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ericmattmann/whisperX-endpoint
ericmattmann
2023-11-29T17:54:03Z
0
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[ "endpoints_compatible", "region:us" ]
2023-11-29T17:54:03Z
2023-11-29T16:32:58.000Z
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ericmattmann/whisperX-endpoint
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chatchatchat/xxxxxx
chatchatchat
2023-11-29T16:33:14Z
0
0
null
[ "region:us" ]
2023-11-29T16:33:14Z
2023-11-29T16:33:14.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
chatchatchat/xxxxxx
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emilstabil/DanSumT5-largeV_32599
emilstabil
2023-11-29T16:34:40Z
0
0
null
[ "region:us" ]
2023-11-29T16:34:40Z
2023-11-29T16:34:40.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-largeV_32599
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scotta01/alan_pics
scotta01
2023-11-29T16:35:21Z
0
0
null
[ "region:us" ]
2023-11-29T16:35:21Z
2023-11-29T16:35:21.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
scotta01/alan_pics
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eszterm/eszterai_1
eszterm
2023-11-29T16:36:27Z
0
0
null
[ "region:us" ]
2023-11-29T16:36:27Z
2023-11-29T16:36:27.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
eszterm/eszterai_1
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RitaGao/Llama-2-7b-chat-finetune
RitaGao
2023-11-29T20:41:10Z
0
0
null
[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T20:41:10Z
2023-11-29T16:37:51.000Z
null
null
--- license: apache-2.0 ---
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
RitaGao/Llama-2-7b-chat-finetune
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nlewins/whisper-base-gen2
nlewins
2023-11-30T01:12:30Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ceb", "dataset:google/fleurs", "base_model:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2023-11-30T01:12:30Z
2023-11-29T16:39:33.000Z
null
null
--- language: - ceb license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - google/fleurs model-index: - name: Whisper finetuned for ceb 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. --> # Whisper finetuned for ceb This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Fleurs Ceb subset 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: 1e-05 - train_batch_size: 16 - 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: 100 - training_steps: 20000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
null
transformers
automatic-speech-recognition
null
null
null
null
null
null
null
null
null
nlewins/whisper-base-gen2
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project2you/quiz-llm-test
project2you
2023-11-29T16:39:37Z
0
0
null
[ "region:us" ]
2023-11-29T16:39:37Z
2023-11-29T16:39:37.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
project2you/quiz-llm-test
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Mlxa/augmented_stories
Mlxa
2023-11-29T16:40:37Z
0
0
null
[ "transformers", "safetensors", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-29T16:40:37Z
2023-11-29T16:40:32.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
Mlxa/augmented_stories
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haryoaw/scenario-kd-from-post-finetune-silver-div-2-data-tyqiangz-multilingual-sentiment
haryoaw
2023-11-29T16:45:55Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:haryoaw/teacher-tyqianz-sentiment-malay", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T16:45:55Z
2023-11-29T16:44:55.000Z
null
null
--- license: mit base_model: haryoaw/teacher-tyqianz-sentiment-malay tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-kd-from-post-finetune-silver-div-2-data-tyqiangz-multilingual-sentiment 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. --> # scenario-kd-from-post-finetune-silver-div-2-data-tyqiangz-multilingual-sentiment This model is a fine-tuned version of [haryoaw/teacher-tyqianz-sentiment-malay](https://huggingface.co/haryoaw/teacher-tyqianz-sentiment-malay) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 6.0831 - Accuracy: 0.7403 - F1: 0.7149 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 6.0544 | 0.7045 | 0.6719 | | No log | 1.36 | 200 | 5.6180 | 0.7154 | 0.6903 | | No log | 2.04 | 300 | 6.3827 | 0.7055 | 0.6858 | | No log | 2.72 | 400 | 5.7204 | 0.7353 | 0.7022 | | 4.731 | 3.4 | 500 | 6.1281 | 0.7045 | 0.6752 | | 4.731 | 4.08 | 600 | 7.7426 | 0.6925 | 0.6691 | | 4.731 | 4.76 | 700 | 5.8761 | 0.7174 | 0.6975 | | 4.731 | 5.44 | 800 | 6.2985 | 0.7264 | 0.7102 | | 4.731 | 6.12 | 900 | 6.5578 | 0.7075 | 0.6714 | | 1.8027 | 6.8 | 1000 | 5.6909 | 0.7104 | 0.6890 | | 1.8027 | 7.48 | 1100 | 7.4650 | 0.7035 | 0.6804 | | 1.8027 | 8.16 | 1200 | 6.0930 | 0.7124 | 0.6806 | | 1.8027 | 8.84 | 1300 | 6.2906 | 0.7204 | 0.7023 | | 1.8027 | 9.52 | 1400 | 6.5270 | 0.7254 | 0.6998 | | 1.3154 | 10.2 | 1500 | 6.6003 | 0.7254 | 0.7052 | | 1.3154 | 10.88 | 1600 | 7.1176 | 0.7025 | 0.6821 | | 1.3154 | 11.56 | 1700 | 5.8288 | 0.7254 | 0.7079 | | 1.3154 | 12.24 | 1800 | 6.6204 | 0.7134 | 0.6935 | | 1.3154 | 12.93 | 1900 | 6.0641 | 0.7104 | 0.6960 | | 0.9883 | 13.61 | 2000 | 6.7124 | 0.7313 | 0.6981 | | 0.9883 | 14.29 | 2100 | 6.7416 | 0.7333 | 0.7144 | | 0.9883 | 14.97 | 2200 | 5.9150 | 0.7373 | 0.7178 | | 0.9883 | 15.65 | 2300 | 5.6811 | 0.7313 | 0.7158 | | 0.9883 | 16.33 | 2400 | 6.4696 | 0.7294 | 0.7088 | | 0.858 | 17.01 | 2500 | 7.2706 | 0.7184 | 0.6938 | | 0.858 | 17.69 | 2600 | 6.2470 | 0.7174 | 0.6918 | | 0.858 | 18.37 | 2700 | 6.1986 | 0.7313 | 0.7200 | | 0.858 | 19.05 | 2800 | 5.8726 | 0.7254 | 0.7030 | | 0.858 | 19.73 | 2900 | 6.5129 | 0.7224 | 0.7029 | | 0.6932 | 20.41 | 3000 | 5.9316 | 0.7323 | 0.7025 | | 0.6932 | 21.09 | 3100 | 6.9943 | 0.7144 | 0.6915 | | 0.6932 | 21.77 | 3200 | 6.2154 | 0.7164 | 0.6908 | | 0.6932 | 22.45 | 3300 | 6.1261 | 0.7174 | 0.6981 | | 0.6932 | 23.13 | 3400 | 7.4358 | 0.7104 | 0.6888 | | 0.5378 | 23.81 | 3500 | 5.6513 | 0.7393 | 0.7126 | | 0.5378 | 24.49 | 3600 | 6.7542 | 0.7313 | 0.7059 | | 0.5378 | 25.17 | 3700 | 5.9334 | 0.7224 | 0.7011 | | 0.5378 | 25.85 | 3800 | 7.2357 | 0.7254 | 0.6949 | | 0.5378 | 26.53 | 3900 | 6.5373 | 0.7264 | 0.7044 | | 0.4586 | 27.21 | 4000 | 6.0928 | 0.7274 | 0.6980 | | 0.4586 | 27.89 | 4100 | 6.1242 | 0.7254 | 0.7043 | | 0.4586 | 28.57 | 4200 | 6.0831 | 0.7403 | 0.7149 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
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haryoaw/scenario-kd-from-post-finetune-silver-div-2-data-tyqiangz-multilingual-sentiment
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow0
FounderOfHuggingface
2023-11-29T16:46:01Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T16:46:01Z
2023-11-29T16:45:57.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow0
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haryoaw/scenario-kd-from-pre-finetune-gold-silver-div-2-data-tyqiangz-multilingual-senti
haryoaw
2023-11-29T16:47:02Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T16:47:02Z
2023-11-29T16:46:07.000Z
null
null
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-kd-from-pre-finetune-gold-silver-div-2-data-tyqiangz-multilingual-senti 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. --> # scenario-kd-from-pre-finetune-gold-silver-div-2-data-tyqiangz-multilingual-senti This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 10.7980 - Accuracy: 0.7015 - F1: 0.6775 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 11.1602 | 0.6179 | 0.5375 | | No log | 1.36 | 200 | 9.8204 | 0.6358 | 0.6160 | | No log | 2.04 | 300 | 9.2062 | 0.6607 | 0.5991 | | No log | 2.72 | 400 | 8.0583 | 0.6915 | 0.6600 | | 10.9216 | 3.4 | 500 | 8.7448 | 0.6995 | 0.6594 | | 10.9216 | 4.08 | 600 | 7.8670 | 0.6806 | 0.6628 | | 10.9216 | 4.76 | 700 | 8.2420 | 0.7035 | 0.6661 | | 10.9216 | 5.44 | 800 | 9.5560 | 0.6915 | 0.6717 | | 10.9216 | 6.12 | 900 | 9.1413 | 0.6806 | 0.6335 | | 5.0534 | 6.8 | 1000 | 9.7632 | 0.7025 | 0.6844 | | 5.0534 | 7.48 | 1100 | 10.7509 | 0.6726 | 0.6492 | | 5.0534 | 8.16 | 1200 | 9.9301 | 0.6945 | 0.6664 | | 5.0534 | 8.84 | 1300 | 9.8570 | 0.6965 | 0.6813 | | 5.0534 | 9.52 | 1400 | 10.7200 | 0.7055 | 0.6780 | | 2.8475 | 10.2 | 1500 | 9.7941 | 0.6945 | 0.6708 | | 2.8475 | 10.88 | 1600 | 10.2912 | 0.6955 | 0.6760 | | 2.8475 | 11.56 | 1700 | 10.2525 | 0.6965 | 0.6663 | | 2.8475 | 12.24 | 1800 | 10.2947 | 0.7134 | 0.6877 | | 2.8475 | 12.93 | 1900 | 10.2570 | 0.7065 | 0.6894 | | 2.0956 | 13.61 | 2000 | 10.7868 | 0.7134 | 0.6972 | | 2.0956 | 14.29 | 2100 | 11.4015 | 0.6836 | 0.6684 | | 2.0956 | 14.97 | 2200 | 10.5019 | 0.7065 | 0.6770 | | 2.0956 | 15.65 | 2300 | 11.0888 | 0.6915 | 0.6603 | | 2.0956 | 16.33 | 2400 | 10.2803 | 0.7174 | 0.6934 | | 1.6305 | 17.01 | 2500 | 9.8610 | 0.7154 | 0.7002 | | 1.6305 | 17.69 | 2600 | 11.2066 | 0.6975 | 0.6801 | | 1.6305 | 18.37 | 2700 | 10.4928 | 0.6935 | 0.6819 | | 1.6305 | 19.05 | 2800 | 10.5211 | 0.7085 | 0.6631 | | 1.6305 | 19.73 | 2900 | 9.9236 | 0.6995 | 0.6877 | | 1.3224 | 20.41 | 3000 | 11.5889 | 0.6905 | 0.6532 | | 1.3224 | 21.09 | 3100 | 10.7814 | 0.7085 | 0.6935 | | 1.3224 | 21.77 | 3200 | 10.4818 | 0.7025 | 0.6760 | | 1.3224 | 22.45 | 3300 | 10.3883 | 0.7164 | 0.6962 | | 1.3224 | 23.13 | 3400 | 9.9823 | 0.7134 | 0.6847 | | 1.0342 | 23.81 | 3500 | 10.3160 | 0.7065 | 0.6877 | | 1.0342 | 24.49 | 3600 | 10.6043 | 0.7075 | 0.6827 | | 1.0342 | 25.17 | 3700 | 10.8022 | 0.7035 | 0.6752 | | 1.0342 | 25.85 | 3800 | 10.8664 | 0.6995 | 0.6679 | | 1.0342 | 26.53 | 3900 | 10.5008 | 0.7134 | 0.6891 | | 0.8143 | 27.21 | 4000 | 10.7980 | 0.7015 | 0.6775 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
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haryoaw/scenario-kd-from-pre-finetune-gold-silver-div-2-data-tyqiangz-multilingual-senti
