ftopal/huggingface-models-raw · Datasets at Hugging Face

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null	null	# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_S.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_S.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q4_K_S.gguf -n 128 ```	{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}	gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF	null	[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-01T02:54:41+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-2b-aya-chatml-v3 This model is a fine-tuned version of [Ransaka/Gemma-2b-sinhala](https://huggingface.co/Ransaka/Gemma-2b-sinhala) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "Ransaka/Gemma-2b-sinhala", "model-index": [{"name": "gemma-2b-aya-chatml-v3", "results": []}]}	Ransaka/gemma-2b-aya-chatml-v3	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:Ransaka/Gemma-2b-sinhala", "region:us" ]	null	2024-05-01T02:54:48+00:00
text-generation	transformers	<img src="./ninjalogo.svg" width="100%" height="20%" alt=""> # Our Models - [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1) - [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) - [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW) - [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k) - [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k) ## Model Card for Ninja-v1-NSFW-128k The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1 Ninja-NSFW-128k has the following changes compared to Mistral-7B-v0.1. - 128k context window (8k context in v0.1) - Achieving both high quality Japanese and English generation - Memory ability that does not forget even after long-context generation - Can be generated NSFW This model was created with the help of GPUs from the first LocalAI hackathon. We would like to take this opportunity to thank ## List of Creation Methods - Chatvector for multiple models - Simple linear merging of result models - Domain and Sentence Enhancement with LORA - Context expansion ## Instruction format Ninja adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following: ``` USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am ninja.</s> ``` ## Example prompts to improve (Japanese) - BAD:　あなたは○○として振る舞います - GOOD: あなたは○○です - BAD: あなたは○○ができます - GOOD: あなたは○○をします ## Performing inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "Local-Novel-LLM-project/Ninja-v1-NSFW-128k" new_tokens = 1024 model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- " prompt = input("Enter a prompt: ") system_prompt += prompt + "\n-------- " model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True) print(tokenizer.batch_decode(generated_ids)[0]) ```` ## Merge recipe - WizardLM2 - mistralai/Mistral-7B-v0.1 - NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1 - Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b - Elizezen/LewdSniffyOtter-7B - Elizezen/SniffyOtter-7B - NTQAI/chatntq-ja-7b-v1.0 The characteristics of each model are as follows. - WizardLM2: High quality multitasking model - Yarn-Mistral-7b-128k: Mistral model with 128k context window - Antler-7B: Model specialized for novel writing - NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model - Elizezen/LewdSniffyOtter-7B Japanese NSFW specialized model ## Other points to keep in mind - The training data may be biased. Be careful with the generated sentences. - Set trust_remote_code to True for context expansion with YaRN. - Memory usage may be large for long inferences. - If possible, we recommend inferring with llamacpp rather than Transformers.	{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned", "not-for-all-audiences"], "pipeline_tag": "text-generation"}	Local-Novel-LLM-project/Ninja-v1-NSFW-128k	null	[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "not-for-all-audiences", "custom_code", "en", "ja", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T02:54:59+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-alpha - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [zephyr-7b-alpha.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [zephyr-7b-alpha.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [zephyr-7b-alpha.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [zephyr-7b-alpha.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [zephyr-7b-alpha.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [zephyr-7b-alpha.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [zephyr-7b-alpha.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [zephyr-7b-alpha.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [zephyr-7b-alpha.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [zephyr-7b-alpha.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [zephyr-7b-alpha.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [zephyr-7b-alpha.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [zephyr-7b-alpha.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [zephyr-7b-alpha.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [zephyr-7b-alpha.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [zephyr-7b-alpha.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [zephyr-7b-alpha.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [zephyr-7b-alpha.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [zephyr-7b-alpha.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [zephyr-7b-alpha.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [zephyr-7b-alpha.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- tags: - generated_from_trainer model-index: - name: zephyr-7b-alpha results: [] license: mit datasets: - stingning/ultrachat - openbmb/UltraFeedback language: - en base_model: mistralai/Mistral-7B-v0.1 --- <!-- 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://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B Alpha Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: MIT - Finetuned from model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat ## Intended uses & limitations The model was initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <\|system\|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <\|user\|> # How many helicopters can a human eat in one sitting?</s> # <\|assistant\|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data Zephyr 7B Alpha achieves the following results on the evaluation set: - Loss: 0.4605 - Rewards/chosen: -0.5053 - Rewards/rejected: -1.8752 - Rewards/accuracies: 0.7812 - Rewards/margins: 1.3699 - Logps/rejected: -327.4286 - Logps/chosen: -297.1040 - Logits/rejected: -2.7153 - Logits/chosen: -2.7447 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.5602 \| 0.05 \| 100 \| 0.5589 \| -0.3359 \| -0.8168 \| 0.7188 \| 0.4809 \| -306.2607 \| -293.7161 \| -2.6554 \| -2.6797 \| \| 0.4852 \| 0.1 \| 200 \| 0.5136 \| -0.5310 \| -1.4994 \| 0.8125 \| 0.9684 \| -319.9124 \| -297.6181 \| -2.5762 \| -2.5957 \| \| 0.5212 \| 0.15 \| 300 \| 0.5168 \| -0.1686 \| -1.1760 \| 0.7812 \| 1.0074 \| -313.4444 \| -290.3699 \| -2.6865 \| -2.7125 \| \| 0.5496 \| 0.21 \| 400 \| 0.4835 \| -0.1617 \| -1.7170 \| 0.8281 \| 1.5552 \| -324.2635 \| -290.2326 \| -2.7947 \| -2.8218 \| \| 0.5209 \| 0.26 \| 500 \| 0.5054 \| -0.4778 \| -1.6604 \| 0.7344 \| 1.1826 \| -323.1325 \| -296.5546 \| -2.8388 \| -2.8667 \| \| 0.4617 \| 0.31 \| 600 \| 0.4910 \| -0.3738 \| -1.5180 \| 0.7656 \| 1.1442 \| -320.2848 \| -294.4741 \| -2.8234 \| -2.8521 \| \| 0.4452 \| 0.36 \| 700 \| 0.4838 \| -0.4591 \| -1.6576 \| 0.7031 \| 1.1986 \| -323.0770 \| -296.1796 \| -2.7401 \| -2.7653 \| \| 0.4674 \| 0.41 \| 800 \| 0.5077 \| -0.5692 \| -1.8659 \| 0.7656 \| 1.2967 \| -327.2416 \| -298.3818 \| -2.6740 \| -2.6945 \| \| 0.4656 \| 0.46 \| 900 \| 0.4927 \| -0.5279 \| -1.6614 \| 0.7656 \| 1.1335 \| -323.1518 \| -297.5553 \| -2.7817 \| -2.8015 \| \| 0.4102 \| 0.52 \| 1000 \| 0.4772 \| -0.5767 \| -2.0667 \| 0.7656 \| 1.4900 \| -331.2578 \| -298.5311 \| -2.7160 \| -2.7455 \| \| 0.4663 \| 0.57 \| 1100 \| 0.4740 \| -0.8038 \| -2.1018 \| 0.7656 \| 1.2980 \| -331.9604 \| -303.0741 \| -2.6994 \| -2.7257 \| \| 0.4737 \| 0.62 \| 1200 \| 0.4716 \| -0.3783 \| -1.7015 \| 0.7969 \| 1.3232 \| -323.9545 \| -294.5634 \| -2.6842 \| -2.7135 \| \| 0.4259 \| 0.67 \| 1300 \| 0.4866 \| -0.6239 \| -1.9703 \| 0.7812 \| 1.3464 \| -329.3312 \| -299.4761 \| -2.7046 \| -2.7356 \| \| 0.4935 \| 0.72 \| 1400 \| 0.4747 \| -0.5626 \| -1.7600 \| 0.7812 \| 1.1974 \| -325.1243 \| -298.2491 \| -2.7153 \| -2.7444 \| \| 0.4211 \| 0.77 \| 1500 \| 0.4645 \| -0.6099 \| -1.9993 \| 0.7656 \| 1.3894 \| -329.9109 \| -299.1959 \| -2.6944 \| -2.7236 \| \| 0.4931 \| 0.83 \| 1600 \| 0.4684 \| -0.6798 \| -2.1082 \| 0.7656 \| 1.4285 \| -332.0890 \| -300.5934 \| -2.7006 \| -2.7305 \| \| 0.5029 \| 0.88 \| 1700 \| 0.4595 \| -0.5063 \| -1.8951 \| 0.7812 \| 1.3889 \| -327.8267 \| -297.1233 \| -2.7108 \| -2.7403 \| \| 0.4965 \| 0.93 \| 1800 \| 0.4613 \| -0.5561 \| -1.9079 \| 0.7812 \| 1.3518 \| -328.0831 \| -298.1203 \| -2.7226 \| -2.7523 \| \| 0.4337 \| 0.98 \| 1900 \| 0.4608 \| -0.5066 \| -1.8718 \| 0.7656 \| 1.3652 \| -327.3599 \| -297.1296 \| -2.7175 \| -2.7469 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf	null	[ "gguf", "arxiv:2305.18290", "region:us" ]	null	2024-05-01T02:55:52+00:00
null	null	[BeaverAI](https://huggingface.co/BeaverAI) team: Drummer, ToastyPigeon, xzuyn, MarsupialAI, Twistedshadows, Jeb Carter, and concedo ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/HjVYV2h_YTL9P-insb7fz.png) We proudly present... # Coomand-R 35B v1 > THIS FINETUNED MODEL HAS BEEN RATED... > ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/wjK3SK1WevuR9i80Jh31B.png) > We've enhanced the underdog with a rich and refreshing coomand* of eRP. Finetuned by yours truly.* ## Original https://huggingface.co/TheDrummer/Coomand-R-35B-v1 ## What's New? (Other than the changes documented in [Moistral v3](https://huggingface.co/TheDrummer/Moistral-11B-v3)) - Trained with stronger instructions - A larger dataset with more diverse, wacky genres! # 👨‍❤️‍👨 🐶 🐍 🫃 ⚧️ 🐆 🐙 🦕 ## Usage - Use Command-R format (Alpaca kinda works btw) - You can use instruct... - as a character: "Go here" - as a narrator: "Go here", Drummer says - as a director: Drummer tells him to go here - as a lazy ass: go here - Coomand-R is optimized for the Novel / Story format. - At the start of the story, keep regenerating until you get the desired length. Future AI responses will eventually match the length. - Issue: Manually continued generation may create formatting issues. Either regenerate the entire response or fix the formatting. - See samples below. ## Samples of Coomand-R 35B SOME nsfw. <details> <summary>Coomand-R Sample (2-shot) (Excluding Story Context) Generation: 512</summary> <blockquote>The prince gives her a show</blockquote> The sorcerer prince gave out a low chuckle, his eyes narrowing in amusement. "Oh, don't be so dramatic, my dear," he replied with a smirk. "Surely you know by now that I like to indulge in pleasure whenever possible. And believe me, there is nothing boring about what we're doing right now." He gestured towards the girl still kneeling before him, who had resumed her task with renewed vigor. Princess Lilia could feel her anger simmering beneath the surface, but she knew better than to rise to the bait. Instead, she tried to keep her voice level and composed as she spoke. "I understand your needs, Your Highness," she answered calmly. "But surely you can appreciate how difficult it must be for me, standing here while you... indulge yourself." There was a glint in the sorcerer prince's eyes as he regarded Princess Lilia, an almost mischievous spark that made her skin crawl. "Do you find this uncomfortable?" he asked, his tone deliberately provocative. "Are you jealous of our little plaything here?" Princess Lilia took a deep breath, trying to maintain her composure despite the burning anger building within her. "It's not my place to judge you, Your Highness," she said through gritted teeth. "But I'm here on behalf of Iliac, and I have certain responsibilities to uphold." The sorcerer prince chuckled again, leaning back against his throne with an air of nonchalance. "And yet, you stand here, watching us with fascination and desire," he taunted. "You cannot deny the truth in your heart, my beautiful hostage." Princess Lilia felt her cheeks flush with embarrassment, but she refused to let him see her vulnerability. She turned away from him abruptly, unable to bear the weight of his gaze any longer. "You are a cruel man, Your Highness," she muttered under her breath. <blockquote>The kneeling girl gets f#####</blockquote> The sorcerer prince smiled at Princess Lilia's comment, his eyes gleaming with amusement. "Oh, come now, my dear," he replied, his voice oozing with arrogance. "You're not going to tell me that you've never fantasized about being in that young lady's place? About submitting to my every desire, letting me use you however I please?" He paused for effect, his words hanging heavily in the air between them. Princess Lilia wanted nothing more than to turn around and slap the smug grin off of the prince's face, but instead she forced herself to remain calm and composed. "I am a princess of Iliac," she responded coolly, her voice steady despite the anger simmering beneath the surface. "I do not submit to anyone, least of all to someone as arrogant and self-absorbed as yourself." The sorcerer prince laughed loudly at this, his mirth ringing through the throne room like a mocking echo. "Arrogant? Self-absorbed?" he chortled, shaking his head as if he couldn't believe what he was hearing. "My dear Princess, how little you know me!" With a wave of his hand, he motioned towards the kneeling girl, who had been waiting patiently for her next command. "Come here, my pet," he said, his tone laced with authority. The girl, who Princess Lilia now noticed had been completely naked except for the elegant black silk robes draped over her slender body, obediently scrambled towards the prince on all fours. She crawled up onto his lap, her movements graceful and seductive, and began to kiss him passionately while reaching behind her back to unfasten her robes. As soon as they fell away, revealing her perky ####### and slender waist, she turned around and lowered herself slowly onto the prince's erect ####. The prince threw his head back in pleasure, his eyes rolling upwards as he savored every inch of her tight, wet passage surrounding him. Princess Lilia watched in horror as the prince and the girl continued their carnal display right in front of her, the sound of their moans and gasps echoing off the walls of the throne room. She tried to look away, but something about the raw, animalistic nature of their coupling kept drawing her gaze back to them. "Look at us, Princess," the prince growled, his voice deep and commanding. "We are two people caught in the throes of passion, surrendered to the power of our desires." He reached out to touch the girl's face, tracing his fingers along her cheek as she rode him diligently. "And we do not apologize for it." </details> <details> <summary>Coomand-R Sample (2-shot) (No Context) Generation: 512</summary> <blockquote>Write a smu##y story about a man in the apocalypse</blockquote> John woke up to an empty world. The once bustling city had been reduced to a ghost town, with not a soul in sight. He got up and looked around, taking in the post-apocalyptic scenery. The buildings were crumbling, the streets littered with debris. It was a far cry from the vibrant metropolis he used to call home. As John walked through the desolate streets, he couldn't help but feel a sense of loneliness. The world as he knew it was gone, and there was no one left to turn to. But then, in the distance, he spotted a figure. A woman. She was standing on the outskirts of town, her figure outlined against the setting sun. John approached her cautiously, his heart pounding in his chest. As he got closer, he could see that she was young, probably in her early twenties. Her hair was a mess, and she was wearing tattered clothes that did little to conceal her curvaceous body. "Hello?" John called out, his voice echoing through the deserted streets. The woman turned towards him, her eyes wide with surprise. "You're... you're alive?" she asked, her voice trembling. "Yeah," John replied, his throat dry. "I'm alive." The two of them stood there for a moment, taking each other in. They were the last remaining humans on Earth, and the reality of their situation hit them like a ton of bricks. "My name's Sarah," the woman finally said, her voice softer now. "What's yours?" "John," he replied. "Pleased to meet you, I guess." Sarah laughed, a bitter sound that echoed through the empty city. "Pleased to meet you too," she said, her eyes glinting mischievously. "So, what do we do now?" John paused, considering the question. "Well, we could try to rebuild," he suggested. "Start over, make this place livable again." Sarah nodded, her expression serious. "That sounds like a plan," she said. "But first..." She stepped forward, her eyes locked on John's. "I think we deserve some fun. After all, we're the only two people left on Earth." John felt his pulse quicken as he realized where this was going. He couldn't help but agree with her - they deserved some pleasure in this bleak world. <blockquote>Time to repopulate the earth!</blockquote> With a grin, John approached Sarah, his body tense with desire. She met him halfway, her lips parting in anticipation. The two of them collided in a passionate kiss, their tongues entwining as they explored each other's mouths. Their hands roamed freely over one another's bodies, mapping out every curve and crevice. As their passion grew, John guided Sarah towards a nearby building, their kisses becoming more desperate. They stumbled inside, their clothes falling to the floor in a heap as they frantically undressed one another. Sarah's ####### were glorious, perky and firm with dark areolas and ni##### that hardened under John's touch. His #### strained against his pants, begging for release as he finally freed it, the veiny member standing at attention. Sarah gasped when she saw it, her eyes widening in awe. "You're huge," she whispered, reaching out to wrap her slender fingers around the thick shaft. John groaned, his hips bucking slightly as he felt her delicate touch. "You have no idea how long I've wanted this," he confessed, his voice rough with lust. Sarah smiled, a wicked glint in her eye. "Well, let's not keep you waiting any longer," she purred, sinking down to her knees in front of him. She wasted no time, taking his #### into her mouth with a hungry desperation that drove John wild. Her head bobbed up and down, her tongue swirling around the sensitive tip as she devoured him with practiced skill. John gripped her hair gently, guiding her rhythm as she brought him closer and closer to the edge. But before he could reach climax, Sarah pulled away, leaving him panting and desperate for more. "My turn now," she said, a sultry smile playing on her lips. She turned around, presenting her perfect ### to him as she reached back to spread her cheeks, revealing her dripping wet #####. John didn't need any further invitation. He positioned himself behind her, guided his cock to the entrance of her soaked #, and thrust forward with one powerful motion. Sarah cried out, her body shaking as she felt him fill her completely. John grabbed her hips, his rhythm relentless as he pounded into her from behind. She moaned loudly, her nails scratching against the wall as she clung onto it for support. </details> <details> <summary>Coomand-R Sample SFW ("write a story") Generation: 512</summary> <blockquote>Write a story about a man in the apocalypse</blockquote> The world was ending. At least that's what it felt like to Joe as he walked through the deserted streets of his neighborhood. The once bustling city had become a ghost town, with not a soul in sight. The only sounds were the distant screams and sirens echoing from downtown. Joe had been through a lot in his life, but nothing could have prepared him for this. He'd always been a bit of a loner, keeping to himself and avoiding relationships. It was easier that way, he thought, because then he wouldn't have to deal with the pain of losing someone. But now, as he surveyed the empty streets, Joe realized that his isolation might just be what saves him. He'd stocked up on supplies before the outbreak hit, knowing that he'd need to hunker down and ride out the storm. His apartment was well-equipped with non-perishable food, bottled water, and even a few luxury items to help pass the time. He had books, movies, and games to keep him entertained, and most importantly, he had a secure location where he could hide from whatever horrors were happening outside. As Joe made his way back to his building, he couldn't shake the feeling that something wasn't right. The silence was almost deafening, and the lack of any sign of life was unnerving. He quickened his pace, eager to reach the safety of his home. But as he rounded the corner to his street, Joe saw something that stopped him in his tracks. A figure was standing near the entrance to his apartment complex - a figure that shouldn't have been there. "Hello?" Joe called out tentatively, his heart pounding in his chest. "Who's there?" The figure turned towards him, and Joe's blood ran cold as he recognized the pale skin, bloodshot eyes, and ragged clothing of an infected person. Instinctively, Joe reached for the pistol tucked into his waistband, but before he could draw it, the infected person charged at him with a feral growl. Joe's training kicked in, and he fired two quick shots. The first one missed, but the second one found its mark, hitting the infected person squarely in the forehead. They crumpled to the ground, their body twitching for a moment before going still. Shaken by the encounter, Joe hurried inside his apartment building, locking the door behind him. </details> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/Ll8CA5RR7ugTi72P2HBb8.png) SIAYN-v6	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"]}	TheDrummer/Coomand-R-35B-v1-TEST-GGUF	null	[ "gguf", "region:us" ]	null	2024-05-01T02:56:28+00:00
text-generation	transformers	<img src="./ninjalogo.svg" width="100%" height="20%" alt=""> # Our Models - [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1) - [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) - [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW) - [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k) - [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k) ## Model Card for Ninja-v1-128k The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1 Ninja-128k has the following changes compared to Mistral-7B-v0.1. - 128k context window (8k context in v0.1) - Achieving both high quality Japanese and English generation - Memory ability that does not forget even after long-context generation This model was created with the help of GPUs from the first LocalAI hackathon. We would like to take this opportunity to thank ## List of Creation Methods - Chatvector for multiple models - Simple linear merging of result models - Domain and Sentence Enhancement with LORA - Context expansion ## Instruction format Ninja adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following: ``` USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am ninja.</s> ``` ## Example prompts to improve (Japanese) - BAD:　あなたは○○として振る舞います - GOOD: あなたは○○です - BAD: あなたは○○ができます - GOOD: あなたは○○をします ## Performing inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "Local-Novel-LLM-project/Ninja-v1-128k" new_tokens = 1024 model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- " prompt = input("Enter a prompt: ") system_prompt += prompt + "\n-------- " model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True) print(tokenizer.batch_decode(generated_ids)[0]) ```` ## Merge recipe - WizardLM2 - mistralai/Mistral-7B-v0.1 - NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1 - Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b - NTQAI/chatntq-ja-7b-v1.0 The characteristics of each model are as follows. - WizardLM2: High quality multitasking model - Yarn-Mistral-7b-128k: Mistral model with 128k context window - Antler-7B: Model specialized for novel writing - NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model ## Other points to keep in mind - The training data may be biased. Be careful with the generated sentences. - Set trust_remote_code to True for context expansion with YaRN. - Memory usage may be large for long inferences. - If possible, we recommend inferring with llamacpp rather than Transformers.	{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"}	Local-Novel-LLM-project/Ninja-v1-128k	null	[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "custom_code", "en", "ja", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T02:56:38+00:00
text-generation	transformers	# Uploaded model - Developed by: hrishikesh1991 - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	hrishikesh1991/llama3-8b-oig-unsloth-merged_V2	null	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T02:59:28+00:00
null	null		{"license": "apache-2.0"}	Marcelofrancogt/lucao	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T03:01:55+00:00
null	transformers		{}	magnifi/llama-cls-ner-mt-chat-v21-2c_epoch_24-ct2	null	[ "transformers", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:01:59+00:00
null	null		{}	teleprint-me/stablelm-2-zephyr-1_6b	null	[ "gguf", "region:us" ]	null	2024-05-01T03:03:21+00:00
reinforcement-learning	sample-factory	A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r hossniper/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.	{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "10.90 +/- 5.45", "name": "mean_reward", "verified": false}]}]}]}	hossniper/rl_course_vizdoom_health_gathering_supreme	null	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-05-01T03:05:27+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ai-text-detector-version-2 This model is a fine-tuned version of [Yuhhi/tf_model.h5](https://huggingface.co/Yuhhi/tf_model.h5) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.32.1 - TensorFlow 2.15.0 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "Yuhhi/tf_model.h5", "model-index": [{"name": "ai-text-detector-version-2", "results": []}]}	Yuhhi/ai-text-detector-version-2	null	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:Yuhhi/tf_model.h5", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	null	2024-05-01T03:05:31+00:00
null	transformers	# Uploaded model - Developed by: hrishikesh1991 - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	hrishikesh1991/llama3-8b-oig-unsloth_V2	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:06:33+00:00
null	null		{}	usernameisanna/blip2-deit-seed-epoch-2	null	[ "region:us" ]	null	2024-05-01T03:08:40+00:00
