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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 2025-06-03 18:27:50	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 466 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-03 18:27:26	card stringlengths 11 1.01M
PrathapThunga/lora_model	PrathapThunga	2024-05-27T13:30:35Z	3	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	2024-05-27T13:15:07Z	--- 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: PrathapThunga - 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)
lemorim/conformer-ctc-l-bp	lemorim	2024-05-27T13:29:21Z	2	0	nemo	[ "nemo", "pt", "region:us" ]	null	2024-05-27T13:24:46Z	--- language: - pt library_name: nemo ---
bdpoff/mistral-7B-json-editor	bdpoff	2024-05-27T13:27:24Z	3	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "dataset:bdpoff/webllm-json-editor", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T12:30:52Z	--- license: apache-2.0 tags: - text-generation-inference - unsloth - trl - sft datasets: - bdpoff/webllm-json-editor ---
bdpoff/mistral-7B-json-editor-MLC	bdpoff	2024-05-27T13:25:52Z	0	0	null	[ "text-generation-inference", "dataset:bdpoff/webllm-json-editor", "license:apache-2.0", "region:us" ]	null	2024-05-27T13:25:15Z	--- license: apache-2.0 datasets: - bdpoff/webllm-json-editor tags: - text-generation-inference ---
NikolayKozloff/Llama-3-Instruct-8B-SimPO-Q4_0-GGUF	NikolayKozloff	2024-05-27T13:25:13Z	8	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us" ]	null	2024-05-27T13:25:00Z	--- tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/Llama-3-Instruct-8B-SimPO-Q4_0-GGUF This model was converted to GGUF format from [`princeton-nlp/Llama-3-Instruct-8B-SimPO`](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO) 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/princeton-nlp/Llama-3-Instruct-8B-SimPO) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Llama-3-Instruct-8B-SimPO-Q4_0-GGUF --model llama-3-instruct-8b-simpo-q4_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Llama-3-Instruct-8B-SimPO-Q4_0-GGUF --model llama-3-instruct-8b-simpo-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m llama-3-instruct-8b-simpo-q4_0.gguf -n 128 ```
sunyuan/save_model	sunyuan	2024-05-27T13:25:02Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-27T12:51:44Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: CompVis/stable-diffusion-v1-4 inference: true instance_prompt: a photo of sks dog --- <!-- 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. --> # DreamBooth - sunyuan/save_model This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
blair129/taxi-v3	blair129	2024-05-27T13:17:52Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-27T13:17:50Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 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="blair129/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"]) ```
automerger/LlamaMahou-8B	automerger	2024-05-27T13:16:37Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:flammenai/Mahou-1.2-llama3-8B", "base_model:finetune:flammenai/Mahou-1.2-llama3-8B", "license:cc-by-nc-4.0", "region:us" ]	null	2024-05-27T13:13:33Z	--- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - flammenai/Mahou-1.2-llama3-8B --- # LlamaMahou-8B LlamaMahou-8B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [flammenai/Mahou-1.2-llama3-8B](https://huggingface.co/flammenai/Mahou-1.2-llama3-8B) ## 🧩 Configuration ```yaml models: - model: Hastagaras/llama-3-8b-okay # No parameters necessary for base model - model: flammenai/Mahou-1.2-llama3-8B parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: Hastagaras/llama-3-8b-okay parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/LlamaMahou-8B" 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", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
automerger/NeuralmillamaKingnish-8B	automerger	2024-05-27T13:09:14Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:Kukedlc/NeuralMiLLaMa-8B-slerp", "base_model:finetune:Kukedlc/NeuralMiLLaMa-8B-slerp", "license:cc-by-nc-4.0", "region:us" ]	null	2024-05-27T13:09:14Z	--- license: cc-by-nc-4.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - Kukedlc/NeuralMiLLaMa-8B-slerp - KingNish/KingNish-Llama3-8b --- # NeuralmillamaKingnish-8B NeuralmillamaKingnish-8B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [Kukedlc/NeuralMiLLaMa-8B-slerp](https://huggingface.co/Kukedlc/NeuralMiLLaMa-8B-slerp) * [KingNish/KingNish-Llama3-8b](https://huggingface.co/KingNish/KingNish-Llama3-8b) ## 🧩 Configuration ```yaml slices: - sources: - model: Kukedlc/NeuralMiLLaMa-8B-slerp layer_range: [0, 32] - model: KingNish/KingNish-Llama3-8b layer_range: [0, 32] merge_method: slerp base_model: Kukedlc/NeuralMiLLaMa-8B-slerp parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/NeuralmillamaKingnish-8B" 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", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
eirsteir/crs-mistral-7b-instruct	eirsteir	2024-05-27T13:08:43Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-20T07:51:30Z	--- 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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kadirnar/yolov10b	kadirnar	2024-05-27T13:07:28Z	3	0	yolov10	[ "yolov10", "object-detection", "computer-vision", "pypi", "dataset:detection-datasets/coco", "arxiv:2405.14458", "license:agpl-3.0", "region:us" ]	object-detection	2024-05-27T12:46:58Z	--- license: agpl-3.0 tags: - object-detection - computer-vision - yolov10 - pypi datasets: - detection-datasets/coco --- ### Model Description [YOLOv10: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2405.14458v1) [Paper Repo: Implementation of paper - YOLOv10](https://github.com/THU-MIG/yolov10) ### Installation ``` pip install supervision git+https://github.com/THU-MIG/yolov10.git ``` ### Yolov10 Inference ```python from ultralytics import YOLOv10 import supervision as sv import cv2 def attempt_download_from_hub(repo_id, hf_token=None): # https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py from huggingface_hub import hf_hub_download, list_repo_files from huggingface_hub.utils._errors import RepositoryNotFoundError from huggingface_hub.utils._validators import HFValidationError try: repo_files = list_repo_files(repo_id=repo_id, repo_type='model', token=hf_token) model_file = [f for f in repo_files if f.endswith('.pt')][0] file = hf_hub_download( repo_id=repo_id, filename=model_file, repo_type='model', token=hf_token, ) return file except (RepositoryNotFoundError, HFValidationError): return None MODEL_PATH = attempt_download_from_hub("kadirnar/yolov10x", hf_token="hf_token") IMAGE_PATH = 'dog.jpeg' model = YOLOv10(MODEL_PATH) image = cv2.imread(IMAGE_PATH) results = model(source=image, conf=0.25, verbose=False)[0] detections = sv.Detections.from_ultralytics(results) box_annotator = sv.BoxAnnotator() category_dict = { 0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush' } labels = [ f"{category_dict[class_id]} {confidence:.2f}" for class_id, confidence in zip(detections.class_id, detections.confidence) ] annotated_image = box_annotator.annotate( image.copy(), detections=detections, labels=labels ) cv2.imwrite('annotated_dog.jpeg', annotated_image) ``` ### BibTeX Entry and Citation Info ``` @misc{wang2024yolov10, title={YOLOv10: Real-Time End-to-End Object Detection}, author={Ao Wang and Hui Chen and Lihao Liu and Kai Chen and Zijia Lin and Jungong Han and Guiguang Ding}, year={2024}, eprint={2405.14458}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
kadirnar/yolov10m	kadirnar	2024-05-27T13:07:02Z	7	1	yolov10	[ "yolov10", "object-detection", "computer-vision", "pypi", "dataset:detection-datasets/coco", "arxiv:2405.14458", "license:agpl-3.0", "region:us" ]	object-detection	2024-05-27T12:49:07Z	--- license: agpl-3.0 tags: - object-detection - computer-vision - yolov10 - pypi datasets: - detection-datasets/coco --- ### Model Description [YOLOv10: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2405.14458v1) [Paper Repo: Implementation of paper - YOLOv10](https://github.com/THU-MIG/yolov10) ### Installation ``` pip install supervision git+https://github.com/THU-MIG/yolov10.git ``` ### Yolov10 Inference ```python from ultralytics import YOLOv10 import supervision as sv import cv2 def attempt_download_from_hub(repo_id, hf_token=None): # https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py from huggingface_hub import hf_hub_download, list_repo_files from huggingface_hub.utils._errors import RepositoryNotFoundError from huggingface_hub.utils._validators import HFValidationError try: repo_files = list_repo_files(repo_id=repo_id, repo_type='model', token=hf_token) model_file = [f for f in repo_files if f.endswith('.pt')][0] file = hf_hub_download( repo_id=repo_id, filename=model_file, repo_type='model', token=hf_token, ) return file except (RepositoryNotFoundError, HFValidationError): return None MODEL_PATH = attempt_download_from_hub("kadirnar/yolov10x", hf_token="hf_token") IMAGE_PATH = 'dog.jpeg' model = YOLOv10(MODEL_PATH) image = cv2.imread(IMAGE_PATH) results = model(source=image, conf=0.25, verbose=False)[0] detections = sv.Detections.from_ultralytics(results) box_annotator = sv.BoxAnnotator() category_dict = { 0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush' } labels = [ f"{category_dict[class_id]} {confidence:.2f}" for class_id, confidence in zip(detections.class_id, detections.confidence) ] annotated_image = box_annotator.annotate( image.copy(), detections=detections, labels=labels ) cv2.imwrite('annotated_dog.jpeg', annotated_image) ``` ### BibTeX Entry and Citation Info ``` @misc{wang2024yolov10, title={YOLOv10: Real-Time End-to-End Object Detection}, author={Ao Wang and Hui Chen and Lihao Liu and Kai Chen and Zijia Lin and Jungong Han and Guiguang Ding}, year={2024}, eprint={2405.14458}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
kadirnar/yolov10x	kadirnar	2024-05-27T13:06:15Z	13	0	yolov10	[ "yolov10", "object-detection", "computer-vision", "pypi", "dataset:detection-datasets/coco", "arxiv:2405.14458", "license:agpl-3.0", "region:us" ]	object-detection	2024-05-27T12:50:41Z	--- license: agpl-3.0 tags: - object-detection - computer-vision - yolov10 - pypi datasets: - detection-datasets/coco --- ### Model Description [YOLOv10: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2405.14458v1) [Paper Repo: Implementation of paper - YOLOv10](https://github.com/THU-MIG/yolov10) ### Installation ``` pip install supervision git+https://github.com/THU-MIG/yolov10.git ``` ### Yolov10 Inference ```python from ultralytics import YOLOv10 import supervision as sv import cv2 def attempt_download_from_hub(repo_id, hf_token=None): # https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py from huggingface_hub import hf_hub_download, list_repo_files from huggingface_hub.utils._errors import RepositoryNotFoundError from huggingface_hub.utils._validators import HFValidationError try: repo_files = list_repo_files(repo_id=repo_id, repo_type='model', token=hf_token) model_file = [f for f in repo_files if f.endswith('.pt')][0] file = hf_hub_download( repo_id=repo_id, filename=model_file, repo_type='model', token=hf_token, ) return file except (RepositoryNotFoundError, HFValidationError): return None MODEL_PATH = attempt_download_from_hub("kadirnar/yolov10x", hf_token="hf_token") IMAGE_PATH = 'dog.jpeg' model = YOLOv10(MODEL_PATH) image = cv2.imread(IMAGE_PATH) results = model(source=image, conf=0.25, verbose=False)[0] detections = sv.Detections.from_ultralytics(results) box_annotator = sv.BoxAnnotator() category_dict = { 0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush' } labels = [ f"{category_dict[class_id]} {confidence:.2f}" for class_id, confidence in zip(detections.class_id, detections.confidence) ] annotated_image = box_annotator.annotate( image.copy(), detections=detections, labels=labels ) cv2.imwrite('annotated_dog.jpeg', annotated_image) ``` ### BibTeX Entry and Citation Info ``` @misc{wang2024yolov10, title={YOLOv10: Real-Time End-to-End Object Detection}, author={Ao Wang and Hui Chen and Lihao Liu and Kai Chen and Zijia Lin and Jungong Han and Guiguang Ding}, year={2024}, eprint={2405.14458}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
Knobi3/StarlingBeagle-dare	Knobi3	2024-05-27T13:05:32Z	5	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "AI-Sweden-Models/tyr", "mlabonne/NeuralBeagle14-7B", "neph1/bellman-7b-mistral-instruct-v0.2", "base_model:AI-Sweden-Models/tyr", "base_model:merge:AI-Sweden-Models/tyr", "base_model:mlabonne/NeuralBeagle14-7B", "base_model:merge:mlabonne/NeuralBeagle14-7B", "base_model:neph1/bellman-7b-mistral-instruct-v0.2", "base_model:merge:neph1/bellman-7b-mistral-instruct-v0.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-28T12:23:55Z	--- tags: - merge - mergekit - lazymergekit - AI-Sweden-Models/tyr - mlabonne/NeuralBeagle14-7B - neph1/bellman-7b-mistral-instruct-v0.2 base_model: - AI-Sweden-Models/tyr - mlabonne/NeuralBeagle14-7B - neph1/bellman-7b-mistral-instruct-v0.2 --- # StarlingBeagle-dare-ties NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [AI-Sweden-Models/tyr](https://huggingface.co/AI-Sweden-Models/tyr) * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) * [neph1/bellman-7b-mistral-instruct-v0.2](https://huggingface.co/neph1/bellman-7b-mistral-instruct-v0.2) ## 🧩 Configuration ```yaml models: - model: Nexusflow/Starling-LM-7B-beta # No parameters necessary for base model - model: AI-Sweden-Models/tyr parameters: density: 0.53 weight: 0.4 - model: mlabonne/NeuralBeagle14-7B parameters: density: 0.53 weight: 0.3 - model: neph1/bellman-7b-mistral-instruct-v0.2 parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: Nexusflow/Starling-LM-7B-beta parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "knobi3/NeuralPipe-7B-slerp" 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", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