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xz-huggingface-0/llama2-7b-sft-lora-20231129-32
xz-huggingface-0
2023-11-29T19:53:55Z
0
0
null
[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]
2023-11-29T19:53:55Z
2023-11-29T16:46:38.000Z
null
null
--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: llama2-7b-sft-lora-20231129-32 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. --> # llama2-7b-sft-lora-20231129-32 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 128 - total_train_batch_size: 4096 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4444 | 0.69 | 35 | 1.4442 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
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xz-huggingface-0/llama2-7b-sft-lora-20231129-32
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emilstabil/DanSumT5-largeV_79725
emilstabil
2023-11-29T16:48:13Z
0
0
null
[ "region:us" ]
2023-11-29T16:48:13Z
2023-11-29T16:48:13.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
emilstabil/DanSumT5-largeV_79725
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mtKhaos/stablecode-completion-alpha-3b-phuire-alpha-001
mtKhaos
2023-11-29T16:48:51Z
0
0
null
[ "license:other", "region:us" ]
2023-11-29T16:48:51Z
2023-11-29T16:48:51.000Z
null
null
--- license: other license_name: gplv3 license_link: https://github.com/Phuire-Research/Stratimux/blob/main/LICENSE ---
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mtKhaos/stablecode-completion-alpha-3b-phuire-alpha-001
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xz-huggingface-0/llama2-7b-sft-lora-20231129-52
xz-huggingface-0
2023-11-29T19:56:57Z
0
0
null
[ "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]
2023-11-29T19:56:57Z
2023-11-29T16:49:58.000Z
null
null
--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: llama2-7b-sft-lora-20231129-52 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. --> # llama2-7b-sft-lora-20231129-52 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4442 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 128 - total_train_batch_size: 4096 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4444 | 0.69 | 35 | 1.4442 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.1 - Datasets 2.14.6 - Tokenizers 0.14.1
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xz-huggingface-0/llama2-7b-sft-lora-20231129-52
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fd3v/DiabloGPT-medium-red
fd3v
2023-11-29T16:52:08Z
0
0
null
[ "region:us" ]
2023-11-29T16:52:08Z
2023-11-29T16:52:08.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
fd3v/DiabloGPT-medium-red
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ag2003/speecht5_finetuned_voxpopuli_french
ag2003
2023-11-29T20:14:32Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "endpoints_compatible", "region:us" ]
2023-11-29T20:14:32Z
2023-11-29T16:53:18.000Z
null
null
Entry not found
null
transformers
text-to-audio
null
null
null
null
null
null
null
null
null
ag2003/speecht5_finetuned_voxpopuli_french
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow1
FounderOfHuggingface
2023-11-29T16:54:28Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T16:54:28Z
2023-11-29T16:54:25.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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peft
null
null
null
null
null
null
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow1
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Realgon/roberta_sst5_padding20model
Realgon
2023-11-29T17:33:06Z
0
0
null
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T17:33:06Z
2023-11-29T16:56:49.000Z
null
null
--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta_sst5_padding20model 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. --> # roberta_sst5_padding20model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8273 - Accuracy: 0.5688 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.2307 | 1.0 | 534 | 1.2559 | 0.4489 | | 0.9732 | 2.0 | 1068 | 1.0476 | 0.5557 | | 0.8222 | 3.0 | 1602 | 1.0850 | 0.5606 | | 0.6916 | 4.0 | 2136 | 1.2357 | 0.5434 | | 0.5447 | 5.0 | 2670 | 1.3221 | 0.5480 | | 0.4339 | 6.0 | 3204 | 1.4764 | 0.5575 | | 0.3467 | 7.0 | 3738 | 1.7786 | 0.5430 | | 0.2612 | 8.0 | 4272 | 1.8505 | 0.5529 | | 0.2177 | 9.0 | 4806 | 2.2441 | 0.5579 | | 0.1935 | 10.0 | 5340 | 2.5254 | 0.5629 | | 0.1837 | 11.0 | 5874 | 2.8216 | 0.5570 | | 0.1332 | 12.0 | 6408 | 2.9686 | 0.5593 | | 0.1321 | 13.0 | 6942 | 3.2822 | 0.5561 | | 0.1078 | 14.0 | 7476 | 3.3137 | 0.5493 | | 0.08 | 15.0 | 8010 | 3.4707 | 0.5629 | | 0.0673 | 16.0 | 8544 | 3.6022 | 0.5661 | | 0.0566 | 17.0 | 9078 | 3.6736 | 0.5561 | | 0.0464 | 18.0 | 9612 | 3.7873 | 0.5597 | | 0.0365 | 19.0 | 10146 | 3.8058 | 0.5706 | | 0.0313 | 20.0 | 10680 | 3.8273 | 0.5688 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1 - Datasets 2.12.0 - Tokenizers 0.13.3
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
Realgon/roberta_sst5_padding20model
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unsloth/llama-2-7b
unsloth
2023-11-29T17:07:34Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T17:07:34Z
2023-11-29T16:57:02.000Z
null
null
--- license: llama2 ---
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
unsloth/llama-2-7b
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DangFutures/7openlaw
DangFutures
2023-11-29T17:01:39Z
0
0
null
[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T17:01:39Z
2023-11-29T16:57:58.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
DangFutures/7openlaw
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haryoaw/scenario-kd-from-pre-finetune-silver-div-2-data-tyqiangz-multilingual-sentiments
haryoaw
2023-11-29T16:59:55Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T16:59:55Z
2023-11-29T16:58:58.000Z
null
null
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-kd-from-pre-finetune-silver-div-2-data-tyqiangz-multilingual-sentiments 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. --> # scenario-kd-from-pre-finetune-silver-div-2-data-tyqiangz-multilingual-sentiments This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 9.5000 - Accuracy: 0.6975 - F1: 0.6826 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 9.5664 | 0.6209 | 0.5597 | | No log | 1.36 | 200 | 7.7629 | 0.6706 | 0.6497 | | No log | 2.04 | 300 | 8.6864 | 0.6498 | 0.5308 | | No log | 2.72 | 400 | 8.0229 | 0.6428 | 0.6096 | | 10.2449 | 3.4 | 500 | 7.3063 | 0.6876 | 0.6512 | | 10.2449 | 4.08 | 600 | 8.1071 | 0.6746 | 0.6571 | | 10.2449 | 4.76 | 700 | 7.3352 | 0.6975 | 0.6596 | | 10.2449 | 5.44 | 800 | 7.7220 | 0.7055 | 0.6845 | | 10.2449 | 6.12 | 900 | 8.5675 | 0.6836 | 0.6527 | | 4.6793 | 6.8 | 1000 | 8.8129 | 0.6856 | 0.6623 | | 4.6793 | 7.48 | 1100 | 7.6661 | 0.7174 | 0.6979 | | 4.6793 | 8.16 | 1200 | 8.8933 | 0.7085 | 0.6564 | | 4.6793 | 8.84 | 1300 | 8.3809 | 0.7035 | 0.6805 | | 4.6793 | 9.52 | 1400 | 9.0804 | 0.7085 | 0.6874 | | 2.6249 | 10.2 | 1500 | 7.9289 | 0.6896 | 0.6652 | | 2.6249 | 10.88 | 1600 | 8.8425 | 0.6955 | 0.6665 | | 2.6249 | 11.56 | 1700 | 10.4711 | 0.6746 | 0.6539 | | 2.6249 | 12.24 | 1800 | 9.2883 | 0.7065 | 0.6910 | | 2.6249 | 12.93 | 1900 | 7.9852 | 0.6836 | 0.6657 | | 1.9302 | 13.61 | 2000 | 8.8038 | 0.6955 | 0.6806 | | 1.9302 | 14.29 | 2100 | 10.6497 | 0.6736 | 0.6546 | | 1.9302 | 14.97 | 2200 | 8.8982 | 0.6935 | 0.6686 | | 1.9302 | 15.65 | 2300 | 9.1197 | 0.6876 | 0.6710 | | 1.9302 | 16.33 | 2400 | 9.2456 | 0.6886 | 0.6604 | | 1.408 | 17.01 | 2500 | 8.7997 | 0.6955 | 0.6738 | | 1.408 | 17.69 | 2600 | 9.5000 | 0.6975 | 0.6826 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
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transformers
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haryoaw/scenario-kd-from-pre-finetune-silver-div-2-data-tyqiangz-multilingual-sentiments
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SkySyrup/RpBird-Yi-34B-200k-quant
SkySyrup
2023-11-29T17:00:38Z
0
0
null
[ "license:other", "region:us" ]
2023-11-29T17:00:38Z
2023-11-29T16:59:39.000Z
null
null
--- license: other license_name: yi license_link: LICENSE ---
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null
SkySyrup/RpBird-Yi-34B-200k-quant
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deepinv/DRUNet
deepinv
2023-11-29T17:06:17Z
0
0
null
[ "license:bsd-3-clause", "region:us" ]
2023-11-29T17:06:17Z
2023-11-29T17:01:12.000Z
null
null
--- license: bsd-3-clause ---
null
null
null
null
null
null
null
null
null
null
null
null
deepinv/DRUNet
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jamiehudson/706_SetFit_paraphrase_A100
jamiehudson
2023-11-29T17:02:10Z
0
0
null
[ "sentence-transformers", "safetensors", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
2023-11-29T17:02:10Z
2023-11-29T17:01:37.000Z
null
null
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # jamiehudson/706_SetFit_paraphrase_A100 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("jamiehudson/706_SetFit_paraphrase_A100") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
null
sentence-transformers
text-classification
null
null
null
null
null
null
null
null
null
jamiehudson/706_SetFit_paraphrase_A100
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow2
FounderOfHuggingface
2023-11-29T17:02:55Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T17:02:55Z
2023-11-29T17:02:52.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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peft
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow2
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Jungwonchang/wav2vec2-large-mms-1b-all-FULL-SPGI-xs
Jungwonchang
2023-11-29T18:22:22Z
0
0
null
[ "region:us" ]