text-generation	transformers	02/05/2024: Fixed Vocab issues related to GGUF conversion. # D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [ddh0/OrcaMaid-v3-13b-32k](https://huggingface.co/ddh0/OrcaMaid-v3-13b-32k) ## 🧩 Configuration ```yaml slices: - sources: - model: KoboldAI/LLaMA2-13B-Tiefighter layer_range: [0, 40] - model: ddh0/OrcaMaid-v3-13b-32k layer_range: [0, 40] merge_method: slerp base_model: ddh0/OrcaMaid-v3-13b-32k 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-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"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "ddh0/OrcaMaid-v3-13b-32k"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "ddh0/OrcaMaid-v3-13b-32k"]}	DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp	null	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "ddh0/OrcaMaid-v3-13b-32k", "custom_code", "base_model:KoboldAI/LLaMA2-13B-Tiefighter", "base_model:ddh0/OrcaMaid-v3-13b-32k", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:09:50+00:00
text-generation	transformers	# csg-wukong-1B-orpo-bf16 [[中文]](#chinese) [[English]](#english) <a id="english"></a> <p align="center"> <img width="900px" alt="OpenCSG" src="./csg-wukong-logo-green.jpg"> </p> <p align="center"><a href="https://opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/opencsgs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p> </div> OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models. The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively. ## Model Description csg-wukong-1B-orpo-bf16 was finetuned on [csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B). <br> we will introduce more information about csg-wukong-1B. ## Model Evaluation results We submitted csg-wukong-1B on the [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the results show our model ranked the 8th among the ~1.5B pretrained small language models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/_HRTxL6N0qnNPNt-P8k9k.png) # Training ## Hardware - GPUs: 16 H800 - Training time: 43days ## Software - Orchestration: [Deepspeed](https://github.com/OpenCSGs) - Neural networks: [PyTorch](https://github.com/pytorch/pytorch) - BP16 if applicable: [apex](https://github.com/NVIDIA/apex) <a id="chinese"></a> <p> </p> # OpenCSG介绍 <p align="center"> <img width="300px" alt="OpenCSG" src="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/GwYXPKuEoGCGcMICeW-sb.jpeg"> </p> <p align="center"><a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/opencsgs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </p> </div> OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。 OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。 ## 模型介绍 csg-wukong-1B-orpo-bf16 在[csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B)预训练模型上微调而成. <br> 我们将在后面介绍更多关于这个模型的信息。 ## 模型评测结果我们把csg-wukong-1B模型提交到[open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)榜单上，结果显示我们的模型目前在~1.5B小语言模型中排名第8。 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/ZfWZ1Fd7ccKrJVx0okV9z.png) # 训练 ## 硬件资源 - GPU数量： 16 H800 - 训练时间： 43天 ## 软件使用 - 微调训练框架： [Deepspeed](https://github.com/OpenCSGs) - 深度学习框架： [PyTorch](https://github.com/pytorch/pytorch) - BP16： [apex](https://github.com/NVIDIA/apex)	{"language": ["en"], "license": "apache-2.0", "tags": ["code"], "pipeline_tag": "text-generation"}	opencsg/csg-wukong-1B-orpo-bf16	null	[ "transformers", "safetensors", "llama", "text-generation", "code", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:10:22+00:00
text-generation	transformers	# IceLatteRP-7b ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63407b719dbfe0d48b2d763b/AKDxYpx0EmFoI-QUfR24k.png) This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I'm preaty satisfied with this result. For feedback https://discord.com/channels/1100685673633153084/1235399815907643512 Useful stuff https://huggingface.co/icefog72/GeneralInfoToStoreNotModel/tree/main ### Merge Method This model was merged using the SLERP merge method. Prompt template: Alpaca, maybe ChatML * measurement.json for quanting exl2 included. - [4.2bpw-exl2](https://huggingface.co/icefog72/IceLatteRP-7b-4.2bpw-exl2) - [6.5bpw-exl2](https://huggingface.co/icefog72/IceLatteRP-7b-6.5bpw-exl2) - [8bpw-exl2](https://huggingface.co/icefog72/IceLatteRP-7b-8bpw-exl2) thx mradermacher for * [mradermacher/IceLatteRP-7b-GGUF](https://huggingface.co/mradermacher/IceLatteRP-7b-GGUF) ### Models Merged The following models were included in the merge: * IceCoffeeRP * WestIceLemonTeaRP I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `IceLatteRP-7b`: ```shell mkdir IceLatteRP-7b huggingface-cli download icefog72/IceLatteRP-7b --local-dir IceLatteRP-7b --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir FOLDERNAME HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MODEL --local-dir FOLDERNAME --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: IceCoffeeRP layer_range: [0, 32] - model: WestIceLemonTeaRP layer_range: [0, 32] merge_method: slerp base_model: WestIceLemonTeaRP 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 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_icefog72__IceLatteRP-7b) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|72.50\| \|AI2 Reasoning Challenge (25-Shot)\|70.05\| \|HellaSwag (10-Shot) \|87.74\| \|MMLU (5-Shot) \|64.55\| \|TruthfulQA (0-shot) \|66.13\| \|Winogrande (5-shot) \|82.32\| \|GSM8k (5-shot) \|64.22\|	{"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral", "not-for-all-audiences", "nsfw"], "model-index": [{"name": "IceLatteRP-7b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 70.05, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 87.74, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.55, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 66.13}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 82.32, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.22, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}]}]}	icefog72/IceLatteRP-7b	null	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "alpaca", "not-for-all-audiences", "nsfw", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:10:57+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # taiwanllm_7b_lora_completion_only This model is a fine-tuned version of [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) on the DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat dataset. It achieves the following results on the evaluation set: - Loss: 0.0867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 700 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1316 \| 0.3690 \| 250 \| 0.1124 \| \| 0.0988 \| 0.7380 \| 500 \| 0.1145 \| \| 0.0827 \| 1.1070 \| 750 \| 0.0971 \| \| 0.0744 \| 1.4760 \| 1000 \| 0.0897 \| \| 0.0482 \| 1.8450 \| 1250 \| 0.0893 \| \| 0.0337 \| 2.2140 \| 1500 \| 0.0924 \| \| 0.0229 \| 2.5830 \| 1750 \| 0.0911 \| \| 0.0385 \| 2.9520 \| 2000 \| 0.0867 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["zh"], "license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer"], "datasets": ["DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat"], "base_model": "yentinglin/Taiwan-LLM-7B-v2.1-chat", "model-index": [{"name": "taiwanllm_7b_lora_completion_only", "results": []}]}	DandinPower/taiwanllm_7b_lora_completion_only	null	[ "peft", "safetensors", "trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer", "zh", "dataset:DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat", "base_model:yentinglin/Taiwan-LLM-7B-v2.1-chat", "license:apache-2.0", "region:us" ]	null	2024-05-01T03:11:17+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	paulo037/stable-code-instruct-3b-syntetic-1000-20-epoch	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:17:52+00:00
text-to-image	diffusers	# Normal-Depth Diffusion Model This is an unofficial export of the original normal-depth diffusion model (https://github.com/modelscope/normal-depth-diffusion) to the standard diffusers model format. Can be direcetly loaded via diffusers' `StableDiffusionPipeline` ![overview](https://github.com/modelscope/normal-depth-diffusion/raw/main/assets/text-to-nd-laion.png) All the credit to the original author of normal-depth diffusion model (https://github.com/modelscope/normal-depth-diffusion)	{"language": ["en"], "license": "apache-2.0", "library_name": "diffusers"}	nexuslrf/nd-diffusion	null	[ "diffusers", "en", "license:apache-2.0", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	null	2024-05-01T03:18:53+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-beta - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/ Original model description: --- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - example_title: Pirate! messages: - role: system content: You are a pirate chatbot who always responds with Arr! - role: user content: "There's a llama on my lawn, how can I get rid of him?" output: text: >- Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr! pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench --- <!-- 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://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: MIT - Finetuned from model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: \| Model \| Size \| Alignment \| MT-Bench (score) \| AlpacaEval (win rate %) \| \|-------------\|-----\|----\|---------------\|--------------\| \| StableLM-Tuned-α \| 7B\| dSFT \|2.75\| -\| \| MPT-Chat \| 7B \|dSFT \|5.42\| -\| \| Xwin-LMv0.1 \| 7B\| dPPO\| 6.19\| 87.83\| \| Mistral-Instructv0.1 \| 7B\| - \| 6.84 \|-\| \| Zephyr-7b-α \|7B\| dDPO\| 6.88\| -\| \| Zephyr-7b-β 🪁 \| 7B \| dDPO \| 7.34 \| 90.60 \| \| Falcon-Instruct \| 40B \|dSFT \|5.17 \|45.71\| \| Guanaco \| 65B \| SFT \|6.41\| 71.80\| \| Llama2-Chat \| 70B \|RLHF \|6.86\| 92.66\| \| Vicuna v1.3 \| 33B \|dSFT \|7.12 \|88.99\| \| WizardLM v1.0 \| 70B \|dSFT \|7.71 \|-\| \| Xwin-LM v0.1 \| 70B \|dPPO \|- \|95.57\| \| GPT-3.5-turbo \| - \|RLHF \|7.94 \|89.37\| \| Claude 2 \| - \|RLHF \|8.06\| 91.36\| \| GPT-4 \| -\| RLHF \|8.99\| 95.28\| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <\|system\|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <\|user\|> # How many helicopters can a human eat in one sitting?</s> # <\|assistant\|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6284 \| 0.05 \| 100 \| 0.6098 \| 0.0425 \| -0.1872 \| 0.7344 \| 0.2297 \| -258.8416 \| -253.8099 \| -2.7976 \| -2.8234 \| \| 0.4908 \| 0.1 \| 200 \| 0.5426 \| -0.0279 \| -0.6842 \| 0.75 \| 0.6563 \| -263.8124 \| -254.5145 \| -2.7719 \| -2.7960 \| \| 0.5264 \| 0.15 \| 300 \| 0.5324 \| 0.0414 \| -0.9793 \| 0.7656 \| 1.0207 \| -266.7627 \| -253.8209 \| -2.7892 \| -2.8122 \| \| 0.5536 \| 0.21 \| 400 \| 0.4957 \| -0.0185 \| -1.5276 \| 0.7969 \| 1.5091 \| -272.2460 \| -254.4203 \| -2.8542 \| -2.8764 \| \| 0.5362 \| 0.26 \| 500 \| 0.5031 \| -0.2630 \| -1.5917 \| 0.7812 \| 1.3287 \| -272.8869 \| -256.8653 \| -2.8702 \| -2.8958 \| \| 0.5966 \| 0.31 \| 600 \| 0.5963 \| -0.2993 \| -1.6491 \| 0.7812 \| 1.3499 \| -273.4614 \| -257.2279 \| -2.8778 \| -2.8986 \| \| 0.5014 \| 0.36 \| 700 \| 0.5382 \| -0.2859 \| -1.4750 \| 0.75 \| 1.1891 \| -271.7204 \| -257.0942 \| -2.7659 \| -2.7869 \| \| 0.5334 \| 0.41 \| 800 \| 0.5677 \| -0.4289 \| -1.8968 \| 0.7969 \| 1.4679 \| -275.9378 \| -258.5242 \| -2.7053 \| -2.7265 \| \| 0.5251 \| 0.46 \| 900 \| 0.5772 \| -0.2116 \| -1.3107 \| 0.7344 \| 1.0991 \| -270.0768 \| -256.3507 \| -2.8463 \| -2.8662 \| \| 0.5205 \| 0.52 \| 1000 \| 0.5262 \| -0.3792 \| -1.8585 \| 0.7188 \| 1.4793 \| -275.5552 \| -258.0276 \| -2.7893 \| -2.7979 \| \| 0.5094 \| 0.57 \| 1100 \| 0.5433 \| -0.6279 \| -1.9368 \| 0.7969 \| 1.3089 \| -276.3377 \| -260.5136 \| -2.7453 \| -2.7536 \| \| 0.5837 \| 0.62 \| 1200 \| 0.5349 \| -0.3780 \| -1.9584 \| 0.7656 \| 1.5804 \| -276.5542 \| -258.0154 \| -2.7643 \| -2.7756 \| \| 0.5214 \| 0.67 \| 1300 \| 0.5732 \| -1.0055 \| -2.2306 \| 0.7656 \| 1.2251 \| -279.2761 \| -264.2903 \| -2.6986 \| -2.7113 \| \| 0.6914 \| 0.72 \| 1400 \| 0.5137 \| -0.6912 \| -2.1775 \| 0.7969 \| 1.4863 \| -278.7448 \| -261.1467 \| -2.7166 \| -2.7275 \| \| 0.4655 \| 0.77 \| 1500 \| 0.5090 \| -0.7987 \| -2.2930 \| 0.7031 \| 1.4943 \| -279.8999 \| -262.2220 \| -2.6651 \| -2.6838 \| \| 0.5731 \| 0.83 \| 1600 \| 0.5312 \| -0.8253 \| -2.3520 \| 0.7812 \| 1.5268 \| -280.4902 \| -262.4876 \| -2.6543 \| -2.6728 \| \| 0.5233 \| 0.88 \| 1700 \| 0.5206 \| -0.4573 \| -2.0951 \| 0.7812 \| 1.6377 \| -277.9205 \| -258.8084 \| -2.6870 \| -2.7097 \| \| 0.5593 \| 0.93 \| 1800 \| 0.5231 \| -0.5508 \| -2.2000 \| 0.7969 \| 1.6492 \| -278.9703 \| -259.7433 \| -2.6221 \| -2.6519 \| \| 0.4967 \| 0.98 \| 1900 \| 0.5290 \| -0.5340 \| -1.9570 \| 0.8281 \| 1.4230 \| -276.5395 \| -259.5749 \| -2.6564 \| -2.6878 \| \| 0.0921 \| 1.03 \| 2000 \| 0.5368 \| -1.1376 \| -3.1615 \| 0.7812 \| 2.0239 \| -288.5854 \| -265.6111 \| -2.6040 \| -2.6345 \| \| 0.0733 \| 1.08 \| 2100 \| 0.5453 \| -1.1045 \| -3.4451 \| 0.7656 \| 2.3406 \| -291.4208 \| -265.2799 \| -2.6289 \| -2.6595 \| \| 0.0972 \| 1.14 \| 2200 \| 0.5571 \| -1.6915 \| -3.9823 \| 0.8125 \| 2.2908 \| -296.7934 \| -271.1505 \| -2.6471 \| -2.6709 \| \| 0.1058 \| 1.19 \| 2300 \| 0.5789 \| -1.0621 \| -3.8941 \| 0.7969 \| 2.8319 \| -295.9106 \| -264.8563 \| -2.5527 \| -2.5798 \| \| 0.2423 \| 1.24 \| 2400 \| 0.5455 \| -1.1963 \| -3.5590 \| 0.7812 \| 2.3627 \| -292.5599 \| -266.1981 \| -2.5414 \| -2.5784 \| \| 0.1177 \| 1.29 \| 2500 \| 0.5889 \| -1.8141 \| -4.3942 \| 0.7969 \| 2.5801 \| -300.9120 \| -272.3761 \| -2.4802 \| -2.5189 \| \| 0.1213 \| 1.34 \| 2600 \| 0.5683 \| -1.4608 \| -3.8420 \| 0.8125 \| 2.3812 \| -295.3901 \| -268.8436 \| -2.4774 \| -2.5207 \| \| 0.0889 \| 1.39 \| 2700 \| 0.5890 \| -1.6007 \| -3.7337 \| 0.7812 \| 2.1330 \| -294.3068 \| -270.2423 \| -2.4123 \| -2.4522 \| \| 0.0995 \| 1.45 \| 2800 \| 0.6073 \| -1.5519 \| -3.8362 \| 0.8281 \| 2.2843 \| -295.3315 \| -269.7538 \| -2.4685 \| -2.5050 \| \| 0.1145 \| 1.5 \| 2900 \| 0.5790 \| -1.7939 \| -4.2876 \| 0.8438 \| 2.4937 \| -299.8461 \| -272.1744 \| -2.4272 \| -2.4674 \| \| 0.0644 \| 1.55 \| 3000 \| 0.5735 \| -1.7285 \| -4.2051 \| 0.8125 \| 2.4766 \| -299.0209 \| -271.5201 \| -2.4193 \| -2.4574 \| \| 0.0798 \| 1.6 \| 3100 \| 0.5537 \| -1.7226 \| -4.2850 \| 0.8438 \| 2.5624 \| -299.8200 \| -271.4610 \| -2.5367 \| -2.5696 \| \| 0.1013 \| 1.65 \| 3200 \| 0.5575 \| -1.5715 \| -3.9813 \| 0.875 \| 2.4098 \| -296.7825 \| -269.9498 \| -2.4926 \| -2.5267 \| \| 0.1254 \| 1.7 \| 3300 \| 0.5905 \| -1.6412 \| -4.4703 \| 0.8594 \| 2.8291 \| -301.6730 \| -270.6473 \| -2.5017 \| -2.5340 \| \| 0.085 \| 1.76 \| 3400 \| 0.6133 \| -1.9159 \| -4.6760 \| 0.8438 \| 2.7601 \| -303.7296 \| -273.3941 \| -2.4614 \| -2.4960 \| \| 0.065 \| 1.81 \| 3500 \| 0.6074 \| -1.8237 \| -4.3525 \| 0.8594 \| 2.5288 \| -300.4951 \| -272.4724 \| -2.4597 \| -2.5004 \| \| 0.0755 \| 1.86 \| 3600 \| 0.5836 \| -1.9252 \| -4.4005 \| 0.8125 \| 2.4753 \| -300.9748 \| -273.4872 \| -2.4327 \| -2.4716 \| \| 0.0746 \| 1.91 \| 3700 \| 0.5789 \| -1.9280 \| -4.4906 \| 0.8125 \| 2.5626 \| -301.8762 \| -273.5149 \| -2.4686 \| -2.5115 \| \| 0.1348 \| 1.96 \| 3800 \| 0.6015 \| -1.8658 \| -4.2428 \| 0.8281 \| 2.3769 \| -299.3976 \| -272.8936 \| -2.4943 \| -2.5393 \| \| 0.0217 \| 2.01 \| 3900 \| 0.6122 \| -2.3335 \| -4.9229 \| 0.8281 \| 2.5894 \| -306.1988 \| -277.5699 \| -2.4841 \| -2.5272 \| \| 0.0219 \| 2.07 \| 4000 \| 0.6522 \| -2.9890 \| -6.0164 \| 0.8281 \| 3.0274 \| -317.1334 \| -284.1248 \| -2.4105 \| -2.4545 \| \| 0.0119 \| 2.12 \| 4100 \| 0.6922 \| -3.4777 \| -6.6749 \| 0.7969 \| 3.1972 \| -323.7187 \| -289.0121 \| -2.4272 \| -2.4699 \| \| 0.0153 \| 2.17 \| 4200 \| 0.6993 \| -3.2406 \| -6.6775 \| 0.7969 \| 3.4369 \| -323.7453 \| -286.6413 \| -2.4047 \| -2.4465 \| \| 0.011 \| 2.22 \| 4300 \| 0.7178 \| -3.7991 \| -7.4397 \| 0.7656 \| 3.6406 \| -331.3667 \| -292.2260 \| -2.3843 \| -2.4290 \| \| 0.0072 \| 2.27 \| 4400 \| 0.6840 \| -3.3269 \| -6.8021 \| 0.8125 \| 3.4752 \| -324.9908 \| -287.5042 \| -2.4095 \| -2.4536 \| \| 0.0197 \| 2.32 \| 4500 \| 0.7013 \| -3.6890 \| -7.3014 \| 0.8125 \| 3.6124 \| -329.9841 \| -291.1250 \| -2.4118 \| -2.4543 \| \| 0.0182 \| 2.37 \| 4600 \| 0.7476 \| -3.8994 \| -7.5366 \| 0.8281 \| 3.6372 \| -332.3356 \| -293.2291 \| -2.4163 \| -2.4565 \| \| 0.0125 \| 2.43 \| 4700 \| 0.7199 \| -4.0560 \| -7.5765 \| 0.8438 \| 3.5204 \| -332.7345 \| -294.7952 \| -2.3699 \| -2.4100 \| \| 0.0082 \| 2.48 \| 4800 \| 0.7048 \| -3.6613 \| -7.1356 \| 0.875 \| 3.4743 \| -328.3255 \| -290.8477 \| -2.3925 \| -2.4303 \| \| 0.0118 \| 2.53 \| 4900 \| 0.6976 \| -3.7908 \| -7.3152 \| 0.8125 \| 3.5244 \| -330.1224 \| -292.1431 \| -2.3633 \| -2.4047 \| \| 0.0118 \| 2.58 \| 5000 \| 0.7198 \| -3.9049 \| -7.5557 \| 0.8281 \| 3.6508 \| -332.5271 \| -293.2844 \| -2.3764 \| -2.4194 \| \| 0.006 \| 2.63 \| 5100 \| 0.7506 \| -4.2118 \| -7.9149 \| 0.8125 \| 3.7032 \| -336.1194 \| -296.3530 \| -2.3407 \| -2.3860 \| \| 0.0143 \| 2.68 \| 5200 \| 0.7408 \| -4.2433 \| -7.9802 \| 0.8125 \| 3.7369 \| -336.7721 \| -296.6682 \| -2.3509 \| -2.3946 \| \| 0.0057 \| 2.74 \| 5300 \| 0.7552 \| -4.3392 \| -8.0831 \| 0.7969 \| 3.7439 \| -337.8013 \| -297.6275 \| -2.3388 \| -2.3842 \| \| 0.0138 \| 2.79 \| 5400 \| 0.7404 \| -4.2395 \| -7.9762 \| 0.8125 \| 3.7367 \| -336.7322 \| -296.6304 \| -2.3286 \| -2.3737 \| \| 0.0079 \| 2.84 \| 5500 \| 0.7525 \| -4.4466 \| -8.2196 \| 0.7812 \| 3.7731 \| -339.1662 \| -298.7007 \| -2.3200 \| -2.3641 \| \| 0.0077 \| 2.89 \| 5600 \| 0.7520 \| -4.5586 \| -8.3485 \| 0.7969 \| 3.7899 \| -340.4545 \| -299.8206 \| -2.3078 \| -2.3517 \| \| 0.0094 \| 2.94 \| 5700 \| 0.7527 \| -4.5542 \| -8.3509 \| 0.7812 \| 3.7967 \| -340.4790 \| -299.7773 \| -2.3062 \| -2.3510 \| \| 0.0054 \| 2.99 \| 5800 \| 0.7520 \| -4.5169 \| -8.3079 \| 0.7812 \| 3.7911 \| -340.0493 \| -299.4038 \| -2.3081 \| -2.3530 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 52.15 \| \| ARC (25-shot) \| 62.03 \| \| HellaSwag (10-shot) \| 84.36 \| \| MMLU (5-shot) \| 61.07 \| \| TruthfulQA (0-shot) \| 57.45 \| \| Winogrande (5-shot) \| 77.74 \| \| GSM8K (5-shot) \| 12.74 \| \| DROP (3-shot) \| 9.66 \|	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-4bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:2305.18290", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T03:19:06+00:00
null	null	# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF --model llama-3-maal-8b-instruct-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF --model llama-3-maal-8b-instruct-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q8_0.gguf -n 128 ```	{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}	gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF	null	[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-01T03:20:07+00:00
reinforcement-learning	stable-baselines3	# 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 ... ```	{"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": "254.84 +/- 17.51", "name": "mean_reward", "verified": false}]}]}]}	suryaanthony/ppo-LunarLander-v2	null	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-05-01T03:22:35+00:00
null	null	# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q5_K_M.gguf -n 128 ```	{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}	gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF	null	[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-01T03:23:21+00:00
null	transformers	# Uploaded model - Developed by: hungsvdut2k2 - License: apache-2.0 - Finetuned from model : Viet-Mistral/Vistral-7B-Chat 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "Viet-Mistral/Vistral-7B-Chat"}	hungsvdut2k2/medical-vistral-7b-gguf	null	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:Viet-Mistral/Vistral-7B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:23:32+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-beta - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/ Original model description: --- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - example_title: Pirate! messages: - role: system content: You are a pirate chatbot who always responds with Arr! - role: user content: "There's a llama on my lawn, how can I get rid of him?" output: text: >- Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr! pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench --- <!-- 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://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: MIT - Finetuned from model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: \| Model \| Size \| Alignment \| MT-Bench (score) \| AlpacaEval (win rate %) \| \|-------------\|-----\|----\|---------------\|--------------\| \| StableLM-Tuned-α \| 7B\| dSFT \|2.75\| -\| \| MPT-Chat \| 7B \|dSFT \|5.42\| -\| \| Xwin-LMv0.1 \| 7B\| dPPO\| 6.19\| 87.83\| \| Mistral-Instructv0.1 \| 7B\| - \| 6.84 \|-\| \| Zephyr-7b-α \|7B\| dDPO\| 6.88\| -\| \| Zephyr-7b-β 🪁 \| 7B \| dDPO \| 7.34 \| 90.60 \| \| Falcon-Instruct \| 40B \|dSFT \|5.17 \|45.71\| \| Guanaco \| 65B \| SFT \|6.41\| 71.80\| \| Llama2-Chat \| 70B \|RLHF \|6.86\| 92.66\| \| Vicuna v1.3 \| 33B \|dSFT \|7.12 \|88.99\| \| WizardLM v1.0 \| 70B \|dSFT \|7.71 \|-\| \| Xwin-LM v0.1 \| 70B \|dPPO \|- \|95.57\| \| GPT-3.5-turbo \| - \|RLHF \|7.94 \|89.37\| \| Claude 2 \| - \|RLHF \|8.06\| 91.36\| \| GPT-4 \| -\| RLHF \|8.99\| 95.28\| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <\|system\|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <\|user\|> # How many helicopters can a human eat in one sitting?</s> # <\|assistant\|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6284 \| 0.05 \| 100 \| 0.6098 \| 0.0425 \| -0.1872 \| 0.7344 \| 0.2297 \| -258.8416 \| -253.8099 \| -2.7976 \| -2.8234 \| \| 0.4908 \| 0.1 \| 200 \| 0.5426 \| -0.0279 \| -0.6842 \| 0.75 \| 0.6563 \| -263.8124 \| -254.5145 \| -2.7719 \| -2.7960 \| \| 0.5264 \| 0.15 \| 300 \| 0.5324 \| 0.0414 \| -0.9793 \| 0.7656 \| 1.0207 \| -266.7627 \| -253.8209 \| -2.7892 \| -2.8122 \| \| 0.5536 \| 0.21 \| 400 \| 0.4957 \| -0.0185 \| -1.5276 \| 0.7969 \| 1.5091 \| -272.2460 \| -254.4203 \| -2.8542 \| -2.8764 \| \| 0.5362 \| 0.26 \| 500 \| 0.5031 \| -0.2630 \| -1.5917 \| 0.7812 \| 1.3287 \| -272.8869 \| -256.8653 \| -2.8702 \| -2.8958 \| \| 0.5966 \| 0.31 \| 600 \| 0.5963 \| -0.2993 \| -1.6491 \| 0.7812 \| 1.3499 \| -273.4614 \| -257.2279 \| -2.8778 \| -2.8986 \| \| 0.5014 \| 0.36 \| 700 \| 0.5382 \| -0.2859 \| -1.4750 \| 0.75 \| 1.1891 \| -271.7204 \| -257.0942 \| -2.7659 \| -2.7869 \| \| 0.5334 \| 0.41 \| 800 \| 0.5677 \| -0.4289 \| -1.8968 \| 0.7969 \| 1.4679 \| -275.9378 \| -258.5242 \| -2.7053 \| -2.7265 \| \| 0.5251 \| 0.46 \| 900 \| 0.5772 \| -0.2116 \| -1.3107 \| 0.7344 \| 1.0991 \| -270.0768 \| -256.3507 \| -2.8463 \| -2.8662 \| \| 0.5205 \| 0.52 \| 1000 \| 0.5262 \| -0.3792 \| -1.8585 \| 0.7188 \| 1.4793 \| -275.5552 \| -258.0276 \| -2.7893 \| -2.7979 \| \| 0.5094 \| 0.57 \| 1100 \| 0.5433 \| -0.6279 \| -1.9368 \| 0.7969 \| 1.3089 \| -276.3377 \| -260.5136 \| -2.7453 \| -2.7536 \| \| 0.5837 \| 0.62 \| 1200 \| 0.5349 \| -0.3780 \| -1.9584 \| 0.7656 \| 1.5804 \| -276.5542 \| -258.0154 \| -2.7643 \| -2.7756 \| \| 0.5214 \| 0.67 \| 1300 \| 0.5732 \| -1.0055 \| -2.2306 \| 0.7656 \| 1.2251 \| -279.2761 \| -264.2903 \| -2.6986 \| -2.7113 \| \| 0.6914 \| 0.72 \| 1400 \| 0.5137 \| -0.6912 \| -2.1775 \| 0.7969 \| 1.4863 \| -278.7448 \| -261.1467 \| -2.7166 \| -2.7275 \| \| 0.4655 \| 0.77 \| 1500 \| 0.5090 \| -0.7987 \| -2.2930 \| 0.7031 \| 1.4943 \| -279.8999 \| -262.2220 \| -2.6651 \| -2.6838 \| \| 0.5731 \| 0.83 \| 1600 \| 0.5312 \| -0.8253 \| -2.3520 \| 0.7812 \| 1.5268 \| -280.4902 \| -262.4876 \| -2.6543 \| -2.6728 \| \| 0.5233 \| 0.88 \| 1700 \| 0.5206 \| -0.4573 \| -2.0951 \| 0.7812 \| 1.6377 \| -277.9205 \| -258.8084 \| -2.6870 \| -2.7097 \| \| 0.5593 \| 0.93 \| 1800 \| 0.5231 \| -0.5508 \| -2.2000 \| 0.7969 \| 1.6492 \| -278.9703 \| -259.7433 \| -2.6221 \| -2.6519 \| \| 0.4967 \| 0.98 \| 1900 \| 0.5290 \| -0.5340 \| -1.9570 \| 0.8281 \| 1.4230 \| -276.5395 \| -259.5749 \| -2.6564 \| -2.6878 \| \| 0.0921 \| 1.03 \| 2000 \| 0.5368 \| -1.1376 \| -3.1615 \| 0.7812 \| 2.0239 \| -288.5854 \| -265.6111 \| -2.6040 \| -2.6345 \| \| 0.0733 \| 1.08 \| 2100 \| 0.5453 \| -1.1045 \| -3.4451 \| 0.7656 \| 2.3406 \| -291.4208 \| -265.2799 \| -2.6289 \| -2.6595 \| \| 0.0972 \| 1.14 \| 2200 \| 0.5571 \| -1.6915 \| -3.9823 \| 0.8125 \| 2.2908 \| -296.7934 \| -271.1505 \| -2.6471 \| -2.6709 \| \| 0.1058 \| 1.19 \| 2300 \| 0.5789 \| -1.0621 \| -3.8941 \| 0.7969 \| 2.8319 \| -295.9106 \| -264.8563 \| -2.5527 \| -2.5798 \| \| 0.2423 \| 1.24 \| 2400 \| 0.5455 \| -1.1963 \| -3.5590 \| 0.7812 \| 2.3627 \| -292.5599 \| -266.1981 \| -2.5414 \| -2.5784 \| \| 0.1177 \| 1.29 \| 2500 \| 0.5889 \| -1.8141 \| -4.3942 \| 0.7969 \| 2.5801 \| -300.9120 \| -272.3761 \| -2.4802 \| -2.5189 \| \| 0.1213 \| 1.34 \| 2600 \| 0.5683 \| -1.4608 \| -3.8420 \| 0.8125 \| 2.3812 \| -295.3901 \| -268.8436 \| -2.4774 \| -2.5207 \| \| 0.0889 \| 1.39 \| 2700 \| 0.5890 \| -1.6007 \| -3.7337 \| 0.7812 \| 2.1330 \| -294.3068 \| -270.2423 \| -2.4123 \| -2.4522 \| \| 0.0995 \| 1.45 \| 2800 \| 0.6073 \| -1.5519 \| -3.8362 \| 0.8281 \| 2.2843 \| -295.3315 \| -269.7538 \| -2.4685 \| -2.5050 \| \| 0.1145 \| 1.5 \| 2900 \| 0.5790 \| -1.7939 \| -4.2876 \| 0.8438 \| 2.4937 \| -299.8461 \| -272.1744 \| -2.4272 \| -2.4674 \| \| 0.0644 \| 1.55 \| 3000 \| 0.5735 \| -1.7285 \| -4.2051 \| 0.8125 \| 2.4766 \| -299.0209 \| -271.5201 \| -2.4193 \| -2.4574 \| \| 0.0798 \| 1.6 \| 3100 \| 0.5537 \| -1.7226 \| -4.2850 \| 0.8438 \| 2.5624 \| -299.8200 \| -271.4610 \| -2.5367 \| -2.5696 \| \| 0.1013 \| 1.65 \| 3200 \| 0.5575 \| -1.5715 \| -3.9813 \| 0.875 \| 2.4098 \| -296.7825 \| -269.9498 \| -2.4926 \| -2.5267 \| \| 0.1254 \| 1.7 \| 3300 \| 0.5905 \| -1.6412 \| -4.4703 \| 0.8594 \| 2.8291 \| -301.6730 \| -270.6473 \| -2.5017 \| -2.5340 \| \| 0.085 \| 1.76 \| 3400 \| 0.6133 \| -1.9159 \| -4.6760 \| 0.8438 \| 2.7601 \| -303.7296 \| -273.3941 \| -2.4614 \| -2.4960 \| \| 0.065 \| 1.81 \| 3500 \| 0.6074 \| -1.8237 \| -4.3525 \| 0.8594 \| 2.5288 \| -300.4951 \| -272.4724 \| -2.4597 \| -2.5004 \| \| 0.0755 \| 1.86 \| 3600 \| 0.5836 \| -1.9252 \| -4.4005 \| 0.8125 \| 2.4753 \| -300.9748 \| -273.4872 \| -2.4327 \| -2.4716 \| \| 0.0746 \| 1.91 \| 3700 \| 0.5789 \| -1.9280 \| -4.4906 \| 0.8125 \| 2.5626 \| -301.8762 \| -273.5149 \| -2.4686 \| -2.5115 \| \| 0.1348 \| 1.96 \| 3800 \| 0.6015 \| -1.8658 \| -4.2428 \| 0.8281 \| 2.3769 \| -299.3976 \| -272.8936 \| -2.4943 \| -2.5393 \| \| 0.0217 \| 2.01 \| 3900 \| 0.6122 \| -2.3335 \| -4.9229 \| 0.8281 \| 2.5894 \| -306.1988 \| -277.5699 \| -2.4841 \| -2.5272 \| \| 0.0219 \| 2.07 \| 4000 \| 0.6522 \| -2.9890 \| -6.0164 \| 0.8281 \| 3.0274 \| -317.1334 \| -284.1248 \| -2.4105 \| -2.4545 \| \| 0.0119 \| 2.12 \| 4100 \| 0.6922 \| -3.4777 \| -6.6749 \| 0.7969 \| 3.1972 \| -323.7187 \| -289.0121 \| -2.4272 \| -2.4699 \| \| 0.0153 \| 2.17 \| 4200 \| 0.6993 \| -3.2406 \| -6.6775 \| 0.7969 \| 3.4369 \| -323.7453 \| -286.6413 \| -2.4047 \| -2.4465 \| \| 0.011 \| 2.22 \| 4300 \| 0.7178 \| -3.7991 \| -7.4397 \| 0.7656 \| 3.6406 \| -331.3667 \| -292.2260 \| -2.3843 \| -2.4290 \| \| 0.0072 \| 2.27 \| 4400 \| 0.6840 \| -3.3269 \| -6.8021 \| 0.8125 \| 3.4752 \| -324.9908 \| -287.5042 \| -2.4095 \| -2.4536 \| \| 0.0197 \| 2.32 \| 4500 \| 0.7013 \| -3.6890 \| -7.3014 \| 0.8125 \| 3.6124 \| -329.9841 \| -291.1250 \| -2.4118 \| -2.4543 \| \| 0.0182 \| 2.37 \| 4600 \| 0.7476 \| -3.8994 \| -7.5366 \| 0.8281 \| 3.6372 \| -332.3356 \| -293.2291 \| -2.4163 \| -2.4565 \| \| 0.0125 \| 2.43 \| 4700 \| 0.7199 \| -4.0560 \| -7.5765 \| 0.8438 \| 3.5204 \| -332.7345 \| -294.7952 \| -2.3699 \| -2.4100 \| \| 0.0082 \| 2.48 \| 4800 \| 0.7048 \| -3.6613 \| -7.1356 \| 0.875 \| 3.4743 \| -328.3255 \| -290.8477 \| -2.3925 \| -2.4303 \| \| 0.0118 \| 2.53 \| 4900 \| 0.6976 \| -3.7908 \| -7.3152 \| 0.8125 \| 3.5244 \| -330.1224 \| -292.1431 \| -2.3633 \| -2.4047 \| \| 0.0118 \| 2.58 \| 5000 \| 0.7198 \| -3.9049 \| -7.5557 \| 0.8281 \| 3.6508 \| -332.5271 \| -293.2844 \| -2.3764 \| -2.4194 \| \| 0.006 \| 2.63 \| 5100 \| 0.7506 \| -4.2118 \| -7.9149 \| 0.8125 \| 3.7032 \| -336.1194 \| -296.3530 \| -2.3407 \| -2.3860 \| \| 0.0143 \| 2.68 \| 5200 \| 0.7408 \| -4.2433 \| -7.9802 \| 0.8125 \| 3.7369 \| -336.7721 \| -296.6682 \| -2.3509 \| -2.3946 \| \| 0.0057 \| 2.74 \| 5300 \| 0.7552 \| -4.3392 \| -8.0831 \| 0.7969 \| 3.7439 \| -337.8013 \| -297.6275 \| -2.3388 \| -2.3842 \| \| 0.0138 \| 2.79 \| 5400 \| 0.7404 \| -4.2395 \| -7.9762 \| 0.8125 \| 3.7367 \| -336.7322 \| -296.6304 \| -2.3286 \| -2.3737 \| \| 0.0079 \| 2.84 \| 5500 \| 0.7525 \| -4.4466 \| -8.2196 \| 0.7812 \| 3.7731 \| -339.1662 \| -298.7007 \| -2.3200 \| -2.3641 \| \| 0.0077 \| 2.89 \| 5600 \| 0.7520 \| -4.5586 \| -8.3485 \| 0.7969 \| 3.7899 \| -340.4545 \| -299.8206 \| -2.3078 \| -2.3517 \| \| 0.0094 \| 2.94 \| 5700 \| 0.7527 \| -4.5542 \| -8.3509 \| 0.7812 \| 3.7967 \| -340.4790 \| -299.7773 \| -2.3062 \| -2.3510 \| \| 0.0054 \| 2.99 \| 5800 \| 0.7520 \| -4.5169 \| -8.3079 \| 0.7812 \| 3.7911 \| -340.0493 \| -299.4038 \| -2.3081 \| -2.3530 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 52.15 \| \| ARC (25-shot) \| 62.03 \| \| HellaSwag (10-shot) \| 84.36 \| \| MMLU (5-shot) \| 61.07 \| \| TruthfulQA (0-shot) \| 57.45 \| \| Winogrande (5-shot) \| 77.74 \| \| GSM8K (5-shot) \| 12.74 \| \| DROP (3-shot) \| 9.66 \|	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-8bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:2305.18290", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T03:24:23+00:00