llmware/slim-sentiment-tool	llmware	2024-05-27T13:03:02Z	1,260	5	transformers	[ "transformers", "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-22T10:32:13Z	--- license: apache-2.0 --- # SLIM-SENTIMENT-TOOL <!-- Provide a quick summary of what the model is/does. --> slim-sentiment-tool is a 4_K_M quantized GGUF version of slim-sentiment, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. [slim-sentiment](https://huggingface.co/llmware/slim-sentiment) is part of the SLIM ("Structured Language Instruction Model") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling. To pull the model via API: from huggingface_hub import snapshot_download snapshot_download("llmware/slim-sentiment-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) Load in your favorite GGUF inference engine, or try with llmware as follows: from llmware.models import ModelCatalog # to load the model and make a basic inference model = ModelCatalog().load_model("slim-sentiment-tool") response = model.function_call(text_sample) # this one line will download the model and run a series of tests ModelCatalog().tool_test_run("slim-sentiment-tool", verbose=True) Slim models can also be orchestrated as part of multi-model, multi-step LLMfx calls: from llmware.agents import LLMfx llm_fx = LLMfx() llm_fx.load_tool("sentiment") response = llm_fx.sentiment(text) Note: please review [config.json](https://huggingface.co/llmware/slim-sentiment-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. ## Model Card Contact Darren Oberst & llmware team [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)
Thodns/openai-whisper-medium-125	Thodns	2024-05-27T13:02:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T13:02:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
llmware/slim-q-gen-tiny-tool	llmware	2024-05-27T13:01:29Z	40	1	transformers	[ "transformers", "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-27T09:59:46Z	--- license: apache-2.0 --- # SLIM-Q-GEN-TINY-TOOL <!-- Provide a quick summary of what the model is/does. --> slim-q-gen-tiny-tool is a 4_K_M quantized GGUF version of slim-q-gen-tiny, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. This model implements a generative 'question' (e.g., 'q-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of one key: `{'question': ['What was the amount of revenue in the quarter?']} ` The model has been designed to accept one of three different parameters to guide the type of question-answer created: 'question' (generates a standard question), 'boolean' (generates a 'yes-no' question), and 'multiple choice' (generates a multiple choice question). slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping. Please also see slim-q-gen-phi-3-tool, which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory. [slim-q-gen-tiny](https://huggingface.co/llmware/slim-q-gen-tiny) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation. To pull the model via API: from huggingface_hub import snapshot_download snapshot_download("llmware/slim-q-gen-tiny-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) Load in your favorite GGUF inference engine, or try with llmware as follows: from llmware.models import ModelCatalog # to load the model and make a basic inference model = ModelCatalog().load_model("slim-q-gen-tiny-tool", sample=True, temperature=0.7) response = model.function_call(text_sample, params=['question']) # this one line will download the model and run a series of tests ModelCatalog().tool_test_run("slim-q-gen-tiny-tool", verbose=True) Note: please review [config.json](https://huggingface.co/llmware/slim-q-gen-tiny-tool/blob/main/config.json) in the repository for prompt template information, details on the model, and full test set. ## Model Card Contact Darren Oberst & llmware team [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h)
juanquivilla/phi-3-mini-NER-PII-Vast3	juanquivilla	2024-05-27T13:00:36Z	62	0	transformers	[ "transformers", "safetensors", "phi3", "token-classification", "generated_from_trainer", "custom_code", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:finetune:microsoft/Phi-3-mini-4k-instruct", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2024-05-26T20:09:44Z	--- license: mit base_model: microsoft/Phi-3-mini-4k-instruct tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: phi-3-mini-NER-PII-Vast3 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. --> # phi-3-mini-NER-PII-Vast3 This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1122 - Precision: 0.6826 - Recall: 0.8382 - F1: 0.7524 - Accuracy: 0.9697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| F1 \| Validation Loss \| Precision \| Recall \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:------:\|:---------------:\|:---------:\|:------:\| \| 0.1017 \| 1.0 \| 11105 \| 0.9693 \| 0.7506 \| 0.1123 \| 0.6807 \| 0.8364 \| \| 0.0782 \| 2.0 \| 22210 \| 0.1119 \| 0.6819 \| 0.8382 \| 0.7520 \| 0.9697 \| \| 0.0944 \| 3.0 \| 33315 \| 0.1122 \| 0.6826 \| 0.8382 \| 0.7524 \| 0.9697 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
slimaneMakh/BinarySuperClass_Lease_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T12:58:31Z	181	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:58:21Z	--- 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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slimaneMakh/BinarySuperClass_Property_Plant_and_Equipment_tableClassification_27may_distilBert_	slimaneMakh	2024-05-27T12:57:49Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:57:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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slimaneMakh/BinarySuperClass_Tax_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T12:57:16Z	181	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:57:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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slimaneMakh/BinarySuperClass_Deferred_tax_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T12:56:47Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:56:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Knobi3/EvoMerge1	Knobi3	2024-05-27T12:54:29Z	47	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "en", "sv", "arxiv:2212.04089", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-10T15:36:57Z	--- base_model: [] library_name: transformers tags: - mergekit - merge license: apache-2.0 language: - en - sv --- # Evolutionary model merging This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). However, only 2 evalations. ## Merge Details ### Merge Method This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using Mistral-7B-Instruct-v0.2 as a base. ### Models Merged The following models were included in the merge: * Starling-LM-7B-beta_581094980 * NeuralBeagle14-7B_2368216670 * Mistral-7B-Instruct-v0.2_674785087 * sol_2748493923 ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: /content/evol_merge_storage/input_models/Mistral-7B-Instruct-v0.2_674785087 dtype: bfloat16 merge_method: task_arithmetic parameters: int8_mask: 1.0 normalize: 0.0 slices: - sources: - layer_range: [0, 8] model: /content/evol_merge_storage/input_models/NeuralBeagle14-7B_2368216670 parameters: weight: 0.445198554642378 - layer_range: [0, 8] model: /content/evol_merge_storage/input_models/Starling-LM-7B-beta_581094980 parameters: weight: 0.005659554655209731 - layer_range: [0, 8] model: /content/evol_merge_storage/input_models/sol_2748493923 parameters: weight: 0.3149085451085008 - layer_range: [0, 8] model: /content/evol_merge_storage/input_models/Mistral-7B-Instruct-v0.2_674785087 - sources: - layer_range: [8, 16] model: /content/evol_merge_storage/input_models/NeuralBeagle14-7B_2368216670 parameters: weight: 0.07684631401806669 - layer_range: [8, 16] model: /content/evol_merge_storage/input_models/Starling-LM-7B-beta_581094980 parameters: weight: 0.4987353671063862 - layer_range: [8, 16] model: /content/evol_merge_storage/input_models/sol_2748493923 parameters: weight: 0.5082785378119663 - layer_range: [8, 16] model: /content/evol_merge_storage/input_models/Mistral-7B-Instruct-v0.2_674785087 - sources: - layer_range: [16, 24] model: /content/evol_merge_storage/input_models/NeuralBeagle14-7B_2368216670 parameters: weight: 0.5687639014384054 - layer_range: [16, 24] model: /content/evol_merge_storage/input_models/Starling-LM-7B-beta_581094980 parameters: weight: 0.5007180726769771 - layer_range: [16, 24] model: /content/evol_merge_storage/input_models/sol_2748493923 parameters: weight: 0.3855348742111532 - layer_range: [16, 24] model: /content/evol_merge_storage/input_models/Mistral-7B-Instruct-v0.2_674785087 - sources: - layer_range: [24, 32] model: /content/evol_merge_storage/input_models/NeuralBeagle14-7B_2368216670 parameters: weight: 0.17691410154696996 - layer_range: [24, 32] model: /content/evol_merge_storage/input_models/Starling-LM-7B-beta_581094980 parameters: weight: 0.2031085126199354 - layer_range: [24, 32] model: /content/evol_merge_storage/input_models/sol_2748493923 parameters: weight: 0.5603073995258283 - layer_range: [24, 32] model: /content/evol_merge_storage/input_models/Mistral-7B-Instruct-v0.2_674785087 ```
Aho-official/Ganyu-Genshin-Impact-JP	Aho-official	2024-05-27T12:53:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T12:50:46Z	--- license: apache-2.0 ---
slimaneMakh/BinarySuperClass_Derivatives_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T12:52:16Z	181	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:52:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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llmware/slim-q-gen-phi-3	llmware	2024-05-27T12:52:10Z	128	3	transformers	[ "transformers", "pytorch", "phi3", "text-generation", "conversational", "custom_code", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-27T10:37:43Z	--- license: apache-2.0 inference: false --- # SLIM-Q-GEN-PHI-3 <!-- Provide a quick summary of what the model is/does. --> slim-q-gen-phi-3 implements a specialized function-calling question generation from a context passage, with output in the form of a python dictionary, e.g.,     `{'question': ['What were earnings per share in the most recent quarter?'] } This model is finetuned on top of phi-3-mini-4k-instruct base. For fast inference use, we would recommend the 'quantized tool' version, e.g., ['slim-q-gen-phi-3-tool'](https://huggingface.co/llmware/slim-q-gen-phi-3-tool). ## Prompt format: `function = "generate"` `params = "{'question', 'boolean', or 'multiple choice'}"` `prompt = "<human> " + {text} + "\n" + `                       `"<{function}> " + {params} + "</{function}>" + "\n<bot>:"` <details> <summary>Transformers Script </summary> model = AutoModelForCausalLM.from_pretrained("llmware/slim-q-gen-phi-3") tokenizer = AutoTokenizer.from_pretrained("llmware/slim-q-gen-phi-3") function = "generate" params = "boolean" text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue." prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:" inputs = tokenizer(prompt, return_tensors="pt") start_of_input = len(inputs.input_ids[0]) outputs = model.generate( inputs.input_ids.to('cpu'), eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.7, max_new_tokens=200 ) output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True) print("output only: ", output_only) [OUTPUT]: {'llm_response': {'question': ['Did Telsa stock decline more than 8% yesterday?']} } # here's the fun part try: output_only = ast.literal_eval(llm_string_output) print("success - converted to python dictionary automatically") except: print("fail - could not convert to python dictionary automatically - ", llm_string_output) </details> <details> <summary>Using as Function Call in LLMWare</summary> from llmware.models import ModelCatalog slim_model = ModelCatalog().load_model("llmware/slim-q-gen-phi-3", sample=True, temperature=0.7) response = slim_model.function_call(text,params=["boolean"], function="generate") print("llmware - llm_response: ", response) </details> ## Model Card Contact Darren Oberst & llmware team [Join us on Discord](https://discord.gg/MhZn5Nc39h)
Thodns/openai-whisper-medium-190	Thodns	2024-05-27T12:45:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T12:44:54Z	--- 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. 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slimaneMakh/BinarySuperClass_Payables_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T12:44:50Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:44:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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slimaneMakh/BinarySuperClass_Borrowings_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T12:43:37Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:43:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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PCS/Extract-O-Matic	PCS	2024-05-27T12:41:31Z	110	0	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "image-to-text", "en", "dataset:katanaml-org/invoices-donut-data-v1", "license:mit", "endpoints_compatible", "region:us" ]	image-to-text	2024-05-27T12:19:04Z	--- license: mit language: - en pipeline_tag: image-to-text datasets: - katanaml-org/invoices-donut-data-v1 ---
DaizeDong/GraphsGPT-4W	DaizeDong	2024-05-27T12:37:59Z	66	0	transformers	[ "transformers", "safetensors", "graphs_gpt", "text-generation", "biology", "medical", "chemistry", "graph-ml", "arxiv:2402.02464", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	graph-ml	2023-12-12T12:35:47Z	--- license: apache-2.0 pipeline_tag: graph-ml tags: - biology - medical - chemistry --- This is the checkpoint of ICML 2024 paper [A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer](https://arxiv.org/abs/2402.02464). For more information, please check the [GitHub Page](https://github.com/DaizeDong/GraphsGPT).