2023-11-29T18:22:22Z
2023-11-29T17:06:21.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
Jungwonchang/wav2vec2-large-mms-1b-all-FULL-SPGI-xs
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KoladeOdunope/mistral-fin
KoladeOdunope
2023-11-29T17:11:03Z
0
0
null
[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T17:11:03Z
2023-11-29T17:07:04.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
KoladeOdunope/mistral-fin
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MNQuinn/Reinforce-CartPole-1
MNQuinn
2023-11-29T17:07:42Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
2023-11-29T17:07:42Z
2023-11-29T17:07:31.000Z
null
null
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 401.90 +/- 192.99 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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reinforcement-learning
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MNQuinn/Reinforce-CartPole-1
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joaobfm/ryan_ia_dataset_C
joaobfm
2023-11-29T17:09:04Z
0
0
null
[ "region:us" ]
2023-11-29T17:09:04Z
2023-11-29T17:08:39.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
joaobfm/ryan_ia_dataset_C
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LarryRArnold/PunDetection
LarryRArnold
2023-11-29T17:08:43Z
0
0
null
[ "region:us" ]
2023-11-29T17:08:43Z
2023-11-29T17:08:43.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
LarryRArnold/PunDetection
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haryoaw/scenario-kd-from-scratch-gold-silver-data-tyqiangz-multilingual-sentiments-malay
haryoaw
2023-11-29T17:10:07Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T17:10:07Z
2023-11-29T17:09:14.000Z
null
null
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-kd-from-scratch-gold-silver-data-tyqiangz-multilingual-sentiments-malay 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. --> # scenario-kd-from-scratch-gold-silver-data-tyqiangz-multilingual-sentiments-malay This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 17.6857 - Accuracy: 0.5980 - F1: 0.5654 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 14.0474 | 0.4905 | 0.3316 | | No log | 1.36 | 200 | 12.2834 | 0.5453 | 0.4729 | | No log | 2.04 | 300 | 12.4374 | 0.5861 | 0.4237 | | No log | 2.72 | 400 | 13.2619 | 0.5741 | 0.5227 | | 13.7796 | 3.4 | 500 | 12.1854 | 0.6129 | 0.5731 | | 13.7796 | 4.08 | 600 | 12.8164 | 0.6040 | 0.5276 | | 13.7796 | 4.76 | 700 | 12.4884 | 0.6219 | 0.5756 | | 13.7796 | 5.44 | 800 | 12.8795 | 0.6438 | 0.5998 | | 13.7796 | 6.12 | 900 | 14.1442 | 0.6020 | 0.5644 | | 7.9325 | 6.8 | 1000 | 12.7533 | 0.6418 | 0.5732 | | 7.9325 | 7.48 | 1100 | 15.2445 | 0.6189 | 0.5581 | | 7.9325 | 8.16 | 1200 | 14.9523 | 0.6229 | 0.5235 | | 7.9325 | 8.84 | 1300 | 13.7360 | 0.6398 | 0.6106 | | 7.9325 | 9.52 | 1400 | 15.1393 | 0.6318 | 0.5720 | | 4.2359 | 10.2 | 1500 | 15.9783 | 0.6259 | 0.5741 | | 4.2359 | 10.88 | 1600 | 14.7346 | 0.6418 | 0.5991 | | 4.2359 | 11.56 | 1700 | 15.2632 | 0.6448 | 0.6049 | | 4.2359 | 12.24 | 1800 | 17.0193 | 0.6259 | 0.5391 | | 4.2359 | 12.93 | 1900 | 16.8662 | 0.6338 | 0.5390 | | 2.918 | 13.61 | 2000 | 16.0299 | 0.6259 | 0.5909 | | 2.918 | 14.29 | 2100 | 15.7952 | 0.6398 | 0.5860 | | 2.918 | 14.97 | 2200 | 16.8918 | 0.6279 | 0.5861 | | 2.918 | 15.65 | 2300 | 17.1674 | 0.6199 | 0.5401 | | 2.918 | 16.33 | 2400 | 15.7054 | 0.6488 | 0.6055 | | 2.1481 | 17.01 | 2500 | 18.5495 | 0.6229 | 0.5230 | | 2.1481 | 17.69 | 2600 | 17.0136 | 0.6209 | 0.5990 | | 2.1481 | 18.37 | 2700 | 16.1657 | 0.6517 | 0.6189 | | 2.1481 | 19.05 | 2800 | 16.8711 | 0.6358 | 0.5855 | | 2.1481 | 19.73 | 2900 | 17.0981 | 0.6199 | 0.5547 | | 2.2033 | 20.41 | 3000 | 14.4731 | 0.6547 | 0.6180 | | 2.2033 | 21.09 | 3100 | 15.9588 | 0.6219 | 0.5892 | | 2.2033 | 21.77 | 3200 | 14.9411 | 0.6587 | 0.5949 | | 2.2033 | 22.45 | 3300 | 14.4769 | 0.6577 | 0.6194 | | 2.2033 | 23.13 | 3400 | 15.6655 | 0.6498 | 0.5920 | | 1.7483 | 23.81 | 3500 | 16.0186 | 0.6408 | 0.5611 | | 1.7483 | 24.49 | 3600 | 16.1892 | 0.6299 | 0.5984 | | 1.7483 | 25.17 | 3700 | 15.2595 | 0.6537 | 0.6043 | | 1.7483 | 25.85 | 3800 | 15.8051 | 0.6478 | 0.5952 | | 1.7483 | 26.53 | 3900 | 17.0388 | 0.6070 | 0.5848 | | 1.5048 | 27.21 | 4000 | 15.9968 | 0.6229 | 0.5960 | | 1.5048 | 27.89 | 4100 | 15.2344 | 0.6577 | 0.6054 | | 1.5048 | 28.57 | 4200 | 15.1395 | 0.6348 | 0.5959 | | 1.5048 | 29.25 | 4300 | 16.6482 | 0.6269 | 0.6032 | | 1.5048 | 29.93 | 4400 | 16.4803 | 0.6388 | 0.5881 | | 1.5532 | 30.61 | 4500 | 14.8284 | 0.6667 | 0.6189 | | 1.5532 | 31.29 | 4600 | 14.4655 | 0.6498 | 0.5978 | | 1.5532 | 31.97 | 4700 | 17.3168 | 0.6189 | 0.5960 | | 1.5532 | 32.65 | 4800 | 17.6857 | 0.5980 | 0.5654 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
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haryoaw/scenario-kd-from-scratch-gold-silver-data-tyqiangz-multilingual-sentiments-malay
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teddyschoenfeld/finetuning-sentiment-model-3000-samples
teddyschoenfeld
2023-11-29T17:51:30Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "endpoints_compatible", "region:us" ]
2023-11-29T17:51:30Z
2023-11-29T17:10:53.000Z
null
null
Entry not found
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
teddyschoenfeld/finetuning-sentiment-model-3000-samples
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow3
FounderOfHuggingface
2023-11-29T17:11:23Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T17:11:23Z
2023-11-29T17:11:19.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow3
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cpriya117/finetuning-sentiment-model-3000-samples
cpriya117
2023-11-29T17:11:34Z
0
0
null
[ "region:us" ]
2023-11-29T17:11:34Z
2023-11-29T17:11:34.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
cpriya117/finetuning-sentiment-model-3000-samples
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Tsuinzues/bills
Tsuinzues
2023-11-29T17:13:00Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-29T17:13:00Z
2023-11-29T17:12:45.000Z
null
null
--- license: openrail ---
null
null
null
null
null
null
null
null
null
null
null
null
Tsuinzues/bills
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Chinxian1121/llama-2-7b-chat-chinxian
Chinxian1121
2023-11-29T17:22:17Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-29T17:22:17Z
2023-11-29T17:15:30.000Z
null
null
--- license: apache-2.0 ---
null
null
null
null
null
null
null
null
null
null
null
null
Chinxian1121/llama-2-7b-chat-chinxian
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rizkaazmira/sidrap-7b-v1-gguf
rizkaazmira
2023-11-29T17:25:22Z
0
0
null
[ "gguf", "region:us" ]
2023-11-29T17:25:22Z
2023-11-29T17:19:18.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
rizkaazmira/sidrap-7b-v1-gguf
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haryoaw/scenario-kd-from-scratch-silver-data-tyqiangz-multilingual-sentiments-malay-mode
haryoaw
2023-11-29T17:20:37Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T17:20:37Z
2023-11-29T17:19:42.000Z
null
null
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-kd-from-scratch-silver-data-tyqiangz-multilingual-sentiments-malay-mode 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. --> # scenario-kd-from-scratch-silver-data-tyqiangz-multilingual-sentiments-malay-mode This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 14.5594 - Accuracy: 0.6080 - F1: 0.5395 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 13.6758 | 0.4766 | 0.3271 | | No log | 1.36 | 200 | 11.9485 | 0.5303 | 0.3802 | | No log | 2.04 | 300 | 11.7084 | 0.5622 | 0.4064 | | No log | 2.72 | 400 | 11.1698 | 0.5821 | 0.5355 | | 12.9548 | 3.4 | 500 | 10.4061 | 0.6249 | 0.5964 | | 12.9548 | 4.08 | 600 | 11.3083 | 0.6209 | 0.5827 | | 12.9548 | 4.76 | 700 | 11.9240 | 0.6209 | 0.5794 | | 12.9548 | 5.44 | 800 | 14.9044 | 0.5771 | 0.5038 | | 12.9548 | 6.12 | 900 | 12.3307 | 0.6308 | 0.5696 | | 5.7647 | 6.8 | 1000 | 11.9038 | 0.6308 | 0.5862 | | 5.7647 | 7.48 | 1100 | 12.3580 | 0.6368 | 0.6095 | | 5.7647 | 8.16 | 1200 | 13.7124 | 0.6289 | 0.5518 | | 5.7647 | 8.84 | 1300 | 12.8972 | 0.6199 | 0.5953 | | 5.7647 | 9.52 | 1400 | 14.2245 | 0.6040 | 0.5771 | | 2.8315 | 10.2 | 1500 | 15.7431 | 0.5980 | 0.5671 | | 2.8315 | 10.88 | 1600 | 14.1603 | 0.6199 | 0.5619 | | 2.8315 | 11.56 | 1700 | 12.6210 | 0.6428 | 0.6158 | | 2.8315 | 12.24 | 1800 | 15.0500 | 0.6119 | 0.5477 | | 2.8315 | 12.93 | 1900 | 13.8910 | 0.6279 | 0.5400 | | 2.0743 | 13.61 | 2000 | 14.2150 | 0.6279 | 0.6067 | | 2.0743 | 14.29 | 2100 | 13.1529 | 0.6478 | 0.6087 | | 2.0743 | 14.97 | 2200 | 13.5424 | 0.6358 | 0.6119 | | 2.0743 | 15.65 | 2300 | 13.8704 | 0.6289 | 0.5959 | | 2.0743 | 16.33 | 2400 | 14.4637 | 0.6119 | 0.5909 | | 1.7132 | 17.01 | 2500 | 13.2986 | 0.6328 | 0.5824 | | 1.7132 | 17.69 | 2600 | 13.5977 | 0.6318 | 0.6104 | | 1.7132 | 18.37 | 2700 | 12.9483 | 0.6517 | 0.6299 | | 1.7132 | 19.05 | 2800 | 13.6398 | 0.6289 | 0.5999 | | 1.7132 | 19.73 | 2900 | 13.0128 | 0.6498 | 0.6151 | | 1.5334 | 20.41 | 3000 | 13.3749 | 0.6478 | 0.6017 | | 1.5334 | 21.09 | 3100 | 13.8551 | 0.6428 | 0.6138 | | 1.5334 | 21.77 | 3200 | 13.7106 | 0.6348 | 0.5988 | | 1.5334 | 22.45 | 3300 | 13.3699 | 0.6378 | 0.6068 | | 1.5334 | 23.13 | 3400 | 13.0654 | 0.6458 | 0.5763 | | 1.3343 | 23.81 | 3500 | 12.9332 | 0.6418 | 0.6108 | | 1.3343 | 24.49 | 3600 | 14.2306 | 0.6209 | 0.5948 | | 1.3343 | 25.17 | 3700 | 13.2935 | 0.6299 | 0.6053 | | 1.3343 | 25.85 | 3800 | 14.0929 | 0.6279 | 0.5748 | | 1.3343 | 26.53 | 3900 | 13.7570 | 0.6308 | 0.5814 | | 1.1461 | 27.21 | 4000 | 13.2298 | 0.6318 | 0.5924 | | 1.1461 | 27.89 | 4100 | 13.9291 | 0.6299 | 0.6149 | | 1.1461 | 28.57 | 4200 | 14.5594 | 0.6080 | 0.5395 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
