_K_M.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q5_K_M.gguf) \| Q5_K_M \| 5.73GB \| High quality, recommended. \| \| [L3-TheSpice-8b-v0.8.3-Q5_K_S.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| High quality, recommended. \| \| [L3-TheSpice-8b-v0.8.3-Q4_K_M.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q4_K_M.gguf) \| Q4_K_M \| 4.92GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [L3-TheSpice-8b-v0.8.3-Q4_K_S.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q4_K_S.gguf) \| Q4_K_S \| 4.69GB \| Slightly lower quality with more space savings, recommended. \| \| [L3-TheSpice-8b-v0.8.3-IQ4_NL.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ4_NL.gguf) \| IQ4_NL \| 4.67GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [L3-TheSpice-8b-v0.8.3-IQ4_XS.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ4_XS.gguf) \| IQ4_XS \| 4.44GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [L3-TheSpice-8b-v0.8.3-Q3_K_L.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q3_K_L.gguf) \| Q3_K_L \| 4.32GB \| Lower quality but usable, good for low RAM availability. \| \| [L3-TheSpice-8b-v0.8.3-Q3_K_M.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q3_K_M.gguf) \| Q3_K_M \| 4.01GB \| Even lower quality. \| \| [L3-TheSpice-8b-v0.8.3-IQ3_M.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ3_M.gguf) \| IQ3_M \| 3.78GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [L3-TheSpice-8b-v0.8.3-IQ3_S.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ3_S.gguf) \| IQ3_S \| 3.68GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [L3-TheSpice-8b-v0.8.3-Q3_K_S.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q3_K_S.gguf) \| Q3_K_S \| 3.66GB \| Low quality, not recommended. \| \| [L3-TheSpice-8b-v0.8.3-IQ3_XS.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ3_XS.gguf) \| IQ3_XS \| 3.51GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [L3-TheSpice-8b-v0.8.3-IQ3_XXS.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ3_XXS.gguf) \| IQ3_XXS \| 3.27GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [L3-TheSpice-8b-v0.8.3-Q2_K.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-Q2_K.gguf) \| Q2_K \| 3.17GB \| Very low quality but surprisingly usable. \| \| [L3-TheSpice-8b-v0.8.3-IQ2_M.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ2_M.gguf) \| IQ2_M \| 2.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [L3-TheSpice-8b-v0.8.3-IQ2_S.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ2_S.gguf) \| IQ2_S \| 2.75GB \| Very low quality, uses SOTA techniques to be usable. \| \| [L3-TheSpice-8b-v0.8.3-IQ2_XS.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ2_XS.gguf) \| IQ2_XS \| 2.60GB \| Very low quality, uses SOTA techniques to be usable. \| \| [L3-TheSpice-8b-v0.8.3-IQ2_XXS.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ2_XXS.gguf) \| IQ2_XXS \| 2.39GB \| Lower quality, uses SOTA techniques to be usable. \| \| [L3-TheSpice-8b-v0.8.3-IQ1_M.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ1_M.gguf) \| IQ1_M \| 2.16GB \| Extremely low quality, not recommended. \| \| [L3-TheSpice-8b-v0.8.3-IQ1_S.gguf](https://huggingface.co/bartowski/L3-TheSpice-8b-v0.8.3-GGUF/blob/main/L3-TheSpice-8b-v0.8.3-IQ1_S.gguf) \| IQ1_S \| 2.01GB \| Extremely low quality, not recommended. \| ## Which file should I choose? 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 and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF	mradermacher	2024-06-05T21:24:19Z	12	0	transformers	[ "transformers", "gguf", "en", "base_model:VictorDCh/Llama-3-8B-Instruct-MoE-4", "base_model:quantized:VictorDCh/Llama-3-8B-Instruct-MoE-4", "endpoints_compatible", "region:us", "conversational" ]	null	2024-06-05T18:32:46Z	--- base_model: VictorDCh/Llama-3-8B-Instruct-MoE-4 language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/VictorDCh/Llama-3-8B-Instruct-MoE-4 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q2_K.gguf) \| Q2_K \| 17.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.IQ3_XS.gguf) \| IQ3_XS \| 20.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.IQ3_S.gguf) \| IQ3_S \| 21.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q3_K_S.gguf) \| Q3_K_S \| 21.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.IQ3_M.gguf) \| IQ3_M \| 22.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q3_K_M.gguf) \| Q3_K_M \| 23.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q3_K_L.gguf) \| Q3_K_L \| 24.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.IQ4_XS.gguf) \| IQ4_XS \| 26.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q4_K_S.gguf) \| Q4_K_S \| 27.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q4_K_M.gguf) \| Q4_K_M \| 29.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q5_K_S.gguf) \| Q5_K_S \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q5_K_M.gguf) \| Q5_K_M \| 33.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q6_K.gguf) \| Q6_K \| 39.1 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-MoE-4-GGUF/resolve/main/Llama-3-8B-Instruct-MoE-4.Q8_0.gguf.part2of2) \| Q8_0 \| 50.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
yzhuang/Qwen1.5-7B-Chat-v0.1_fictional_Chinese_v1	yzhuang	2024-06-05T21:20:11Z	156	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:Qwen/Qwen1.5-7B-Chat", "base_model:finetune:Qwen/Qwen1.5-7B-Chat", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-06-05T16:05:22Z	--- license: other base_model: Qwen/Qwen1.5-7B-Chat tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: Qwen1.5-7B-Chat-v0.1_fictional_Chinese_v1 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. --> # Qwen1.5-7B-Chat-v0.1_fictional_Chinese_v1 This model is a fine-tuned version of [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Shansqw/Shans	Shansqw	2024-06-05T21:18:59Z	0	0	fairseq	[ "fairseq", "code", "translation", "aa", "dataset:Replete-AI/code_bagel", "license:apache-2.0", "region:us" ]	translation	2024-06-05T21:17:14Z	--- license: apache-2.0 datasets: - Replete-AI/code_bagel language: - aa metrics: - character library_name: fairseq pipeline_tag: translation tags: - code ---

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