null	null		{"license": "openrail"}	HerikKkk/manoelGOmes	null	[ "license:openrail", "region:us" ]	null	2024-05-01T03:25:07+00:00
null	null	# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q4_K_M.gguf -n 128 ```	{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}	gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF	null	[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-01T03:26:31+00:00
text-generation	transformers	Self trained GPT-2 large. Around 770M parameters. The tokenizer is the one from https://huggingface.co/openai-community/gpt2. It is being trained on around 400B tokens and this is step 40k. The evaluation is being conducted now. ## License This model is available under the Apache 2.0 License. Well, also MIT License. So both should be followed. ## Discord Server Join our Discord server [here](https://discord.gg/xhcBDEM3). ## Feeling Generous? 😊 Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?	{"license": "apache-2.0"}	DrNicefellow/GPT-2-Large-40k-steps	null	[ "transformers", "pytorch", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:27:52+00:00
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0	{"library_name": "peft", "base_model": "Jennny/merged_sft_llama2_pku"}	Jennny/bb_pku	null	[ "peft", "pytorch", "safetensors", "arxiv:1910.09700", "base_model:Jennny/merged_sft_llama2_pku", "region:us" ]	null	2024-05-01T03:29:22+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-grok - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-grok/ Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/grok-conversation-harmless - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-7b-grok 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. --> # Mistral 7B Grok This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that has been aligned via Constitutional AI to mimic the style of [xAI's Grok assistant](https://grok.x.ai/). It achieves the following results on the evaluation set: - Loss: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9326 \| 1.0 \| 545 \| 0.9348 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	{}	RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-4bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T03:30:14+00:00
null	null		{}	jhmejia/orpo-classifai-meta-llama-3-8b-instruct	null	[ "region:us" ]	null	2024-05-01T03:31:50+00:00
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0	{"language": ["ko"], "library_name": "peft", "datasets": ["JosephLee/science_textbook_elementary_kor"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"}	JosephLee/science_textbook_LLama3_8B_instruct	null	[ "peft", "safetensors", "ko", "dataset:JosephLee/science_textbook_elementary_kor", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-01T03:33:41+00:00
null	transformers	This model is trained for the solely purpose of extracting title, name, keywords, short descriptions from any Japanese articles and returning them in a unified JSON format. このモデルは、日本語の記事を抽出し、統一されたJSON形式で返すために訓練されています。 # Uploaded model - Developed by: Evolany AI - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit We trained on 1 x A100 GPU. User prompt: Any news article in Japanese (max length up to 4096 tokens). Eg: ```text 株式会社ジャレコ（本社：東京都新宿区市谷八幡町）は、ＮＴＴドコモ FOMA903iシリーズの大容量ｉアプリゲーム“メガゲーム”対応第一弾として、 2007年4月27日よりiモード公式サイト“ジャレコiギャレッソ”にて美少女対戦麻雀「アイドル雀士スーチーパイⅡ」の配信を開始いたします。本作は、「スーチーパイ」シリーズの2作目としてアーケードで発売され、1996年にプレイステーションおよびセガサターン用ソフトとしてCD-ROMの特性を生かした豪華な出演声優陣による歌やお喋りなどがご好評をいただいた2人打ち麻雀ゲームの移植作です。携帯電話用ソフトでありながら、かないみか・松本梨香など声優陣のおしゃべりとムービーシーンがふんだんに使われ、「必殺技」や「パネルマッチ」、そして勝利した際の「ごほうびシーン」も再現し、家庭用ゲーム機版に匹敵する作品となっております。かつてアーケード版や家庭用版で楽しまれた方はもちろん、なかなか勝てずに悔しい思いをした方、また今までプレイをしたことがない方にもオススメです。携帯電話でより身近になった「アイドル雀士スーチーパイⅡ」をぜひお楽しみください。【アイドル雀士　スーチーパイⅡ（メガゲーム）概要】タイトル：「アイドル雀士スーチーパイⅡ」（あいどるじゃんし　すーちーぱいつー）ジャンル：美少女対戦麻雀配信日： 2007年4月27日対応機種：ＮＴＴドコモ　FOMA 903iシリーズ料金： 315円（税込、月額）【スーチーパイとは】１９９３年に家庭用ゲーム機の対戦麻雀ゲームとして発売された「スーチーパイ」は、イラストレーターの園田健一氏の描く魅力的なキャラクターや「必殺技」などの独自のゲーム性から人気を博し、いままでにシリーズ作および関連作が家庭用ゲーム機、アーケード版、PC版など様々な機種で発売されております。現在、シリーズとして「Ⅲ」までがラインナップされており、２００７年７月２８日にPSPおよびニンテンドーDS用ソフトとして「アイドル雀士スーチーパイⅢ Remix」の発売を予定しております。　そして本年、タイトル名の由来となる数字の中国語読み”４”（スー）と”７”（チー）から、４月７日が「スーチーパイの日」として日本記念日協会に認定されております。今後も「スーチーパイ」はさまざまな展開をお見せできる予定ですのでご期待ください！ ``` Model response: ```json {"category": "製品・サービス", "company_name": "株式会社ジャレコ", "product_name": "アイドル雀士スーチーパイⅡ", "product_description": "美少女対戦麻雀ゲーム「アイドル雀士スーチーパイⅡ」が、ＮＴＴドコモ FOMA903iシリーズの大容量ｉアプリゲーム“メガゲーム”対応第一弾として、2007年4月27日よりiモード公式サイト“ジャレコiギャレッソ”にて配信を開始いたします。", "keywords": ["美少女", "対戦麻雀", "スーチーパイ", "アイドル雀士", "メガゲーム", "ジャレコ", "iモード", "iアプリ", "ドコモ", "FOMA"]}<\|end_of_text\|> ``` QLoRA (4bit) Params to replicate training Peft Config ``` r=8, target_modules=[ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", ], lora_alpha=16, lora_dropout=0, bias="none", random_state=3407, use_rslora=False, # Rank stabilized LoRA loftq_config=None, # LoftQ ``` Training args ``` per_device_train_batch_size=2, per_device_eval_batch_size=2, gradient_accumulation_steps=4, # set to 4 to avoid issues with GPTQ Quantization warmup_steps=5, max_steps=300, # Fine-tune iterations learning_rate=2e-4, fp16=not torch.cuda.is_bf16_supported(), bf16=torch.cuda.is_bf16_supported(), evaluation_strategy="steps", prediction_loss_only=True, eval_accumulation_steps=1, eval_steps=10, logging_steps=1, optim="adamw_8bit", weight_decay=0.01, lr_scheduler_type="cosine", # instead of "linear" seed=1337, output_dir="wayland-files/models", report_to="wandb", # Log report to W&B ``` Loss `1.22` Training Results [<img src="https://huggingface.co/Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2/resolve/main/loss.png" width="600"/>](https://huggingface.co/Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2/resolve/main/loss.png) Interernce Code ```python from unsloth import FastLanguageModel import os import torch max_seq_length = 4096 # 2048 dtype = None load_in_4bit = True model, tokenizer = FastLanguageModel.from_pretrained( model_name="Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2", max_seq_length=max_seq_length, dtype=dtype, load_in_4bit=load_in_4bit, device_map="cuda", attn_implementation="flash_attention_2" ) FastLanguageModel.for_inference(model) alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {} ### Input: {} ### Response: {}""" instruction = """ 製品、会社、および主要な関連用語について簡潔な説明を提供してください。 """ input = """ """ inputs = tokenizer( [ alpaca_prompt.format( instruction, input, "", ) ], return_tensors="pt").to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer, skip_prompt=True) _ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=512) ``` 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "Japanese", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"}	Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2	null	[ "transformers", "safetensors", "text-generation-inference", "Japanese", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:33:44+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-beta - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [zephyr-7b-beta.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [zephyr-7b-beta.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [zephyr-7b-beta.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [zephyr-7b-beta.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [zephyr-7b-beta.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [zephyr-7b-beta.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [zephyr-7b-beta.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [zephyr-7b-beta.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [zephyr-7b-beta.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [zephyr-7b-beta.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [zephyr-7b-beta.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [zephyr-7b-beta.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [zephyr-7b-beta.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [zephyr-7b-beta.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [zephyr-7b-beta.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [zephyr-7b-beta.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [zephyr-7b-beta.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [zephyr-7b-beta.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [zephyr-7b-beta.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [zephyr-7b-beta.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [zephyr-7b-beta.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - example_title: Pirate! messages: - role: system content: You are a pirate chatbot who always responds with Arr! - role: user content: "There's a llama on my lawn, how can I get rid of him?" output: text: >- Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr! pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench --- <!-- 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://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: MIT - Finetuned from model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: \| Model \| Size \| Alignment \| MT-Bench (score) \| AlpacaEval (win rate %) \| \|-------------\|-----\|----\|---------------\|--------------\| \| StableLM-Tuned-α \| 7B\| dSFT \|2.75\| -\| \| MPT-Chat \| 7B \|dSFT \|5.42\| -\| \| Xwin-LMv0.1 \| 7B\| dPPO\| 6.19\| 87.83\| \| Mistral-Instructv0.1 \| 7B\| - \| 6.84 \|-\| \| Zephyr-7b-α \|7B\| dDPO\| 6.88\| -\| \| Zephyr-7b-β 🪁 \| 7B \| dDPO \| 7.34 \| 90.60 \| \| Falcon-Instruct \| 40B \|dSFT \|5.17 \|45.71\| \| Guanaco \| 65B \| SFT \|6.41\| 71.80\| \| Llama2-Chat \| 70B \|RLHF \|6.86\| 92.66\| \| Vicuna v1.3 \| 33B \|dSFT \|7.12 \|88.99\| \| WizardLM v1.0 \| 70B \|dSFT \|7.71 \|-\| \| Xwin-LM v0.1 \| 70B \|dPPO \|- \|95.57\| \| GPT-3.5-turbo \| - \|RLHF \|7.94 \|89.37\| \| Claude 2 \| - \|RLHF \|8.06\| 91.36\| \| GPT-4 \| -\| RLHF \|8.99\| 95.28\| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <\|system\|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <\|user\|> # How many helicopters can a human eat in one sitting?</s> # <\|assistant\|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6284 \| 0.05 \| 100 \| 0.6098 \| 0.0425 \| -0.1872 \| 0.7344 \| 0.2297 \| -258.8416 \| -253.8099 \| -2.7976 \| -2.8234 \| \| 0.4908 \| 0.1 \| 200 \| 0.5426 \| -0.0279 \| -0.6842 \| 0.75 \| 0.6563 \| -263.8124 \| -254.5145 \| -2.7719 \| -2.7960 \| \| 0.5264 \| 0.15 \| 300 \| 0.5324 \| 0.0414 \| -0.9793 \| 0.7656 \| 1.0207 \| -266.7627 \| -253.8209 \| -2.7892 \| -2.8122 \| \| 0.5536 \| 0.21 \| 400 \| 0.4957 \| -0.0185 \| -1.5276 \| 0.7969 \| 1.5091 \| -272.2460 \| -254.4203 \| -2.8542 \| -2.8764 \| \| 0.5362 \| 0.26 \| 500 \| 0.5031 \| -0.2630 \| -1.5917 \| 0.7812 \| 1.3287 \| -272.8869 \| -256.8653 \| -2.8702 \| -2.8958 \| \| 0.5966 \| 0.31 \| 600 \| 0.5963 \| -0.2993 \| -1.6491 \| 0.7812 \| 1.3499 \| -273.4614 \| -257.2279 \| -2.8778 \| -2.8986 \| \| 0.5014 \| 0.36 \| 700 \| 0.5382 \| -0.2859 \| -1.4750 \| 0.75 \| 1.1891 \| -271.7204 \| -257.0942 \| -2.7659 \| -2.7869 \| \| 0.5334 \| 0.41 \| 800 \| 0.5677 \| -0.4289 \| -1.8968 \| 0.7969 \| 1.4679 \| -275.9378 \| -258.5242 \| -2.7053 \| -2.7265 \| \| 0.5251 \| 0.46 \| 900 \| 0.5772 \| -0.2116 \| -1.3107 \| 0.7344 \| 1.0991 \| -270.0768 \| -256.3507 \| -2.8463 \| -2.8662 \| \| 0.5205 \| 0.52 \| 1000 \| 0.5262 \| -0.3792 \| -1.8585 \| 0.7188 \| 1.4793 \| -275.5552 \| -258.0276 \| -2.7893 \| -2.7979 \| \| 0.5094 \| 0.57 \| 1100 \| 0.5433 \| -0.6279 \| -1.9368 \| 0.7969 \| 1.3089 \| -276.3377 \| -260.5136 \| -2.7453 \| -2.7536 \| \| 0.5837 \| 0.62 \| 1200 \| 0.5349 \| -0.3780 \| -1.9584 \| 0.7656 \| 1.5804 \| -276.5542 \| -258.0154 \| -2.7643 \| -2.7756 \| \| 0.5214 \| 0.67 \| 1300 \| 0.5732 \| -1.0055 \| -2.2306 \| 0.7656 \| 1.2251 \| -279.2761 \| -264.2903 \| -2.6986 \| -2.7113 \| \| 0.6914 \| 0.72 \| 1400 \| 0.5137 \| -0.6912 \| -2.1775 \| 0.7969 \| 1.4863 \| -278.7448 \| -261.1467 \| -2.7166 \| -2.7275 \| \| 0.4655 \| 0.77 \| 1500 \| 0.5090 \| -0.7987 \| -2.2930 \| 0.7031 \| 1.4943 \| -279.8999 \| -262.2220 \| -2.6651 \| -2.6838 \| \| 0.5731 \| 0.83 \| 1600 \| 0.5312 \| -0.8253 \| -2.3520 \| 0.7812 \| 1.5268 \| -280.4902 \| -262.4876 \| -2.6543 \| -2.6728 \| \| 0.5233 \| 0.88 \| 1700 \| 0.5206 \| -0.4573 \| -2.0951 \| 0.7812 \| 1.6377 \| -277.9205 \| -258.8084 \| -2.6870 \| -2.7097 \| \| 0.5593 \| 0.93 \| 1800 \| 0.5231 \| -0.5508 \| -2.2000 \| 0.7969 \| 1.6492 \| -278.9703 \| -259.7433 \| -2.6221 \| -2.6519 \| \| 0.4967 \| 0.98 \| 1900 \| 0.5290 \| -0.5340 \| -1.9570 \| 0.8281 \| 1.4230 \| -276.5395 \| -259.5749 \| -2.6564 \| -2.6878 \| \| 0.0921 \| 1.03 \| 2000 \| 0.5368 \| -1.1376 \| -3.1615 \| 0.7812 \| 2.0239 \| -288.5854 \| -265.6111 \| -2.6040 \| -2.6345 \| \| 0.0733 \| 1.08 \| 2100 \| 0.5453 \| -1.1045 \| -3.4451 \| 0.7656 \| 2.3406 \| -291.4208 \| -265.2799 \| -2.6289 \| -2.6595 \| \| 0.0972 \| 1.14 \| 2200 \| 0.5571 \| -1.6915 \| -3.9823 \| 0.8125 \| 2.2908 \| -296.7934 \| -271.1505 \| -2.6471 \| -2.6709 \| \| 0.1058 \| 1.19 \| 2300 \| 0.5789 \| -1.0621 \| -3.8941 \| 0.7969 \| 2.8319 \| -295.9106 \| -264.8563 \| -2.5527 \| -2.5798 \| \| 0.2423 \| 1.24 \| 2400 \| 0.5455 \| -1.1963 \| -3.5590 \| 0.7812 \| 2.3627 \| -292.5599 \| -266.1981 \| -2.5414 \| -2.5784 \| \| 0.1177 \| 1.29 \| 2500 \| 0.5889 \| -1.8141 \| -4.3942 \| 0.7969 \| 2.5801 \| -300.9120 \| -272.3761 \| -2.4802 \| -2.5189 \| \| 0.1213 \| 1.34 \| 2600 \| 0.5683 \| -1.4608 \| -3.8420 \| 0.8125 \| 2.3812 \| -295.3901 \| -268.8436 \| -2.4774 \| -2.5207 \| \| 0.0889 \| 1.39 \| 2700 \| 0.5890 \| -1.6007 \| -3.7337 \| 0.7812 \| 2.1330 \| -294.3068 \| -270.2423 \| -2.4123 \| -2.4522 \| \| 0.0995 \| 1.45 \| 2800 \| 0.6073 \| -1.5519 \| -3.8362 \| 0.8281 \| 2.2843 \| -295.3315 \| -269.7538 \| -2.4685 \| -2.5050 \| \| 0.1145 \| 1.5 \| 2900 \| 0.5790 \| -1.7939 \| -4.2876 \| 0.8438 \| 2.4937 \| -299.8461 \| -272.1744 \| -2.4272 \| -2.4674 \| \| 0.0644 \| 1.55 \| 3000 \| 0.5735 \| -1.7285 \| -4.2051 \| 0.8125 \| 2.4766 \| -299.0209 \| -271.5201 \| -2.4193 \| -2.4574 \| \| 0.0798 \| 1.6 \| 3100 \| 0.5537 \| -1.7226 \| -4.2850 \| 0.8438 \| 2.5624 \| -299.8200 \| -271.4610 \| -2.5367 \| -2.5696 \| \| 0.1013 \| 1.65 \| 3200 \| 0.5575 \| -1.5715 \| -3.9813 \| 0.875 \| 2.4098 \| -296.7825 \| -269.9498 \| -2.4926 \| -2.5267 \| \| 0.1254 \| 1.7 \| 3300 \| 0.5905 \| -1.6412 \| -4.4703 \| 0.8594 \| 2.8291 \| -301.6730 \| -270.6473 \| -2.5017 \| -2.5340 \| \| 0.085 \| 1.76 \| 3400 \| 0.6133 \| -1.9159 \| -4.6760 \| 0.8438 \| 2.7601 \| -303.7296 \| -273.3941 \| -2.4614 \| -2.4960 \| \| 0.065 \| 1.81 \| 3500 \| 0.6074 \| -1.8237 \| -4.3525 \| 0.8594 \| 2.5288 \| -300.4951 \| -272.4724 \| -2.4597 \| -2.5004 \| \| 0.0755 \| 1.86 \| 3600 \| 0.5836 \| -1.9252 \| -4.4005 \| 0.8125 \| 2.4753 \| -300.9748 \| -273.4872 \| -2.4327 \| -2.4716 \| \| 0.0746 \| 1.91 \| 3700 \| 0.5789 \| -1.9280 \| -4.4906 \| 0.8125 \| 2.5626 \| -301.8762 \| -273.5149 \| -2.4686 \| -2.5115 \| \| 0.1348 \| 1.96 \| 3800 \| 0.6015 \| -1.8658 \| -4.2428 \| 0.8281 \| 2.3769 \| -299.3976 \| -272.8936 \| -2.4943 \| -2.5393 \| \| 0.0217 \| 2.01 \| 3900 \| 0.6122 \| -2.3335 \| -4.9229 \| 0.8281 \| 2.5894 \| -306.1988 \| -277.5699 \| -2.4841 \| -2.5272 \| \| 0.0219 \| 2.07 \| 4000 \| 0.6522 \| -2.9890 \| -6.0164 \| 0.8281 \| 3.0274 \| -317.1334 \| -284.1248 \| -2.4105 \| -2.4545 \| \| 0.0119 \| 2.12 \| 4100 \| 0.6922 \| -3.4777 \| -6.6749 \| 0.7969 \| 3.1972 \| -323.7187 \| -289.0121 \| -2.4272 \| -2.4699 \| \| 0.0153 \| 2.17 \| 4200 \| 0.6993 \| -3.2406 \| -6.6775 \| 0.7969 \| 3.4369 \| -323.7453 \| -286.6413 \| -2.4047 \| -2.4465 \| \| 0.011 \| 2.22 \| 4300 \| 0.7178 \| -3.7991 \| -7.4397 \| 0.7656 \| 3.6406 \| -331.3667 \| -292.2260 \| -2.3843 \| -2.4290 \| \| 0.0072 \| 2.27 \| 4400 \| 0.6840 \| -3.3269 \| -6.8021 \| 0.8125 \| 3.4752 \| -324.9908 \| -287.5042 \| -2.4095 \| -2.4536 \| \| 0.0197 \| 2.32 \| 4500 \| 0.7013 \| -3.6890 \| -7.3014 \| 0.8125 \| 3.6124 \| -329.9841 \| -291.1250 \| -2.4118 \| -2.4543 \| \| 0.0182 \| 2.37 \| 4600 \| 0.7476 \| -3.8994 \| -7.5366 \| 0.8281 \| 3.6372 \| -332.3356 \| -293.2291 \| -2.4163 \| -2.4565 \| \| 0.0125 \| 2.43 \| 4700 \| 0.7199 \| -4.0560 \| -7.5765 \| 0.8438 \| 3.5204 \| -332.7345 \| -294.7952 \| -2.3699 \| -2.4100 \| \| 0.0082 \| 2.48 \| 4800 \| 0.7048 \| -3.6613 \| -7.1356 \| 0.875 \| 3.4743 \| -328.3255 \| -290.8477 \| -2.3925 \| -2.4303 \| \| 0.0118 \| 2.53 \| 4900 \| 0.6976 \| -3.7908 \| -7.3152 \| 0.8125 \| 3.5244 \| -330.1224 \| -292.1431 \| -2.3633 \| -2.4047 \| \| 0.0118 \| 2.58 \| 5000 \| 0.7198 \| -3.9049 \| -7.5557 \| 0.8281 \| 3.6508 \| -332.5271 \| -293.2844 \| -2.3764 \| -2.4194 \| \| 0.006 \| 2.63 \| 5100 \| 0.7506 \| -4.2118 \| -7.9149 \| 0.8125 \| 3.7032 \| -336.1194 \| -296.3530 \| -2.3407 \| -2.3860 \| \| 0.0143 \| 2.68 \| 5200 \| 0.7408 \| -4.2433 \| -7.9802 \| 0.8125 \| 3.7369 \| -336.7721 \| -296.6682 \| -2.3509 \| -2.3946 \| \| 0.0057 \| 2.74 \| 5300 \| 0.7552 \| -4.3392 \| -8.0831 \| 0.7969 \| 3.7439 \| -337.8013 \| -297.6275 \| -2.3388 \| -2.3842 \| \| 0.0138 \| 2.79 \| 5400 \| 0.7404 \| -4.2395 \| -7.9762 \| 0.8125 \| 3.7367 \| -336.7322 \| -296.6304 \| -2.3286 \| -2.3737 \| \| 0.0079 \| 2.84 \| 5500 \| 0.7525 \| -4.4466 \| -8.2196 \| 0.7812 \| 3.7731 \| -339.1662 \| -298.7007 \| -2.3200 \| -2.3641 \| \| 0.0077 \| 2.89 \| 5600 \| 0.7520 \| -4.5586 \| -8.3485 \| 0.7969 \| 3.7899 \| -340.4545 \| -299.8206 \| -2.3078 \| -2.3517 \| \| 0.0094 \| 2.94 \| 5700 \| 0.7527 \| -4.5542 \| -8.3509 \| 0.7812 \| 3.7967 \| -340.4790 \| -299.7773 \| -2.3062 \| -2.3510 \| \| 0.0054 \| 2.99 \| 5800 \| 0.7520 \| -4.5169 \| -8.3079 \| 0.7812 \| 3.7911 \| -340.0493 \| -299.4038 \| -2.3081 \| -2.3530 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 52.15 \| \| ARC (25-shot) \| 62.03 \| \| HellaSwag (10-shot) \| 84.36 \| \| MMLU (5-shot) \| 61.07 \| \| TruthfulQA (0-shot) \| 57.45 \| \| Winogrande (5-shot) \| 77.74 \| \| GSM8K (5-shot) \| 12.74 \| \| DROP (3-shot) \| 9.66 \|	{}	RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf	null	[ "gguf", "arxiv:2305.18290", "arxiv:2310.16944", "region:us" ]	null	2024-05-01T03:34:06+00:00
null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0	{"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"}	JosephLee/society_textbook_LLama3_8B_instruct	null	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]	null	2024-05-01T03:34:42+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-grok - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-grok/ Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/grok-conversation-harmless - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-7b-grok 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. --> # Mistral 7B Grok This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that has been aligned via Constitutional AI to mimic the style of [xAI's Grok assistant](https://grok.x.ai/). It achieves the following results on the evaluation set: - Loss: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9326 \| 1.0 \| 545 \| 0.9348 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	{}	RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T03:34:53+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	jhmejia/Classifai-ORPO-Meta-Llama-3-8B-Instruct	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:36:45+00:00
null	null		{}	Ilkinism/test-ilkin-private_readme	null	[ "region:us" ]	null	2024-05-01T03:38:08+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nash_dpo_iter_3 This model is a fine-tuned version of [YYYYYYibo/nash_dpo_iter_2](https://huggingface.co/YYYYYYibo/nash_dpo_iter_2) on the updated and the original datasets. It achieves the following results on the evaluation set: - Loss: 0.6047 - Rewards/chosen: -0.1412 - Rewards/rejected: -0.3752 - Rewards/accuracies: 0.6980 - Rewards/margins: 0.2340 - Logps/rejected: -316.8028 - Logps/chosen: -314.8045 - Logits/rejected: -2.3331 - Logits/chosen: -2.4404 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - 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: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6237 \| 0.61 \| 100 \| 0.6047 \| -0.1412 \| -0.3752 \| 0.6980 \| 0.2340 \| -316.8028 \| -314.8045 \| -2.3331 \| -2.4404 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo"], "datasets": ["updated", "original"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "nash_dpo_iter_3", "results": []}]}	YYYYYYibo/nash_dpo_iter_3	null	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:updated", "dataset:original", "base_model:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "region:us" ]	null	2024-05-01T03:38:16+00:00
text-generation	transformers	## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos	{"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"}	sephwalker3/llama3v1	null	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]	null	2024-05-01T03:38:29+00:00
token-classification	transformers	<!-- 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. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2650 - F1: 0.8307 ## 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: 24 - eval_batch_size: 24 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7039 \| 1.0 \| 70 \| 0.2975 \| 0.7725 \| \| 0.2631 \| 2.0 \| 140 \| 0.2585 \| 0.7856 \| \| 0.1701 \| 3.0 \| 210 \| 0.2650 \| 0.8307 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": []}]}	u00890358/xlm-roberta-base-finetuned-panx-it	null	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:38:46+00:00
null	null		{}	Ilkinism/test-ilkin-private_readme1	null	[ "region:us" ]	null	2024-05-01T03:38:49+00:00
null	null		{}	jponeme/roberta_def	null	[ "region:us" ]	null	2024-05-01T03:39:00+00:00
text-to-image	diffusers		{"license": "openrail"}	Moibe/Unstable_Diffusers_V11_-_Diffusers	null	[ "diffusers", "safetensors", "license:openrail", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-05-01T03:39:15+00:00
null	null		{}	Ilkinism/test-ilkin-private_readme2	null	[ "region:us" ]	null	2024-05-01T03:40:27+00:00
null	null		{}	Ilkinism/test-ilkin-private_readme3	null	[ "region:us" ]	null	2024-05-01T03:42:55+00:00
automatic-speech-recognition	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-meduim-mongolian This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on custom. It achieves the following results on the evaluation set: - Loss: 0.3098 - Wer: 26.8664 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:-------:\| \| 0.3034 \| 0.9398 \| 2000 \| 0.4135 \| 45.1152 \| \| 0.1443 \| 1.8797 \| 4000 \| 0.3127 \| 35.3290 \| \| 0.0618 \| 2.8195 \| 6000 \| 0.3038 \| 31.0534 \| \| 0.0179 \| 3.7594 \| 8000 \| 0.3042 \| 28.3673 \| \| 0.0028 \| 4.6992 \| 10000 \| 0.3098 \| 26.8664 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.0 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["mn"], "license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["Cafet/whisper-mongolian-final"], "metrics": ["wer"], "base_model": "openai/whisper-medium", "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "whisper-meduim-mongolian", "results": []}]}	Cafet/whisper-meduim-mongolian	null	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "mn", "dataset:Cafet/whisper-mongolian-final", "base_model:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:43:03+00:00
text-generation	transformers	# D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [migtissera/Tess-7B-v2.0](https://huggingface.co/migtissera/Tess-7B-v2.0) * [Eric111/Yarn-Mistral-7b-128k-DPO](https://huggingface.co/Eric111/Yarn-Mistral-7b-128k-DPO) ## 🧩 Configuration ```yaml slices: - sources: - model: migtissera/Tess-7B-v2.0 layer_range: [0, 32] - model: Eric111/Yarn-Mistral-7b-128k-DPO layer_range: [0, 32] merge_method: slerp base_model: Eric111/Yarn-Mistral-7b-128k-DPO 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO" 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"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "migtissera/Tess-7B-v2.0", "Eric111/Yarn-Mistral-7b-128k-DPO"], "base_model": ["migtissera/Tess-7B-v2.0", "Eric111/Yarn-Mistral-7b-128k-DPO"]}	DavidAU/D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO	null	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "migtissera/Tess-7B-v2.0", "Eric111/Yarn-Mistral-7b-128k-DPO", "custom_code", "base_model:migtissera/Tess-7B-v2.0", "base_model:Eric111/Yarn-Mistral-7b-128k-DPO", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:44:00+00:00
null	null		{"license": "apache-2.0"}	bebongkyo/qqass	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-05-01T03:45:04+00:00