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transformers
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haryoaw/scenario-kd-from-scratch-silver-data-tyqiangz-multilingual-sentiments-malay-mode
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow4
FounderOfHuggingface
2023-11-29T17:19:48Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T17:19:48Z
2023-11-29T17:19:45.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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peft
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow4
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deepinv/DnCNN
deepinv
2023-11-29T17:23:34Z
0
0
null
[ "license:bsd-3-clause", "region:us" ]
2023-11-29T17:23:34Z
2023-11-29T17:20:40.000Z
null
null
--- license: bsd-3-clause ---
null
null
null
null
null
null
null
null
null
null
null
null
deepinv/DnCNN
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A2H0H0R1/llama2-7B-gpt4-gguf
A2H0H0R1
2023-11-29T17:20:43Z
0
0
null
[ "region:us" ]
2023-11-29T17:20:43Z
2023-11-29T17:20:43.000Z
null
null
Entry not found
null
null
null
null
null
null
null
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A2H0H0R1/llama2-7B-gpt4-gguf
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OpenNMT/Mistral-7B-v0.1-instruct-onmt-awq
OpenNMT
2023-11-29T17:25:54Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-29T17:25:54Z
2023-11-29T17:22:20.000Z
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--- license: apache-2.0 ---
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OpenNMT/Mistral-7B-v0.1-instruct-onmt-awq
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thewhiterider27/1123regesses
thewhiterider27
2023-11-29T20:02:51Z
0
0
null
[ "region:us" ]
2023-11-29T20:02:51Z
2023-11-29T17:23:45.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
thewhiterider27/1123regesses
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MarkrAI/DopeorNope-maestro-v1.1-DPO-13b
MarkrAI
2023-11-29T17:59:27Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T17:59:27Z
2023-11-29T17:24:45.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
MarkrAI/DopeorNope-maestro-v1.1-DPO-13b
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smc/videomae-base-finetuned-ucf101-subset
smc
2023-11-29T17:25:01Z
0
0
null
[ "region:us" ]
2023-11-29T17:25:01Z
2023-11-29T17:25:01.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
smc/videomae-base-finetuned-ucf101-subset
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lorelupo/twitter-xlm-large-user-gender-it
lorelupo
2023-11-29T17:25:59Z
0
0
null
[ "region:us" ]
2023-11-29T17:25:59Z
2023-11-29T17:25:59.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-large-user-gender-it
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lorelupo/twitter-xlm-large-user-gender-it-extra
lorelupo
2023-11-29T17:27:01Z
0
0
null
[ "region:us" ]
2023-11-29T17:27:01Z
2023-11-29T17:27:01.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-large-user-gender-it-extra
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maxschlake/roberta-base_disaster_tweets_bombing
maxschlake
2023-11-29T17:27:45Z
0
0
null
[ "region:us" ]
2023-11-29T17:27:45Z
2023-11-29T17:27:42.000Z
null
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maxschlake/roberta-base_disaster_tweets_bombing
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OpenNMT/Mistral-7B-v0.1-instruct-ct2-int8
OpenNMT
2023-11-29T17:32:25Z
0
0
null
[ "transformers", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2023-11-29T17:32:25Z
2023-11-29T17:27:44.000Z
null
null
--- license: apache-2.0 ---
null
transformers
null
null
null
null
null
null
null
null
null
null
OpenNMT/Mistral-7B-v0.1-instruct-ct2-int8
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jtsuei/bert-finetuned-ner
jtsuei
2023-11-30T00:12:12Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-30T00:12:12Z
2023-11-29T17:27:50.000Z
null
null
Entry not found
null
transformers
token-classification
null
null
null
null
null
null
null
null
null
jtsuei/bert-finetuned-ner
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lorelupo/twitter-xlm-base-user-age-4g-it
lorelupo
2023-11-29T17:28:04Z
0
0
null
[ "region:us" ]
2023-11-29T17:28:04Z
2023-11-29T17:28:04.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-base-user-age-4g-it
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow5
FounderOfHuggingface
2023-11-29T17:28:16Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T17:28:16Z
2023-11-29T17:28:11.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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peft
null
null
null
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow5
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lorelupo/twitter-xlm-large-user-age-4g-it
lorelupo
2023-11-29T17:28:35Z
0
0
null
[ "region:us" ]
2023-11-29T17:28:35Z
2023-11-29T17:28:35.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-large-user-age-4g-it
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Arthuerwang/cm-cifar10-32-low_dimension_control
Arthuerwang
2023-11-29T18:44:56Z
0
0
null
[ "diffusers", "diffusers:ConsistencyPipeline", "region:us" ]
2023-11-29T18:44:56Z
2023-11-29T17:28:43.000Z
null
null
Entry not found
null
diffusers
null
null
null
null
null
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null
null
null
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Arthuerwang/cm-cifar10-32-low_dimension_control
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lorelupo/twitter-xlm-base-user-age-5g-it
lorelupo
2023-11-29T17:30:56Z
0
0
null
[ "region:us" ]
2023-11-29T17:30:56Z
2023-11-29T17:30:56.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-base-user-age-5g-it
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lorelupo/twitter-xlm-large-user-age-5g-it
lorelupo
2023-11-29T17:31:48Z
0
0
null
[ "region:us" ]
2023-11-29T17:31:48Z
2023-11-29T17:31:48.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-large-user-age-5g-it
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Matupom/thainer-corpus-v2-dataset-new_old
Matupom
2023-11-29T17:32:32Z
0
0
null
[ "transformers", "safetensors", "camembert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2023-11-29T17:32:32Z
2023-11-29T17:32:09.000Z
null
null
Entry not found
null
transformers
token-classification
null
null
null
null
null
null
null
null
null
Matupom/thainer-corpus-v2-dataset-new_old
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lorelupo/twitter-xlm-base-user-age-5g-it-extra
lorelupo
2023-11-29T17:32:10Z
0
0
null
[ "region:us" ]
2023-11-29T17:32:10Z
2023-11-29T17:32:10.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-base-user-age-5g-it-extra
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lorelupo/twitter-xlm-large-user-age-5g-it-extra
lorelupo
2023-11-29T17:32:37Z
0
0
null
[ "region:us" ]
2023-11-29T17:32:37Z
2023-11-29T17:32:37.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
lorelupo/twitter-xlm-large-user-age-5g-it-extra
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haryoaw/scenario-kd-from-post-finetune-gold-silver-div-2-data-tyqiangz-multilingual-sent
haryoaw
2023-11-29T17:33:45Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:haryoaw/teacher-tyqianz-sentiment-malay", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T17:33:45Z
2023-11-29T17:32:48.000Z
null
null
--- license: mit base_model: haryoaw/teacher-tyqianz-sentiment-malay tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-kd-from-post-finetune-gold-silver-div-2-data-tyqiangz-multilingual-sent 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. --> # scenario-kd-from-post-finetune-gold-silver-div-2-data-tyqiangz-multilingual-sent This model is a fine-tuned version of [haryoaw/teacher-tyqianz-sentiment-malay](https://huggingface.co/haryoaw/teacher-tyqianz-sentiment-malay) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 8.2048 - Accuracy: 0.7274 - F1: 0.6998 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 6.8995 | 0.7095 | 0.6829 | | No log | 1.36 | 200 | 6.5239 | 0.7303 | 0.7067 | | No log | 2.04 | 300 | 6.7391 | 0.7045 | 0.6793 | | No log | 2.72 | 400 | 9.2571 | 0.6945 | 0.6477 | | 5.1077 | 3.4 | 500 | 7.7229 | 0.7154 | 0.6858 | | 5.1077 | 4.08 | 600 | 7.6829 | 0.7204 | 0.6927 | | 5.1077 | 4.76 | 700 | 7.8409 | 0.7254 | 0.6945 | | 5.1077 | 5.44 | 800 | 8.1633 | 0.7065 | 0.6868 | | 5.1077 | 6.12 | 900 | 8.5666 | 0.7005 | 0.6846 | | 1.9703 | 6.8 | 1000 | 9.1410 | 0.6945 | 0.6686 | | 1.9703 | 7.48 | 1100 | 7.9084 | 0.7224 | 0.7048 | | 1.9703 | 8.16 | 1200 | 8.2947 | 0.7164 | 0.6885 | | 1.9703 | 8.84 | 1300 | 8.7847 | 0.7164 | 0.7006 | | 1.9703 | 9.52 | 1400 | 10.0835 | 0.7164 | 0.6828 | | 1.5565 | 10.2 | 1500 | 8.6227 | 0.7323 | 0.7039 | | 1.5565 | 10.88 | 1600 | 8.5941 | 0.7095 | 0.6875 | | 1.5565 | 11.56 | 1700 | 7.9987 | 0.7294 | 0.7082 | | 1.5565 | 12.24 | 1800 | 9.3364 | 0.7055 | 0.6701 | | 1.5565 | 12.93 | 1900 | 8.2019 | 0.7164 | 0.6966 | | 1.1837 | 13.61 | 2000 | 8.8541 | 0.7224 | 0.6996 | | 1.1837 | 14.29 | 2100 | 7.7262 | 0.7353 | 0.7122 | | 1.1837 | 14.97 | 2200 | 7.6075 | 0.7363 | 0.7090 | | 1.1837 | 15.65 | 2300 | 7.9963 | 0.7164 | 0.6956 | | 1.1837 | 16.33 | 2400 | 7.3705 | 0.7224 | 0.7017 | | 0.9988 | 17.01 | 2500 | 10.3905 | 0.6975 | 0.6798 | | 0.9988 | 17.69 | 2600 | 8.6213 | 0.7214 | 0.6919 | | 0.9988 | 18.37 | 2700 | 8.2232 | 0.7164 | 0.6989 | | 0.9988 | 19.05 | 2800 | 9.6971 | 0.6975 | 0.6788 | | 0.9988 | 19.73 | 2900 | 7.8717 | 0.7294 | 0.7004 | | 0.8846 | 20.41 | 3000 | 8.0931 | 0.7323 | 0.7090 | | 0.8846 | 21.09 | 3100 | 9.0185 | 0.7104 | 0.6835 | | 0.8846 | 21.77 | 3200 | 8.2613 | 0.7075 | 0.6871 | | 0.8846 | 22.45 | 3300 | 8.5072 | 0.7274 | 0.7006 | | 0.8846 | 23.13 | 3400 | 7.9912 | 0.7353 | 0.7149 | | 0.6487 | 23.81 | 3500 | 8.5033 | 0.7512 | 0.7235 | | 0.6487 | 24.49 | 3600 | 8.2848 | 0.7284 | 0.7015 | | 0.6487 | 25.17 | 3700 | 8.1872 | 0.7194 | 0.6866 | | 0.6487 | 25.85 | 3800 | 9.5836 | 0.7015 | 0.6707 | | 0.6487 | 26.53 | 3900 | 7.9580 | 0.7204 | 0.6923 | | 0.5645 | 27.21 | 4000 | 9.4211 | 0.7055 | 0.6652 | | 0.5645 | 27.89 | 4100 | 7.5209 | 0.7164 | 0.6889 | | 0.5645 | 28.57 | 4200 | 7.6929 | 0.7284 | 0.7081 | | 0.5645 | 29.25 | 4300 | 8.7319 | 0.7254 | 0.6857 | | 0.5645 | 29.93 | 4400 | 8.9702 | 0.7214 | 0.6994 | | 0.4539 | 30.61 | 4500 | 7.4409 | 0.7154 | 0.6924 | | 0.4539 | 31.29 | 4600 | 8.2603 | 0.7174 | 0.6960 | | 0.4539 | 31.97 | 4700 | 8.6107 | 0.7164 | 0.6836 | | 0.4539 | 32.65 | 4800 | 8.3044 | 0.7264 | 0.7044 | | 0.4539 | 33.33 | 4900 | 8.3015 | 0.7204 | 0.7034 | | 0.4166 | 34.01 | 5000 | 8.2048 | 0.7274 | 0.6998 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