DaizeDong/GraphsGPT-2W	DaizeDong	2024-05-27T12:37:44Z	64	1	transformers	[ "transformers", "safetensors", "graphs_gpt", "text-generation", "biology", "medical", "chemistry", "graph-ml", "arxiv:2402.02464", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	graph-ml	2023-12-12T12:34:39Z	--- license: apache-2.0 pipeline_tag: graph-ml tags: - biology - medical - chemistry --- This is the checkpoint of ICML 2024 paper [A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer](https://arxiv.org/abs/2402.02464). For more information, please check the [GitHub Page](https://github.com/DaizeDong/GraphsGPT).
DaizeDong/GraphsGPT-1W	DaizeDong	2024-05-27T12:37:32Z	209	0	transformers	[ "transformers", "safetensors", "graphs_gpt", "text-generation", "biology", "medical", "chemistry", "graph-ml", "arxiv:2402.02464", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	graph-ml	2023-12-12T12:32:05Z	--- license: apache-2.0 pipeline_tag: graph-ml tags: - biology - medical - chemistry --- This is the checkpoint of ICML 2024 paper [A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer](https://arxiv.org/abs/2402.02464). For more information, please check the [GitHub Page](https://github.com/DaizeDong/GraphsGPT).
vgarg/results	vgarg	2024-05-27T12:36:51Z	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	2024-05-27T12:27:19Z	--- license: apache-2.0 base_model: bert-base-uncased 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-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6240 ## 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: 512 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 0.6822 \| \| No log \| 2.0 \| 2 \| 0.6416 \| \| No log \| 3.0 \| 3 \| 0.6240 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
slimaneMakh/BinarySuperClass_Inventories_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T12:36:44Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:36:30Z	--- 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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OneFly7/T5-base-finetuned-on-webnlg-train-eredat-Q1-epoch10	OneFly7	2024-05-27T12:36:29Z	163	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T12:35:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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CreitinGameplays/ConvAI-9b	CreitinGameplays	2024-05-27T12:36:18Z	18	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:CreitinGameplays/merged-data-v2", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:finetune:HuggingFaceH4/zephyr-7b-beta", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-18T16:14:34Z	--- license: mit datasets: - CreitinGameplays/merged-data-v2 base_model: - HuggingFaceH4/zephyr-7b-beta - mistral-community/Mistral-7B-v0.2 language: - en --- # ConvAI-9b: A Conversational AI Model ![img](https://huggingface.co/CreitinGameplays/ConvAI-9b/resolve/main/convai.png) ## 1. Model Details * Model Name: ConvAI-9b * Authors: CreitinGameplays * Date: April 18th, 2024 ## 2. Model Description ConvAI-9b is a fine-tuned conversational AI model with 9 billion parameters. It is based on the following models: * Base Model: [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) * Merged Model: [mistral-community/Mistral-7B-v0.2](https://huggingface.co/mistral-community/Mistral-7B-v0.2) ## 3. Training Data The model was fine-tuned on a custom dataset of conversations between an AI assistant and a user. The dataset format followed a specific structure: ``` <\|system\|> (system prompt, e.g.: You are a helpful AI language model called ChatGPT, your goal is helping users with their questions) </s> <\|user\|> (user prompt) </s> ``` ## 4. Intended Uses ConvAI-9b is intended for use in conversational AI applications, such as: * Chatbots * Virtual assistants * Interactive storytelling * Educational tools ## 5. Limitations * Like any other language model, ConvAI-9b may generate incorrect or misleading responses. * It may exhibit biases present in the training data. * The model's performance can be affected by the quality and format of the input text. ## 6. Evaluation \| Metrics \|Value\| \|----------\|-----\| \|ARC \|57.50\| \|HellaSwag \|80.34\| \|TruthfulQA\|49.54\| \|Winogrande\|76.24\| More detailed evaluation [here](https://huggingface.co/datasets/open-llm-leaderboard/details_CreitinGameplays__ConvAI-9b)
casque/Greater_Lord_Rukkhadevata_costume_1_v1	casque	2024-05-27T12:36:17Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-27T12:34:26Z	--- license: creativeml-openrail-m ---
frikh-said/Phi-3-mini-GameStory	frikh-said	2024-05-27T12:35:14Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:HaitameLaframe/Phi3_StoryGenerator", "base_model:finetune:HaitameLaframe/Phi3_StoryGenerator", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-27T12:35:06Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: HaitameLaf/Phi3-Game16bit --- # Uploaded model - Developed by: frikh-said - License: apache-2.0 - Finetuned from model : HaitameLaf/Phi3-Game16bit 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)
slimaneMakh/BinarySuperClass_Intangible_Assets_and_Goodwill_tableClassification_27may_distilBer	slimaneMakh	2024-05-27T12:33:48Z	163	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T12:33:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
croissantllm/CroissantLLMChat-v0.1-q4f16_1-MLC	croissantllm	2024-05-27T12:33:23Z	0	0	null	[ "text-generation", "conversational", "fr", "en", "license:mit", "region:us" ]	text-generation	2024-04-26T10:42:14Z	--- license: mit language: - fr - en pipeline_tag: text-generation --- CroissantLLM weights in Machine Learning Compilation (MLC) format, with q4f16 quantization. Learn more about Machine Learning Compilation and how to use these weights [here](https://llm.mlc.ai/docs/get_started/introduction.html).
croissantllm/CroissantLLMChat-v0.1-q0f16-MLC	croissantllm	2024-05-27T12:32:27Z	0	0	null	[ "text-generation", "conversational", "fr", "en", "license:mit", "region:us" ]	text-generation	2024-04-26T10:43:48Z	--- license: mit language: - fr - en pipeline_tag: text-generation --- CroissantLLM weights in Machine Learning Compilation (MLC) format, with q0f16 quantization. Learn more about Machine Learning Compilation and how to use these weights [here](https://llm.mlc.ai/docs/get_started/introduction.html).
croissantllm/CroissantLLMChat-v0.1-q0f32-MLC	croissantllm	2024-05-27T12:31:41Z	0	0	null	[ "text-generation", "conversational", "fr", "en", "license:mit", "region:us" ]	text-generation	2024-04-26T10:44:07Z	--- license: mit language: - fr - en pipeline_tag: text-generation --- CroissantLLM weights in Machine Learning Compilation (MLC) format, with q0f32 quantization. Learn more about Machine Learning Compilation and how to use these weights [here](https://llm.mlc.ai/docs/get_started/introduction.html).
Rachel9916/ppo-Huggy	Rachel9916	2024-05-27T12:29:36Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-27T12:26:58Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Rachel9916/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Stern5497/nir-test-xlm-roberta-base	Stern5497	2024-05-27T12:27:27Z	10	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-27T12:26:59Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Stern5497/nir-test-xlm-roberta-base This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('Stern5497/nir-test-xlm-roberta-base') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('Stern5497/nir-test-xlm-roberta-base') model = AutoModel.from_pretrained('Stern5497/nir-test-xlm-roberta-base') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=Stern5497/nir-test-xlm-roberta-base) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 6 with parameters: ``` {'batch_size': 16} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 5000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 0, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
lctzz540/llamasft	lctzz540	2024-05-27T12:27:18Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ura-hcmut/ura-llama-7b", "base_model:adapter:ura-hcmut/ura-llama-7b", "region:us" ]	null	2024-05-27T07:30:14Z	--- library_name: peft base_model: ura-hcmut/ura-llama-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Chatbot-DL/GameStory-Chatboot	Chatbot-DL	2024-05-27T12:26:31Z	79	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:HaitameLaframe/Phi3_StoryGenerator", "base_model:quantized:HaitameLaframe/Phi3_StoryGenerator", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-27T12:25:03Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: HaitameLaf/Phi3-Game16bit --- # Uploaded model - Developed by: Chatbot-DL - License: apache-2.0 - Finetuned from model : HaitameLaf/Phi3-Game16bit 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)
Classacre/AMC_Pantheon	Classacre	2024-05-27T12:26:11Z	8	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/sdxl-turbo", "base_model:adapter:stabilityai/sdxl-turbo", "region:us" ]	text-to-image	2024-05-27T11:39:32Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: not the real photo parameters: negative_prompt: this is test photo output: url: images/images.jpg base_model: stabilityai/sdxl-turbo instance_prompt: null --- # AMCPantheon <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Classacre/AMC_Pantheon/tree/main) them in the Files & versions tab.
Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-5_0bpw_exl2	Zoyd	2024-05-27T12:24:47Z	5	0	transformers	[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "conversational", "en", "zh", "arxiv:2309.16609", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-27T12:09:23Z	--- license: other license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-4B/blob/main/LICENSE language: - en - zh --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_2bpw_exl2)</center> \| <center>1869 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_5bpw_exl2)</center> \| <center>1981 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_0bpw_exl2)</center> \| <center>2170 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_5bpw_exl2)</center> \| <center>2359 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_75bpw_exl2)</center> \| <center>2453 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_0bpw_exl2)</center> \| <center>2548 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_25bpw_exl2)</center> \| <center>2642 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-5_0bpw_exl2)</center> \| <center>2924 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_0bpw_exl2)</center> \| <center>3383 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_5bpw_exl2)</center> \| <center>3571 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-8_0bpw_exl2)</center> \| <center>4119 MB</center> \| <center>8</center> \| # Qwen1.5-Wukong-4B ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/655dc641accde1bbc8b41aec/xOe1Nb3S9Nb53us7_Ja3s.jpeg) Join Our Discord! https://discord.gg/cognitivecomputations Qwen1.5-Wukong-4B is a dealigned chat finetune of the original fantastic Qwen1.5-4B model by the Qwen team. This model was trained on a selection of datasets from Cognitive Computations Dolphin 2.9 https://erichartford.com/dolphin 🐬 This model was trained for 3 epochs. # Example Outputs TBD # Orignal Model Card Below # Qwen1.5-4B ## Introduction Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: * 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated; * Significant performance improvement in Chat models; * Multilingual support of both base and chat models; * Stable support of 32K context length for models of all sizes * No need of `trust_remote_code`. For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). ## Model Details Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. ## Requirements The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2'. ``` ## Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen, title={Qwen Technical Report}, author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, journal={arXiv preprint arXiv:2309.16609}, year={2023} } ```
Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_5bpw_exl2	Zoyd	2024-05-27T12:24:20Z	6	0	transformers	[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "conversational", "en", "zh", "arxiv:2309.16609", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-27T11:56:35Z	--- license: other license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-4B/blob/main/LICENSE language: - en - zh --- Exllamav2 quant (exl2 / 3.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_2bpw_exl2)</center> \| <center>1869 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_5bpw_exl2)</center> \| <center>1981 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_0bpw_exl2)</center> \| <center>2170 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_5bpw_exl2)</center> \| <center>2359 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_75bpw_exl2)</center> \| <center>2453 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_0bpw_exl2)</center> \| <center>2548 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_25bpw_exl2)</center> \| <center>2642 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-5_0bpw_exl2)</center> \| <center>2924 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_0bpw_exl2)</center> \| <center>3383 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_5bpw_exl2)</center> \| <center>3571 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-8_0bpw_exl2)</center> \| <center>4119 MB</center> \| <center>8</center> \| # Qwen1.5-Wukong-4B ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/655dc641accde1bbc8b41aec/xOe1Nb3S9Nb53us7_Ja3s.jpeg) Join Our Discord! https://discord.gg/cognitivecomputations Qwen1.5-Wukong-4B is a dealigned chat finetune of the original fantastic Qwen1.5-4B model by the Qwen team. This model was trained on a selection of datasets from Cognitive Computations Dolphin 2.9 https://erichartford.com/dolphin 🐬 This model was trained for 3 epochs. # Example Outputs TBD # Orignal Model Card Below # Qwen1.5-4B ## Introduction Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: * 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated; * Significant performance improvement in Chat models; * Multilingual support of both base and chat models; * Stable support of 32K context length for models of all sizes * No need of `trust_remote_code`. For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). ## Model Details Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. ## Requirements The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2'. ``` ## Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen, title={Qwen Technical Report}, author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, journal={arXiv preprint arXiv:2309.16609}, year={2023} } ```
Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_5bpw_exl2	Zoyd	2024-05-27T12:23:39Z	4	0	transformers	[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "conversational", "en", "zh", "arxiv:2309.16609", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-27T11:50:16Z	--- license: other license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-4B/blob/main/LICENSE language: - en - zh --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_2bpw_exl2)</center> \| <center>1869 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_5bpw_exl2)</center> \| <center>1981 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_0bpw_exl2)</center> \| <center>2170 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_5bpw_exl2)</center> \| <center>2359 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_75bpw_exl2)</center> \| <center>2453 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_0bpw_exl2)</center> \| <center>2548 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_25bpw_exl2)</center> \| <center>2642 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-5_0bpw_exl2)</center> \| <center>2924 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_0bpw_exl2)</center> \| <center>3383 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_5bpw_exl2)</center> \| <center>3571 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-8_0bpw_exl2)</center> \| <center>4119 MB</center> \| <center>8</center> \| # Qwen1.5-Wukong-4B ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/655dc641accde1bbc8b41aec/xOe1Nb3S9Nb53us7_Ja3s.jpeg) Join Our Discord! https://discord.gg/cognitivecomputations Qwen1.5-Wukong-4B is a dealigned chat finetune of the original fantastic Qwen1.5-4B model by the Qwen team. This model was trained on a selection of datasets from Cognitive Computations Dolphin 2.9 https://erichartford.com/dolphin 🐬 This model was trained for 3 epochs. # Example Outputs TBD # Orignal Model Card Below # Qwen1.5-4B ## Introduction Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: * 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated; * Significant performance improvement in Chat models; * Multilingual support of both base and chat models; * Stable support of 32K context length for models of all sizes * No need of `trust_remote_code`. For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). ## Model Details Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. ## Requirements The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2'. ``` ## Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen, title={Qwen Technical Report}, author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, journal={arXiv preprint arXiv:2309.16609}, year={2023} } ```
Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_2bpw_exl2	Zoyd	2024-05-27T12:22:38Z	5	0	transformers	[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "conversational", "en", "zh", "arxiv:2309.16609", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-27T11:43:55Z	--- license: other license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-4B/blob/main/LICENSE language: - en - zh --- Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_2bpw_exl2)</center> \| <center>1869 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-2_5bpw_exl2)</center> \| <center>1981 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_0bpw_exl2)</center> \| <center>2170 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_5bpw_exl2)</center> \| <center>2359 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-3_75bpw_exl2)</center> \| <center>2453 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_0bpw_exl2)</center> \| <center>2548 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-4_25bpw_exl2)</center> \| <center>2642 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-5_0bpw_exl2)</center> \| <center>2924 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_0bpw_exl2)</center> \| <center>3383 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-6_5bpw_exl2)</center> \| <center>3571 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/RESMPDEV_Qwen1.5-Wukong-4B-8_0bpw_exl2)</center> \| <center>4119 MB</center> \| <center>8</center> \| # Qwen1.5-Wukong-4B ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/655dc641accde1bbc8b41aec/xOe1Nb3S9Nb53us7_Ja3s.jpeg) Join Our Discord! https://discord.gg/cognitivecomputations Qwen1.5-Wukong-4B is a dealigned chat finetune of the original fantastic Qwen1.5-4B model by the Qwen team. This model was trained on a selection of datasets from Cognitive Computations Dolphin 2.9 https://erichartford.com/dolphin 🐬 This model was trained for 3 epochs. # Example Outputs TBD # Orignal Model Card Below # Qwen1.5-4B ## Introduction Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include: * 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated; * Significant performance improvement in Chat models; * Multilingual support of both base and chat models; * Stable support of 32K context length for models of all sizes * No need of `trust_remote_code`. For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). ## Model Details Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. ## Requirements The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2'. ``` ## Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen, title={Qwen Technical Report}, author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu}, journal={arXiv preprint arXiv:2309.16609}, year={2023} } ```
anonydass/tinyllama-colorist-v0	anonydass	2024-05-27T12:18:49Z	124	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T12:17:38Z	--- 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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mradermacher/Daredevil-8B-abliterated-GGUF	mradermacher	2024-05-27T12:11:43Z	517	1	transformers	[ "transformers", "gguf", "en", "base_model:mlabonne/Daredevil-8B-abliterated", "base_model:quantized:mlabonne/Daredevil-8B-abliterated", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-27T11:22:03Z	--- base_model: mlabonne/Daredevil-8B-abliterated 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/mlabonne/Daredevil-8B-abliterated <!-- 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/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-abliterated-GGUF/resolve/main/Daredevil-8B-abliterated.f16.gguf) \| f16 \| 16.2 \| 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 -->
bragour/Camel-13b-chat-3000s	bragour	2024-05-27T12:11:07Z	1	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-13b-bnb-4bit", "base_model:finetune:unsloth/llama-2-13b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T02:49:13Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-13b-bnb-4bit --- # Uploaded model - Developed by: bragour - License: apache-2.0 - Finetuned from model : unsloth/llama-2-13b-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)
Mantis-VL/mantis-8b-idefics2-video-eval-95k-mantis_4096	Mantis-VL	2024-05-27T12:10:33Z	4	0	transformers	[ "transformers", "safetensors", "idefics2", "image-text-to-text", "generated_from_trainer", "base_model:TIGER-Lab/Mantis-8B-Idefics2", "base_model:finetune:TIGER-Lab/Mantis-8B-Idefics2", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-26T23:59:23Z	--- license: apache-2.0 base_model: TIGER-Lab/Mantis-8B-Idefics2 tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-video-eval-95k-mantis_4096 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dongfu/Mantis/runs/ti9gjutf) # mantis-8b-idefics2-video-eval-95k-mantis_4096 This model is a fine-tuned version of [TIGER-Lab/Mantis-8B-Idefics2](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