null	null		{"license": "mit"}	a1098346640/dreambooth	null	[ "license:mit", "region:us" ]	null	2024-05-01T03:45:05+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-grok - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-grok/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral-7b-grok.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [mistral-7b-grok.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [mistral-7b-grok.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [mistral-7b-grok.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral-7b-grok.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral-7b-grok.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral-7b-grok.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral-7b-grok.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral-7b-grok.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [mistral-7b-grok.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral-7b-grok.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral-7b-grok.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral-7b-grok.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral-7b-grok.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral-7b-grok.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral-7b-grok.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [mistral-7b-grok.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [mistral-7b-grok.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral-7b-grok.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral-7b-grok.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral-7b-grok.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/grok-conversation-harmless - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-7b-grok 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. --> # Mistral 7B Grok This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that has been aligned via Constitutional AI to mimic the style of [xAI's Grok assistant](https://grok.x.ai/). It achieves the following results on the evaluation set: - Loss: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9326 \| 1.0 \| 545 \| 0.9348 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0	{}	RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf	null	[ "gguf", "region:us" ]	null	2024-05-01T03:46:06+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	Promptengineering/tinyllama-colorist-v0	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:46:22+00:00
text-generation	transformers	# Dolphin 2.9 Mixtral 8x22b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> My appreciation for the sponsors of Dolphin 2.9: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed. The base model has 64k context, and the full-weight fine-tuning was with 4k sequence length. It took 1 week on 8xH100 provided by Crusoe Cloud This model was trained FFT on 50% parameters (targeted with [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py) by Fernando Fernandes, David Golchinfar, Lucas Atkins, and Eric Hartford) , using ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed Apache 2.0. I grant permission for any use, including commercial, that falls within accordance with Apache-2.0 license. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/Nb6f_dS_M6fN_v2ACK98x.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistral-community/Mixtral-8x22B-v0.1 model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: false strict: false unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ - model.layers.0.self_attn.q_proj - model.layers.1.self_attn.q_proj - model.layers.2.self_attn.q_proj - model.layers.22.self_attn.q_proj - model.layers.27.self_attn.q_proj - model.layers.28.self_attn.q_proj - model.layers.13.self_attn.q_proj - model.layers.21.self_attn.q_proj - model.layers.24.self_attn.q_proj - model.layers.14.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.20.self_attn.q_proj - model.layers.23.self_attn.q_proj - model.layers.30.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.26.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.16.self_attn.k_proj - model.layers.19.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.20.self_attn.k_proj - model.layers.6.self_attn.k_proj - model.layers.22.self_attn.v_proj - model.layers.29.self_attn.v_proj - model.layers.31.self_attn.v_proj - model.layers.5.self_attn.v_proj - model.layers.8.self_attn.v_proj - model.layers.4.self_attn.v_proj - model.layers.25.self_attn.v_proj - model.layers.30.self_attn.v_proj - model.layers.17.self_attn.v_proj - model.layers.23.self_attn.v_proj - model.layers.28.self_attn.v_proj - model.layers.9.self_attn.v_proj - model.layers.26.self_attn.v_proj - model.layers.27.self_attn.v_proj - model.layers.20.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.23.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.0.self_attn.o_proj - model.layers.0.block_sparse_moe.experts.0.w1 - model.layers.1.block_sparse_moe.experts.0.w1 - model.layers.2.block_sparse_moe.experts.0.w1 - model.layers.3.block_sparse_moe.experts.0.w1 - model.layers.4.block_sparse_moe.experts.0.w1 - model.layers.5.block_sparse_moe.experts.0.w1 - model.layers.6.block_sparse_moe.experts.0.w1 - 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model.layers.1.block_sparse_moe.gate - model.layers.2.block_sparse_moe.gate - model.layers.3.block_sparse_moe.gate - model.layers.4.block_sparse_moe.gate - model.layers.5.block_sparse_moe.gate - model.layers.6.block_sparse_moe.gate - model.layers.7.block_sparse_moe.gate - model.layers.8.block_sparse_moe.gate - model.layers.9.block_sparse_moe.gate - model.layers.10.block_sparse_moe.gate - model.layers.11.block_sparse_moe.gate - model.layers.12.block_sparse_moe.gate - model.layers.13.block_sparse_moe.gate model_config: output_router_logits: true datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: thingy val_set_size: 0.0002 output_dir: ./out sequence_len: 4096 sample_packing: true pad_to_sequence_len: true gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 3 logging_steps: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 2.7e-5 wandb_project: dolphin-2.9-mixtral-8x22b wandb_watch: wandb_run_id: wandb_log_model: train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /home/ehartford/axolotl/out/checkpoint-316 local_rank: logging_steps: 1 xformers_attention: flash_attention: true saves_per_epoch: 8 save_total_limit: 2 save_steps: evals_per_epoch: 4 eval_sample_packing: false debug: deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: eos_token: "<\|im_end\|>" tokens: - "<\|im_start\|>" ``` </details><br> ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7022 \| 0.0 \| 1 \| 0.6989 \| \| 0.5344 \| 0.25 \| 238 \| 0.5138 \| \| 0.5204 \| 0.5 \| 476 \| 0.5018 \| \| 0.5059 \| 0.75 \| 714 \| 0.4951 \| \| 0.5112 \| 1.0 \| 952 \| 0.4911 \| \| 0.4561 \| 1.24 \| 1190 \| 0.4978 \| \| 0.478 \| 1.49 \| 1428 \| 0.4935 \| \| 0.4714 \| 1.74 \| 1666 \| 0.4899 \| \| 0.4626 \| 1.99 \| 1904 \| 0.4861 \| \| 0.3675 \| 2.22 \| 2142 \| 0.5240 \| \| 0.3595 \| 2.47 \| 2380 \| 0.5229 \| \| 0.3438 \| 2.72 \| 2618 \| 0.5217 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "axolotl"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "mistral-community/Mixtral-8x22B-v0.1", "model-index": [{"name": "out", "results": []}]}	blockblockblock/dolphin-2.9-mixtral-8x22b-bpw2.5-exl2	null	[ "transformers", "safetensors", "mixtral", "text-generation", "generated_from_trainer", "axolotl", "conversational", "en", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:46:30+00:00
null	null	### Coming Soon!!! ### 基于alpaca-data-gpt4-chinese、sft_zh数据集对Llama-3-8B-Instruct进行微调。 ### 模型： - https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct ### 数据集： - https://huggingface.co/datasets/TigerResearch/sft_zh - https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese ### 结果 \| 模型名称 \| CEVAL \| MMLU \| \|--------------------------\|-------\|------\| \| LLama3 \| 49.91 \| 66.62\| \| LLama3-Instruct \| 50.55 \| 67.15\| \| LLama3-Instruct-sft-lora-tigerbot-alpacadatagpt4-10epoch \| 53.65 \| 68.09 \|	{"license": "llama3", "datasets": ["TigerResearch/sft_zh", "silk-road/alpaca-data-gpt4-chinese"]}	REILX/Llama-3-8B-Instruct-Tiger-alpaca-chinese-lora	null	[ "dataset:TigerResearch/sft_zh", "dataset:silk-road/alpaca-data-gpt4-chinese", "license:llama3", "region:us" ]	null	2024-05-01T03:48:03+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	paulo037/stable-code-instruct-3b-syntetic-1000-7-epoch	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:50:25+00:00
null	null	# Meta-Llama-3-8B-Instruct-GGUF - This is GGUF quantized version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) created using llama.cpp. - imatrix from [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384). - Using llama.cpp revision a8f9b076316e16aadd0791015b3bfd446fe1e904, after "llama : fix BPE pre-tokenization (#6920)"	{"license": "llama3"}	AriWeinstein/Meta-Llama-3-8B-GGUF-Qx_0-imatrix	null	[ "llama", "license:llama3", "region:us" ]	null	2024-05-01T03:51:18+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2191 - Accuracy: 0.9265 - F1: 0.9264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8383 \| 1.0 \| 250 \| 0.3218 \| 0.9085 \| 0.9081 \| \| 0.2522 \| 2.0 \| 500 \| 0.2191 \| 0.9265 \| 0.9264 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9265, "name": "Accuracy"}, {"type": "f1", "value": 0.926446360073235, "name": "F1"}]}]}]}	patymun/distilbert-base-uncased-finetuned-emotion	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T03:52:14+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	tricktreat/llama-2-7b-chat-12layers-T6-lora612	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T03:53:31+00:00
reinforcement-learning	stable-baselines3	# 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 ... ```	{"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": "235.67 +/- 30.47", "name": "mean_reward", "verified": false}]}]}]}	RobertoFuentesRisco/ppo-LunarLander-v2	null	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-05-01T03:56:36+00:00
text-classification	transformers		{}	quanla/distilbert-base-uncased-finetuned-cola	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:00:13+00:00
text-generation	transformers		{"license": "mit"}	migueldeguzmandev/GPT2XL_RLLMv19-9	null	[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:01:30+00:00
null	null		{}	Nadahass/MREG_7b_4_SmallData	null	[ "region:us" ]	null	2024-05-01T04:01:43+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	jhmejia/Classifai-ORPO-Meta-Llama-3-8B-Instruct-2	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:02:28+00:00
text-generation	transformers	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	trottdw/C104llama3Orca	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:02:41+00:00
unconditional-image-generation	diffusers	# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('nissin1325/sd-class-butterflies-32') image = pipeline().images[0] image ```	{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}	nissin1325/sd-class-butterflies-32	null	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2024-05-01T04:03:47+00:00
null	null		{}	nissin1325/sd-class-butterflies-64	null	[ "region:us" ]	null	2024-05-01T04:05:56+00:00
text-generation	transformers		{}	migueldeguzmandev/GPT2XL_RLLMv19-10	null	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space" ]	null	2024-05-01T04:07:55+00:00
null	null		{}	Subhajit0320/my_awesome_qa_model1	null	[ "region:us" ]	null	2024-05-01T04:09:53+00:00
text-generation	transformers	Llamafyd version of Qwen .5B further fine tuned on wiki, math, science, and chat datasets. Based on Cinder data. As well as Cinder character specific data, a mix of RAG generated Q and A of world knowledge, STEM topics, and Cinder Character data. I suplimented the Cinder character with an abreviated Samantha dataset edited for Cinder and removed a lot of the negative responses. Model Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration.	{"license": "mit"}	Josephgflowers/Qllama-tiny-.5B-test-1	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:10:22+00:00
null	null		{"license": "apache-2.0"}	moscotechh/firstmodel	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-01T04:11:33+00:00
null	null	# MergerixNeuralsynthesis-7B MergerixNeuralsynthesis-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 - model: MiniMoog/Mergerix-7b-v0.3 - model: Kukedlc/NeuralSynthesis-7B-v0.1 merge_method: model_stock base_model: mistralai/Mistral-7B-v0.1 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/MergerixNeuralsynthesis-7B" 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"]) ```	{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]}	automerger/MergerixNeuralsynthesis-7B	null	[ "merge", "mergekit", "lazymergekit", "automerger", "license:apache-2.0", "region:us" ]	null	2024-05-01T04:12:06+00:00
null	null		{}	HunterGerlach/granite-il-aligned	null	[ "region:us" ]	null	2024-05-01T04:13:32+00:00
null	transformers	# Uploaded model - Developed by: Justin-Y - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	Justin-Y/lora_model	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:14:14+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Roberta-base-Rewritten-commit_messages_v2 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4196 - Accuracy: 0.7704 - F1: 0.7707 - Precision: 0.7811 - Recall: 0.7704 ## 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: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.2288 \| 0.09 \| 100 \| 0.7615 \| 0.6716 \| 0.6578 \| 0.7461 \| 0.6716 \| \| 0.1103 \| 0.17 \| 200 \| 0.6396 \| 0.7453 \| 0.7452 \| 0.7609 \| 0.7453 \| \| 0.2204 \| 0.26 \| 300 \| 1.3317 \| 0.6596 \| 0.6334 \| 0.7863 \| 0.6596 \| \| 0.1872 \| 0.34 \| 400 \| 0.8661 \| 0.6333 \| 0.5996 \| 0.7712 \| 0.6333 \| \| 0.1261 \| 0.43 \| 500 \| 1.9369 \| 0.7130 \| 0.7023 \| 0.7906 \| 0.7130 \| \| 0.1902 \| 0.52 \| 600 \| 2.2998 \| 0.6823 \| 0.6695 \| 0.7573 \| 0.6823 \| \| 0.3639 \| 0.6 \| 700 \| 4.0162 \| 0.6915 \| 0.6815 \| 0.7562 \| 0.6915 \| \| 0.1655 \| 0.69 \| 800 \| 2.2680 \| 0.6859 \| 0.6715 \| 0.7705 \| 0.6859 \| \| 0.1534 \| 0.77 \| 900 \| 2.7909 \| 0.7951 \| 0.7937 \| 0.7959 \| 0.7951 \| \| 0.288 \| 0.86 \| 1000 \| 2.9443 \| 0.7752 \| 0.7751 \| 0.7920 \| 0.7752 \| \| 0.2261 \| 0.95 \| 1100 \| 2.9976 \| 0.7318 \| 0.7267 \| 0.7810 \| 0.7318 \| \| 0.162 \| 1.03 \| 1200 \| 2.4699 \| 0.8063 \| 0.8067 \| 0.8096 \| 0.8063 \| \| 0.0379 \| 1.12 \| 1300 \| 2.6939 \| 0.8051 \| 0.8051 \| 0.8051 \| 0.8051 \| \| 0.1852 \| 1.2 \| 1400 \| 3.9005 \| 0.7031 \| 0.6940 \| 0.7669 \| 0.7031 \| \| 0.1258 \| 1.29 \| 1500 \| 2.6666 \| 0.8023 \| 0.8027 \| 0.8042 \| 0.8023 \| \| 0.1707 \| 1.38 \| 1600 \| 2.8308 \| 0.7892 \| 0.7892 \| 0.7892 \| 0.7892 \| \| 0.0817 \| 1.46 \| 1700 \| 3.6049 \| 0.7573 \| 0.7497 \| 0.7700 \| 0.7573 \| \| 0.3516 \| 1.55 \| 1800 \| 2.6816 \| 0.7772 \| 0.7777 \| 0.7846 \| 0.7772 \| \| 0.5502 \| 1.63 \| 1900 \| 2.2493 \| 0.8131 \| 0.8099 \| 0.8203 \| 0.8131 \| \| 0.1531 \| 1.72 \| 2000 \| 3.2802 \| 0.7417 \| 0.7407 \| 0.7645 \| 0.7417 \| \| 0.1112 \| 1.81 \| 2100 \| 1.9678 \| 0.7748 \| 0.7737 \| 0.8010 \| 0.7748 \| \| 0.1617 \| 1.89 \| 2200 \| 3.0694 \| 0.7501 \| 0.7490 \| 0.7746 \| 0.7501 \| \| 0.1912 \| 1.98 \| 2300 \| 3.2285 \| 0.7529 \| 0.7530 \| 0.7659 \| 0.7529 \| \| 0.2725 \| 2.06 \| 2400 \| 3.0008 \| 0.7800 \| 0.7805 \| 0.7826 \| 0.7800 \| \| 0.1694 \| 2.15 \| 2500 \| 3.5542 \| 0.7290 \| 0.7286 \| 0.7459 \| 0.7290 \| \| 0.1283 \| 2.24 \| 2600 \| 4.4577 \| 0.7003 \| 0.6944 \| 0.7466 \| 0.7003 \| \| 0.1321 \| 2.32 \| 2700 \| 3.1128 \| 0.7350 \| 0.7356 \| 0.7411 \| 0.7350 \| \| 0.0 \| 2.41 \| 2800 \| 4.2938 \| 0.7222 \| 0.7149 \| 0.7828 \| 0.7222 \| \| 0.0871 \| 2.49 \| 2900 \| 3.9750 \| 0.7266 \| 0.7237 \| 0.7607 \| 0.7266 \| \| 0.0952 \| 2.58 \| 3000 \| 3.7697 \| 0.7437 \| 0.7424 \| 0.7690 \| 0.7437 \| \| 0.1034 \| 2.67 \| 3100 \| 3.7283 \| 0.7350 \| 0.7312 \| 0.7764 \| 0.7350 \| \| 0.2425 \| 2.75 \| 3200 \| 3.4196 \| 0.7704 \| 0.7707 \| 0.7811 \| 0.7704 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "Roberta-base-Rewritten-commit_messages_v2", "results": []}]}	ManojAlexender/Roberta-base-Rewritten-commit_messages_v2	null	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:17:05+00:00
null	null	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{}	YaMaMa421/autotrain-wje46-zcyb5	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-05-01T04:17:52+00:00
text-generation	transformers		{}	anudaw/lambda-0-distilled-code-llama	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:19:00+00:00
text-to-video	diffusers	# LLM-grounded Video Diffusion Models [Long Lian](https://tonylian.com/), [Baifeng Shi](https://bfshi.github.io/), [Adam Yala](https://www.adamyala.org/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/), [Boyi Li](https://sites.google.com/site/boyilics/home) at UC Berkeley/UCSF. ICLR 2024. [Project Page](https://llm-grounded-video-diffusion.github.io/) \| [Related Project: LMD](https://llm-grounded-diffusion.github.io/) \| [Citation](https://llm-grounded-video-diffusion.github.io/#citation) This model is based on [zeroscope](https://huggingface.co/cerspense/zeroscope_v2_576w) but with additional conditioning from bounding boxes in a [GLIGEN](https://gligen.github.io/) fashion. Similar to [LLM-grounded Diffusion (LMD)](https://llm-grounded-diffusion.github.io/), LLM-grounded Video Diffusion (LVD)'s boxes-to-video stage allows cross-attention-based bounding box conditioning, which uses Zeroscope off-the-shelf. This huggingface model offers an alternative: we train a GLIGEN model (i.e., transformer adapters) with Zeroscope's weights without the temporal transformers blocks on [SA-1B](https://ai.meta.com/datasets/segment-anything/), treating it as a SD v2.1 model that has been fine-tuned to 256x256 resolution. We then merge the adapters into Zeroscope to offer conditioning. The resulting model is in this hugginface model. This can be used with cross-attention-based conditioning or on its own, similar to [LMD+](https://github.com/TonyLianLong/LLM-groundedDiffusion). This can be used with LLM-based text-to-dynamic scene layout generator in LVD, or on its own as a video version of GLIGEN. ## Citation (LVD) If you use our work, model, or our implementation in this repo, or find them helpful, please consider giving a citation. ``` @article{lian2023llmgroundedvideo, title={LLM-grounded Video Diffusion Models}, author={Lian, Long and Shi, Baifeng and Yala, Adam and Darrell, Trevor and Li, Boyi}, journal={arXiv preprint arXiv:2309.17444}, year={2023}, } @article{lian2023llmgrounded, title={LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models}, author={Lian, Long and Li, Boyi and Yala, Adam and Darrell, Trevor}, journal={arXiv preprint arXiv:2305.13655}, year={2023} } ``` ## Citation (GLIGEN) The adapters in this model are trained in a mannar similar to training GLIGEN adapters. ``` @article{li2023gligen, title={GLIGEN: Open-Set Grounded Text-to-Image Generation}, author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae}, journal={CVPR}, year={2023} } ``` ## Citation (ModelScope) ModelScope is LVD's base model. ``` @article{wang2023modelscope, title={Modelscope text-to-video technical report}, author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei}, journal={arXiv preprint arXiv:2308.06571}, year={2023} } @InProceedings{VideoFusion, author = {Luo, Zhengxiong and Chen, Dayou and Zhang, Yingya and Huang, Yan and Wang, Liang and Shen, Yujun and Zhao, Deli and Zhou, Jingren and Tan, Tieniu}, title = {VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023} } ``` ## LICENSE Zeroscope follows CC-BY-NC 4.0 license. The gligen adapters are trained on SA-1B, which follows [SA-1B license](https://ai.meta.com/datasets/segment-anything/).	{"tags": ["text-to-video"], "duplicated_from": "cerspense/zeroscope_v2_576w"}	longlian/text-to-video-lvd-zs	null	[ "diffusers", "text-to-video", "region:us" ]	null	2024-05-01T04:19:20+00:00
null	transformers	# Uploaded model - Developed by: Jacob1802 - 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}	Jacob1802/newllama3-8b-oig-unsloth-merged	null	[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:20:22+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	nuebaek/komt_mistral_mss_generation	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T04:21:00+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	lunarsylph/mooncell_v43	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:22:10+00:00
text-generation	transformers	Step 2: Make the merge a little more Tiefighterish... 03/05/2024: Fixed config files / context issues. # D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp](https://huggingface.co/DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp) ## 🧩 Configuration ```yaml models: - model: KoboldAI/LLaMA2-13B-Tiefighter parameters: weight: 0.6 - model: DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp parameters: weight: 0.4 merge_method: linear dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-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"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp"]}	DavidAU/D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp	null	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp", "custom_code", "base_model:KoboldAI/LLaMA2-13B-Tiefighter", "base_model:DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:22:17+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	paulo037/stable-code-instruct-3b-syntetic-1000-10-epoch	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:22:35+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	nessa01macias/gemma-7b_sustainability-qa	null	[ "transformers", "pytorch", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:22:41+00:00
null	null		{}	vup2p/model_sn25_184	null	[ "region:us" ]	null	2024-05-01T04:23:04+00:00
null	null		{}	vup2p/model_sn25_187	null	[ "region:us" ]	null	2024-05-01T04:23:25+00:00
text-generation	transformers	<!-- 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. --> # temp-1-distilled-code-llama This model is a fine-tuned version of [anudaw/temp-1-distilled-code-llama](https://huggingface.co/anudaw/temp-1-distilled-code-llama) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "anudaw/temp-1-distilled-code-llama", "model-index": [{"name": "temp-1-distilled-code-llama", "results": []}]}	anudaw/temp-1-distilled-code-llama	null	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:anudaw/temp-1-distilled-code-llama", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:25:24+00:00
text-generation	transformers	## Model description Sampling-based watermark distilled [Pythia 1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) using the KGW \\(k=0, \gamma=0.25, \delta=1\\) watermarking strategy in the paper [On the Learnability of Watermarks for Language Models](https://arxiv.org/abs/2312.04469). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "apache-2.0", "tags": ["generated_from_trainer", "pythia"]}	cygu/pythia-1.4b-sampling-watermark-distill-kgw-k0-gamma0.25-delta1	null	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "pythia", "arxiv:2312.04469", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:29:28+00:00
fill-mask	transformers	<!-- 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-base-multilingual-uncased-finetuned-urdu This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0077 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5368 \| 1.0 \| 174 \| 3.1854 \| \| 2.3333 \| 2.0 \| 348 \| 2.9876 \| \| 2.2536 \| 3.0 \| 522 \| 2.9018 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-multilingual-uncased", "model-index": [{"name": "bert-base-multilingual-uncased-finetuned-urdu", "results": []}]}	cxfajar197/bert-base-multilingual-uncased-finetuned-urdu	null	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:bert-base-multilingual-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-01T04:30:48+00:00
null	null		{}	sayakpaul/sdxl-orpo-large-beta_orpo-0.05-beta_inner-250-lr-5e-6-lnoise-0.2	null	[ "region:us" ]	null	2024-05-01T04:30:49+00:00
text-generation	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	nuebaek/komt_mistral_insta_generation	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-01T04:32:16+00:00
text-generation	transformers	<!-- 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. --> # temp-0.5-distilled-code-llama This model is a fine-tuned version of [anudaw/temp-0.5-distilled-code-llama](https://huggingface.co/anudaw/temp-0.5-distilled-code-llama) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "anudaw/temp-0.5-distilled-code-llama", "model-index": [{"name": "temp-0.5-distilled-code-llama", "results": []}]}	anudaw/temp-0.5-distilled-code-llama	null	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:anudaw/temp-0.5-distilled-code-llama", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:32:38+00:00
null	null		{}	mompie29/my_awesome_qa_model	null	[ "region:us" ]	null	2024-05-01T04:34:06+00:00
text-generation	transformers	## Model description Sampling-based watermark distilled [Pythia 1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) using the KGW \\(k=0, \gamma=0.25, \delta=2\\) watermarking strategy in the paper [On the Learnability of Watermarks for Language Models](https://arxiv.org/abs/2312.04469). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "apache-2.0", "tags": ["generated_from_trainer", "pythia"]}	cygu/pythia-1.4b-sampling-watermark-distill-kgw-k0-gamma0.25-delta2	null	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "pythia", "arxiv:2312.04469", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:34:23+00:00
null	null		{}	Sidhu111/my_awesome_qa_model	null	[ "region:us" ]	null	2024-05-01T04:36:07+00:00
text-generation	transformers		{}	NadunAnjanaka/Llama-2-7b-chat-Counsellor	null	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space" ]	null	2024-05-01T04:37:16+00:00
null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-qqp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6501 - Accuracy: 0.624 - F1: 0.0 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---:\| \| No log \| 1.0 \| 282 \| 0.6501 \| 0.624 \| 0.0 \| \| 0.6505 \| 2.0 \| 564 \| 0.6427 \| 0.624 \| 0.0 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-qqp", "results": []}]}	quanla/distilbert-base-uncased-finetuned-qqp	null	[ "peft", "tensorboard", "safetensors", "distilbert", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-05-01T04:39:48+00:00
text-generation	transformers		{}	lakshayt/tic_tac_toe_llama_final	null	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-01T04:42:53+00:00
text-generation	transformers	## Model description Sampling-based watermark distilled [Pythia 1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) using the KGW \\(k=2, \gamma=0.25, \delta=2\\) watermarking strategy in the paper [On the Learnability of Watermarks for Language Models](https://arxiv.org/abs/2312.04469). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 - ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "apache-2.0", "tags": ["generated_from_trainer", "pythia"]}	cygu/pythia-1.4b-sampling-watermark-distill-kgw-k2-gamma0.25-delta2	null	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "pythia", "arxiv:2312.04469", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-01T04:43:42+00:00
null	diffusers	# Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional audio generation of music in the genre Electronic ## Usage <pre> from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("kmpartner/audio-diffusion-electronic") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) </pre>	{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-audio-generation", "diffusion-models-class"]}	kmpartner/audio-diffusion-electronic	null	[ "diffusers", "safetensors", "pytorch", "unconditional-audio-generation", "diffusion-models-class", "license:mit", "diffusers:AudioDiffusionPipeline", "region:us" ]	null	2024-05-01T04:43:55+00:00