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transformers
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haryoaw/scenario-kd-from-post-finetune-gold-silver-div-2-data-tyqiangz-multilingual-sent
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Realgon/roberta_sst5_padding30model
Realgon
2023-11-29T18:18:30Z
0
0
null
[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T18:18:30Z
2023-11-29T17:33:09.000Z
null
null
--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta_sst5_padding30model 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. --> # roberta_sst5_padding30model This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7712 - Accuracy: 0.5674 ## 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.2819 | 1.0 | 534 | 1.1781 | 0.4683 | | 1.0082 | 2.0 | 1068 | 0.9981 | 0.5715 | | 0.8514 | 3.0 | 1602 | 1.0001 | 0.5715 | | 0.7126 | 4.0 | 2136 | 1.1284 | 0.5665 | | 0.582 | 5.0 | 2670 | 1.3241 | 0.5462 | | 0.4655 | 6.0 | 3204 | 1.4761 | 0.5466 | | 0.3524 | 7.0 | 3738 | 1.6291 | 0.5457 | | 0.2865 | 8.0 | 4272 | 1.9088 | 0.5335 | | 0.2393 | 9.0 | 4806 | 2.0502 | 0.5538 | | 0.2127 | 10.0 | 5340 | 2.3658 | 0.5516 | | 0.1828 | 11.0 | 5874 | 2.9156 | 0.5493 | | 0.1483 | 12.0 | 6408 | 2.9242 | 0.5566 | | 0.1433 | 13.0 | 6942 | 3.2224 | 0.5362 | | 0.1184 | 14.0 | 7476 | 3.3634 | 0.5502 | | 0.1026 | 15.0 | 8010 | 3.4638 | 0.5602 | | 0.0826 | 16.0 | 8544 | 3.5596 | 0.5511 | | 0.0571 | 17.0 | 9078 | 3.5359 | 0.5710 | | 0.0416 | 18.0 | 9612 | 3.7094 | 0.5579 | | 0.0345 | 19.0 | 10146 | 3.7244 | 0.5674 | | 0.0297 | 20.0 | 10680 | 3.7712 | 0.5674 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1 - Datasets 2.12.0 - Tokenizers 0.13.3
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
Realgon/roberta_sst5_padding30model
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CarolLiu999/llama-2-7b-int4-python-code-20k
CarolLiu999
2023-11-29T17:50:47Z
0
0
null
[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2023-11-29T17:50:47Z
2023-11-29T17:35:19.000Z
null
null
Entry not found
null
transformers
text-generation
null
null
null
null
null
null
null
null
null
CarolLiu999/llama-2-7b-int4-python-code-20k
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LarryAIDraw/mata_hari_fgo
LarryAIDraw
2023-11-29T17:42:55Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-29T17:42:55Z
2023-11-29T17:35:43.000Z
null
null
--- license: creativeml-openrail-m --- https://civitai.com/models/197502/matahari-fategrand-order
null
null
null
null
null
null
null
null
null
null
null
null
LarryAIDraw/mata_hari_fgo
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow6
FounderOfHuggingface
2023-11-29T17:36:43Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T17:36:43Z
2023-11-29T17:36:39.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow6
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dvilasuero/notus-7b-v1-sft
dvilasuero
2023-11-29T17:36:48Z
0
0
null
[ "region:us" ]
2023-11-29T17:36:48Z
2023-11-29T17:36:48.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
dvilasuero/notus-7b-v1-sft
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LarryAIDraw/illustrious_azur_lane_
LarryAIDraw
2023-11-29T17:43:04Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-29T17:43:04Z
2023-11-29T17:36:52.000Z
null
null
--- license: creativeml-openrail-m --- https://civitai.com/models/215439/illustriousazur-lane
null
null
null
null
null
null
null
null
null
null
null
null
LarryAIDraw/illustrious_azur_lane_
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OpenNMT/Nous-hermes-13B-onmt-awq
OpenNMT
2023-11-29T17:45:05Z
0
0
null
[ "region:us" ]
2023-11-29T17:45:05Z
2023-11-29T17:37:25.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
OpenNMT/Nous-hermes-13B-onmt-awq
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LarryAIDraw/fusou_kantaicollection
LarryAIDraw
2023-11-29T17:43:17Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-29T17:43:17Z
2023-11-29T17:37:38.000Z
null
null
--- license: creativeml-openrail-m --- https://civitai.com/models/197496/fusou-kantai-collection
null
null
null
null
null
null
null
null
null
null
null
null
LarryAIDraw/fusou_kantaicollection
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LarryAIDraw/Ko_Yagami
LarryAIDraw
2023-11-29T17:43:28Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-29T17:43:28Z
2023-11-29T17:38:00.000Z
null
null
--- license: creativeml-openrail-m --- https://civitai.com/models/215403/kou-yagami-oror-new-game
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LarryAIDraw/Ko_Yagami
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briefai/LongShort-Llama-2-7B
briefai
2023-11-29T20:28:22Z
0
0
null
[ "pytorch", "llama-2", "Gen-AI", "Finance", "KPI Extraction", "text-generation", "en", "dataset:briefai/LongShort-Dataset", "license:apache-2.0", "region:us" ]
2023-11-29T20:28:22Z
2023-11-29T17:38:06.000Z
null
null
--- license: apache-2.0 datasets: - briefai/LongShort-Dataset language: - en pipeline_tag: text-generation tags: - pytorch - llama-2 - Gen-AI - Finance - KPI Extraction --- # LongShort-Llama-2-7B ### Model Description LongShort-Llama-2-7B is a large language model fine-tuned on earnings call documents to extract financial KPIs from the earnings call documents. It is based on the Llama-2-7B Architecture. - Model creator: [Brief AI](https://huggingface.co/briefai) - Original model: [Llama 2 7B Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) ### Dataset Description - Data Source: Factiva - Data Description: 28K+ Earnings Call Documents - Data Scope: 1K+ public companies - Fine Tuning Data: Collection of 60K+ samples. ## Prompt template: LongShort-Llama-2-7B ``` [INST]Given the context, answer the question. ### Question: Extract all the finance-based performance indicators and evaluation metrics. ### Context: {context} ### Answer: [/INST] ``` ## Basics *This section provides information about the model type, version, license, funders, release date, developers, and contact information.* *It is useful for anyone who wants to reference the model.* **Developed by:** [Brief AI Team](https://huggingface.co/briefai) **Model Type:** Transformer-based Large Language Model **Version:** 1.0.0 **Languages:** English **License:** Apache 2.0 **Release Date Estimate:** Wednesday, 29.November.2023 **Send Questions to:** vishalparameswaran96@gmail.com **Cite as:** Brief AI LongShort Language Model **Funded by:** UChicago Data Science Institute **Mentored by:** Nick Kadochnikov ## Technical Specifications *This section includes details about the model objective and architecture, and the compute infrastructure.* *It is useful for people interested in model development.* Please see [the LongShort training README](https://github.com/brief-ai-uchicago/LongShort-Dataset) for full details on replicating training. ### Model Architecture and Objective * Modified from Llama-2-7B **Objective:** Financial KPI extraction from earnings call documents. ### Hardware and Software - Compute Infrastructure * 4 NVIDIA L4 GPUs & 48 vCPUs * Environment: PyTorch (pytorch-2.0 w/ CUDA-11.8; see [Github link](https://github.com/pytorch/pytorch)) * CPU: GCP G2 Standard 48 (Platform: Intel Cascade Lake) (Accelerator Optimized) * CPU memory: 192GB RAM * GPU memory: 30GB per GPU ## Training *This section provides information about the training.* *It is useful for people who want to learn more about the model inputs and training footprint.* The following bits and bytes quantization config was used during training: * quant_method: bitsandbytes * load_in_8bit: False * load_in_4bit: True * llm_int8_threshold: 6.0 * llm_int8_skip_modules: None * llm_int8_enable_fp32_cpu_offload: False * llm_int8_has_fp16_weight: False * bnb_4bit_quant_type: nf4 * bnb_4bit_use_double_quant: True * bnb_4bit_compute_dtype: float16 Framework versions * PEFT 0.4.0 ### Training Data *This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.* Details for the dataset can be found in [LongShort Dataset](https://github.com/brief-ai-uchicago/LongShort-Dataset) Training data includes: - 5000 Earnings Call Documents ## How to use This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows: [LongShort-Llama-2-7B](https://huggingface.co/briefai/LongShort-Llama-2-7B) ## Intended Use This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pre-trained base model that can be further fine-tuned for specific tasks. The use cases below are not exhaustive. ### Direct Use - Text generation - Exploring characteristics of language generated by a language model - Examples: Cloze tests, counterfactuals, generations with reframings ### Downstream Use - Tasks that leverage language models include: Information Extraction, Question Answering, Summarization #### Out-of-scope Uses Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. Out-of-scope Uses Include: - Usage for evaluating or scoring individuals, such as for employment, education, or credit - Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct #### Misuse Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes: - Spam generation - Disinformation and influence operations - Disparagement and defamation - Harassment and abuse - [Deception](#deception) - Unconsented impersonation and imitation - Unconsented surveillance - Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license) ## Intended Users ### Direct Users - General Public - Researchers - Students - Educators - Engineers/developers - Non-commercial entities - Financial Industry # Risks and Limitations *This section identifies foreseeable harms and misunderstandings.