baek26/all_9899_bart-all_rl	baek26	2024-05-27T12:09:55Z	51	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-05-27T12:08:55Z	--- license: apache-2.0 tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="baek26//tmp/tmp1n688cbf/baek26/all_9899_bart-all_rl") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmp1n688cbf/baek26/all_9899_bart-all_rl") model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmp1n688cbf/baek26/all_9899_bart-all_rl") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
mehdirafiei/SQLCODER3V6	mehdirafiei	2024-05-27T12:08:49Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T11:58:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
tqiqbal/lora_model	tqiqbal	2024-05-27T12:06:38Z	0	1	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-27T12:06:26Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: tqiqbal - 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)
SHAMS-R/AraT5-1500context-sample-epoch-0-v1	SHAMS-R	2024-05-27T12:05:54Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T11:37:53Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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cyr19/llama3-8b-de-quatrain	cyr19	2024-05-27T12:04:54Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T12:02:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Zangs3011/tester_123123	Zangs3011	2024-05-27T12:02:36Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-19T04:34:57Z	--- 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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YorkieOH10/internlm2-math-plus-7b-Q8_0-GGUF	YorkieOH10	2024-05-27T12:02:15Z	0	0	null	[ "gguf", "math", "llama-cpp", "gguf-my-repo", "text-generation", "en", "zh", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-27T12:01:50Z	--- language: - en - zh license: other tags: - math - llama-cpp - gguf-my-repo pipeline_tag: text-generation --- # YorkieOH10/internlm2-math-plus-7b-Q8_0-GGUF This model was converted to GGUF format from [`internlm/internlm2-math-plus-7b`](https://huggingface.co/internlm/internlm2-math-plus-7b) 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/internlm/internlm2-math-plus-7b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo YorkieOH10/internlm2-math-plus-7b-Q8_0-GGUF --model internlm2-math-plus-7b-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo YorkieOH10/internlm2-math-plus-7b-Q8_0-GGUF --model internlm2-math-plus-7b-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. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m internlm2-math-plus-7b-q8_0.gguf -n 128 ```
Treza12/mixtral	Treza12	2024-05-27T12:01:42Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T12:00:46Z	--- 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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Benphil/pegasum-samsum	Benphil	2024-05-27T12:00:32Z	104	0	transformers	[ "transformers", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:google/pegasus-cnn_dailymail", "base_model:finetune:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T10:51:38Z	--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasum-samsum 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. --> # pegasum-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4827 ## 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.6533 \| 0.5430 \| 500 \| 1.4827 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
slimaneMakh/BinarySuperClass_not_found_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T11:58:55Z	181	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T11:58:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
OduguSusmitha/llama-3-8b-Instruct-bnb-4bit-updated_json	OduguSusmitha	2024-05-27T11:57:49Z	10	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-27T05:45:25Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: OduguSusmitha - 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)
huangcanlu/mlc	huangcanlu	2024-05-27T11:48:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T11:48:29Z	--- license: apache-2.0 ---
aydink/hubert-base-ls960-finetuned-common_voice	aydink	2024-05-27T11:44:32Z	34	0	transformers	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "base_model:facebook/hubert-base-ls960", "base_model:finetune:facebook/hubert-base-ls960", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-05-16T08:55:53Z	--- license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - generated_from_trainer metrics: - accuracy - f1 - recall - precision model-index: - name: hubert-base-ls960-finetuned-common_voice 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. --> # hubert-base-ls960-finetuned-common_voice This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0451 - Accuracy: 0.99 - F1: 0.9900 - Recall: 0.99 - Precision: 0.9900 - Mcc: 0.9875 - Auc: 0.9994 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Recall \| Precision \| Mcc \| Auc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:---------:\|:------:\|:------:\| \| 0.2557 \| 1.0 \| 200 \| 0.1431 \| 0.965 \| 0.9647 \| 0.9650 \| 0.9676 \| 0.9570 \| 0.9965 \| \| 0.1858 \| 2.0 \| 400 \| 0.0567 \| 0.985 \| 0.9849 \| 0.985 \| 0.9854 \| 0.9814 \| 0.9994 \| \| 0.0626 \| 3.0 \| 600 \| 0.0612 \| 0.9875 \| 0.9875 \| 0.9875 \| 0.9876 \| 0.9844 \| 0.9996 \| \| 0.2167 \| 4.0 \| 800 \| 0.0340 \| 0.995 \| 0.9950 \| 0.9950 \| 0.9950 \| 0.9938 \| 0.9999 \| \| 0.0217 \| 5.0 \| 1000 \| 0.0454 \| 0.9925 \| 0.9925 \| 0.9925 \| 0.9925 \| 0.9906 \| 0.9997 \| \| 0.1366 \| 6.0 \| 1200 \| 0.0659 \| 0.985 \| 0.9850 \| 0.985 \| 0.9852 \| 0.9813 \| 0.9992 \| \| 0.0167 \| 7.0 \| 1400 \| 0.0515 \| 0.9925 \| 0.9925 \| 0.9925 \| 0.9927 \| 0.9907 \| 0.9991 \| \| 0.015 \| 8.0 \| 1600 \| 0.0414 \| 0.9925 \| 0.9925 \| 0.9925 \| 0.9927 \| 0.9907 \| 0.9993 \| \| 0.0312 \| 9.0 \| 1800 \| 0.0432 \| 0.9925 \| 0.9925 \| 0.9925 \| 0.9926 \| 0.9906 \| 0.9993 \| \| 0.0091 \| 10.0 \| 2000 \| 0.0451 \| 0.99 \| 0.9900 \| 0.99 \| 0.9900 \| 0.9875 \| 0.9994 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
rlaorrn/jeju_stt_eval	rlaorrn	2024-05-27T11:40:23Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-27T10:51:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Ntchinda-Giscard/en_pipeline	Ntchinda-Giscard	2024-05-27T11:38:32Z	0	0	spacy	[ "spacy", "token-classification", "en", "model-index", "region:us" ]	token-classification	2024-05-27T11:37:57Z	--- tags: - spacy - token-classification language: - en model-index: - name: en_pipeline results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 1.0 - name: NER Recall type: recall value: 1.0 - name: NER F Score type: f_score value: 1.0 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_pipeline` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.7.4,<3.8.0` \| \| Default Pipeline \| `tok2vec`, `ner` \| \| Components \| `tok2vec`, `ner` \| \| Vectors \| 514157 keys, 514157 unique vectors (300 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `DOB`, `fnames`, `lnames`, `serial` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 100.00 \| \| `ENTS_P` \| 100.00 \| \| `ENTS_R` \| 100.00 \| \| `TOK2VEC_LOSS` \| 0.00 \| \| `NER_LOSS` \| 0.00 \|
QuantFactory/deepseek-coder-1.3B-kexer-GGUF	QuantFactory	2024-05-27T11:35:35Z	477	1	null	[ "gguf", "code", "text-generation", "dataset:JetBrains/KExercises", "base_model:JetBrains/deepseek-coder-1.3B-kexer", "base_model:quantized:JetBrains/deepseek-coder-1.3B-kexer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T06:38:23Z	--- license: apache-2.0 datasets: - JetBrains/KExercises base_model: JetBrains/deepseek-coder-1.3B-kexer results: - task: type: text-generation dataset: name: MultiPL-HumanEval (Kotlin) type: openai_humaneval metrics: - name: pass@1 type: pass@1 value: 36.65 tags: - code pipeline_tag: text-generation --- # Deepseek-Coder-1.3B-kexer-GGUF This is quantized version of [JetBrains/deepseek-coder-1.3B-kexer](https://huggingface.co/JetBrains/deepseek-coder-1.3B-kexer) created using llama.cpp # Kexer models Kexer models are a collection of open-source generative text models fine-tuned on the [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. This is a repository for the fine-tuned Deepseek-coder-1.3b model in the Hugging Face Transformers format. # How to use As with the base model, we can use FIM. To do this, the following format must be used: ``` '<｜fim▁begin｜>' + prefix + '<｜fim▁hole｜>' + suffix + '<｜fim▁end｜>' ``` # Training setup The model was trained on one A100 GPU with following hyperparameters: \| Hyperparameter \| Value \| \|:---------------------------:\|:----------------------------------------:\| \| `warmup` \| 10% \| \| `max_lr` \| 1e-4 \| \| `scheduler` \| linear \| \| `total_batch_size` \| 256 (~130K tokens per step) \| \| `num_epochs` \| 4 \| More details about fine-tuning can be found in the technical report (coming soon!). # Fine-tuning data For tuning this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. Every example follows the HumanEval format. In total, the dataset contains about 3.5M tokens. # Evaluation For evaluation, we used the [Kotlin HumanEval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval) dataset, which contains all 161 tasks from HumanEval translated into Kotlin by human experts. You can find more details about the pre-processing necessary to obtain our results, including the code for running, on the [datasets's page](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval). Here are the results of our evaluation: \| Model name \| Kotlin HumanEval Pass Rate \| \|:---------------------------:\|:----------------------------------------:\| \| `Deepseek-coder-1.3B` \| 26.71 \| \| `Deepseek-coder-1.3B-Kexer` \| 36.65 \| # Ethical considerations and limitations Deepseek-coder-1.3B-Kexer is a new technology that carries risks with use. The testing conducted to date has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Deepseek-coder-1.3B-Kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of Deepseek-coder-1.3B-Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.
Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2	Zoyd	2024-05-27T11:31:19Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:nbeerbower/llama-3-Stheno-Mahou-8B", "base_model:quantized:nbeerbower/llama-3-Stheno-Mahou-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-27T11:04:51Z	--- library_name: transformers license: llama3 base_model: - nbeerbower/llama-3-Stheno-Mahou-8B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)</center> \| <center>3893 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)</center> \| <center>5557 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)</center> \| <center>6496 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)</center> \| <center>6902 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)</center> \| <center>8131 MB</center> \| <center>8</center> \| ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2a-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` # Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=2000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) # Fine-tune model with DPO dpo_trainer.train() ```
Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2	Zoyd	2024-05-27T11:30:50Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:nbeerbower/llama-3-Stheno-Mahou-8B", "base_model:quantized:nbeerbower/llama-3-Stheno-Mahou-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-27T10:39:35Z	--- library_name: transformers license: llama3 base_model: - nbeerbower/llama-3-Stheno-Mahou-8B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML --- Exllamav2 quant (exl2 / 3.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)</center> \| <center>3893 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)</center> \| <center>5557 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)</center> \| <center>6496 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)</center> \| <center>6902 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)</center> \| <center>8131 MB</center> \| <center>8</center> \| ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2a-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` # Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=2000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) # Fine-tune model with DPO dpo_trainer.train() ```
alppo/vae-conditioned-diffusion-model_v2	alppo	2024-05-27T11:30:48Z	2	0	diffusers	[ "diffusers", "safetensors", "dataset:teticio/audio-diffusion-256", "diffusers:DDPMPipeline", "region:us" ]	null	2024-05-21T20:55:30Z	--- datasets: - teticio/audio-diffusion-256 library_name: diffusers --- # Variational Autoencoder Conditioned Diffusion Model This model is designed to generate music tracks based on input playlists by extracting the "taste" from the playlists using a combination of a Variational Autoencoder (VAE) and a conditioned diffusion model. ## Model Details - VAE: Learns a compressed latent space representation of the input data, specifically mel spectrogram images of audio samples. - Diffusion Model: Generates new data points by progressively refining random noise into meaningful data, conditioned on the VAE's latent space.
Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2	Zoyd	2024-05-27T11:30:12Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:nbeerbower/llama-3-Stheno-Mahou-8B", "base_model:quantized:nbeerbower/llama-3-Stheno-Mahou-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-05-27T10:52:17Z	--- library_name: transformers license: llama3 base_model: - nbeerbower/llama-3-Stheno-Mahou-8B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML --- Exllamav2 quant (exl2 / 4.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)</center> \| <center>3893 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)</center> \| <center>5557 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)</center> \| <center>6496 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)</center> \| <center>6902 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)</center> \| <center>8131 MB</center> \| <center>8</center> \| ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2a-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` # Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=2000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) # Fine-tune model with DPO dpo_trainer.train() ```
SHAMS-R/AraT5-1500context-sample-epoch-3-v1	SHAMS-R	2024-05-27T11:30:01Z	106	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T10:48:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2	Zoyd	2024-05-27T11:29:25Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:nbeerbower/llama-3-Stheno-Mahou-8B", "base_model:quantized:nbeerbower/llama-3-Stheno-Mahou-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-27T11:09:37Z	--- library_name: transformers license: llama3 base_model: - nbeerbower/llama-3-Stheno-Mahou-8B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML --- Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)</center> \| <center>3893 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)</center> \| <center>5557 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)</center> \| <center>6496 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)</center> \| <center>6902 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)</center> \| <center>8131 MB</center> \| <center>8</center> \| ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2a-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` # Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=2000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) # Fine-tune model with DPO dpo_trainer.train() ```
Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2	Zoyd	2024-05-27T11:29:14Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:nbeerbower/llama-3-Stheno-Mahou-8B", "base_model:quantized:nbeerbower/llama-3-Stheno-Mahou-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-27T10:57:11Z	--- library_name: transformers license: llama3 base_model: - nbeerbower/llama-3-Stheno-Mahou-8B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)</center> \| <center>3893 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)</center> \| <center>5557 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)</center> \| <center>6496 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)</center> \| <center>6902 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)</center> \| <center>8131 MB</center> \| <center>8</center> \| ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2a-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` # Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=2000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) # Fine-tune model with DPO dpo_trainer.train() ```
Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2	Zoyd	2024-05-27T11:29:05Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:nbeerbower/llama-3-Stheno-Mahou-8B", "base_model:quantized:nbeerbower/llama-3-Stheno-Mahou-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-27T10:44:31Z	--- library_name: transformers license: llama3 base_model: - nbeerbower/llama-3-Stheno-Mahou-8B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML --- Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)</center> \| <center>3893 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)</center> \| <center>5557 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)</center> \| <center>6496 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)</center> \| <center>6902 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)</center> \| <center>8131 MB</center> \| <center>8</center> \| ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2a-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` # Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=2000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) # Fine-tune model with DPO dpo_trainer.train() ```
Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2	Zoyd	2024-05-27T11:28:56Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:flammenai/FlameMix-DPO-v1", "dataset:flammenai/Grill-preprod-v1_chatML", "dataset:flammenai/Grill-preprod-v2_chatML", "base_model:nbeerbower/llama-3-Stheno-Mahou-8B", "base_model:quantized:nbeerbower/llama-3-Stheno-Mahou-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-27T10:31:46Z	--- library_name: transformers license: llama3 base_model: - nbeerbower/llama-3-Stheno-Mahou-8B datasets: - flammenai/FlameMix-DPO-v1 - flammenai/Grill-preprod-v1_chatML - flammenai/Grill-preprod-v2_chatML --- Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_2bpw_exl2)</center> \| <center>3250 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-2_5bpw_exl2)</center> \| <center>3479 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_0bpw_exl2)</center> \| <center>3893 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_5bpw_exl2)</center> \| <center>4311 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-3_75bpw_exl2)</center> \| <center>4518 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_0bpw_exl2)</center> \| <center>4727 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-4_25bpw_exl2)</center> \| <center>4935 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-5_0bpw_exl2)</center> \| <center>5557 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_0bpw_exl2)</center> \| <center>6496 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-6_5bpw_exl2)</center> \| <center>6902 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/flammenai_Mahou-1.2a-llama3-8B-8_0bpw_exl2)</center> \| <center>8131 MB</center> \| <center>8</center> \| ![image/png](https://huggingface.co/flammenai/Mahou-1.0-mistral-7B/resolve/main/mahou1.png) # Mahou-1.2a-llama3-8B Mahou is our attempt to build a production-ready conversational/roleplay LLM. Future versions will be released iteratively and finetuned from flammen.ai conversational data. ### Chat Format This model has been trained to use ChatML format. ``` <\|im_start\|>system {{system}}<\|im_end\|> <\|im_start\|>{{char}} {{message}}<\|im_end\|> <\|im_start\|>{{user}} {{message}}<\|im_end\|> ``` # Roleplay Format - Speech without quotes. - Actions in `asterisks` ``` leans against wall cooly so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass. ``` ### ST Settings 1. Use ChatML for the Context Template. 2. Turn on Instruct Mode for ChatML. 3. Use the following stopping strings: `["<", "\|", "<\|", "\n"]` ### Method Finetuned using an A100 on Google Colab. [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne) ### Configuration LoRA, model, and training settings: ```python # LoRA configuration peft_config = LoraConfig( r=16, lora_alpha=16, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] ) # Model to fine-tune model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) model.config.use_cache = False # Reference model ref_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, load_in_4bit=True ) # Training arguments training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=2000, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", ) # Create DPO trainer dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, force_use_ref_model=True ) # Fine-tune model with DPO dpo_trainer.train() ```
slimaneMakh/BinarySuperClass_Receivable_tableClassification_27may_distilBert_BASELINE	slimaneMakh	2024-05-27T11:24:22Z	183	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-27T11:23:58Z	--- 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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xrz99/ppo-Huggy	xrz99	2024-05-27T11:23:24Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-27T11:22:55Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: xrz99/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
classla/xlm-r-bertic	classla	2024-05-27T11:21:50Z	23	3	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "hr", "bs", "sr", "dataset:classla/xlm-r-bertic-data", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-08T10:18:10Z	--- license: cc-by-sa-4.0 language: - hr - bs - sr datasets: - classla/xlm-r-bertic-data --- # XLM-R-BERTić This model was produced by pre-training [XLM-Roberta-large](https://huggingface.co/xlm-roberta-large) 48k steps on South Slavic languages using [XLM-R-BERTić dataset](https://huggingface.co/datasets/classla/xlm-r-bertic-data) # Benchmarking Three tasks were chosen for model evaluation: * Named Entity Recognition (NER) * Sentiment regression * COPA (Choice of plausible alternatives) In all cases, this model was finetuned for specific downstream tasks. ## NER Average macro-F1 scores from three runs were used to evaluate performance. Datasets used: [hr500k](https://huggingface.co/datasets/classla/hr500k), [ReLDI-sr](https://huggingface.co/datasets/classla/reldi_sr), [ReLDI-hr](https://huggingface.co/datasets/classla/reldi_hr), and [SETimes.SR](https://huggingface.co/datasets/classla/setimes_sr). \| system \| dataset \| F1 score \| \|:-----------------------------------------------------------------------\|:--------\|---------:\| \| [XLM-R-BERTić](https://huggingface.co/classla/xlm-r-bertic) \| hr500k \| 0.927 \| \| [BERTić](https://huggingface.co/classla/bcms-bertic) \| hr500k \| 0.925 \| \| [XLM-R-SloBERTić](https://huggingface.co/classla/xlm-r-slobertic) \| hr500k \| 0.923 \| \| [XLM-Roberta-Large](https://huggingface.co/xlm-roberta-large) \| hr500k \| 0.919 \| \| [crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) \| hr500k \| 0.918 \| \| [XLM-Roberta-Base](https://huggingface.co/xlm-roberta-base) \| hr500k \| 0.903 \| \| system \| dataset \| F1 score \| \|:-----------------------------------------------------------------------\|:---------\|---------:\| \| [XLM-R-SloBERTić](https://huggingface.co/classla/xlm-r-slobertic) \| ReLDI-hr \| 0.812 \| \| [XLM-R-BERTić](https://huggingface.co/classla/xlm-r-bertic) \| ReLDI-hr \| 0.809 \| \| [crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) \| ReLDI-hr \| 0.794 \| \| [BERTić](https://huggingface.co/classla/bcms-bertic) \| ReLDI-hr \| 0.792 \| \| [XLM-Roberta-Large](https://huggingface.co/xlm-roberta-large) \| ReLDI-hr \| 0.791 \| \| [XLM-Roberta-Base](https://huggingface.co/xlm-roberta-base) \| ReLDI-hr \| 0.763 \| \| system \| dataset \| F1 score \| \|:-----------------------------------------------------------------------\|:-----------\|---------:\| \| [XLM-R-SloBERTić](https://huggingface.co/classla/xlm-r-slobertic) \| SETimes.SR \| 0.949 \| \| [XLM-R-BERTić](https://huggingface.co/classla/xlm-r-bertic) \| SETimes.SR \| 0.940 \| \| [BERTić](https://huggingface.co/classla/bcms-bertic) \| SETimes.SR \| 0.936 \| \| [XLM-Roberta-Large](https://huggingface.co/xlm-roberta-large) \| SETimes.SR \| 0.933 \| \| [crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) \| SETimes.SR \| 0.922 \| \| [XLM-Roberta-Base](https://huggingface.co/xlm-roberta-base) \| SETimes.SR \| 0.914 \| \| system \| dataset \| F1 score \| \|:-----------------------------------------------------------------------\|:---------\|---------:\| \| [XLM-R-BERTić](https://huggingface.co/classla/xlm-r-bertic) \| ReLDI-sr \| 0.841 \| \| [XLM-R-SloBERTić](https://huggingface.co/classla/xlm-r-slobertic) \| ReLDI-sr \| 0.824 \| \| [BERTić](https://huggingface.co/classla/bcms-bertic) \| ReLDI-sr \| 0.798 \| \| [XLM-Roberta-Large](https://huggingface.co/xlm-roberta-large) \| ReLDI-sr \| 0.774 \| \| [crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) \| ReLDI-sr \| 0.751 \| \| [XLM-Roberta-Base](https://huggingface.co/xlm-roberta-base) \| ReLDI-sr \| 0.734 \| ## Sentiment regression [ParlaSent dataset](https://huggingface.co/datasets/classla/ParlaSent) was used to evaluate sentiment regression for Bosnian, Croatian, and Serbian languages. The procedure is explained in greater detail in the dedicated [benchmarking repository](https://github.com/clarinsi/benchich/tree/main/sentiment). \| system \| train \| test \| r^2 \| \|:-----------------------------------------------------------------------\|:--------------------\|:-------------------------\|------:\| \| [xlm-r-parlasent](https://huggingface.co/classla/xlm-r-parlasent) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| 0.615 \| \| [BERTić](https://huggingface.co/classla/bcms-bertic) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| 0.612 \| \| [XLM-R-SloBERTić](https://huggingface.co/classla/xlm-r-slobertic) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| 0.607 \| \| [XLM-Roberta-Large](https://huggingface.co/xlm-roberta-large) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| 0.605 \| \| [XLM-R-BERTić](https://huggingface.co/classla/xlm-r-bertic) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| 0.601 \| \| [crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| 0.537 \| \| [XLM-Roberta-Base](https://huggingface.co/xlm-roberta-base) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| 0.500 \| \| dummy (mean) \| ParlaSent_BCS.jsonl \| ParlaSent_BCS_test.jsonl \| -0.12 \| ## COPA \| system \| dataset \| Accuracy score \| \|:-----------------------------------------------------------------------\|:--------\|---------------:\| \| [BERTić](https://huggingface.co/classla/bcms-bertic) \| Copa-SR \| 0.689 \| \| [XLM-R-SloBERTić](https://huggingface.co/classla/xlm-r-slobertic) \| Copa-SR \| 0.665 \| \| [XLM-R-BERTić](https://huggingface.co/classla/xlm-r-bertic) \| Copa-SR \| 0.637 \| \| [crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) \| Copa-SR \| 0.607 \| \| [XLM-Roberta-Base](https://huggingface.co/xlm-roberta-base) \| Copa-SR \| 0.573 \| \| [XLM-Roberta-Large](https://huggingface.co/xlm-roberta-large) \| Copa-SR \| 0.570 \| \| system \| dataset \| Accuracy score \| \|:-----------------------------------------------------------------------\|:--------\|---------------:\| \| [BERTić](https://huggingface.co/classla/bcms-bertic) \| Copa-HR \| 0.669 \| \| [XLM-R-SloBERTić](https://huggingface.co/classla/xlm-r-slobertic) \| Copa-HR \| 0.628 \| \| [XLM-R-BERTić](https://huggingface.co/classla/xlm-r-bertic) \| Copa-HR \| 0.635 \| \| [crosloengual-bert](https://huggingface.co/EMBEDDIA/crosloengual-bert) \| Copa-HR \| 0.669 \| \| [XLM-Roberta-Base](https://huggingface.co/xlm-roberta-base) \| Copa-HR \| 0.585 \| \| [XLM-Roberta-Large](https://huggingface.co/xlm-roberta-large) \| Copa-HR \| 0.571 \| # Citation Please cite the following paper: ``` @inproceedings{ljubesic-etal-2024-language, title = "Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining", author = "Ljube{\v{s}}i{\'c}, Nikola and Suchomel, V{\'\i}t and Rupnik, Peter and Kuzman, Taja and van Noord, Rik", editor = "Melero, Maite and Sakti, Sakriani and Soria, Claudia", booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.sigul-1.23", pages = "189--203", } ```
ahsenali/falcon-7b-med_dialogue-symptoms	ahsenali	2024-05-27T11:21:48Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-10T15:05:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AmirrezaV1/emotion_strong_data	AmirrezaV1	2024-05-27T11:20:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T11:20:07Z	--- license: apache-2.0 ---
mrchantey/one-small-step	mrchantey	2024-05-27T11:15:28Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-27T11:15:06Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.59 +/- 15.55 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
DownwardSpiral33/gpt2-imdb-pos-roberta16-256_0_02-full-2024.05.27.10.28	DownwardSpiral33	2024-05-27T11:10:17Z	122	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T11:09:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Klevin/Decyphers-2b-v0	Klevin	2024-05-27T11:08:07Z	77	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-27T11:05:29Z	--- 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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Apel-sin/nxcode-CQ-7B-orpo-exl2	Apel-sin	2024-05-27T11:06:41Z	2	0	null	[ "code", "text-generation", "arxiv:2403.07691", "license:mit", "region:us" ]	text-generation	2024-05-26T13:17:03Z	--- license: mit tags: - code pipeline_tag: text-generation --- # Exllama v2 NTQAI/Nxcode-CQ-7B-orpo Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.21">turboderp's ExLlamaV2 v0.0.21</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: <a href="https://huggingface.co/NTQAI/Nxcode-CQ-7B-orpo">NTQAI/Nxcode-CQ-7B-orpo</a><br> Calibration dataset is based on: <a href="https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction">m-a-p/CodeFeedback-Filtered-Instruction</a> ## Available sizes \| Branch \| Bits \| lm_head bits \| Description \| \| ----- \| ---- \| ------- \| ------------ \| \| [8_0](https://huggingface.co/Apel-sin/nxcode-CQ-7B-orpo-exl2/tree/8_0) \| 8.0 \| 8.0 \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/Apel-sin/nxcode-CQ-7B-orpo-exl2/tree/6_5) \| 6.5 \| 8.0 \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_5](https://huggingface.co/Apel-sin/nxcode-CQ-7B-orpo-exl2/tree/5_5) \| 5.5 \| 8.0 \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| <a href="https://ntq.com.vn" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/etbfTJuVdAub2evNP_E4g.png" width="200"/></a> ## Introduction Nxcode-CQ-7B-orpo is an [Monolithic Preference Optimization without Reference Model](https://arxiv.org/abs/2403.07691) fine-tune of Qwen/CodeQwen1.5-7B on 100k samples of high-quality ranking data. ## [Evalplus](https://github.com/evalplus/evalplus) \| EvalPlus \| pass@1 \| \| --- \| --- \| \| HumanEval \| 86.6 \| \| HumanEval+ \| 83.5 \| \| MBPP(v0.2.0) \| 82.3 \| \| MBPP+(v0.2.0) \| 70.4 \| We use a simple template to generate the solution for evalplus: ```python "Complete the following Python function:\n{prompt}" ``` [Evalplus Leaderboard](https://evalplus.github.io/leaderboard.html) \| Models \| HumanEval \| HumanEval+\| \|------ \| ------ \| ------ \| \| GPT-4-Turbo (April 2024)\| 90.2\| 86.6\| \| GPT-4 (May 2023)\| 88.4\| 81.17\| \| GPT-4-Turbo (Nov 2023)\| 85.4\| 79.3\| \| CodeQwen1.5-7B-Chat\| 83.5\| 78.7\| \| claude-3-opus (Mar 2024)\| 82.9\| 76.8\| \| DeepSeek-Coder-33B-instruct\| 81.1\| 75.0\| \| WizardCoder-33B-V1.1\| 79.9\| 73.2\| \| OpenCodeInterpreter-DS-33B\| 79.3\| 73.8\| \| speechless-codellama-34B-v2.0\| 77.4\| 72\| \| GPT-3.5-Turbo (Nov 2023)\| 76.8\| 70.7\| \| Llama3-70B-instruct\| 76.2\| 70.7\| ## Bigcode Leaderboard [Bigcode Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard) 09/05/2024 Top 1 average score. Top 2 winrate. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/OQonD6a7aNjnN9SsTkFp-.png) ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. You should upgrade the transformers if you receive an error when loading the tokenizer ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "NTQAI/Nxcode-CQ-7B-orpo", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("NTQAI/Nxcode-CQ-7B-orpo") prompt = """Complete the following Python function: from typing import List def has_close_elements(numbers: List[float], threshold: float) -> bool: """ Check if in given list of numbers, are any two numbers closer to each other than given threshold. >>> has_close_elements([1.0, 2.0, 3.0], 0.5) False >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True """ """ messages = [ {"role": "user", "content": prompt} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) res = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) ``` ### Contact information For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).
research-dump/Llama-2-7b-chat-hf_taqa_mixed_sftt	research-dump	2024-05-27T11:06:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T00:18:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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scepter-studio/stylebooth	scepter-studio	2024-05-27T11:05:30Z	0	2	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T11:02:03Z	--- frameworks: - Pytorch license: apache-2.0 tasks: - image-style-transfer #model-type: ##如 gpt、phi、llama、chatglm、baichuan 等 #- gpt #domain: ##如 nlp、cv、audio、multi-modal #- nlp #language: ##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa #- cn #metrics: ##如 CIDEr、Blue、ROUGE 等 #- CIDEr #tags: ##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他 #- pretrained #tools: ##如 vllm、fastchat、llamacpp、AdaSeq 等 #- vllm --- # StyleBooth: Image Style Editing with Multimodal Instruction ## Run StyleBooth - Code implementation: See model configuration and code based on [🪄SCEPTER](https://github.com/modelscope/scepter). - Demo: Try [🖥️SCEPTER Studio](https://github.com/modelscope/scepter/tree/main?tab=readme-ov-file#%EF%B8%8F-scepter-studio). - Easy run: Try the following example script to run StyleBooth modified from [tests/modules/test_diffusion_inference.py](https://github.com/modelscope/scepter/blob/main/tests/modules/test_diffusion_inference.py): ```python # `pip install scepter>0.0.4` or # clone newest SCEPTER and run `PYTHONPATH=./ python <this_script>` at the main branch root. import os import unittest from PIL import Image from torchvision.utils import save_image from scepter.modules.inference.stylebooth_inference import StyleboothInference from scepter.modules.utils.config import Config from scepter.modules.utils.file_system import FS from scepter.modules.utils.logger import get_logger class DiffusionInferenceTest(unittest.TestCase): def setUp(self): print(('Testing %s.%s' % (type(self).__name__, self._testMethodName))) self.logger = get_logger(name='scepter') config_file = 'scepter/methods/studio/scepter_ui.yaml' cfg = Config(cfg_file=config_file) if 'FILE_SYSTEM' in cfg: for fs_info in cfg['FILE_SYSTEM']: FS.init_fs_client(fs_info) self.tmp_dir = './cache/save_data/diffusion_inference' if not os.path.exists(self.tmp_dir): os.makedirs(self.tmp_dir) def tearDown(self): super().tearDown() # uncomment this line to skip this module. # @unittest.skip('') def test_stylebooth(self): config_file = 'scepter/methods/studio/inference/edit/stylebooth_tb_pro.yaml' cfg = Config(cfg_file=config_file) diff_infer = StyleboothInference(logger=self.logger) diff_infer.init_from_cfg(cfg) output = diff_infer({'prompt': 'Let this image be in the style of sai-lowpoly'}, style_edit_image=Image.open('asset/images/inpainting_text_ref/ex4_scene_im.jpg'), style_guide_scale_text=7.5, style_guide_scale_image=1.5, stylebooth_state=True) save_path = os.path.join(self.tmp_dir, 'stylebooth_test_lowpoly_cute_dog.png') save_image(output['images'], save_path) if __name__ == '__main__': unittest.main() ```
phongtintruong/mistral_7b-instruct-apirec	phongtintruong	2024-05-27T11:01:39Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T18:03:58Z	--- 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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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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jonyoon/bert-finetuned-ner	jonyoon	2024-05-27T10:59:29Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-27T10:50:58Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9223078183904277 - name: Recall type: recall value: 0.9469875462807136 - name: F1 type: f1 value: 0.934484762932824 - name: Accuracy type: accuracy value: 0.9857096603284865 --- <!-- 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. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0539 - Precision: 0.9223 - Recall: 0.9470 - F1: 0.9345 - Accuracy: 0.9857 ## 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 439 \| 0.0746 \| 0.8824 \| 0.9164 \| 0.8990 \| 0.9779 \| \| 0.194 \| 2.0 \| 878 \| 0.0562 \| 0.9200 \| 0.9428 \| 0.9313 \| 0.9850 \| \| 0.0488 \| 3.0 \| 1317 \| 0.0539 \| 0.9223 \| 0.9470 \| 0.9345 \| 0.9857 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
PaawanPurdhani/MHA-Text-Classifier	PaawanPurdhani	2024-05-27T10:56:01Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T10:54:41Z	--- license: apache-2.0 ---