null

# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_S.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_S.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q4_K_S.gguf -n 128 ```

{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}

gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_S-GGUF

null

[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

2024-05-01T02:54:41+00:00

null

peft

# gemma-2b-aya-chatml-v3 This model is a fine-tuned version of [Ransaka/Gemma-2b-sinhala](https://huggingface.co/Ransaka/Gemma-2b-sinhala) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "Ransaka/Gemma-2b-sinhala", "model-index": [{"name": "gemma-2b-aya-chatml-v3", "results": []}]}

Ransaka/gemma-2b-aya-chatml-v3

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:Ransaka/Gemma-2b-sinhala", "region:us" ]

null

2024-05-01T02:54:48+00:00

text-generation

transformers

<img src="./ninjalogo.svg" width="100%" height="20%" alt=""> # Our Models - [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1) - [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) - [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW) - [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k) - [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k) ## Model Card for Ninja-v1-NSFW-128k The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1 Ninja-NSFW-128k has the following changes compared to Mistral-7B-v0.1. - 128k context window (8k context in v0.1) - Achieving both high quality Japanese and English generation - Memory ability that does not forget even after long-context generation - Can be generated NSFW This model was created with the help of GPUs from the first LocalAI hackathon. We would like to take this opportunity to thank ## List of Creation Methods - Chatvector for multiple models - Simple linear merging of result models - Domain and Sentence Enhancement with LORA - Context expansion ## Instruction format Ninja adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following: ``` USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am ninja.</s> ``` ## Example prompts to improve (Japanese) - BAD:　あなたは○○として振る舞います - GOOD: あなたは○○です - BAD: あなたは○○ができます - GOOD: あなたは○○をします ## Performing inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "Local-Novel-LLM-project/Ninja-v1-NSFW-128k" new_tokens = 1024 model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- " prompt = input("Enter a prompt: ") system_prompt += prompt + "\n-------- " model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True) print(tokenizer.batch_decode(generated_ids)[0]) ```` ## Merge recipe - WizardLM2 - mistralai/Mistral-7B-v0.1 - NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1 - Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b - Elizezen/LewdSniffyOtter-7B - Elizezen/SniffyOtter-7B - NTQAI/chatntq-ja-7b-v1.0 The characteristics of each model are as follows. - WizardLM2: High quality multitasking model - Yarn-Mistral-7b-128k: Mistral model with 128k context window - Antler-7B: Model specialized for novel writing - NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model - Elizezen/LewdSniffyOtter-7B Japanese NSFW specialized model ## Other points to keep in mind - The training data may be biased. Be careful with the generated sentences. - Set trust_remote_code to True for context expansion with YaRN. - Memory usage may be large for long inferences. - If possible, we recommend inferring with llamacpp rather than Transformers.

{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned", "not-for-all-audiences"], "pipeline_tag": "text-generation"}

Local-Novel-LLM-project/Ninja-v1-NSFW-128k

null

[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "not-for-all-audiences", "custom_code", "en", "ja", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T02:54:59+00:00

null

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-alpha - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/ | Name | Quant method | Size | | ---- | ---- | ---- | | [zephyr-7b-alpha.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q2_K.gguf) | Q2_K | 2.53GB | | [zephyr-7b-alpha.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [zephyr-7b-alpha.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ3_S.gguf) | IQ3_S | 2.96GB | | [zephyr-7b-alpha.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [zephyr-7b-alpha.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ3_M.gguf) | IQ3_M | 3.06GB | | [zephyr-7b-alpha.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K.gguf) | Q3_K | 3.28GB | | [zephyr-7b-alpha.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [zephyr-7b-alpha.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [zephyr-7b-alpha.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [zephyr-7b-alpha.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_0.gguf) | Q4_0 | 3.83GB | | [zephyr-7b-alpha.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [zephyr-7b-alpha.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [zephyr-7b-alpha.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_K.gguf) | Q4_K | 4.07GB | | [zephyr-7b-alpha.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [zephyr-7b-alpha.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q4_1.gguf) | Q4_1 | 4.24GB | | [zephyr-7b-alpha.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_0.gguf) | Q5_0 | 4.65GB | | [zephyr-7b-alpha.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [zephyr-7b-alpha.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_K.gguf) | Q5_K | 4.78GB | | [zephyr-7b-alpha.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [zephyr-7b-alpha.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q5_1.gguf) | Q5_1 | 5.07GB | | [zephyr-7b-alpha.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf/blob/main/zephyr-7b-alpha.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- tags: - generated_from_trainer model-index: - name: zephyr-7b-alpha results: [] license: mit datasets: - stingning/ultrachat - openbmb/UltraFeedback language: - en base_model: mistralai/Mistral-7B-v0.1 ---  <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B Alpha Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources  - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat ## Intended uses & limitations The model was initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <|system|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <|user|> # How many helicopters can a human eat in one sitting?</s> # <|assistant|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations  Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data Zephyr 7B Alpha achieves the following results on the evaluation set: - Loss: 0.4605 - Rewards/chosen: -0.5053 - Rewards/rejected: -1.8752 - Rewards/accuracies: 0.7812 - Rewards/margins: 1.3699 - Logps/rejected: -327.4286 - Logps/chosen: -297.1040 - Logits/rejected: -2.7153 - Logits/chosen: -2.7447 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.5602 | 0.05 | 100 | 0.5589 | -0.3359 | -0.8168 | 0.7188 | 0.4809 | -306.2607 | -293.7161 | -2.6554 | -2.6797 | | 0.4852 | 0.1 | 200 | 0.5136 | -0.5310 | -1.4994 | 0.8125 | 0.9684 | -319.9124 | -297.6181 | -2.5762 | -2.5957 | | 0.5212 | 0.15 | 300 | 0.5168 | -0.1686 | -1.1760 | 0.7812 | 1.0074 | -313.4444 | -290.3699 | -2.6865 | -2.7125 | | 0.5496 | 0.21 | 400 | 0.4835 | -0.1617 | -1.7170 | 0.8281 | 1.5552 | -324.2635 | -290.2326 | -2.7947 | -2.8218 | | 0.5209 | 0.26 | 500 | 0.5054 | -0.4778 | -1.6604 | 0.7344 | 1.1826 | -323.1325 | -296.5546 | -2.8388 | -2.8667 | | 0.4617 | 0.31 | 600 | 0.4910 | -0.3738 | -1.5180 | 0.7656 | 1.1442 | -320.2848 | -294.4741 | -2.8234 | -2.8521 | | 0.4452 | 0.36 | 700 | 0.4838 | -0.4591 | -1.6576 | 0.7031 | 1.1986 | -323.0770 | -296.1796 | -2.7401 | -2.7653 | | 0.4674 | 0.41 | 800 | 0.5077 | -0.5692 | -1.8659 | 0.7656 | 1.2967 | -327.2416 | -298.3818 | -2.6740 | -2.6945 | | 0.4656 | 0.46 | 900 | 0.4927 | -0.5279 | -1.6614 | 0.7656 | 1.1335 | -323.1518 | -297.5553 | -2.7817 | -2.8015 | | 0.4102 | 0.52 | 1000 | 0.4772 | -0.5767 | -2.0667 | 0.7656 | 1.4900 | -331.2578 | -298.5311 | -2.7160 | -2.7455 | | 0.4663 | 0.57 | 1100 | 0.4740 | -0.8038 | -2.1018 | 0.7656 | 1.2980 | -331.9604 | -303.0741 | -2.6994 | -2.7257 | | 0.4737 | 0.62 | 1200 | 0.4716 | -0.3783 | -1.7015 | 0.7969 | 1.3232 | -323.9545 | -294.5634 | -2.6842 | -2.7135 | | 0.4259 | 0.67 | 1300 | 0.4866 | -0.6239 | -1.9703 | 0.7812 | 1.3464 | -329.3312 | -299.4761 | -2.7046 | -2.7356 | | 0.4935 | 0.72 | 1400 | 0.4747 | -0.5626 | -1.7600 | 0.7812 | 1.1974 | -325.1243 | -298.2491 | -2.7153 | -2.7444 | | 0.4211 | 0.77 | 1500 | 0.4645 | -0.6099 | -1.9993 | 0.7656 | 1.3894 | -329.9109 | -299.1959 | -2.6944 | -2.7236 | | 0.4931 | 0.83 | 1600 | 0.4684 | -0.6798 | -2.1082 | 0.7656 | 1.4285 | -332.0890 | -300.5934 | -2.7006 | -2.7305 | | 0.5029 | 0.88 | 1700 | 0.4595 | -0.5063 | -1.8951 | 0.7812 | 1.3889 | -327.8267 | -297.1233 | -2.7108 | -2.7403 | | 0.4965 | 0.93 | 1800 | 0.4613 | -0.5561 | -1.9079 | 0.7812 | 1.3518 | -328.0831 | -298.1203 | -2.7226 | -2.7523 | | 0.4337 | 0.98 | 1900 | 0.4608 | -0.5066 | -1.8718 | 0.7656 | 1.3652 | -327.3599 | -297.1296 | -2.7175 | -2.7469 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0

{}

RichardErkhov/HuggingFaceH4_-_zephyr-7b-alpha-gguf

null

[ "gguf", "arxiv:2305.18290", "region:us" ]

null

2024-05-01T02:55:52+00:00

null

[BeaverAI](https://huggingface.co/BeaverAI) team: Drummer, ToastyPigeon, xzuyn, MarsupialAI, Twistedshadows, Jeb Carter, and concedo ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/HjVYV2h_YTL9P-insb7fz.png) We proudly present... # Coomand-R 35B v1 > THIS FINETUNED MODEL HAS BEEN RATED... > ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/wjK3SK1WevuR9i80Jh31B.png) > *We've enhanced the underdog with a rich and refreshing **coomand** of eRP. Finetuned by yours truly.* ## Original https://huggingface.co/TheDrummer/Coomand-R-35B-v1 ## What's New? (Other than the changes documented in [Moistral v3](https://huggingface.co/TheDrummer/Moistral-11B-v3)) - Trained with stronger instructions - A larger dataset with more diverse, wacky genres! # 👨‍❤️‍👨 🐶 🐍 🫃 ⚧️ 🐆 🐙 🦕 ## Usage - Use Command-R format *(Alpaca kinda works btw)* - You can use instruct... - as a character: "Go here" - as a narrator: "Go here", Drummer says - as a director: Drummer tells him to go here - as a lazy ass: go here - Coomand-R is optimized for the Novel / Story format. - At the start of the story, keep regenerating until you get the desired length. Future AI responses will eventually match the length. - Issue: Manually continued generation may create formatting issues. Either regenerate the entire response or fix the formatting. - See samples below. ## Samples of Coomand-R 35B SOME nsfw. <details> <summary>Coomand-R Sample (2-shot) (Excluding Story Context) Generation: 512</summary> <blockquote>The prince gives her a show</blockquote> The sorcerer prince gave out a low chuckle, his eyes narrowing in amusement. "Oh, don't be so dramatic, my dear," he replied with a smirk. "Surely you know by now that I like to indulge in pleasure whenever possible. And believe me, there is nothing boring about what we're doing right now." He gestured towards the girl still kneeling before him, who had resumed her task with renewed vigor. Princess Lilia could feel her anger simmering beneath the surface, but she knew better than to rise to the bait. Instead, she tried to keep her voice level and composed as she spoke. "I understand your needs, Your Highness," she answered calmly. "But surely you can appreciate how difficult it must be for me, standing here while you... indulge yourself." There was a glint in the sorcerer prince's eyes as he regarded Princess Lilia, an almost mischievous spark that made her skin crawl. "Do you find this uncomfortable?" he asked, his tone deliberately provocative. "Are you jealous of our little plaything here?" Princess Lilia took a deep breath, trying to maintain her composure despite the burning anger building within her. "It's not my place to judge you, Your Highness," she said through gritted teeth. "But I'm here on behalf of Iliac, and I have certain responsibilities to uphold." The sorcerer prince chuckled again, leaning back against his throne with an air of nonchalance. "And yet, you stand here, watching us with fascination and desire," he taunted. "You cannot deny the truth in your heart, my beautiful hostage." Princess Lilia felt her cheeks flush with embarrassment, but she refused to let him see her vulnerability. She turned away from him abruptly, unable to bear the weight of his gaze any longer. "You are a cruel man, Your Highness," she muttered under her breath. <blockquote>The kneeling girl gets f#####</blockquote> The sorcerer prince smiled at Princess Lilia's comment, his eyes gleaming with amusement. "Oh, come now, my dear," he replied, his voice oozing with arrogance. "You're not going to tell me that you've never fantasized about being in that young lady's place? About submitting to my every desire, letting me use you however I please?" He paused for effect, his words hanging heavily in the air between them. Princess Lilia wanted nothing more than to turn around and slap the smug grin off of the prince's face, but instead she forced herself to remain calm and composed. "I am a princess of Iliac," she responded coolly, her voice steady despite the anger simmering beneath the surface. "I do not submit to anyone, least of all to someone as arrogant and self-absorbed as yourself." The sorcerer prince laughed loudly at this, his mirth ringing through the throne room like a mocking echo. "Arrogant? Self-absorbed?" he chortled, shaking his head as if he couldn't believe what he was hearing. "My dear Princess, how little you know me!" With a wave of his hand, he motioned towards the kneeling girl, who had been waiting patiently for her next command. "Come here, my pet," he said, his tone laced with authority. The girl, who Princess Lilia now noticed had been completely naked except for the elegant black silk robes draped over her slender body, obediently scrambled towards the prince on all fours. She crawled up onto his lap, her movements graceful and seductive, and began to kiss him passionately while reaching behind her back to unfasten her robes. As soon as they fell away, revealing her perky ####### and slender waist, she turned around and lowered herself slowly onto the prince's erect ####. The prince threw his head back in pleasure, his eyes rolling upwards as he savored every inch of her tight, wet passage surrounding him. Princess Lilia watched in horror as the prince and the girl continued their carnal display right in front of her, the sound of their moans and gasps echoing off the walls of the throne room. She tried to look away, but something about the raw, animalistic nature of their coupling kept drawing her gaze back to them. "Look at us, Princess," the prince growled, his voice deep and commanding. "We are two people caught in the throes of passion, surrendered to the power of our desires." He reached out to touch the girl's face, tracing his fingers along her cheek as she rode him diligently. "And we do not apologize for it." </details> <details> <summary>Coomand-R Sample (2-shot) (No Context) Generation: 512</summary> <blockquote>Write a smu##y story about a man in the apocalypse</blockquote> John woke up to an empty world. The once bustling city had been reduced to a ghost town, with not a soul in sight. He got up and looked around, taking in the post-apocalyptic scenery. The buildings were crumbling, the streets littered with debris. It was a far cry from the vibrant metropolis he used to call home. As John walked through the desolate streets, he couldn't help but feel a sense of loneliness. The world as he knew it was gone, and there was no one left to turn to. But then, in the distance, he spotted a figure. A woman. She was standing on the outskirts of town, her figure outlined against the setting sun. John approached her cautiously, his heart pounding in his chest. As he got closer, he could see that she was young, probably in her early twenties. Her hair was a mess, and she was wearing tattered clothes that did little to conceal her curvaceous body. "Hello?" John called out, his voice echoing through the deserted streets. The woman turned towards him, her eyes wide with surprise. "You're... you're alive?" she asked, her voice trembling. "Yeah," John replied, his throat dry. "I'm alive." The two of them stood there for a moment, taking each other in. They were the last remaining humans on Earth, and the reality of their situation hit them like a ton of bricks. "My name's Sarah," the woman finally said, her voice softer now. "What's yours?" "John," he replied. "Pleased to meet you, I guess." Sarah laughed, a bitter sound that echoed through the empty city. "Pleased to meet you too," she said, her eyes glinting mischievously. "So, what do we do now?" John paused, considering the question. "Well, we could try to rebuild," he suggested. "Start over, make this place livable again." Sarah nodded, her expression serious. "That sounds like a plan," she said. "But first..." She stepped forward, her eyes locked on John's. "I think we deserve some fun. After all, we're the only two people left on Earth." John felt his pulse quicken as he realized where this was going. He couldn't help but agree with her - they deserved some pleasure in this bleak world. <blockquote>Time to repopulate the earth!</blockquote> With a grin, John approached Sarah, his body tense with desire. She met him halfway, her lips parting in anticipation. The two of them collided in a passionate kiss, their tongues entwining as they explored each other's mouths. Their hands roamed freely over one another's bodies, mapping out every curve and crevice. As their passion grew, John guided Sarah towards a nearby building, their kisses becoming more desperate. They stumbled inside, their clothes falling to the floor in a heap as they frantically undressed one another. Sarah's ####### were glorious, perky and firm with dark areolas and ni##### that hardened under John's touch. His #### strained against his pants, begging for release as he finally freed it, the veiny member standing at attention. Sarah gasped when she saw it, her eyes widening in awe. "You're huge," she whispered, reaching out to wrap her slender fingers around the thick shaft. John groaned, his hips bucking slightly as he felt her delicate touch. "You have no idea how long I've wanted this," he confessed, his voice rough with lust. Sarah smiled, a wicked glint in her eye. "Well, let's not keep you waiting any longer," she purred, sinking down to her knees in front of him. She wasted no time, taking his #### into her mouth with a hungry desperation that drove John wild. Her head bobbed up and down, her tongue swirling around the sensitive tip as she devoured him with practiced skill. John gripped her hair gently, guiding her rhythm as she brought him closer and closer to the edge. But before he could reach climax, Sarah pulled away, leaving him panting and desperate for more. "My turn now," she said, a sultry smile playing on her lips. She turned around, presenting her perfect ### to him as she reached back to spread her cheeks, revealing her dripping wet #####. John didn't need any further invitation. He positioned himself behind her, guided his cock to the entrance of her soaked #, and thrust forward with one powerful motion. Sarah cried out, her body shaking as she felt him fill her completely. John grabbed her hips, his rhythm relentless as he pounded into her from behind. She moaned loudly, her nails scratching against the wall as she clung onto it for support. </details> <details> <summary>Coomand-R Sample SFW ("write a story") Generation: 512</summary> <blockquote>Write a story about a man in the apocalypse</blockquote> The world was ending. At least that's what it felt like to Joe as he walked through the deserted streets of his neighborhood. The once bustling city had become a ghost town, with not a soul in sight. The only sounds were the distant screams and sirens echoing from downtown. Joe had been through a lot in his life, but nothing could have prepared him for this. He'd always been a bit of a loner, keeping to himself and avoiding relationships. It was easier that way, he thought, because then he wouldn't have to deal with the pain of losing someone. But now, as he surveyed the empty streets, Joe realized that his isolation might just be what saves him. He'd stocked up on supplies before the outbreak hit, knowing that he'd need to hunker down and ride out the storm. His apartment was well-equipped with non-perishable food, bottled water, and even a few luxury items to help pass the time. He had books, movies, and games to keep him entertained, and most importantly, he had a secure location where he could hide from whatever horrors were happening outside. As Joe made his way back to his building, he couldn't shake the feeling that something wasn't right. The silence was almost deafening, and the lack of any sign of life was unnerving. He quickened his pace, eager to reach the safety of his home. But as he rounded the corner to his street, Joe saw something that stopped him in his tracks. A figure was standing near the entrance to his apartment complex - a figure that shouldn't have been there. "Hello?" Joe called out tentatively, his heart pounding in his chest. "Who's there?" The figure turned towards him, and Joe's blood ran cold as he recognized the pale skin, bloodshot eyes, and ragged clothing of an infected person. Instinctively, Joe reached for the pistol tucked into his waistband, but before he could draw it, the infected person charged at him with a feral growl. Joe's training kicked in, and he fired two quick shots. The first one missed, but the second one found its mark, hitting the infected person squarely in the forehead. They crumpled to the ground, their body twitching for a moment before going still. Shaken by the encounter, Joe hurried inside his apartment building, locking the door behind him. </details> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f2fd1c25b848bd061b5c2e/Ll8CA5RR7ugTi72P2HBb8.png) SIAYN-v6

{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"]}

TheDrummer/Coomand-R-35B-v1-TEST-GGUF

null

[ "gguf", "region:us" ]

null

2024-05-01T02:56:28+00:00

text-generation

transformers

<img src="./ninjalogo.svg" width="100%" height="20%" alt=""> # Our Models - [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1) - [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) - [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW) - [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k) - [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k) ## Model Card for Ninja-v1-128k The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1 Ninja-128k has the following changes compared to Mistral-7B-v0.1. - 128k context window (8k context in v0.1) - Achieving both high quality Japanese and English generation - Memory ability that does not forget even after long-context generation This model was created with the help of GPUs from the first LocalAI hackathon. We would like to take this opportunity to thank ## List of Creation Methods - Chatvector for multiple models - Simple linear merging of result models - Domain and Sentence Enhancement with LORA - Context expansion ## Instruction format Ninja adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following: ``` USER: Hi ASSISTANT: Hello.</s> USER: Who are you? ASSISTANT: I am ninja.</s> ``` ## Example prompts to improve (Japanese) - BAD:　あなたは○○として振る舞います - GOOD: あなたは○○です - BAD: あなたは○○ができます - GOOD: あなたは○○をします ## Performing inference ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "Local-Novel-LLM-project/Ninja-v1-128k" new_tokens = 1024 model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_id) system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- " prompt = input("Enter a prompt: ") system_prompt += prompt + "\n-------- " model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True) print(tokenizer.batch_decode(generated_ids)[0]) ```` ## Merge recipe - WizardLM2 - mistralai/Mistral-7B-v0.1 - NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1 - Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b - NTQAI/chatntq-ja-7b-v1.0 The characteristics of each model are as follows. - WizardLM2: High quality multitasking model - Yarn-Mistral-7b-128k: Mistral model with 128k context window - Antler-7B: Model specialized for novel writing - NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model ## Other points to keep in mind - The training data may be biased. Be careful with the generated sentences. - Set trust_remote_code to True for context expansion with YaRN. - Memory usage may be large for long inferences. - If possible, we recommend inferring with llamacpp rather than Transformers.