* Model may: - Overrepresent some viewpoints and underrepresent others - Contain stereotypes - Contain [personal information](#personal-data-and-information) - Generate: - Hateful, abusive, or violent language - Discriminatory or prejudicial language - Content that may not be appropriate for all settings, including sexual content - Make errors, including producing incorrect information as if it were factual - Generate irrelevant or repetitive outputs - Induce users into attributing human traits to it, such as sentience or consciousness # Evaluation *This section describes the evaluation protocols and provides the results.* Result: LongShort-Llama-2-7B gives 44.4% accuracy on a validation set of 10% of the original training dataset. **Train-time Evaluation:** Final checkpoint after 700 epochs: - Training Loss: 1.245 ## Recommendations *This section provides information on warnings and potential mitigations.* - Indirect users should be made aware when the content they're working with is created by the LLM. - Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary. - Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments. # Model Card Authors Vishal Parameshwaran, Garima Sohi, Jose Gerala, Sanchit Narayan Kumar
null
null
text-generation
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null
null
null
null
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briefai/LongShort-Llama-2-7B
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Someone7777/Wingom7
Someone7777
2023-11-29T17:38:57Z
0
0
null
[ "region:us" ]
2023-11-29T17:38:57Z
2023-11-29T17:38:29.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
Someone7777/Wingom7
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LarryAIDraw/hiei_kantaicollection
LarryAIDraw
2023-11-29T17:43:40Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-29T17:43:40Z
2023-11-29T17:38:33.000Z
null
null
--- license: creativeml-openrail-m --- https://civitai.com/models/205528/hiei-kantai-collection
null
null
null
null
null
null
null
null
null
null
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LarryAIDraw/hiei_kantaicollection
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LarryAIDraw/ashigara_kantaicollection
LarryAIDraw
2023-11-29T17:43:52Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-29T17:43:52Z
2023-11-29T17:39:36.000Z
null
null
--- license: creativeml-openrail-m --- https://civitai.com/models/205641/ashigara-kantai-collection
null
null
null
null
null
null
null
null
null
null
null
null
LarryAIDraw/ashigara_kantaicollection
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LarryAIDraw/mayer_arknights
LarryAIDraw
2023-11-29T17:44:04Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
2023-11-29T17:44:04Z
2023-11-29T17:40:15.000Z
null
null
--- license: creativeml-openrail-m --- https://civitai.com/models/205634/mayer-arknights
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LarryAIDraw/mayer_arknights
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Preuss01/goularte
Preuss01
2023-11-29T17:43:11Z
0
0
null
[ "region:us" ]
2023-11-29T17:43:11Z
2023-11-29T17:43:11.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
Preuss01/goularte
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow7
FounderOfHuggingface
2023-11-29T17:45:09Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T17:45:09Z
2023-11-29T17:45:06.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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peft
null
null
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow7
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ribes14/demo-onnx
ribes14
2023-11-29T17:47:18Z
0
0
null
[ "region:us" ]
2023-11-29T17:47:18Z
2023-11-29T17:47:16.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
ribes14/demo-onnx
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haryoaw/scenario-non-kd-from-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-ma
haryoaw
2023-11-29T17:48:16Z
0
0
null
[ "transformers", "pytorch", "xlm-roberta", "generated_from_trainer", "dataset:multilingual-sentiments", "base_model:xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]
2023-11-29T17:48:16Z
2023-11-29T17:47:23.000Z
null
null
--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - multilingual-sentiments metrics: - accuracy - f1 model-index: - name: scenario-non-kd-from-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-ma 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. --> # scenario-non-kd-from-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-ma This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset. It achieves the following results on the evaluation set: - Loss: 2.2224 - Accuracy: 0.6985 - F1: 0.6661 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6969 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 0.68 | 100 | 0.7717 | 0.6189 | 0.5854 | | No log | 1.36 | 200 | 0.8048 | 0.6468 | 0.6332 | | No log | 2.04 | 300 | 0.7043 | 0.6647 | 0.6364 | | No log | 2.72 | 400 | 0.7412 | 0.6438 | 0.6260 | | 0.6872 | 3.4 | 500 | 0.7609 | 0.6955 | 0.6748 | | 0.6872 | 4.08 | 600 | 0.8408 | 0.6876 | 0.6697 | | 0.6872 | 4.76 | 700 | 0.8482 | 0.6846 | 0.6562 | | 0.6872 | 5.44 | 800 | 0.9626 | 0.6876 | 0.6719 | | 0.6872 | 6.12 | 900 | 1.0625 | 0.7154 | 0.6978 | | 0.3167 | 6.8 | 1000 | 1.1384 | 0.6836 | 0.6588 | | 0.3167 | 7.48 | 1100 | 1.1296 | 0.7035 | 0.6766 | | 0.3167 | 8.16 | 1200 | 1.4336 | 0.6736 | 0.6469 | | 0.3167 | 8.84 | 1300 | 1.4899 | 0.6736 | 0.6529 | | 0.3167 | 9.52 | 1400 | 1.2895 | 0.7154 | 0.6956 | | 0.1513 | 10.2 | 1500 | 1.5986 | 0.7114 | 0.6769 | | 0.1513 | 10.88 | 1600 | 1.7920 | 0.6905 | 0.6679 | | 0.1513 | 11.56 | 1700 | 1.6600 | 0.6975 | 0.6608 | | 0.1513 | 12.24 | 1800 | 1.6425 | 0.6915 | 0.6704 | | 0.1513 | 12.93 | 1900 | 1.8379 | 0.6915 | 0.6745 | | 0.0825 | 13.61 | 2000 | 1.6874 | 0.6925 | 0.6748 | | 0.0825 | 14.29 | 2100 | 1.8852 | 0.6955 | 0.6725 | | 0.0825 | 14.97 | 2200 | 1.9019 | 0.7075 | 0.6864 | | 0.0825 | 15.65 | 2300 | 2.1659 | 0.7065 | 0.6808 | | 0.0825 | 16.33 | 2400 | 2.2224 | 0.6985 | 0.6661 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
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transformers
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haryoaw/scenario-non-kd-from-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-ma
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delsin/mhmod
delsin
2023-11-29T17:50:55Z
0
0
null
[ "region:us" ]
2023-11-29T17:50:55Z
2023-11-29T17:49:07.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
delsin/mhmod
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DRXD1000/Stelar_SFT_Lora
DRXD1000
2023-11-29T17:50:03Z
0
0
null
[ "region:us" ]
2023-11-29T17:50:03Z
2023-11-29T17:50:03.000Z
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DRXD1000/Stelar_SFT_Lora
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vvkropochev/hw5calc-peft
vvkropochev
2023-11-29T20:31:32Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:google/flan-t5-base", "region:us" ]
2023-11-29T20:31:32Z
2023-11-29T17:50:37.000Z
null
null
--- library_name: peft base_model: google/flan-t5-base --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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peft
null
null
null
null
null
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null
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vvkropochev/hw5calc-peft
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lafual/Uzoth_Test
lafual
2023-11-29T17:52:42Z
0
0
null
[ "license:openrail", "region:us" ]
2023-11-29T17:52:42Z
2023-11-29T17:52:21.000Z
null
null
--- license: openrail ---
null
null
null
null
null
null
null
null
null
null
null
null
lafual/Uzoth_Test
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kyle0518/HW3_rank64
kyle0518
2023-11-29T23:11:52Z
0
0
null
[ "region:us" ]
2023-11-29T23:11:52Z
2023-11-29T17:52:55.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
kyle0518/HW3_rank64
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow8
FounderOfHuggingface
2023-11-29T17:53:35Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T17:53:35Z
2023-11-29T17:53:32.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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peft
null
null
null
null
null
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow8
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Leandro862/58
Leandro862
2023-11-29T17:54:25Z
0
0
null
[ "region:us" ]
2023-11-29T17:54:25Z
2023-11-29T17:54:25.000Z
null
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Leandro862/58
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healthcontentwriter/first
healthcontentwriter
2023-11-29T17:55:19Z
0
0
null
[ "license:cc", "region:us" ]
2023-11-29T17:55:19Z
2023-11-29T17:55:19.000Z
null
null
--- license: cc ---
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healthcontentwriter/first
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bhadauriaupendra062/distillbert
bhadauriaupendra062
2023-11-29T17:55:36Z
0
0
null
[ "license:apache-2.0", "region:us" ]