pranavthombare/Phi-3-mini-4k-instruct	pranavthombare	2024-05-27T10:53:55Z	124	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "nlp", "code", "conversational", "custom_code", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-27T10:35:08Z	--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - nlp - code inference: parameters: temperature: 0.0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- ## Model Summary The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support. The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. Resources and Technical Documentation: + [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) + [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) + [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) + [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) \| \| Short Context \| Long Context \| \| ------- \| ------------- \| ------------ \| \| Mini \| 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)\| \| Small \| 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)\| \| Medium \| 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)\| \| Vision \| \| 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct)\| ## Intended Uses Primary use cases The model is intended for commercial and research use in English. The model provides uses for applications which require: 1) Memory/compute constrained environments 2) Latency bound scenarios 3) Strong reasoning (especially code, math and logic) Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. Use case considerations Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. ## How to Use Phi-3 Mini-4K-Instruct has been integrated in the development version (4.41.0.dev0) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following: * When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function. * Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source. The current `transformers` version can be verified with: `pip list \| grep transformers`. Phi-3 Mini-4K-Instruct is also available in [HuggingChat](https://aka.ms/try-phi3-hf-chat). ### Tokenizer Phi-3 Mini-4K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size. ### Chat Format Given the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. You can provide the prompt as a question with a generic template as follow: ```markdown <\|user\|>\nQuestion <\|end\|>\n<\|assistant\|> ``` For example: ```markdown <\|user\|> How to explain Internet for a medieval knight?<\|end\|> <\|assistant\|> ``` where the model generates the text after `<\|assistant\|>` . In case of few-shots prompt, the prompt can be formatted as the following: ```markdown <\|user\|> I am going to Paris, what should I see?<\|end\|> <\|assistant\|> Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<\|end\|> <\|user\|> What is so great about #1?<\|end\|> <\|assistant\|> ``` ### Sample inference code This code snippets show how to get quickly started with running the model on a GPU: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline torch.random.manual_seed(0) model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-4k-instruct", device_map="cuda", torch_dtype="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") messages = [ {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 500, "return_full_text": False, "temperature": 0.0, "do_sample": False, } output = pipe(messages, *generation_args) print(output[0]['generated_text']) ``` Some applications/frameworks might not include a BOS token (`<s>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.* ## Responsible AI Considerations Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include: + Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. + Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. + Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. + Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. + Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include: + Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques. + High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. + Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). + Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. + Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations. ## Training ### Model * Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines. * Inputs: Text. It is best suited for prompts using chat format. * Context length: 4K tokens * GPUs: 512 H100-80G * Training time: 7 days * Training data: 3.3T tokens * Outputs: Generated text in response to the input * Dates: Our models were trained between February and April 2024 * Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models. ### Datasets Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of 1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; 2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); 3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness. ### Fine-tuning A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/sample_finetune.py). ## Benchmarks We report the results for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5. All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation. As is now standard, we use few-shot prompts to evaluate the models, at temperature 0. The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3. More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model. The number of k–shot examples is listed per-benchmark. \| \| Phi-3-Mini-4K-In<br>3.8b \| Phi-3-Small<br>7b (preview) \| Phi-3-Medium<br>14b (preview) \| Phi-2<br>2.7b \| Mistral<br>7b \| Gemma<br>7b \| Llama-3-In<br>8b \| Mixtral<br>8x7b \| GPT-3.5<br>version 1106 \| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| MMLU <br>5-Shot \| 68.8 \| 75.3 \| 78.2 \| 56.3 \| 61.7 \| 63.6 \| 66.5 \| 68.4 \| 71.4 \| \| HellaSwag <br> 5-Shot \| 76.7 \| 78.7 \| 83.2 \| 53.6 \| 58.5 \| 49.8 \| 71.1 \| 70.4 \| 78.8 \| \| ANLI <br> 7-Shot \| 52.8 \| 55.0 \| 58.7 \| 42.5 \| 47.1 \| 48.7 \| 57.3 \| 55.2 \| 58.1 \| \| GSM-8K <br> 0-Shot; CoT \| 82.5 \| 86.4 \| 90.8 \| 61.1 \| 46.4 \| 59.8 \| 77.4 \| 64.7 \| 78.1 \| \| MedQA <br> 2-Shot \| 53.8 \| 58.2 \| 69.8 \| 40.9 \| 49.6 \| 50.0 \| 60.5 \| 62.2 \| 63.4 \| \| AGIEval <br> 0-Shot \| 37.5 \| 45.0 \| 49.7 \| 29.8 \| 35.1 \| 42.1 \| 42.0 \| 45.2 \| 48.4 \| \| TriviaQA <br> 5-Shot \| 64.0 \| 59.1 \| 73.3 \| 45.2 \| 72.3 \| 75.2 \| 67.7 \| 82.2 \| 85.8 \| \| Arc-C <br> 10-Shot \| 84.9 \| 90.7 \| 91.9 \| 75.9 \| 78.6 \| 78.3 \| 82.8 \| 87.3 \| 87.4 \| \| Arc-E <br> 10-Shot \| 94.6 \| 97.1 \| 98.0 \| 88.5 \| 90.6 \| 91.4 \| 93.4 \| 95.6 \| 96.3 \| \| PIQA <br> 5-Shot \| 84.2 \| 87.8 \| 88.2 \| 60.2 \| 77.7 \| 78.1 \| 75.7 \| 86.0 \| 86.6 \| \| SociQA <br> 5-Shot \| 76.6 \| 79.0 \| 79.4 \| 68.3 \| 74.6 \| 65.5 \| 73.9 \| 75.9 \| 68.3 \| \| BigBench-Hard <br> 0-Shot \| 71.7 \| 75.0 \| 82.5 \| 59.4 \| 57.3 \| 59.6 \| 51.5 \| 69.7 \| 68.32 \| \| WinoGrande <br> 5-Shot \| 70.8 \| 82.5 \| 81.2 \| 54.7 \| 54.2 \| 55.6 \| 65 \| 62.0 \| 68.8 \| \| OpenBookQA <br> 10-Shot \| 83.2 \| 88.4 \| 86.6 \| 73.6 \| 79.8 \| 78.6 \| 82.6 \| 85.8 \| 86.0 \| \| BoolQ <br> 0-Shot \| 77.6 \| 82.9 \| 86.5 \| -- \| 72.2 \| 66.0 \| 80.9 \| 77.6 \| 79.1 \| \| CommonSenseQA <br> 10-Shot \| 80.2 \| 80.3 \| 82.6 \| 69.3 \| 72.6 \| 76.2 \| 79 \| 78.1 \| 79.6 \| \| TruthfulQA <br> 10-Shot \| 65.0 \| 68.1 \| 74.8 \| -- \| 52.1 \| 53.0 \| 63.2 \| 60.1 \| 85.8 \| \| HumanEval <br> 0-Shot \| 59.1 \| 59.1 \| 54.7 \| 47.0 \| 28.0 \| 34.1 \| 60.4 \| 37.8 \| 62.2 \| \| MBPP <br> 3-Shot \| 53.8 \| 71.4 \| 73.7 \| 60.6 \| 50.8 \| 51.5 \| 67.7 \| 60.2 \| 77.8 \| ## Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Transformers](https://github.com/huggingface/transformers) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ## Hardware Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types: * NVIDIA A100 * NVIDIA A6000 * NVIDIA H100 If you want to run the model on: * NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager" * CPU: use the GGUF quantized models [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf) + Optimized inference on GPU, CPU, and Mobile: use the ONNX models [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx) ## Cross Platform Support ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model [here](https://aka.ms/phi3-mini-4k-instruct-onnx). Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile. Here are some of the optimized configurations we have added: 1. ONNX models for int4 DML: Quantized to int4 via AWQ 2. ONNX model for fp16 CUDA 3. ONNX model for int4 CUDA: Quantized to int4 via RTN 4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN ## License The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-4k/resolve/main/LICENSE). ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
HachiML/Mistral-7B-v0.3-m1-lora	HachiML	2024-05-27T10:53:44Z	0	0	peft	[ "peft", "safetensors", "mistral", "trl", "sft", "generated_from_trainer", "dataset:HachiML/oasst1_for_self-rewarding_IFT", "dataset:HachiML/oasst1_for_self-rewarding_EFT_MSv0.3", "base_model:mistralai/Mistral-7B-v0.3", "base_model:adapter:mistralai/Mistral-7B-v0.3", "license:apache-2.0", "region:us" ]	null	2024-05-25T12:58:40Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-v0.3 datasets: - HachiML/oasst1_for_self-rewarding_IFT - HachiML/oasst1_for_self-rewarding_EFT_MSv0.3 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> - [HachiML/Mistral-7B-v0.3-sft-lora_sr_5ep](https://huggingface.co/HachiML/Mistral-7B-v0.3-sft-lora_sr_5ep)のAdapterをマージしたモデル - This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on following datasets. - [HachiML/oasst1_for_self-rewarding_IFT](https://huggingface.co/datasets/HachiML/oasst1_for_self-rewarding_IFT) - [HachiML/oasst1_for_self-rewarding_EFT_MSv0.3](https://huggingface.co/datasets/HachiML/oasst1_for_self-rewarding_EFT_MSv0.3) - It achieves the following results on the evaluation set: - Loss: 0.4237 ## 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: [HachiML](https://huggingface.co/HachiML) - Model type: Mistral-7B - Language(s) (NLP): Japanese - License: Apache-2.0 - Finetuned from model: [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) - Finetuned type: SFT - Finetuned dataset: - [HachiML/oasst1_for_self-rewarding_IFT](https://huggingface.co/datasets/HachiML/oasst1_for_self-rewarding_IFT) - [HachiML/oasst1_for_self-rewarding_EFT_MSv0.3](https://huggingface.co/datasets/HachiML/oasst1_for_self-rewarding_EFT_MSv0.3) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.5e-06 - 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: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.1951 \| 1.0 \| 262 \| 0.4563 \| \| 0.9304 \| 2.0 \| 524 \| 0.4279 \| \| 0.9129 \| 3.0 \| 786 \| 0.4242 \| \| 0.9088 \| 4.0 \| 1048 \| 0.4237 \| \| 0.9089 \| 5.0 \| 1310 \| 0.4237 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ObserverAI/bart-cnn-samsum-finetuned	ObserverAI	2024-05-27T10:50:46Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-27T10:29:20Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer datasets: - samsum model-index: - name: bart-cnn-samsum-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-cnn-samsum-finetuned This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.0241 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1883 \| 1.0 \| 37 \| 1.0241 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lucienbaumgartner/PAG-annotation	lucienbaumgartner	2024-05-27T10:38:47Z	7	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/all-mpnet-base-v2", "base_model:finetune:sentence-transformers/all-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-05-22T12:41:02Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer base_model: sentence-transformers/all-mpnet-base-v2 metrics: - accuracy - precision - recall - f1 widget: - text: I appreciate the smooth operation of our public transit system every day. - text: Precision shines through in your flawless execution of the project. - text: Your assistance with the move was invaluable; I couldn’t have managed without you. - text: Well done on orchestrating such a seamless event! - text: The presentation Anne gave was remarkably insightful and comprehensive. pipeline_tag: text-classification inference: false model-index: - name: SetFit with sentence-transformers/all-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.5 name: Accuracy - type: precision value: 0.8 name: Precision - type: recall value: 0.8888888888888888 name: Recall - type: f1 value: 0.8421052631578947 name: F1 --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A MultiOutputClassifier instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - Classification head: a MultiOutputClassifier instance - Maximum Sequence Length: 384 tokens - Number of Classes: 3 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Evaluation ### Metrics \| Label \| Accuracy \| Precision \| Recall \| F1 \| \|:--------\|:---------\|:----------\|:-------\|:-------\| \| all \| 0.5 \| 0.8 \| 0.8889 \| 0.8421 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("setfit_model_id") # Run inference preds = model("Well done on orchestrating such a seamless event!") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 6 \| 10.75 \| 16 \| ### Training Hyperparameters - batch_size: (32, 2) - num_epochs: (10, 10) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:------:\|:-------------:\|:---------------:\| \| 0.0769 \| 1 \| 0.3115 \| - \| \| 1.0 \| 13 \| - \| 0.1928 \| \| 2.0 \| 26 \| - \| 0.1831 \| \| 3.0 \| 39 \| - \| 0.1724 \| \| 3.8462 \| 50 \| 0.08 \| - \| \| 4.0 \| 52 \| - \| 0.1614 \| \| 5.0 \| 65 \| - \| 0.1695 \| \| 6.0 \| 78 \| - \| 0.1837 \| \| 7.0 \| 91 \| - \| 0.1904 \| \| 7.6923 \| 100 \| 0.0364 \| - \| \| 8.0 \| 104 \| - \| 0.1997 \| \| 9.0 \| 117 \| - \| 0.1994 \| \| 10.0 \| 130 \| - \| 0.1967 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.1 - SetFit: 1.0.3 - Sentence Transformers: 2.7.0 - Transformers: 4.37.2 - PyTorch: 2.2.0 - Datasets: 2.19.1 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
sardukar/physiology-8k-llama3-8b-qlora	sardukar	2024-05-27T10:35:51Z	33	0	peft	[ "peft", "safetensors", "en", "dataset:sardukar/physiology-mcqa-8k", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:adapter:NousResearch/Meta-Llama-3-8B-Instruct", "license:mit", "region:us" ]	null	2024-05-27T10:22:28Z	--- library_name: peft base_model: NousResearch/Meta-Llama-3-8B-Instruct license: mit datasets: - sardukar/physiology-mcqa-8k language: - en --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This model is a 1 epoch training with ORPO Trainer on the [sardukar/physiology-mcqa-8k](https://huggingface.co/datasets/sardukar/physiology-mcqa-8k) dataset Base model is NousResearch/Meta-Llama-3-8B-Instruct Training results ![train_results](physiology-8k-rtx3060-train-complete.png)

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