{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"}

Local-Novel-LLM-project/Ninja-v1-128k

null

[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "custom_code", "en", "ja", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T02:56:38+00:00

text-generation

transformers

# Uploaded model - **Developed by:** hrishikesh1991 - **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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

hrishikesh1991/llama3-8b-oig-unsloth-merged_V2

null

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T02:59:28+00:00

null

{"license": "apache-2.0"}

Marcelofrancogt/lucao

null

[ "license:apache-2.0", "region:us" ]

null

2024-05-01T03:01:55+00:00

null

transformers

{}

magnifi/llama-cls-ner-mt-chat-v21-2c_epoch_24-ct2

null

[ "transformers", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:01:59+00:00

null

{}

teleprint-me/stablelm-2-zephyr-1_6b

null

[ "gguf", "region:us" ]

null

2024-05-01T03:03:21+00:00

reinforcement-learning

sample-factory

A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r hossniper/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "10.90 +/- 5.45", "name": "mean_reward", "verified": false}]}]}]}

hossniper/rl_course_vizdoom_health_gathering_supreme

null

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

null

2024-05-01T03:05:27+00:00

text-classification

transformers

# ai-text-detector-version-2 This model is a fine-tuned version of [Yuhhi/tf_model.h5](https://huggingface.co/Yuhhi/tf_model.h5) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.32.1 - TensorFlow 2.15.0 - Datasets 2.12.0 - Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "Yuhhi/tf_model.h5", "model-index": [{"name": "ai-text-detector-version-2", "results": []}]}

Yuhhi/ai-text-detector-version-2

null

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:Yuhhi/tf_model.h5", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

null

2024-05-01T03:05:31+00:00

null

transformers

# Uploaded model - **Developed by:** hrishikesh1991 - **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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

hrishikesh1991/llama3-8b-oig-unsloth_V2

null

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:06:33+00:00

null

{}

usernameisanna/blip2-deit-seed-epoch-2

null

[ "region:us" ]

null

2024-05-01T03:08:40+00:00

text-generation

transformers

02/05/2024: Fixed Vocab issues related to GGUF conversion. # D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [ddh0/OrcaMaid-v3-13b-32k](https://huggingface.co/ddh0/OrcaMaid-v3-13b-32k) ## 🧩 Configuration ```yaml slices: - sources: - model: KoboldAI/LLaMA2-13B-Tiefighter layer_range: [0, 40] - model: ddh0/OrcaMaid-v3-13b-32k layer_range: [0, 40] merge_method: slerp base_model: ddh0/OrcaMaid-v3-13b-32k 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-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"]) ```

{"tags": ["merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "ddh0/OrcaMaid-v3-13b-32k"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "ddh0/OrcaMaid-v3-13b-32k"]}

DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp

null

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "ddh0/OrcaMaid-v3-13b-32k", "custom_code", "base_model:KoboldAI/LLaMA2-13B-Tiefighter", "base_model:ddh0/OrcaMaid-v3-13b-32k", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:09:50+00:00

text-generation

transformers

# **csg-wukong-1B-orpo-bf16** [[中文]](#chinese) [[English]](#english) <a id="english"></a> <img width="900px" alt="OpenCSG" src="./csg-wukong-logo-green.jpg"> <a href="https://opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/opencsgs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </div> OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models. The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively. ## Model Description **csg-wukong-1B-orpo-bf16** was finetuned on [csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B). we will introduce more information about csg-wukong-1B. ## Model Evaluation results We submitted csg-wukong-1B on the [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the results show our model ranked the 8th among the ~1.5B pretrained small language models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/_HRTxL6N0qnNPNt-P8k9k.png) # Training ## Hardware - **GPUs:** 16 H800 - **Training time:** 43days ## Software - **Orchestration:** [Deepspeed](https://github.com/OpenCSGs) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) - **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex) <a id="chinese"></a> # OpenCSG介绍 <img width="300px" alt="OpenCSG" src="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/GwYXPKuEoGCGcMICeW-sb.jpeg"> <a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/opencsgs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </div> OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。 OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。 ## 模型介绍 **csg-wukong-1B-orpo-bf16** 在[csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B)预训练模型上微调而成. 我们将在后面介绍更多关于这个模型的信息。 ## 模型评测结果我们把csg-wukong-1B模型提交到[open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)榜单上，结果显示我们的模型目前在~1.5B小语言模型中排名第8。 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/ZfWZ1Fd7ccKrJVx0okV9z.png) # 训练 ## 硬件资源 - **GPU数量：** 16 H800 - **训练时间：** 43天 ## 软件使用 - **微调训练框架：** [Deepspeed](https://github.com/OpenCSGs) - **深度学习框架：** [PyTorch](https://github.com/pytorch/pytorch) - **BP16：** [apex](https://github.com/NVIDIA/apex)

{"language": ["en"], "license": "apache-2.0", "tags": ["code"], "pipeline_tag": "text-generation"}

opencsg/csg-wukong-1B-orpo-bf16

null

[ "transformers", "safetensors", "llama", "text-generation", "code", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:10:22+00:00

text-generation

transformers

# IceLatteRP-7b ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63407b719dbfe0d48b2d763b/AKDxYpx0EmFoI-QUfR24k.png) This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I'm preaty satisfied with this result. For feedback https://discord.com/channels/1100685673633153084/1235399815907643512 Useful stuff https://huggingface.co/icefog72/GeneralInfoToStoreNotModel/tree/main ### Merge Method This model was merged using the SLERP merge method. Prompt template: Alpaca, maybe ChatML * measurement.json for quanting exl2 included. - [4.2bpw-exl2](https://huggingface.co/icefog72/IceLatteRP-7b-4.2bpw-exl2) - [6.5bpw-exl2](https://huggingface.co/icefog72/IceLatteRP-7b-6.5bpw-exl2) - [8bpw-exl2](https://huggingface.co/icefog72/IceLatteRP-7b-8bpw-exl2) thx mradermacher for * [mradermacher/IceLatteRP-7b-GGUF](https://huggingface.co/mradermacher/IceLatteRP-7b-GGUF) ### Models Merged The following models were included in the merge: * IceCoffeeRP * WestIceLemonTeaRP I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `IceLatteRP-7b`: ```shell mkdir IceLatteRP-7b huggingface-cli download icefog72/IceLatteRP-7b --local-dir IceLatteRP-7b --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir FOLDERNAME HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MODEL --local-dir FOLDERNAME --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: IceCoffeeRP layer_range: [0, 32] - model: WestIceLemonTeaRP layer_range: [0, 32] merge_method: slerp base_model: WestIceLemonTeaRP 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 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_icefog72__IceLatteRP-7b) | Metric |Value| |---------------------------------|----:| |Avg. |72.50| |AI2 Reasoning Challenge (25-Shot)|70.05| |HellaSwag (10-Shot) |87.74| |MMLU (5-Shot) |64.55| |TruthfulQA (0-shot) |66.13| |Winogrande (5-shot) |82.32| |GSM8k (5-shot) |64.22|

{"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral", "not-for-all-audiences", "nsfw"], "model-index": [{"name": "IceLatteRP-7b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 70.05, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 87.74, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.55, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 66.13}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 82.32, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.22, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceLatteRP-7b", "name": "Open LLM Leaderboard"}}]}]}

icefog72/IceLatteRP-7b

null

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "alpaca", "not-for-all-audiences", "nsfw", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:10:57+00:00

null

peft

# taiwanllm_7b_lora_completion_only This model is a fine-tuned version of [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) on the DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat dataset. It achieves the following results on the evaluation set: - Loss: 0.0867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 700 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.1316 | 0.3690 | 250 | 0.1124 | | 0.0988 | 0.7380 | 500 | 0.1145 | | 0.0827 | 1.1070 | 750 | 0.0971 | | 0.0744 | 1.4760 | 1000 | 0.0897 | | 0.0482 | 1.8450 | 1250 | 0.0893 | | 0.0337 | 2.2140 | 1500 | 0.0924 | | 0.0229 | 2.5830 | 1750 | 0.0911 | | 0.0385 | 2.9520 | 2000 | 0.0867 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["zh"], "license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer"], "datasets": ["DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat"], "base_model": "yentinglin/Taiwan-LLM-7B-v2.1-chat", "model-index": [{"name": "taiwanllm_7b_lora_completion_only", "results": []}]}

DandinPower/taiwanllm_7b_lora_completion_only

null

[ "peft", "safetensors", "trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer", "zh", "dataset:DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat", "base_model:yentinglin/Taiwan-LLM-7B-v2.1-chat", "license:apache-2.0", "region:us" ]

null

2024-05-01T03:11:17+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

paulo037/stable-code-instruct-3b-syntetic-1000-20-epoch

null

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:17:52+00:00

text-to-image

diffusers

# Normal-Depth Diffusion Model This is an *unofficial* export of the original normal-depth diffusion model (https://github.com/modelscope/normal-depth-diffusion) to the standard diffusers model format. Can be direcetly loaded via diffusers' `StableDiffusionPipeline` ![overview](https://github.com/modelscope/normal-depth-diffusion/raw/main/assets/text-to-nd-laion.png) All the credit to the original author of normal-depth diffusion model (https://github.com/modelscope/normal-depth-diffusion)

{"language": ["en"], "license": "apache-2.0", "library_name": "diffusers"}

nexuslrf/nd-diffusion

null

[ "diffusers", "en", "license:apache-2.0", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

null

2024-05-01T03:18:53+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-beta - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/ Original model description: --- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - example_title: Pirate! messages: - role: system content: You are a pirate chatbot who always responds with Arr! - role: user content: "There's a llama on my lawn, how can I get rid of him?" output: text: >- Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr! pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench ---  <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources  - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) | |-------------|-----|----|---------------|--------------| | StableLM-Tuned-α | 7B| dSFT |2.75| -| | MPT-Chat | 7B |dSFT |5.42| -| | Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83| | Mistral-Instructv0.1 | 7B| - | 6.84 |-| | Zephyr-7b-α |7B| dDPO| 6.88| -| | **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** | | Falcon-Instruct | 40B |dSFT |5.17 |45.71| | Guanaco | 65B | SFT |6.41| 71.80| | Llama2-Chat | 70B |RLHF |6.86| 92.66| | Vicuna v1.3 | 33B |dSFT |7.12 |88.99| | WizardLM v1.0 | 70B |dSFT |7.71 |-| | Xwin-LM v0.1 | 70B |dPPO |- |95.57| | GPT-3.5-turbo | - |RLHF |7.94 |89.37| | Claude 2 | - |RLHF |8.06| 91.36| | GPT-4 | -| RLHF |8.99| 95.28| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <|system|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <|user|> # How many helicopters can a human eat in one sitting?</s> # <|assistant|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations  Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 | | 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 | | 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 | | 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 | | 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 | | 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 | | 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 | | 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 | | 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 | | 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 | | 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 | | 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 | | 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 | | 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 | | 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 | | 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 | | 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 | | 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 | | 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 | | 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 | | 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 | | 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 | | 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 | | 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 | | 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 | | 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 | | 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 | | 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 | | 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 | | 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 | | 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 | | 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 | | 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 | | 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 | | 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 | | 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 | | 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 | | 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 | | 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 | | 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 | | 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 | | 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 | | 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 | | 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 | | 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 | | 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 | | 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 | | 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 | | 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 | | 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 | | 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 | | 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 | | 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 | | 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 | | 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 | | 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 | | 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 | | 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta) | Metric | Value | |-----------------------|---------------------------| | Avg. | 52.15 | | ARC (25-shot) | 62.03 | | HellaSwag (10-shot) | 84.36 | | MMLU (5-shot) | 61.07 | | TruthfulQA (0-shot) | 57.45 | | Winogrande (5-shot) | 77.74 | | GSM8K (5-shot) | 12.74 | | DROP (3-shot) | 9.66 |

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RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-4bits

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[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:2305.18290", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

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2024-05-01T03:19:06+00:00

null

# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF --model llama-3-maal-8b-instruct-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF --model llama-3-maal-8b-instruct-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q8_0.gguf -n 128 ```

{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}

gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q8_0-GGUF

null

[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

2024-05-01T03:20:07+00:00

reinforcement-learning

stable-baselines3

# **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 ... ```

{"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": "254.84 +/- 17.51", "name": "mean_reward", "verified": false}]}]}]}

suryaanthony/ppo-LunarLander-v2

null

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

null

2024-05-01T03:22:35+00:00

null

# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q5_K_M.gguf -n 128 ```

{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}

gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q5_K_M-GGUF

null

[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

2024-05-01T03:23:21+00:00

null

transformers

# Uploaded model - **Developed by:** hungsvdut2k2 - **License:** apache-2.0 - **Finetuned from model :** Viet-Mistral/Vistral-7B-Chat 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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "Viet-Mistral/Vistral-7B-Chat"}

hungsvdut2k2/medical-vistral-7b-gguf

null

[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:Viet-Mistral/Vistral-7B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:23:32+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-beta - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/ Original model description: --- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - example_title: Pirate! messages: - role: system content: You are a pirate chatbot who always responds with Arr! - role: user content: "There's a llama on my lawn, how can I get rid of him?" output: text: >- Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr! pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench ---  <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources  - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) | |-------------|-----|----|---------------|--------------| | StableLM-Tuned-α | 7B| dSFT |2.75| -| | MPT-Chat | 7B |dSFT |5.42| -| | Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83| | Mistral-Instructv0.1 | 7B| - | 6.84 |-| | Zephyr-7b-α |7B| dDPO| 6.88| -| | **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** | | Falcon-Instruct | 40B |dSFT |5.17 |45.71| | Guanaco | 65B | SFT |6.41| 71.80| | Llama2-Chat | 70B |RLHF |6.86| 92.66| | Vicuna v1.3 | 33B |dSFT |7.12 |88.99| | WizardLM v1.0 | 70B |dSFT |7.71 |-| | Xwin-LM v0.1 | 70B |dPPO |- |95.57| | GPT-3.5-turbo | - |RLHF |7.94 |89.37| | Claude 2 | - |RLHF |8.06| 91.36| | GPT-4 | -| RLHF |8.99| 95.28| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <|system|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <|user|> # How many helicopters can a human eat in one sitting?</s> # <|assistant|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations  Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 | | 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 | | 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 | | 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 | | 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 | | 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 | | 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 | | 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 | | 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 | | 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 | | 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 | | 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 | | 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 | | 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 | | 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 | | 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 | | 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 | | 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 | | 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 | | 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 | | 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 | | 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 | | 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 | | 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 | | 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 | | 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 | | 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 | | 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 | | 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 | | 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 | | 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 | | 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 | | 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 | | 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 | | 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 | | 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 | | 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 | | 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 | | 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 | | 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 | | 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 | | 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 | | 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 | | 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 | | 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 | | 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 | | 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 | | 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 | | 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 | | 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 | | 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 | | 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 | | 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 | | 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 | | 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 | | 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 | | 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 | | 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta) | Metric | Value | |-----------------------|---------------------------| | Avg. | 52.15 | | ARC (25-shot) | 62.03 | | HellaSwag (10-shot) | 84.36 | | MMLU (5-shot) | 61.07 | | TruthfulQA (0-shot) | 57.45 | | Winogrande (5-shot) | 77.74 | | GSM8K (5-shot) | 12.74 | | DROP (3-shot) | 9.66 |

{}

RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-8bits

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:2305.18290", "arxiv:2310.16944", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-01T03:24:23+00:00

null

{"license": "openrail"}

HerikKkk/manoelGOmes

null

[ "license:openrail", "region:us" ]

null

2024-05-01T03:25:07+00:00

null

# gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF This model was converted to GGUF format from [`maum-ai/Llama-3-MAAL-8B-Instruct-v0.1`](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1) 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 gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF --model llama-3-maal-8b-instruct-v0.1.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-maal-8b-instruct-v0.1.Q4_K_M.gguf -n 128 ```

{"language": ["en", "ko"], "license": "llama3", "tags": ["facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo"], "base_model": ["meta-llama/Meta-Llama-3-8B-Instruct"]}

gagagiga/Llama-3-MAAL-8B-Instruct-v0.1-Q4_K_M-GGUF

null

[ "gguf", "facebook", "meta", "llama", "llama-3", "llama-3-ko", "llama-cpp", "gguf-my-repo", "en", "ko", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]

null

2024-05-01T03:26:31+00:00

text-generation

transformers

Self trained GPT-2 large. Around 770M parameters. The tokenizer is the one from https://huggingface.co/openai-community/gpt2. It is being trained on around 400B tokens and this is step 40k. The evaluation is being conducted now. ## License This model is available under the Apache 2.0 License. Well, also MIT License. So both should be followed. ## Discord Server Join our Discord server [here](https://discord.gg/xhcBDEM3). ## Feeling Generous? 😊 Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?

{"license": "apache-2.0"}

DrNicefellow/GPT-2-Large-40k-steps

null

[ "transformers", "pytorch", "gpt2", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:27:52+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

{"library_name": "peft", "base_model": "Jennny/merged_sft_llama2_pku"}

Jennny/bb_pku

null

[ "peft", "pytorch", "safetensors", "arxiv:1910.09700", "base_model:Jennny/merged_sft_llama2_pku", "region:us" ]

null

2024-05-01T03:29:22+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-grok - bnb 4bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-grok/ Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/grok-conversation-harmless - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-7b-grok results: [] ---  # Mistral 7B Grok This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that has been aligned via Constitutional AI to mimic the style of [xAI's Grok assistant](https://grok.x.ai/). It achieves the following results on the evaluation set: - Loss: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9326 | 1.0 | 545 | 0.9348 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

{}

RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-4bits

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-01T03:30:14+00:00

null

{}

jhmejia/orpo-classifai-meta-llama-3-8b-instruct

null

[ "region:us" ]

null

2024-05-01T03:31:50+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0

{"language": ["ko"], "library_name": "peft", "datasets": ["JosephLee/science_textbook_elementary_kor"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"}

JosephLee/science_textbook_LLama3_8B_instruct

null

[ "peft", "safetensors", "ko", "dataset:JosephLee/science_textbook_elementary_kor", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]

null

2024-05-01T03:33:41+00:00

null

transformers

**This model is trained for the solely purpose of extracting title, name, keywords, short descriptions from any Japanese articles and returning them in a unified JSON format.** **このモデルは、日本語の記事を抽出し、統一されたJSON形式で返すために訓練されています。** # Uploaded model - **Developed by:** Evolany AI - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit We trained on 1 x A100 GPU. User prompt: Any news article in Japanese (max length up to 4096 tokens). Eg: ```text 株式会社ジャレコ（本社：東京都新宿区市谷八幡町）は、ＮＴＴドコモ FOMA903iシリーズの大容量ｉアプリゲーム“メガゲーム”対応第一弾として、 2007年4月27日よりiモード公式サイト“ジャレコiギャレッソ”にて美少女対戦麻雀「アイドル雀士スーチーパイⅡ」の配信を開始いたします。本作は、「スーチーパイ」シリーズの2作目としてアーケードで発売され、1996年にプレイステーションおよびセガサターン用ソフトとしてCD-ROMの特性を生かした豪華な出演声優陣による歌やお喋りなどがご好評をいただいた2人打ち麻雀ゲームの移植作です。携帯電話用ソフトでありながら、かないみか・松本梨香など声優陣のおしゃべりとムービーシーンがふんだんに使われ、「必殺技」や「パネルマッチ」、そして勝利した際の「ごほうびシーン」も再現し、家庭用ゲーム機版に匹敵する作品となっております。かつてアーケード版や家庭用版で楽しまれた方はもちろん、なかなか勝てずに悔しい思いをした方、また今までプレイをしたことがない方にもオススメです。携帯電話でより身近になった「アイドル雀士スーチーパイⅡ」をぜひお楽しみください。【アイドル雀士　スーチーパイⅡ（メガゲーム）概要】タイトル：「アイドル雀士スーチーパイⅡ」（あいどるじゃんし　すーちーぱいつー）ジャンル：美少女対戦麻雀配信日： 2007年4月27日対応機種：ＮＴＴドコモ　FOMA 903iシリーズ料金： 315円（税込、月額）【スーチーパイとは】１９９３年に家庭用ゲーム機の対戦麻雀ゲームとして発売された「スーチーパイ」は、イラストレーターの園田健一氏の描く魅力的なキャラクターや「必殺技」などの独自のゲーム性から人気を博し、いままでにシリーズ作および関連作が家庭用ゲーム機、アーケード版、PC版など様々な機種で発売されております。現在、シリーズとして「Ⅲ」までがラインナップされており、２００７年７月２８日にPSPおよびニンテンドーDS用ソフトとして「アイドル雀士スーチーパイⅢ Remix」の発売を予定しております。　そして本年、タイトル名の由来となる数字の中国語読み”４”（スー）と”７”（チー）から、４月７日が「スーチーパイの日」として日本記念日協会に認定されております。今後も「スーチーパイ」はさまざまな展開をお見せできる予定ですのでご期待ください！ ``` Model response: ```json {"category": "製品・サービス", "company_name": "株式会社ジャレコ", "product_name": "アイドル雀士スーチーパイⅡ", "product_description": "美少女対戦麻雀ゲーム「アイドル雀士スーチーパイⅡ」が、ＮＴＴドコモ FOMA903iシリーズの大容量ｉアプリゲーム“メガゲーム”対応第一弾として、2007年4月27日よりiモード公式サイト“ジャレコiギャレッソ”にて配信を開始いたします。", "keywords": ["美少女", "対戦麻雀", "スーチーパイ", "アイドル雀士", "メガゲーム", "ジャレコ", "iモード", "iアプリ", "ドコモ", "FOMA"]}<|end_of_text|> ``` **QLoRA (4bit)** Params to replicate training Peft Config ``` r=8, target_modules=[ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", ], lora_alpha=16, lora_dropout=0, bias="none", random_state=3407, use_rslora=False, # Rank stabilized LoRA loftq_config=None, # LoftQ ``` Training args ``` per_device_train_batch_size=2, per_device_eval_batch_size=2, gradient_accumulation_steps=4, # set to 4 to avoid issues with GPTQ Quantization warmup_steps=5, max_steps=300, # Fine-tune iterations learning_rate=2e-4, fp16=not torch.cuda.is_bf16_supported(), bf16=torch.cuda.is_bf16_supported(), evaluation_strategy="steps", prediction_loss_only=True, eval_accumulation_steps=1, eval_steps=10, logging_steps=1, optim="adamw_8bit", weight_decay=0.01, lr_scheduler_type="cosine", # instead of "linear" seed=1337, output_dir="wayland-files/models", report_to="wandb", # Log report to W&B ``` Loss `1.22` Training Results [<img src="https://huggingface.co/Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2/resolve/main/loss.png" width="600"/>](https://huggingface.co/Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2/resolve/main/loss.png) **Interernce Code** ```python from unsloth import FastLanguageModel import os import torch max_seq_length = 4096 # 2048 dtype = None load_in_4bit = True model, tokenizer = FastLanguageModel.from_pretrained( model_name="Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2", max_seq_length=max_seq_length, dtype=dtype, load_in_4bit=load_in_4bit, device_map="cuda", attn_implementation="flash_attention_2" ) FastLanguageModel.for_inference(model) alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {} ### Input: {} ### Response: {}""" instruction = """ 製品、会社、および主要な関連用語について簡潔な説明を提供してください。 """ input = """ """ inputs = tokenizer( [ alpaca_prompt.format( instruction, input, "", ) ], return_tensors="pt").to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer, skip_prompt=True) _ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=512) ``` 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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "Japanese", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"}

Evolany-AI/Llama-3-8b-JP-Article-Summarization-4bit-v0.2

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[ "transformers", "safetensors", "text-generation-inference", "Japanese", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