2023-11-29T17:55:36Z
2023-11-29T17:55:36.000Z
null
null
--- license: apache-2.0 ---
null
null
null
null
null
null
null
null
null
null
null
null
bhadauriaupendra062/distillbert
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Dacavi/whisper-small-hi
Dacavi
2023-11-29T21:47:37Z
0
0
null
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]
2023-11-29T21:47:37Z
2023-11-29T17:56:18.000Z
null
null
Entry not found
null
transformers
automatic-speech-recognition
null
null
null
null
null
null
null
null
null
Dacavi/whisper-small-hi
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Silemo/asr
Silemo
2023-11-29T17:56:40Z
0
0
null
[ "region:us" ]
2023-11-29T17:56:40Z
2023-11-29T17:56:40.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
Silemo/asr
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khanhnto/ilytototo
khanhnto
2023-11-29T17:57:04Z
0
0
null
[ "region:us" ]
2023-11-29T17:57:04Z
2023-11-29T17:57:02.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
khanhnto/ilytototo
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TheBloke/deepseek-llm-7B-base-GGUF
TheBloke
2023-11-29T23:14:38Z
0
0
null
[ "transformers", "gguf", "deepseek", "base_model:deepseek-ai/deepseek-llm-7b-base", "license:other", "region:us" ]
2023-11-29T23:14:38Z
2023-11-29T17:58:06.000Z
null
null
--- base_model: deepseek-ai/deepseek-llm-7b-base inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek LLM 7B Base model_type: deepseek prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Deepseek LLM 7B Base - GGUF - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base) <!-- description start --> ## Description This repo contains GGUF format model files for [DeepSeek's Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [deepseek-llm-7b-base.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q2_K.gguf) | Q2_K | 2 | 2.99 GB| 5.49 GB | smallest, significant quality loss - not recommended for most purposes | | [deepseek-llm-7b-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q3_K_S.gguf) | Q3_K_S | 3 | 3.14 GB| 5.64 GB | very small, high quality loss | | [deepseek-llm-7b-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q3_K_M.gguf) | Q3_K_M | 3 | 3.47 GB| 5.97 GB | very small, high quality loss | | [deepseek-llm-7b-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q3_K_L.gguf) | Q3_K_L | 3 | 3.75 GB| 6.25 GB | small, substantial quality loss | | [deepseek-llm-7b-base.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q4_0.gguf) | Q4_0 | 4 | 4.00 GB| 6.50 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [deepseek-llm-7b-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q4_K_S.gguf) | Q4_K_S | 4 | 4.03 GB| 6.53 GB | small, greater quality loss | | [deepseek-llm-7b-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q4_K_M.gguf) | Q4_K_M | 4 | 4.22 GB| 6.72 GB | medium, balanced quality - recommended | | [deepseek-llm-7b-base.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q5_0.gguf) | Q5_0 | 5 | 4.81 GB| 7.31 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [deepseek-llm-7b-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q5_K_S.gguf) | Q5_K_S | 5 | 4.81 GB| 7.31 GB | large, low quality loss - recommended | | [deepseek-llm-7b-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q5_K_M.gguf) | Q5_K_M | 5 | 4.93 GB| 7.43 GB | large, very low quality loss - recommended | | [deepseek-llm-7b-base.Q6_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q6_K.gguf) | Q6_K | 6 | 5.67 GB| 8.17 GB | very large, extremely low quality loss | | [deepseek-llm-7b-base.Q8_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF/blob/main/deepseek-llm-7b-base.Q8_0.gguf) | Q8_0 | 8 | 7.35 GB| 9.85 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/deepseek-llm-7B-base-GGUF and below it, a specific filename to download, such as: deepseek-llm-7b-base.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/deepseek-llm-7B-base-GGUF deepseek-llm-7b-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/deepseek-llm-7B-base-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` 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 HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-llm-7B-base-GGUF deepseek-llm-7b-base.Q4_K_M.gguf --local-dir . --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> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m deepseek-llm-7b-base.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./deepseek-llm-7b-base.Q4_K_M.gguf", # Download the model file first n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./deepseek-llm-7b-base.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: DeepSeek's Deepseek LLM 7B Base <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-7b-base` is a 7B parameter model with Multi-Head Attention trained on 2 trillion tokens from scratch. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Text Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-7b-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com). <!-- original-model-card end -->
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TheBloke/deepseek-llm-7B-base-GGUF
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TheBloke/deepseek-llm-7B-base-AWQ
TheBloke
2023-11-29T23:14:53Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "base_model:deepseek-ai/deepseek-llm-7b-base", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]
2023-11-29T23:14:53Z
2023-11-29T17:58:06.000Z
null
null
--- base_model: deepseek-ai/deepseek-llm-7b-base inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek LLM 7B Base model_type: deepseek prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Deepseek LLM 7B Base - AWQ - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base) <!-- description start --> ## Description This repo contains AWQ model files for [DeepSeek's Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/deepseek-llm-7B-base-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.83 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/deepseek-llm-7B-base-AWQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `deepseek-llm-7B-base-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/deepseek-llm-7B-base-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''{prompt} ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/deepseek-llm-7B-base-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/deepseek-llm-7B-base-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/deepseek-llm-7B-base-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: DeepSeek's Deepseek LLM 7B Base <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-7b-base` is a 7B parameter model with Multi-Head Attention trained on 2 trillion tokens from scratch. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Text Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-7b-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
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transformers
text-generation
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TheBloke/deepseek-llm-7B-base-AWQ
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finiteautomata/roberta-large-bne-reranker-2
finiteautomata
2023-11-29T21:49:18Z
0
0
null
[ "transformers", "pytorch", "roberta", "text-classification", "endpoints_compatible", "region:us" ]
2023-11-29T21:49:18Z
2023-11-29T17:59:48.000Z
null
null
--- {} --- # Reranker with roberta ## Metrics | Metric | Value | | ------ | ----- | | MRR | 0.675 | | MRR Grouped | 0.727 | | Accuracy | 0.598 | | Accuracy Grouped | 0.656 |
null
transformers
text-classification
null
null
null
null
null
null
null
null
null
finiteautomata/roberta-large-bne-reranker-2
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kyle0518/HW3_rank128
kyle0518
2023-11-29T23:22:44Z
0
0
null
[ "region:us" ]
2023-11-29T23:22:44Z
2023-11-29T18:01:15.000Z
null
null
Entry not found
null
null
null
null
null
null
null
null
null
null
null
null
kyle0518/HW3_rank128
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow9
FounderOfHuggingface
2023-11-29T18:01:59Z
0
0
null
[ "peft", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2023-11-29T18:01:59Z
2023-11-29T18:01:56.000Z
null
null
--- library_name: peft base_model: gpt2 --- # 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] ## Training procedure ### Framework versions - PEFT 0.6.2
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FounderOfHuggingface/gpt2_lora_ag_news_t200_e5_member_shadow9
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TheBloke/deepseek-llm-67b-base-AWQ
TheBloke
2023-11-29T20:30:59Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "base_model:deepseek-ai/deepseek-llm-67b-base", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]
2023-11-29T20:30:59Z
2023-11-29T18:02:39.000Z
null
null
--- base_model: deepseek-ai/deepseek-llm-67b-base inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek LLM 67B Base model_type: yi prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Deepseek LLM 67B Base - AWQ - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek LLM 67B Base](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base) <!-- description start --> ## Description This repo contains AWQ model files for [DeepSeek's Deepseek LLM 67B Base](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-67b-base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/deepseek-llm-67b-base-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 37.52 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/deepseek-llm-67b-base-AWQ`. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `deepseek-llm-67b-base-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/deepseek-llm-67b-base-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''{prompt} ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/deepseek-llm-67b-base-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/deepseek-llm-67b-base-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/deepseek-llm-67b-base-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: DeepSeek's Deepseek LLM 67B Base <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-67b-base` is a 67B parameter model with Grouped-Query Attention trained on 2 trillion tokens from scratch. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Text Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-67b-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