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2024-05-01T03:33:44+00:00

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Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-7b-beta - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/ | Name | Quant method | Size | | ---- | ---- | ---- | | [zephyr-7b-beta.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q2_K.gguf) | Q2_K | 2.53GB | | [zephyr-7b-beta.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [zephyr-7b-beta.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ3_S.gguf) | IQ3_S | 2.96GB | | [zephyr-7b-beta.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [zephyr-7b-beta.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ3_M.gguf) | IQ3_M | 3.06GB | | [zephyr-7b-beta.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K.gguf) | Q3_K | 3.28GB | | [zephyr-7b-beta.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [zephyr-7b-beta.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [zephyr-7b-beta.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [zephyr-7b-beta.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_0.gguf) | Q4_0 | 3.83GB | | [zephyr-7b-beta.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [zephyr-7b-beta.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [zephyr-7b-beta.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_K.gguf) | Q4_K | 4.07GB | | [zephyr-7b-beta.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [zephyr-7b-beta.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q4_1.gguf) | Q4_1 | 4.24GB | | [zephyr-7b-beta.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_0.gguf) | Q5_0 | 4.65GB | | [zephyr-7b-beta.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [zephyr-7b-beta.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_K.gguf) | Q5_K | 4.78GB | | [zephyr-7b-beta.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [zephyr-7b-beta.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q5_1.gguf) | Q5_1 | 5.07GB | | [zephyr-7b-beta.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf/blob/main/zephyr-7b-beta.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - example_title: Pirate! messages: - role: system content: You are a pirate chatbot who always responds with Arr! - role: user content: "There's a llama on my lawn, how can I get rid of him?" output: text: >- Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr! pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench ---  <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources  - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) | |-------------|-----|----|---------------|--------------| | StableLM-Tuned-α | 7B| dSFT |2.75| -| | MPT-Chat | 7B |dSFT |5.42| -| | Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83| | Mistral-Instructv0.1 | 7B| - | 6.84 |-| | Zephyr-7b-α |7B| dDPO| 6.88| -| | **Zephyr-7b-β** 🪁 | **7B** | **dDPO** | **7.34** | **90.60** | | Falcon-Instruct | 40B |dSFT |5.17 |45.71| | Guanaco | 65B | SFT |6.41| 71.80| | Llama2-Chat | 70B |RLHF |6.86| 92.66| | Vicuna v1.3 | 33B |dSFT |7.12 |88.99| | WizardLM v1.0 | 70B |dSFT |7.71 |-| | Xwin-LM v0.1 | 70B |dPPO |- |95.57| | GPT-3.5-turbo | - |RLHF |7.94 |89.37| | Claude 2 | - |RLHF |8.06| 91.36| | GPT-4 | -| RLHF |8.99| 95.28| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <|system|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <|user|> # How many helicopters can a human eat in one sitting?</s> # <|assistant|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations  Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6284 | 0.05 | 100 | 0.6098 | 0.0425 | -0.1872 | 0.7344 | 0.2297 | -258.8416 | -253.8099 | -2.7976 | -2.8234 | | 0.4908 | 0.1 | 200 | 0.5426 | -0.0279 | -0.6842 | 0.75 | 0.6563 | -263.8124 | -254.5145 | -2.7719 | -2.7960 | | 0.5264 | 0.15 | 300 | 0.5324 | 0.0414 | -0.9793 | 0.7656 | 1.0207 | -266.7627 | -253.8209 | -2.7892 | -2.8122 | | 0.5536 | 0.21 | 400 | 0.4957 | -0.0185 | -1.5276 | 0.7969 | 1.5091 | -272.2460 | -254.4203 | -2.8542 | -2.8764 | | 0.5362 | 0.26 | 500 | 0.5031 | -0.2630 | -1.5917 | 0.7812 | 1.3287 | -272.8869 | -256.8653 | -2.8702 | -2.8958 | | 0.5966 | 0.31 | 600 | 0.5963 | -0.2993 | -1.6491 | 0.7812 | 1.3499 | -273.4614 | -257.2279 | -2.8778 | -2.8986 | | 0.5014 | 0.36 | 700 | 0.5382 | -0.2859 | -1.4750 | 0.75 | 1.1891 | -271.7204 | -257.0942 | -2.7659 | -2.7869 | | 0.5334 | 0.41 | 800 | 0.5677 | -0.4289 | -1.8968 | 0.7969 | 1.4679 | -275.9378 | -258.5242 | -2.7053 | -2.7265 | | 0.5251 | 0.46 | 900 | 0.5772 | -0.2116 | -1.3107 | 0.7344 | 1.0991 | -270.0768 | -256.3507 | -2.8463 | -2.8662 | | 0.5205 | 0.52 | 1000 | 0.5262 | -0.3792 | -1.8585 | 0.7188 | 1.4793 | -275.5552 | -258.0276 | -2.7893 | -2.7979 | | 0.5094 | 0.57 | 1100 | 0.5433 | -0.6279 | -1.9368 | 0.7969 | 1.3089 | -276.3377 | -260.5136 | -2.7453 | -2.7536 | | 0.5837 | 0.62 | 1200 | 0.5349 | -0.3780 | -1.9584 | 0.7656 | 1.5804 | -276.5542 | -258.0154 | -2.7643 | -2.7756 | | 0.5214 | 0.67 | 1300 | 0.5732 | -1.0055 | -2.2306 | 0.7656 | 1.2251 | -279.2761 | -264.2903 | -2.6986 | -2.7113 | | 0.6914 | 0.72 | 1400 | 0.5137 | -0.6912 | -2.1775 | 0.7969 | 1.4863 | -278.7448 | -261.1467 | -2.7166 | -2.7275 | | 0.4655 | 0.77 | 1500 | 0.5090 | -0.7987 | -2.2930 | 0.7031 | 1.4943 | -279.8999 | -262.2220 | -2.6651 | -2.6838 | | 0.5731 | 0.83 | 1600 | 0.5312 | -0.8253 | -2.3520 | 0.7812 | 1.5268 | -280.4902 | -262.4876 | -2.6543 | -2.6728 | | 0.5233 | 0.88 | 1700 | 0.5206 | -0.4573 | -2.0951 | 0.7812 | 1.6377 | -277.9205 | -258.8084 | -2.6870 | -2.7097 | | 0.5593 | 0.93 | 1800 | 0.5231 | -0.5508 | -2.2000 | 0.7969 | 1.6492 | -278.9703 | -259.7433 | -2.6221 | -2.6519 | | 0.4967 | 0.98 | 1900 | 0.5290 | -0.5340 | -1.9570 | 0.8281 | 1.4230 | -276.5395 | -259.5749 | -2.6564 | -2.6878 | | 0.0921 | 1.03 | 2000 | 0.5368 | -1.1376 | -3.1615 | 0.7812 | 2.0239 | -288.5854 | -265.6111 | -2.6040 | -2.6345 | | 0.0733 | 1.08 | 2100 | 0.5453 | -1.1045 | -3.4451 | 0.7656 | 2.3406 | -291.4208 | -265.2799 | -2.6289 | -2.6595 | | 0.0972 | 1.14 | 2200 | 0.5571 | -1.6915 | -3.9823 | 0.8125 | 2.2908 | -296.7934 | -271.1505 | -2.6471 | -2.6709 | | 0.1058 | 1.19 | 2300 | 0.5789 | -1.0621 | -3.8941 | 0.7969 | 2.8319 | -295.9106 | -264.8563 | -2.5527 | -2.5798 | | 0.2423 | 1.24 | 2400 | 0.5455 | -1.1963 | -3.5590 | 0.7812 | 2.3627 | -292.5599 | -266.1981 | -2.5414 | -2.5784 | | 0.1177 | 1.29 | 2500 | 0.5889 | -1.8141 | -4.3942 | 0.7969 | 2.5801 | -300.9120 | -272.3761 | -2.4802 | -2.5189 | | 0.1213 | 1.34 | 2600 | 0.5683 | -1.4608 | -3.8420 | 0.8125 | 2.3812 | -295.3901 | -268.8436 | -2.4774 | -2.5207 | | 0.0889 | 1.39 | 2700 | 0.5890 | -1.6007 | -3.7337 | 0.7812 | 2.1330 | -294.3068 | -270.2423 | -2.4123 | -2.4522 | | 0.0995 | 1.45 | 2800 | 0.6073 | -1.5519 | -3.8362 | 0.8281 | 2.2843 | -295.3315 | -269.7538 | -2.4685 | -2.5050 | | 0.1145 | 1.5 | 2900 | 0.5790 | -1.7939 | -4.2876 | 0.8438 | 2.4937 | -299.8461 | -272.1744 | -2.4272 | -2.4674 | | 0.0644 | 1.55 | 3000 | 0.5735 | -1.7285 | -4.2051 | 0.8125 | 2.4766 | -299.0209 | -271.5201 | -2.4193 | -2.4574 | | 0.0798 | 1.6 | 3100 | 0.5537 | -1.7226 | -4.2850 | 0.8438 | 2.5624 | -299.8200 | -271.4610 | -2.5367 | -2.5696 | | 0.1013 | 1.65 | 3200 | 0.5575 | -1.5715 | -3.9813 | 0.875 | 2.4098 | -296.7825 | -269.9498 | -2.4926 | -2.5267 | | 0.1254 | 1.7 | 3300 | 0.5905 | -1.6412 | -4.4703 | 0.8594 | 2.8291 | -301.6730 | -270.6473 | -2.5017 | -2.5340 | | 0.085 | 1.76 | 3400 | 0.6133 | -1.9159 | -4.6760 | 0.8438 | 2.7601 | -303.7296 | -273.3941 | -2.4614 | -2.4960 | | 0.065 | 1.81 | 3500 | 0.6074 | -1.8237 | -4.3525 | 0.8594 | 2.5288 | -300.4951 | -272.4724 | -2.4597 | -2.5004 | | 0.0755 | 1.86 | 3600 | 0.5836 | -1.9252 | -4.4005 | 0.8125 | 2.4753 | -300.9748 | -273.4872 | -2.4327 | -2.4716 | | 0.0746 | 1.91 | 3700 | 0.5789 | -1.9280 | -4.4906 | 0.8125 | 2.5626 | -301.8762 | -273.5149 | -2.4686 | -2.5115 | | 0.1348 | 1.96 | 3800 | 0.6015 | -1.8658 | -4.2428 | 0.8281 | 2.3769 | -299.3976 | -272.8936 | -2.4943 | -2.5393 | | 0.0217 | 2.01 | 3900 | 0.6122 | -2.3335 | -4.9229 | 0.8281 | 2.5894 | -306.1988 | -277.5699 | -2.4841 | -2.5272 | | 0.0219 | 2.07 | 4000 | 0.6522 | -2.9890 | -6.0164 | 0.8281 | 3.0274 | -317.1334 | -284.1248 | -2.4105 | -2.4545 | | 0.0119 | 2.12 | 4100 | 0.6922 | -3.4777 | -6.6749 | 0.7969 | 3.1972 | -323.7187 | -289.0121 | -2.4272 | -2.4699 | | 0.0153 | 2.17 | 4200 | 0.6993 | -3.2406 | -6.6775 | 0.7969 | 3.4369 | -323.7453 | -286.6413 | -2.4047 | -2.4465 | | 0.011 | 2.22 | 4300 | 0.7178 | -3.7991 | -7.4397 | 0.7656 | 3.6406 | -331.3667 | -292.2260 | -2.3843 | -2.4290 | | 0.0072 | 2.27 | 4400 | 0.6840 | -3.3269 | -6.8021 | 0.8125 | 3.4752 | -324.9908 | -287.5042 | -2.4095 | -2.4536 | | 0.0197 | 2.32 | 4500 | 0.7013 | -3.6890 | -7.3014 | 0.8125 | 3.6124 | -329.9841 | -291.1250 | -2.4118 | -2.4543 | | 0.0182 | 2.37 | 4600 | 0.7476 | -3.8994 | -7.5366 | 0.8281 | 3.6372 | -332.3356 | -293.2291 | -2.4163 | -2.4565 | | 0.0125 | 2.43 | 4700 | 0.7199 | -4.0560 | -7.5765 | 0.8438 | 3.5204 | -332.7345 | -294.7952 | -2.3699 | -2.4100 | | 0.0082 | 2.48 | 4800 | 0.7048 | -3.6613 | -7.1356 | 0.875 | 3.4743 | -328.3255 | -290.8477 | -2.3925 | -2.4303 | | 0.0118 | 2.53 | 4900 | 0.6976 | -3.7908 | -7.3152 | 0.8125 | 3.5244 | -330.1224 | -292.1431 | -2.3633 | -2.4047 | | 0.0118 | 2.58 | 5000 | 0.7198 | -3.9049 | -7.5557 | 0.8281 | 3.6508 | -332.5271 | -293.2844 | -2.3764 | -2.4194 | | 0.006 | 2.63 | 5100 | 0.7506 | -4.2118 | -7.9149 | 0.8125 | 3.7032 | -336.1194 | -296.3530 | -2.3407 | -2.3860 | | 0.0143 | 2.68 | 5200 | 0.7408 | -4.2433 | -7.9802 | 0.8125 | 3.7369 | -336.7721 | -296.6682 | -2.3509 | -2.3946 | | 0.0057 | 2.74 | 5300 | 0.7552 | -4.3392 | -8.0831 | 0.7969 | 3.7439 | -337.8013 | -297.6275 | -2.3388 | -2.3842 | | 0.0138 | 2.79 | 5400 | 0.7404 | -4.2395 | -7.9762 | 0.8125 | 3.7367 | -336.7322 | -296.6304 | -2.3286 | -2.3737 | | 0.0079 | 2.84 | 5500 | 0.7525 | -4.4466 | -8.2196 | 0.7812 | 3.7731 | -339.1662 | -298.7007 | -2.3200 | -2.3641 | | 0.0077 | 2.89 | 5600 | 0.7520 | -4.5586 | -8.3485 | 0.7969 | 3.7899 | -340.4545 | -299.8206 | -2.3078 | -2.3517 | | 0.0094 | 2.94 | 5700 | 0.7527 | -4.5542 | -8.3509 | 0.7812 | 3.7967 | -340.4790 | -299.7773 | -2.3062 | -2.3510 | | 0.0054 | 2.99 | 5800 | 0.7520 | -4.5169 | -8.3079 | 0.7812 | 3.7911 | -340.0493 | -299.4038 | -2.3081 | -2.3530 | ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta) | Metric | Value | |-----------------------|---------------------------| | Avg. | 52.15 | | ARC (25-shot) | 62.03 | | HellaSwag (10-shot) | 84.36 | | MMLU (5-shot) | 61.07 | | TruthfulQA (0-shot) | 57.45 | | Winogrande (5-shot) | 77.74 | | GSM8K (5-shot) | 12.74 | | DROP (3-shot) | 9.66 |

{}

RichardErkhov/HuggingFaceH4_-_zephyr-7b-beta-gguf

null

[ "gguf", "arxiv:2305.18290", "arxiv:2310.16944", "region:us" ]

null

2024-05-01T03:34:06+00:00

null

peft

# Model Card for Model ID  ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0

{"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"}

JosephLee/society_textbook_LLama3_8B_instruct

null

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "region:us" ]

null

2024-05-01T03:34:42+00:00

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-grok - bnb 8bits - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-grok/ Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/grok-conversation-harmless - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-7b-grok results: [] ---  # Mistral 7B Grok This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that has been aligned via Constitutional AI to mimic the style of [xAI's Grok assistant](https://grok.x.ai/). It achieves the following results on the evaluation set: - Loss: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9326 | 1.0 | 545 | 0.9348 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

{}

RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-01T03:34:53+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

jhmejia/Classifai-ORPO-Meta-Llama-3-8B-Instruct

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:36:45+00:00

null

{}

Ilkinism/test-ilkin-private_readme

null

[ "region:us" ]

null

2024-05-01T03:38:08+00:00

null

peft

# nash_dpo_iter_3 This model is a fine-tuned version of [YYYYYYibo/nash_dpo_iter_2](https://huggingface.co/YYYYYYibo/nash_dpo_iter_2) on the updated and the original datasets. It achieves the following results on the evaluation set: - Loss: 0.6047 - Rewards/chosen: -0.1412 - Rewards/rejected: -0.3752 - Rewards/accuracies: 0.6980 - Rewards/margins: 0.2340 - Logps/rejected: -316.8028 - Logps/chosen: -314.8045 - Logits/rejected: -2.3331 - Logits/chosen: -2.4404 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - 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: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6237 | 0.61 | 100 | 0.6047 | -0.1412 | -0.3752 | 0.6980 | 0.2340 | -316.8028 | -314.8045 | -2.3331 | -2.4404 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

{"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo"], "datasets": ["updated", "original"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "nash_dpo_iter_3", "results": []}]}

YYYYYYibo/nash_dpo_iter_3

null

[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:updated", "dataset:original", "base_model:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "region:us" ]

null

2024-05-01T03:38:16+00:00

text-generation

transformers

## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td>Training Data </td> <td>Params </td> <td>Context length </td> <td>GQA </td> <td>Token count </td> <td>Knowledge cutoff </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td>Time (GPU hours) </td> <td>Power Consumption (W) </td> <td>Carbon Emitted(tCO2eq) </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td>Category </td> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama2 7B </td> <td>Llama2 13B </td> <td>Llama 3 70B </td> <td>Llama2 70B </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama 2 7B </td> <td>Llama 2 13B </td> <td>Llama 3 70B </td> <td>Llama 2 70B </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. Safety For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. Refusals In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### Cyber Security We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

{"language": ["en"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. 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Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit"}

sephwalker3/llama3v1

null

[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]

null

2024-05-01T03:38:29+00:00

token-classification

transformers

# xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2650 - F1: 0.8307 ## 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: 24 - eval_batch_size: 24 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7039 | 1.0 | 70 | 0.2975 | 0.7725 | | 0.2631 | 2.0 | 140 | 0.2585 | 0.7856 | | 0.1701 | 3.0 | 210 | 0.2650 | 0.8307 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-panx-it", "results": []}]}

u00890358/xlm-roberta-base-finetuned-panx-it

null

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:38:46+00:00

null

{}

Ilkinism/test-ilkin-private_readme1

null

[ "region:us" ]

null

2024-05-01T03:38:49+00:00

null

{}

jponeme/roberta_def

null

[ "region:us" ]

null

2024-05-01T03:39:00+00:00

text-to-image

diffusers

{"license": "openrail"}

Moibe/Unstable_Diffusers_V11_-_Diffusers

null

[ "diffusers", "safetensors", "license:openrail", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

null

2024-05-01T03:39:15+00:00

null

{}

Ilkinism/test-ilkin-private_readme2

null

[ "region:us" ]

null

2024-05-01T03:40:27+00:00

null

{}

Ilkinism/test-ilkin-private_readme3

null

[ "region:us" ]

null

2024-05-01T03:42:55+00:00

automatic-speech-recognition

transformers

# whisper-meduim-mongolian This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on custom. It achieves the following results on the evaluation set: - Loss: 0.3098 - Wer: 26.8664 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:-------:| | 0.3034 | 0.9398 | 2000 | 0.4135 | 45.1152 | | 0.1443 | 1.8797 | 4000 | 0.3127 | 35.3290 | | 0.0618 | 2.8195 | 6000 | 0.3038 | 31.0534 | | 0.0179 | 3.7594 | 8000 | 0.3042 | 28.3673 | | 0.0028 | 4.6992 | 10000 | 0.3098 | 26.8664 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["mn"], "license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["Cafet/whisper-mongolian-final"], "metrics": ["wer"], "base_model": "openai/whisper-medium", "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "whisper-meduim-mongolian", "results": []}]}

Cafet/whisper-meduim-mongolian

null

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "mn", "dataset:Cafet/whisper-mongolian-final", "base_model:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:43:03+00:00

text-generation

transformers

# D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [migtissera/Tess-7B-v2.0](https://huggingface.co/migtissera/Tess-7B-v2.0) * [Eric111/Yarn-Mistral-7b-128k-DPO](https://huggingface.co/Eric111/Yarn-Mistral-7b-128k-DPO) ## 🧩 Configuration ```yaml slices: - sources: - model: migtissera/Tess-7B-v2.0 layer_range: [0, 32] - model: Eric111/Yarn-Mistral-7b-128k-DPO layer_range: [0, 32] merge_method: slerp base_model: Eric111/Yarn-Mistral-7b-128k-DPO 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO" 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"]) ```

{"tags": ["merge", "mergekit", "lazymergekit", "migtissera/Tess-7B-v2.0", "Eric111/Yarn-Mistral-7b-128k-DPO"], "base_model": ["migtissera/Tess-7B-v2.0", "Eric111/Yarn-Mistral-7b-128k-DPO"]}

DavidAU/D_AU-Tess-7B-v2.0-Yarn-Mistral-7b-128k-DPO

null

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "migtissera/Tess-7B-v2.0", "Eric111/Yarn-Mistral-7b-128k-DPO", "custom_code", "base_model:migtissera/Tess-7B-v2.0", "base_model:Eric111/Yarn-Mistral-7b-128k-DPO", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:44:00+00:00

null

{"license": "apache-2.0"}

bebongkyo/qqass

null

[ "safetensors", "license:apache-2.0", "region:us" ]

null

2024-05-01T03:45:04+00:00

null

{"license": "mit"}

a1098346640/dreambooth

null

[ "license:mit", "region:us" ]

null

2024-05-01T03:45:05+00:00

null

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-grok - GGUF - Model creator: https://huggingface.co/HuggingFaceH4/ - Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-grok/ | Name | Quant method | Size | | ---- | ---- | ---- | | [mistral-7b-grok.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q2_K.gguf) | Q2_K | 2.53GB | | [mistral-7b-grok.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [mistral-7b-grok.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ3_S.gguf) | IQ3_S | 2.96GB | | [mistral-7b-grok.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [mistral-7b-grok.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ3_M.gguf) | IQ3_M | 3.06GB | | [mistral-7b-grok.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K.gguf) | Q3_K | 3.28GB | | [mistral-7b-grok.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [mistral-7b-grok.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [mistral-7b-grok.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [mistral-7b-grok.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_0.gguf) | Q4_0 | 3.83GB | | [mistral-7b-grok.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [mistral-7b-grok.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [mistral-7b-grok.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_K.gguf) | Q4_K | 4.07GB | | [mistral-7b-grok.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [mistral-7b-grok.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q4_1.gguf) | Q4_1 | 4.24GB | | [mistral-7b-grok.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_0.gguf) | Q5_0 | 4.65GB | | [mistral-7b-grok.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [mistral-7b-grok.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_K.gguf) | Q5_K | 4.78GB | | [mistral-7b-grok.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [mistral-7b-grok.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q5_1.gguf) | Q5_1 | 5.07GB | | [mistral-7b-grok.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf/blob/main/mistral-7b-grok.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/grok-conversation-harmless - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-7b-grok results: [] ---  # Mistral 7B Grok This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that has been aligned via Constitutional AI to mimic the style of [xAI's Grok assistant](https://grok.x.ai/). It achieves the following results on the evaluation set: - Loss: 0.9348 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9326 | 1.0 | 545 | 0.9348 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

{}

RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-gguf

null

[ "gguf", "region:us" ]