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transformers
text-generation
null
null
null
null
null
null
null
null
null
TheBloke/deepseek-llm-67b-base-AWQ
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TheBloke/deepseek-llm-67b-base-GPTQ
TheBloke
2023-11-29T22:06:12Z
0
0
null
[ "transformers", "safetensors", "llama", "text-generation", "base_model:deepseek-ai/deepseek-llm-67b-base", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]
2023-11-29T22:06:12Z
2023-11-29T18:02:39.000Z
null
null
--- base_model: deepseek-ai/deepseek-llm-67b-base inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek LLM 67B Base model_type: yi prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Deepseek LLM 67B Base - GPTQ - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek LLM 67B Base](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base) <!-- description start --> # Description This repo contains GPTQ model files for [DeepSeek's Deepseek LLM 67B Base](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-67b-base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 36.29 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 37.56 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 41.41 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 28.07 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 29.27 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | | [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 32.93 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/deepseek-llm-67b-base-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/deepseek-llm-67b-base-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `deepseek-llm-67b-base-GPTQ`: ```shell mkdir deepseek-llm-67b-base-GPTQ huggingface-cli download TheBloke/deepseek-llm-67b-base-GPTQ --local-dir deepseek-llm-67b-base-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir deepseek-llm-67b-base-GPTQ huggingface-cli download TheBloke/deepseek-llm-67b-base-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir deepseek-llm-67b-base-GPTQ --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 deepseek-llm-67b-base-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-llm-67b-base-GPTQ --local-dir deepseek-llm-67b-base-GPTQ --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> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/deepseek-llm-67b-base-GPTQ`. - To download from a specific branch, enter for example `TheBloke/deepseek-llm-67b-base-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `deepseek-llm-67b-base-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/deepseek-llm-67b-base-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/deepseek-llm-67b-base-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: DeepSeek's Deepseek LLM 67B Base <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-67b-base` is a 67B parameter model with Grouped-Query Attention trained on 2 trillion tokens from scratch. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Text Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-67b-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
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transformers
text-generation
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TheBloke/deepseek-llm-67b-base-GPTQ
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TheBloke/deepseek-llm-67b-base-GGUF
TheBloke
2023-11-29T18:44:06Z
0
0
null
[ "transformers", "gguf", "yi", "base_model:deepseek-ai/deepseek-llm-67b-base", "license:other", "region:us" ]
2023-11-29T18:44:06Z
2023-11-29T18:02:39.000Z
null
null
--- base_model: deepseek-ai/deepseek-llm-67b-base inference: false license: other license_link: LICENSE license_name: deepseek model_creator: DeepSeek model_name: Deepseek LLM 67B Base model_type: yi prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Deepseek LLM 67B Base - GGUF - Model creator: [DeepSeek](https://huggingface.co/deepseek-ai) - Original model: [Deepseek LLM 67B Base](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base) <!-- description start --> ## Description This repo contains GGUF format model files for [DeepSeek's Deepseek LLM 67B Base](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-67b-base-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF) * [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-67b-base) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [deepseek-llm-67b-base.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q2_K.gguf) | Q2_K | 2 | 28.50 GB| 31.00 GB | smallest, significant quality loss - not recommended for most purposes | | [deepseek-llm-67b-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q3_K_S.gguf) | Q3_K_S | 3 | 29.31 GB| 31.81 GB | very small, high quality loss | | [deepseek-llm-67b-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q3_K_M.gguf) | Q3_K_M | 3 | 32.58 GB| 35.08 GB | very small, high quality loss | | [deepseek-llm-67b-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q3_K_L.gguf) | Q3_K_L | 3 | 35.58 GB| 38.08 GB | small, substantial quality loss | | [deepseek-llm-67b-base.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q4_0.gguf) | Q4_0 | 4 | 38.15 GB| 40.65 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [deepseek-llm-67b-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q4_K_S.gguf) | Q4_K_S | 4 | 38.25 GB| 40.75 GB | small, greater quality loss | | [deepseek-llm-67b-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q4_K_M.gguf) | Q4_K_M | 4 | 40.44 GB| 42.94 GB | medium, balanced quality - recommended | | [deepseek-llm-67b-base.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q5_0.gguf) | Q5_0 | 5 | 46.48 GB| 48.98 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [deepseek-llm-67b-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q5_K_S.gguf) | Q5_K_S | 5 | 46.48 GB| 48.98 GB | large, low quality loss - recommended | | [deepseek-llm-67b-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-base-GGUF/blob/main/deepseek-llm-67b-base.Q5_K_M.gguf) | Q5_K_M | 5 | 47.65 GB| 50.15 GB | large, very low quality loss - recommended | | deepseek-llm-67b-base.Q6_K.gguf | Q6_K | 6 | 55.32 GB| 57.82 GB | very large, extremely low quality loss | | deepseek-llm-67b-base.Q8_0.gguf | Q8_0 | 8 | 71.65 GB| 74.15 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ### Q6_K and Q8_0 files are split and require joining **Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files. <details> <summary>Click for instructions regarding Q6_K and Q8_0 files</summary> ### q6_K Please download: * `deepseek-llm-67b-base.Q6_K.gguf-split-a` * `deepseek-llm-67b-base.Q6_K.gguf-split-b` ### q8_0 Please download: * `deepseek-llm-67b-base.Q8_0.gguf-split-a` * `deepseek-llm-67b-base.Q8_0.gguf-split-b` To join the files, do the following: Linux and macOS: ``` cat deepseek-llm-67b-base.Q6_K.gguf-split-* > deepseek-llm-67b-base.Q6_K.gguf && rm deepseek-llm-67b-base.Q6_K.gguf-split-* cat deepseek-llm-67b-base.Q8_0.gguf-split-* > deepseek-llm-67b-base.Q8_0.gguf && rm deepseek-llm-67b-base.Q8_0.gguf-split-* ``` Windows command line: ``` COPY /B deepseek-llm-67b-base.Q6_K.gguf-split-a + deepseek-llm-67b-base.Q6_K.gguf-split-b deepseek-llm-67b-base.Q6_K.gguf del deepseek-llm-67b-base.Q6_K.gguf-split-a deepseek-llm-67b-base.Q6_K.gguf-split-b COPY /B deepseek-llm-67b-base.Q8_0.gguf-split-a + deepseek-llm-67b-base.Q8_0.gguf-split-b deepseek-llm-67b-base.Q8_0.gguf del deepseek-llm-67b-base.Q8_0.gguf-split-a deepseek-llm-67b-base.Q8_0.gguf-split-b ``` </details> <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/deepseek-llm-67b-base-GGUF and below it, a specific filename to download, such as: deepseek-llm-67b-base.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/deepseek-llm-67b-base-GGUF deepseek-llm-67b-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/deepseek-llm-67b-base-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` 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 HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-llm-67b-base-GGUF deepseek-llm-67b-base.Q4_K_M.gguf --local-dir . --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> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m deepseek-llm-67b-base.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./deepseek-llm-67b-base.Q4_K_M.gguf", # Download the model file first n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "{prompt}", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./deepseek-llm-67b-base.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: DeepSeek's Deepseek LLM 67B Base <p align="center"> <img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-67b-base` is a 67B parameter model with Grouped-Query Attention trained on 2 trillion tokens from scratch. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Text Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-67b-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com). <!-- original-model-card end -->
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TheBloke/deepseek-llm-67b-base-GGUF
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