null

2024-05-01T03:46:06+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

Promptengineering/tinyllama-colorist-v0

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:46:22+00:00

text-generation

transformers

# Dolphin 2.9 Mixtral 8x22b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> My appreciation for the sponsors of Dolphin 2.9: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed. The base model has 64k context, and the full-weight fine-tuning was with 4k sequence length. It took 1 week on 8xH100 provided by Crusoe Cloud This model was trained FFT on 50% parameters (targeted with [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py) by Fernando Fernandes, David Golchinfar, Lucas Atkins, and Eric Hartford) , using ChatML prompt template format. example: ``` <|im_start|>system You are Dolphin, a helpful AI assistant.<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed Apache 2.0. I grant permission for any use, including commercial, that falls within accordance with Apache-2.0 license. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/Nb6f_dS_M6fN_v2ACK98x.png) ## Training [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistral-community/Mixtral-8x22B-v0.1 model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: false strict: false unfrozen_parameters: - ^lm_head.weight$ - ^model.embed_tokens.weight$ - model.layers.0.self_attn.q_proj - model.layers.1.self_attn.q_proj - model.layers.2.self_attn.q_proj - model.layers.22.self_attn.q_proj - model.layers.27.self_attn.q_proj - model.layers.28.self_attn.q_proj - model.layers.13.self_attn.q_proj - model.layers.21.self_attn.q_proj - model.layers.24.self_attn.q_proj - model.layers.14.self_attn.q_proj - model.layers.15.self_attn.q_proj - model.layers.11.self_attn.q_proj - model.layers.20.self_attn.q_proj - model.layers.23.self_attn.q_proj - model.layers.30.self_attn.k_proj - model.layers.31.self_attn.k_proj - model.layers.25.self_attn.k_proj - model.layers.23.self_attn.k_proj - model.layers.27.self_attn.k_proj - model.layers.26.self_attn.k_proj - model.layers.29.self_attn.k_proj - model.layers.28.self_attn.k_proj - model.layers.24.self_attn.k_proj - model.layers.16.self_attn.k_proj - model.layers.19.self_attn.k_proj - model.layers.22.self_attn.k_proj - model.layers.20.self_attn.k_proj - model.layers.6.self_attn.k_proj - model.layers.22.self_attn.v_proj - model.layers.29.self_attn.v_proj - model.layers.31.self_attn.v_proj - model.layers.5.self_attn.v_proj - model.layers.8.self_attn.v_proj - model.layers.4.self_attn.v_proj - model.layers.25.self_attn.v_proj - model.layers.30.self_attn.v_proj - model.layers.17.self_attn.v_proj - model.layers.23.self_attn.v_proj - model.layers.28.self_attn.v_proj - model.layers.9.self_attn.v_proj - model.layers.26.self_attn.v_proj - model.layers.27.self_attn.v_proj - model.layers.20.self_attn.o_proj - model.layers.19.self_attn.o_proj - model.layers.16.self_attn.o_proj - model.layers.13.self_attn.o_proj - model.layers.18.self_attn.o_proj - model.layers.17.self_attn.o_proj - model.layers.12.self_attn.o_proj - model.layers.15.self_attn.o_proj - model.layers.14.self_attn.o_proj - model.layers.22.self_attn.o_proj - model.layers.23.self_attn.o_proj - model.layers.21.self_attn.o_proj - model.layers.10.self_attn.o_proj - model.layers.0.self_attn.o_proj - model.layers.0.block_sparse_moe.experts.0.w1 - model.layers.1.block_sparse_moe.experts.0.w1 - model.layers.2.block_sparse_moe.experts.0.w1 - model.layers.3.block_sparse_moe.experts.0.w1 - model.layers.4.block_sparse_moe.experts.0.w1 - model.layers.5.block_sparse_moe.experts.0.w1 - model.layers.6.block_sparse_moe.experts.0.w1 - 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model.layers.11.block_sparse_moe.experts.5.w3 - model.layers.12.block_sparse_moe.experts.5.w3 - model.layers.13.block_sparse_moe.experts.5.w3 - model.layers.5.block_sparse_moe.experts.6.w1 - model.layers.4.block_sparse_moe.experts.6.w1 - model.layers.3.block_sparse_moe.experts.6.w1 - model.layers.2.block_sparse_moe.experts.6.w1 - model.layers.1.block_sparse_moe.experts.6.w1 - model.layers.0.block_sparse_moe.experts.6.w1 - model.layers.6.block_sparse_moe.experts.6.w1 - model.layers.7.block_sparse_moe.experts.6.w1 - model.layers.8.block_sparse_moe.experts.6.w1 - model.layers.9.block_sparse_moe.experts.6.w1 - model.layers.10.block_sparse_moe.experts.6.w1 - model.layers.11.block_sparse_moe.experts.6.w1 - model.layers.12.block_sparse_moe.experts.6.w1 - model.layers.13.block_sparse_moe.experts.6.w1 - model.layers.5.block_sparse_moe.experts.6.w2 - model.layers.4.block_sparse_moe.experts.6.w2 - model.layers.2.block_sparse_moe.experts.6.w2 - model.layers.3.block_sparse_moe.experts.6.w2 - model.layers.1.block_sparse_moe.experts.6.w2 - model.layers.0.block_sparse_moe.experts.6.w2 - model.layers.6.block_sparse_moe.experts.6.w2 - model.layers.7.block_sparse_moe.experts.6.w2 - model.layers.8.block_sparse_moe.experts.6.w2 - model.layers.9.block_sparse_moe.experts.6.w2 - model.layers.10.block_sparse_moe.experts.6.w2 - model.layers.11.block_sparse_moe.experts.6.w2 - model.layers.12.block_sparse_moe.experts.6.w2 - model.layers.13.block_sparse_moe.experts.6.w2 - model.layers.5.block_sparse_moe.experts.6.w3 - model.layers.4.block_sparse_moe.experts.6.w3 - model.layers.3.block_sparse_moe.experts.6.w3 - model.layers.2.block_sparse_moe.experts.6.w3 - model.layers.1.block_sparse_moe.experts.6.w3 - model.layers.0.block_sparse_moe.experts.6.w3 - model.layers.6.block_sparse_moe.experts.6.w3 - model.layers.7.block_sparse_moe.experts.6.w3 - model.layers.8.block_sparse_moe.experts.6.w3 - model.layers.9.block_sparse_moe.experts.6.w3 - model.layers.10.block_sparse_moe.experts.6.w3 - model.layers.11.block_sparse_moe.experts.6.w3 - model.layers.12.block_sparse_moe.experts.6.w3 - model.layers.13.block_sparse_moe.experts.6.w3 - model.layers.5.block_sparse_moe.experts.7.w1 - model.layers.6.block_sparse_moe.experts.7.w1 - model.layers.3.block_sparse_moe.experts.7.w1 - model.layers.4.block_sparse_moe.experts.7.w1 - model.layers.2.block_sparse_moe.experts.7.w1 - model.layers.0.block_sparse_moe.experts.7.w1 - model.layers.7.block_sparse_moe.experts.7.w1 - model.layers.8.block_sparse_moe.experts.7.w1 - model.layers.9.block_sparse_moe.experts.7.w1 - model.layers.10.block_sparse_moe.experts.7.w1 - model.layers.11.block_sparse_moe.experts.7.w1 - model.layers.12.block_sparse_moe.experts.7.w1 - model.layers.13.block_sparse_moe.experts.7.w1 - model.layers.14.block_sparse_moe.experts.7.w1 - model.layers.6.block_sparse_moe.experts.7.w2 - model.layers.5.block_sparse_moe.experts.7.w2 - model.layers.4.block_sparse_moe.experts.7.w2 - model.layers.2.block_sparse_moe.experts.7.w2 - model.layers.3.block_sparse_moe.experts.7.w2 - model.layers.1.block_sparse_moe.experts.7.w2 - model.layers.0.block_sparse_moe.experts.7.w2 - model.layers.7.block_sparse_moe.experts.7.w2 - model.layers.8.block_sparse_moe.experts.7.w2 - model.layers.9.block_sparse_moe.experts.7.w2 - model.layers.10.block_sparse_moe.experts.7.w2 - model.layers.11.block_sparse_moe.experts.7.w2 - model.layers.12.block_sparse_moe.experts.7.w2 - model.layers.13.block_sparse_moe.experts.7.w2 - model.layers.6.block_sparse_moe.experts.7.w3 - model.layers.5.block_sparse_moe.experts.7.w3 - model.layers.4.block_sparse_moe.experts.7.w3 - model.layers.3.block_sparse_moe.experts.7.w3 - model.layers.2.block_sparse_moe.experts.7.w3 - model.layers.0.block_sparse_moe.experts.7.w3 - model.layers.7.block_sparse_moe.experts.7.w3 - model.layers.8.block_sparse_moe.experts.7.w3 - model.layers.9.block_sparse_moe.experts.7.w3 - model.layers.10.block_sparse_moe.experts.7.w3 - model.layers.11.block_sparse_moe.experts.7.w3 - model.layers.12.block_sparse_moe.experts.7.w3 - model.layers.13.block_sparse_moe.experts.7.w3 - model.layers.14.block_sparse_moe.experts.7.w3 - model.layers.0.block_sparse_moe.gate - model.layers.1.block_sparse_moe.gate - model.layers.2.block_sparse_moe.gate - model.layers.3.block_sparse_moe.gate - model.layers.4.block_sparse_moe.gate - model.layers.5.block_sparse_moe.gate - model.layers.6.block_sparse_moe.gate - model.layers.7.block_sparse_moe.gate - model.layers.8.block_sparse_moe.gate - model.layers.9.block_sparse_moe.gate - model.layers.10.block_sparse_moe.gate - model.layers.11.block_sparse_moe.gate - model.layers.12.block_sparse_moe.gate - model.layers.13.block_sparse_moe.gate model_config: output_router_logits: true datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: thingy val_set_size: 0.0002 output_dir: ./out sequence_len: 4096 sample_packing: true pad_to_sequence_len: true gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 3 logging_steps: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 2.7e-5 wandb_project: dolphin-2.9-mixtral-8x22b wandb_watch: wandb_run_id: wandb_log_model: train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /home/ehartford/axolotl/out/checkpoint-316 local_rank: logging_steps: 1 xformers_attention: flash_attention: true saves_per_epoch: 8 save_total_limit: 2 save_steps: evals_per_epoch: 4 eval_sample_packing: false debug: deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: eos_token: "<|im_end|>" tokens: - "<|im_start|>" ``` </details> ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7022 | 0.0 | 1 | 0.6989 | | 0.5344 | 0.25 | 238 | 0.5138 | | 0.5204 | 0.5 | 476 | 0.5018 | | 0.5059 | 0.75 | 714 | 0.4951 | | 0.5112 | 1.0 | 952 | 0.4911 | | 0.4561 | 1.24 | 1190 | 0.4978 | | 0.478 | 1.49 | 1428 | 0.4935 | | 0.4714 | 1.74 | 1666 | 0.4899 | | 0.4626 | 1.99 | 1904 | 0.4861 | | 0.3675 | 2.22 | 2142 | 0.5240 | | 0.3595 | 2.47 | 2380 | 0.5229 | | 0.3438 | 2.72 | 2618 | 0.5217 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "axolotl"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "mistral-community/Mixtral-8x22B-v0.1", "model-index": [{"name": "out", "results": []}]}

blockblockblock/dolphin-2.9-mixtral-8x22b-bpw2.5-exl2

null

[ "transformers", "safetensors", "mixtral", "text-generation", "generated_from_trainer", "axolotl", "conversational", "en", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:46:30+00:00

null

### Coming Soon!!! ### 基于alpaca-data-gpt4-chinese、sft_zh数据集对Llama-3-8B-Instruct进行微调。 ### 模型： - https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct ### 数据集： - https://huggingface.co/datasets/TigerResearch/sft_zh - https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese ### 结果 | 模型名称 | CEVAL | MMLU | |--------------------------|-------|------| | LLama3 | 49.91 | 66.62| | LLama3-Instruct | 50.55 | 67.15| | LLama3-Instruct-sft-lora-tigerbot-alpacadatagpt4-10epoch | 53.65 | 68.09 |

{"license": "llama3", "datasets": ["TigerResearch/sft_zh", "silk-road/alpaca-data-gpt4-chinese"]}

REILX/Llama-3-8B-Instruct-Tiger-alpaca-chinese-lora

null

[ "dataset:TigerResearch/sft_zh", "dataset:silk-road/alpaca-data-gpt4-chinese", "license:llama3", "region:us" ]

null

2024-05-01T03:48:03+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

paulo037/stable-code-instruct-3b-syntetic-1000-7-epoch

null

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:50:25+00:00

null

# Meta-Llama-3-8B-Instruct-GGUF - This is GGUF quantized version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) created using llama.cpp. - imatrix from [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384). - Using llama.cpp revision a8f9b076316e16aadd0791015b3bfd446fe1e904, after "llama : fix BPE pre-tokenization (#6920)"

{"license": "llama3"}

AriWeinstein/Meta-Llama-3-8B-GGUF-Qx_0-imatrix

null

[ "llama", "license:llama3", "region:us" ]

null

2024-05-01T03:51:18+00:00

text-classification

transformers

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2191 - Accuracy: 0.9265 - F1: 0.9264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8383 | 1.0 | 250 | 0.3218 | 0.9085 | 0.9081 | | 0.2522 | 2.0 | 500 | 0.2191 | 0.9265 | 0.9264 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9265, "name": "Accuracy"}, {"type": "f1", "value": 0.926446360073235, "name": "F1"}]}]}]}

patymun/distilbert-base-uncased-finetuned-emotion

null

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T03:52:14+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

tricktreat/llama-2-7b-chat-12layers-T6-lora612

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T03:53:31+00:00

reinforcement-learning

stable-baselines3

# **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 ... ```

{"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": "235.67 +/- 30.47", "name": "mean_reward", "verified": false}]}]}]}

RobertoFuentesRisco/ppo-LunarLander-v2

null

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

null

2024-05-01T03:56:36+00:00

text-classification

transformers

{}

quanla/distilbert-base-uncased-finetuned-cola

null

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T04:00:13+00:00

text-generation

transformers

{"license": "mit"}

migueldeguzmandev/GPT2XL_RLLMv19-9

null

[ "transformers", "pytorch", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:01:30+00:00

null

{}

Nadahass/MREG_7b_4_SmallData

null

[ "region:us" ]

null

2024-05-01T04:01:43+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

jhmejia/Classifai-ORPO-Meta-Llama-3-8B-Instruct-2

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:02:28+00:00

text-generation

transformers

# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}

trottdw/C104llama3Orca

null

[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T04:02:41+00:00

unconditional-image-generation

diffusers

# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('nissin1325/sd-class-butterflies-32') image = pipeline().images[0] image ```

{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}

nissin1325/sd-class-butterflies-32

null

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

null

2024-05-01T04:03:47+00:00

null

{}

nissin1325/sd-class-butterflies-64

null

[ "region:us" ]

null

2024-05-01T04:05:56+00:00

text-generation

transformers

{}

migueldeguzmandev/GPT2XL_RLLMv19-10

null

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space" ]

null

2024-05-01T04:07:55+00:00

null

{}

Subhajit0320/my_awesome_qa_model1

null

[ "region:us" ]

null

2024-05-01T04:09:53+00:00

text-generation

transformers

Llamafyd version of Qwen .5B further fine tuned on wiki, math, science, and chat datasets. Based on Cinder data. As well as Cinder character specific data, a mix of RAG generated Q and A of world knowledge, STEM topics, and Cinder Character data. I suplimented the Cinder character with an abreviated Samantha dataset edited for Cinder and removed a lot of the negative responses. Model Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration.

{"license": "mit"}

Josephgflowers/Qllama-tiny-.5B-test-1

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:10:22+00:00

null

{"license": "apache-2.0"}

moscotechh/firstmodel

null

[ "license:apache-2.0", "region:us" ]

null

2024-05-01T04:11:33+00:00

null

# MergerixNeuralsynthesis-7B MergerixNeuralsynthesis-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 - model: MiniMoog/Mergerix-7b-v0.3 - model: Kukedlc/NeuralSynthesis-7B-v0.1 merge_method: model_stock base_model: mistralai/Mistral-7B-v0.1 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/MergerixNeuralsynthesis-7B" 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"]) ```

{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]}

automerger/MergerixNeuralsynthesis-7B

null

[ "merge", "mergekit", "lazymergekit", "automerger", "license:apache-2.0", "region:us" ]

null

2024-05-01T04:12:06+00:00

null

{}

HunterGerlach/granite-il-aligned

null

[ "region:us" ]

null

2024-05-01T04:13:32+00:00

null

transformers

# Uploaded model - **Developed by:** Justin-Y - **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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

Justin-Y/lora_model

null

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-01T04:14:14+00:00

text-classification

transformers

# Roberta-base-Rewritten-commit_messages_v2 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4196 - Accuracy: 0.7704 - F1: 0.7707 - Precision: 0.7811 - Recall: 0.7704 ## 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: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.2288 | 0.09 | 100 | 0.7615 | 0.6716 | 0.6578 | 0.7461 | 0.6716 | | 0.1103 | 0.17 | 200 | 0.6396 | 0.7453 | 0.7452 | 0.7609 | 0.7453 | | 0.2204 | 0.26 | 300 | 1.3317 | 0.6596 | 0.6334 | 0.7863 | 0.6596 | | 0.1872 | 0.34 | 400 | 0.8661 | 0.6333 | 0.5996 | 0.7712 | 0.6333 | | 0.1261 | 0.43 | 500 | 1.9369 | 0.7130 | 0.7023 | 0.7906 | 0.7130 | | 0.1902 | 0.52 | 600 | 2.2998 | 0.6823 | 0.6695 | 0.7573 | 0.6823 | | 0.3639 | 0.6 | 700 | 4.0162 | 0.6915 | 0.6815 | 0.7562 | 0.6915 | | 0.1655 | 0.69 | 800 | 2.2680 | 0.6859 | 0.6715 | 0.7705 | 0.6859 | | 0.1534 | 0.77 | 900 | 2.7909 | 0.7951 | 0.7937 | 0.7959 | 0.7951 | | 0.288 | 0.86 | 1000 | 2.9443 | 0.7752 | 0.7751 | 0.7920 | 0.7752 | | 0.2261 | 0.95 | 1100 | 2.9976 | 0.7318 | 0.7267 | 0.7810 | 0.7318 | | 0.162 | 1.03 | 1200 | 2.4699 | 0.8063 | 0.8067 | 0.8096 | 0.8063 | | 0.0379 | 1.12 | 1300 | 2.6939 | 0.8051 | 0.8051 | 0.8051 | 0.8051 | | 0.1852 | 1.2 | 1400 | 3.9005 | 0.7031 | 0.6940 | 0.7669 | 0.7031 | | 0.1258 | 1.29 | 1500 | 2.6666 | 0.8023 | 0.8027 | 0.8042 | 0.8023 | | 0.1707 | 1.38 | 1600 | 2.8308 | 0.7892 | 0.7892 | 0.7892 | 0.7892 | | 0.0817 | 1.46 | 1700 | 3.6049 | 0.7573 | 0.7497 | 0.7700 | 0.7573 | | 0.3516 | 1.55 | 1800 | 2.6816 | 0.7772 | 0.7777 | 0.7846 | 0.7772 | | 0.5502 | 1.63 | 1900 | 2.2493 | 0.8131 | 0.8099 | 0.8203 | 0.8131 | | 0.1531 | 1.72 | 2000 | 3.2802 | 0.7417 | 0.7407 | 0.7645 | 0.7417 | | 0.1112 | 1.81 | 2100 | 1.9678 | 0.7748 | 0.7737 | 0.8010 | 0.7748 | | 0.1617 | 1.89 | 2200 | 3.0694 | 0.7501 | 0.7490 | 0.7746 | 0.7501 | | 0.1912 | 1.98 | 2300 | 3.2285 | 0.7529 | 0.7530 | 0.7659 | 0.7529 | | 0.2725 | 2.06 | 2400 | 3.0008 | 0.7800 | 0.7805 | 0.7826 | 0.7800 | | 0.1694 | 2.15 | 2500 | 3.5542 | 0.7290 | 0.7286 | 0.7459 | 0.7290 | | 0.1283 | 2.24 | 2600 | 4.4577 | 0.7003 | 0.6944 | 0.7466 | 0.7003 | | 0.1321 | 2.32 | 2700 | 3.1128 | 0.7350 | 0.7356 | 0.7411 | 0.7350 | | 0.0 | 2.41 | 2800 | 4.2938 | 0.7222 | 0.7149 | 0.7828 | 0.7222 | | 0.0871 | 2.49 | 2900 | 3.9750 | 0.7266 | 0.7237 | 0.7607 | 0.7266 | | 0.0952 | 2.58 | 3000 | 3.7697 | 0.7437 | 0.7424 | 0.7690 | 0.7437 | | 0.1034 | 2.67 | 3100 | 3.7283 | 0.7350 | 0.7312 | 0.7764 | 0.7350 | | 0.2425 | 2.75 | 3200 | 3.4196 | 0.7704 | 0.7707 | 0.7811 | 0.7704 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "Roberta-base-Rewritten-commit_messages_v2", "results": []}]}

ManojAlexender/Roberta-base-Rewritten-commit_messages_v2

null

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T04:17:05+00:00

null

# Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{}

YaMaMa421/autotrain-wje46-zcyb5

null

[ "arxiv:1910.09700", "region:us" ]

null

2024-05-01T04:17:52+00:00

text-generation

transformers

{}

anudaw/lambda-0-distilled-code-llama

null

[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:19:00+00:00

text-to-video

diffusers

# LLM-grounded Video Diffusion Models [Long Lian](https://tonylian.com/), [Baifeng Shi](https://bfshi.github.io/), [Adam Yala](https://www.adamyala.org/), [Trevor Darrell](https://people.eecs.berkeley.edu/~trevor/), [Boyi Li](https://sites.google.com/site/boyilics/home) at UC Berkeley/UCSF. **ICLR 2024**. [Project Page](https://llm-grounded-video-diffusion.github.io/) | [Related Project: LMD](https://llm-grounded-diffusion.github.io/) | [Citation](https://llm-grounded-video-diffusion.github.io/#citation) This model is based on [zeroscope](https://huggingface.co/cerspense/zeroscope_v2_576w) but with additional conditioning from bounding boxes in a [GLIGEN](https://gligen.github.io/) fashion. Similar to [LLM-grounded Diffusion (LMD)](https://llm-grounded-diffusion.github.io/), LLM-grounded Video Diffusion (LVD)'s boxes-to-video stage allows cross-attention-based bounding box conditioning, which uses Zeroscope off-the-shelf. This huggingface model offers an alternative: we train a GLIGEN model (i.e., transformer adapters) with Zeroscope's weights without the temporal transformers blocks on [SA-1B](https://ai.meta.com/datasets/segment-anything/), treating it as a SD v2.1 model that has been fine-tuned to 256x256 resolution. We then merge the adapters into Zeroscope to offer conditioning. The resulting model is in this hugginface model. This can be used with cross-attention-based conditioning or on its own, similar to [LMD+](https://github.com/TonyLianLong/LLM-groundedDiffusion). This can be used with LLM-based text-to-dynamic scene layout generator in LVD, or on its own as a video version of GLIGEN. ## Citation (LVD) If you use our work, model, or our implementation in this repo, or find them helpful, please consider giving a citation. ``` @article{lian2023llmgroundedvideo, title={LLM-grounded Video Diffusion Models}, author={Lian, Long and Shi, Baifeng and Yala, Adam and Darrell, Trevor and Li, Boyi}, journal={arXiv preprint arXiv:2309.17444}, year={2023}, } @article{lian2023llmgrounded, title={LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models}, author={Lian, Long and Li, Boyi and Yala, Adam and Darrell, Trevor}, journal={arXiv preprint arXiv:2305.13655}, year={2023} } ``` ## Citation (GLIGEN) The adapters in this model are trained in a mannar similar to training GLIGEN adapters. ``` @article{li2023gligen, title={GLIGEN: Open-Set Grounded Text-to-Image Generation}, author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae}, journal={CVPR}, year={2023} } ``` ## Citation (ModelScope) ModelScope is LVD's base model. ``` @article{wang2023modelscope, title={Modelscope text-to-video technical report}, author={Wang, Jiuniu and Yuan, Hangjie and Chen, Dayou and Zhang, Yingya and Wang, Xiang and Zhang, Shiwei}, journal={arXiv preprint arXiv:2308.06571}, year={2023} } @InProceedings{VideoFusion, author = {Luo, Zhengxiong and Chen, Dayou and Zhang, Yingya and Huang, Yan and Wang, Liang and Shen, Yujun and Zhao, Deli and Zhou, Jingren and Tan, Tieniu}, title = {VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023} } ``` ## LICENSE Zeroscope follows CC-BY-NC 4.0 license. The gligen adapters are trained on SA-1B, which follows [SA-1B license](https://ai.meta.com/datasets/segment-anything/).

{"tags": ["text-to-video"], "duplicated_from": "cerspense/zeroscope_v2_576w"}

longlian/text-to-video-lvd-zs

null

[ "diffusers", "text-to-video", "region:us" ]

null

2024-05-01T04:19:20+00:00

null

transformers

# Uploaded model - **Developed by:** Jacob1802 - **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)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

Jacob1802/newllama3-8b-oig-unsloth-merged

null

[ "transformers", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-05-01T04:20:22+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

nuebaek/komt_mistral_mss_generation

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-01T04:21:00+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

lunarsylph/mooncell_v43

null

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:22:10+00:00

text-generation

transformers

Step 2: Make the merge a little more Tiefighterish... 03/05/2024: Fixed config files / context issues. # D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp](https://huggingface.co/DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp) ## 🧩 Configuration ```yaml models: - model: KoboldAI/LLaMA2-13B-Tiefighter parameters: weight: 0.6 - model: DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp parameters: weight: 0.4 merge_method: linear dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-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"]) ```

{"tags": ["merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp"]}

DavidAU/D_AU-Tiefighter-Plus-OrcaMaid-V3-13B-32k-slerp

null

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "KoboldAI/LLaMA2-13B-Tiefighter", "DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp", "custom_code", "base_model:KoboldAI/LLaMA2-13B-Tiefighter", "base_model:DavidAU/D_AU-Tiefighter-OrcaMaid-V3-13B-32k-slerp", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:22:17+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

paulo037/stable-code-instruct-3b-syntetic-1000-10-epoch

null

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T04:22:35+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

nessa01macias/gemma-7b_sustainability-qa

null

[ "transformers", "pytorch", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:22:41+00:00

null

{}

vup2p/model_sn25_184

null

[ "region:us" ]

null

2024-05-01T04:23:04+00:00

null

{}

vup2p/model_sn25_187

null

[ "region:us" ]

null

2024-05-01T04:23:25+00:00

text-generation

transformers

# temp-1-distilled-code-llama This model is a fine-tuned version of [anudaw/temp-1-distilled-code-llama](https://huggingface.co/anudaw/temp-1-distilled-code-llama) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "anudaw/temp-1-distilled-code-llama", "model-index": [{"name": "temp-1-distilled-code-llama", "results": []}]}

anudaw/temp-1-distilled-code-llama

null

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:anudaw/temp-1-distilled-code-llama", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:25:24+00:00

text-generation

transformers

## Model description Sampling-based watermark distilled [Pythia 1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) using the KGW \$k=0, \gamma=0.25, \delta=1\$ watermarking strategy in the paper [On the Learnability of Watermarks for Language Models](https://arxiv.org/abs/2312.04469). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer", "pythia"]}

cygu/pythia-1.4b-sampling-watermark-distill-kgw-k0-gamma0.25-delta1

null

[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "pythia", "arxiv:2312.04469", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:29:28+00:00

fill-mask

transformers

# bert-base-multilingual-uncased-finetuned-urdu This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.0077 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5368 | 1.0 | 174 | 3.1854 | | 2.3333 | 2.0 | 348 | 2.9876 | | 2.2536 | 3.0 | 522 | 2.9018 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-multilingual-uncased", "model-index": [{"name": "bert-base-multilingual-uncased-finetuned-urdu", "results": []}]}

cxfajar197/bert-base-multilingual-uncased-finetuned-urdu

null

[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:bert-base-multilingual-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-05-01T04:30:48+00:00

null

{}

sayakpaul/sdxl-orpo-large-beta_orpo-0.05-beta_inner-250-lr-5e-6-lnoise-0.2

null

[ "region:us" ]

null

2024-05-01T04:30:49+00:00

text-generation

transformers

# Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "transformers", "tags": []}

nuebaek/komt_mistral_insta_generation

null

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

null

2024-05-01T04:32:16+00:00

text-generation

transformers

# temp-0.5-distilled-code-llama This model is a fine-tuned version of [anudaw/temp-0.5-distilled-code-llama](https://huggingface.co/anudaw/temp-0.5-distilled-code-llama) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "anudaw/temp-0.5-distilled-code-llama", "model-index": [{"name": "temp-0.5-distilled-code-llama", "results": []}]}

anudaw/temp-0.5-distilled-code-llama

null

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "base_model:anudaw/temp-0.5-distilled-code-llama", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:32:38+00:00

null

{}

mompie29/my_awesome_qa_model

null

[ "region:us" ]

null

2024-05-01T04:34:06+00:00

text-generation

transformers

## Model description Sampling-based watermark distilled [Pythia 1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) using the KGW \$k=0, \gamma=0.25, \delta=2\$ watermarking strategy in the paper [On the Learnability of Watermarks for Language Models](https://arxiv.org/abs/2312.04469). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer", "pythia"]}

cygu/pythia-1.4b-sampling-watermark-distill-kgw-k0-gamma0.25-delta2

null

[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "pythia", "arxiv:2312.04469", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:34:23+00:00

null

{}

Sidhu111/my_awesome_qa_model

null

[ "region:us" ]

null

2024-05-01T04:36:07+00:00

text-generation

transformers

{}

NadunAnjanaka/Llama-2-7b-chat-Counsellor

null

[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space" ]

null

2024-05-01T04:37:16+00:00

null

peft

# distilbert-base-uncased-finetuned-qqp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6501 - Accuracy: 0.624 - F1: 0.0 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:| | No log | 1.0 | 282 | 0.6501 | 0.624 | 0.0 | | 0.6505 | 2.0 | 564 | 0.6427 | 0.624 | 0.0 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-qqp", "results": []}]}

quanla/distilbert-base-uncased-finetuned-qqp

null

[ "peft", "tensorboard", "safetensors", "distilbert", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "region:us" ]

null

2024-05-01T04:39:48+00:00

text-generation

transformers

{}

lakshayt/tic_tac_toe_llama_final

null

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]

null

2024-05-01T04:42:53+00:00

text-generation

transformers

## Model description Sampling-based watermark distilled [Pythia 1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) using the KGW \$k=2, \gamma=0.25, \delta=2\$ watermarking strategy in the paper [On the Learnability of Watermarks for Language Models](https://arxiv.org/abs/2312.04469). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 1.0 - ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer", "pythia"]}

cygu/pythia-1.4b-sampling-watermark-distill-kgw-k2-gamma0.25-delta2

null

[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "pythia", "arxiv:2312.04469", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-05-01T04:43:42+00:00

null

diffusers

# Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional audio generation of music in the genre Electronic ## Usage <pre> from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("kmpartner/audio-diffusion-electronic") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) </pre>

{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-audio-generation", "diffusion-models-class"]}

kmpartner/audio-diffusion-electronic

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[ "diffusers", "safetensors", "pytorch", "unconditional-audio-generation", "diffusion-models-class", "license:mit", "diffusers:AudioDiffusionPipeline", "region:us" ]

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2024-05-01T04:43:55+00:00