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modelId stringlengths 5 122	author stringlengths 2 42	last_modified unknown	likes int64 0 10.6k	library_name stringclasses 189 values	tags sequencelengths 1 1.84k	pipeline_tag stringclasses 48 values	createdAt unknown	card stringlengths 1 901k
sosoai/unsloth-hansoldeco-mistral-7b-openhermes-mixed-lora-v0.1	sosoai	"2024-04-14T22:13:47Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	"2024-04-14T21:56:00Z"	Entry not found
Proteinamino/doggy_style_for_asian	Proteinamino	"2024-04-14T21:59:07Z"	0	null	[ "region:us" ]	null	"2024-04-14T21:56:46Z"	Entry not found
ashishp-wiai/ClipArt_LoRA_50-2024-04-14	ashishp-wiai	"2024-04-14T22:52:49Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-04-14T21:56:59Z"	Entry not found
Ffohturk/mistral-7b_ablated_model_layer_1_gate_only_healed	Ffohturk	"2024-04-15T02:40:23Z"	0	transformers	[ "transformers", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T21:58:12Z"	Entry not found
frankmurray/girl	frankmurray	"2024-04-14T21:59:04Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-14T21:58:14Z"	--- license: openrail ---
femindharamshi/receiptItemizer	femindharamshi	"2024-04-14T22:00:16Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:00:16Z"	Entry not found
Sikontil/Char.gi	Sikontil	"2024-04-14T22:19:36Z"	0	null	[ "arxiv:1910.09700", "region:us" ]	null	"2024-04-14T22:02:02Z"	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mazzaqq/mistral_sigmoid_lr2e-05_b0.1	mazzaqq	"2024-04-14T22:02:38Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:02:38Z"	Invalid username or password.
WesPro/MisHum	WesPro	"2024-04-14T22:15:12Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:05:10Z"	Entry not found
BrandonM001/bert-finetuned-ner-accelerate	BrandonM001	"2024-04-15T02:45:04Z"	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2024-04-14T22:07:02Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
adamjweintraut/bart-finetuned-lyrlen-64-lines-light	adamjweintraut	"2024-04-14T22:11:46Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:11:46Z"	Entry not found
SeoulStreamingStation/KLMv7s	SeoulStreamingStation	"2024-04-15T00:45:36Z"	0	null	[ "license:other", "region:us" ]	null	"2024-04-14T22:13:30Z"	--- license: other license_name: seoulstreamingstation license_link: LICENSE ---
adamjweintraut/bart-finetuned-kwsylgen-64-simple_input_BARTlarge	adamjweintraut	"2024-04-15T02:00:49Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-04-14T22:13:43Z"	--- license: apache-2.0 tags: - generated_from_trainer base_model: facebook/bart-large model-index: - name: bart-finetuned-kwsylgen-64-simple_input_BARTlarge results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-finetuned-kwsylgen-64-simple_input_BARTlarge This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1785 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0641 \| 0.18 \| 500 \| 0.2451 \| \| 0.2194 \| 0.36 \| 1000 \| 0.2228 \| \| 0.1989 \| 0.54 \| 1500 \| 0.2086 \| \| 0.1888 \| 0.72 \| 2000 \| 0.2027 \| \| 0.177 \| 0.9 \| 2500 \| 0.1976 \| \| 0.1703 \| 1.08 \| 3000 \| 0.1933 \| \| 0.1647 \| 1.26 \| 3500 \| 0.1928 \| \| 0.159 \| 1.44 \| 4000 \| 0.1890 \| \| 0.1538 \| 1.61 \| 4500 \| 0.1864 \| \| 0.151 \| 1.79 \| 5000 \| 0.1857 \| \| 0.1471 \| 1.97 \| 5500 \| 0.1828 \| \| 0.1436 \| 2.15 \| 6000 \| 0.1814 \| \| 0.1435 \| 2.33 \| 6500 \| 0.1806 \| \| 0.141 \| 2.51 \| 7000 \| 0.1799 \| \| 0.1393 \| 2.69 \| 7500 \| 0.1790 \| \| 0.1388 \| 2.87 \| 8000 \| 0.1785 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Kukedlc/NeuralStockFusion-7b	Kukedlc	"2024-04-14T23:58:45Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2403.19522", "base_model:Kukedlc/NeuralSirKrishna-7b", "base_model:Kukedlc/NeuralArjuna-7B-DT", "base_model:Kukedlc/NeuralMaths-Experiment-7b", "base_model:Kukedlc/NeuralSynthesis-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:18:13Z"	--- base_model: - Kukedlc/NeuralSirKrishna-7b - Kukedlc/NeuralArjuna-7B-DT - Kukedlc/NeuralMaths-Experiment-7b - Kukedlc/NeuralSynthesis-7B-v0.1 library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # NeuralStockFusion-7b ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/64d71ab4089bc502ceb44d29/5Ex2YG8H1oLXaS25gvZQs.webp) # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) as a base. ### Models Merged The following models were included in the merge: * [Kukedlc/NeuralArjuna-7B-DT](https://huggingface.co/Kukedlc/NeuralArjuna-7B-DT) * [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b) * [Kukedlc/NeuralSynthesis-7B-v0.1](https://huggingface.co/Kukedlc/NeuralSynthesis-7B-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Kukedlc/NeuralMaths-Experiment-7b - model: Kukedlc/NeuralArjuna-7B-DT - model: Kukedlc/NeuralSirKrishna-7b - model: Kukedlc/NeuralSynthesis-7B-v0.1 merge_method: model_stock base_model: Kukedlc/NeuralSirKrishna-7b dtype: bfloat16 ``` # Model Inference: ``` python !pip install -qU transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig import torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) MODEL_NAME = 'Kukedlc/NeuralStockFusion-7b' tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:0', quantization_config=bnb_config) inputs = tokenizer(["[INST] What is a large language model, in spanish \n[/INST]\n"], return_tensors="pt").to('cuda') streamer = TextStreamer(tokenizer) # Despite returning the usual output, the streamer will also print the generated text to stdout. _ = model.generate(**inputs, streamer=streamer, max_new_tokens=256, do_sample=True, temperature=0.7, repetition_penalty=1.4, top_p=0.9) ```
Dracones/mixtral-8x22b-instruct-oh_exl2_7.0bpw	Dracones	"2024-04-14T22:38:51Z"	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "exl2", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "7-bit", "region:us" ]	text-generation	"2024-04-14T22:19:33Z"	--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 7.0bpw This is a 7.0bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "\| Quant Level \| Perplexity Score \|" echo "\|-------------\|------------------\|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" \| grep -oP 'Evaluation perplexity: \K[\d.]+') echo "\| $BIT_PRECISION \| $score \|" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```
uday91/llama-2-7b-customer-support-llm	uday91	"2024-04-14T22:54:08Z"	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-14T22:20:05Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
K00B404/Hitotsume_xB_v0.1	K00B404	"2024-04-14T22:20:44Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:20:43Z"	Invalid username or password.
Shalazary/ruBert-base-sberquad-0.001-filtered	Shalazary	"2024-04-14T22:21:22Z"	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:ai-forever/ruBert-base", "license:apache-2.0", "region:us" ]	null	"2024-04-14T22:20:59Z"	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: ai-forever/ruBert-base model-index: - name: ruBert-base-sberquad-0.001-filtered 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. --> # ruBert-base-sberquad-0.001-filtered This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Lalazn/Mirada	Lalazn	"2024-04-14T22:24:34Z"	0	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-14T22:22:39Z"	--- license: apache-2.0 ---
ShenaoZ/0.001_ablation_iter_2	ShenaoZ	"2024-04-14T23:37:13Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.001_ablation_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:23:00Z"	Invalid username or password.
WesPro/MisHumHyp	WesPro	"2024-04-15T00:25:14Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:23:09Z"	Entry not found
jetx/hznvrae	jetx	"2024-04-14T22:25:26Z"	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-14T22:23:38Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Arczisan/tattoo-world	Arczisan	"2024-04-14T22:25:35Z"	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "region:us" ]	text-to-image	"2024-04-14T22:25:27Z"	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\0<\0l\0o\0r\0a\0:\0T\0a\0t\0t\0o\0o\0W\0o\0r\0l\0d\0:\01\0>\0 \0(\0T\0a\0t\0t\0o\0o\0W\0o\0r\0l\0d\0:\00\0.\09\0)\0 \0 \0 \0m\0a\0s\0t\0e\0r\0p\0i\0e\0c\0e\0,\0 \0b\0e\0s\0t\0 \0q\0u\0a\0l\0i\0t\0y\0,\0s\0h\0c\0h\0,\0(\01\0w\0o\0m\0a\0n\0:\01\0.\04\0)\0 \0(\0b\0l\0u\0e\0 \0b\0i\0k\0i\0n\0i\0 \0:\01\0.\03\0)\0 \0,\0s\0o\0l\0o\0,\0b\0e\0a\0u\0t\0i\0f\0u\0l\0,\0a\0t\0t\0r\0a\0c\0t\0i\0v\0e\0,\0c\0u\0t\0e\0 \0<\0l\0o\0r\0a\0:\0m\0o\0r\0e\0_\0d\0e\0t\0a\0i\0l\0s\0:\0.\06\0>\0 \0(\0b\0e\0a\0c\0h\0 \0b\0a\0c\0k\0g\0r\0o\0u\0n\0d\0:\01\0.\05\0)\0 \0" output: url: images/00051-2622726513.jpeg base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null --- # Tattoo World <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Arczisan/tattoo-world/tree/main) them in the Files & versions tab.
mergekit-community/mergekit-slerp-fodinzo	mergekit-community	"2024-04-14T22:29:41Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:Equall/Saul-Base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:26:07Z"	--- base_model: - HuggingFaceH4/zephyr-7b-beta - Equall/Saul-Base library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) * [Equall/Saul-Base](https://huggingface.co/Equall/Saul-Base) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Equall/Saul-Base layer_range: [0, 32] - model: HuggingFaceH4/zephyr-7b-beta layer_range: [0, 32] merge_method: slerp base_model: HuggingFaceH4/zephyr-7b-beta parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
ShenaoZ/0.0_ablation_iter_2	ShenaoZ	"2024-04-14T23:39:57Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0_ablation_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:26:49Z"	Entry not found
EdBerg/quotes_Llama-2-7b-chat-hf	EdBerg	"2024-04-14T22:45:47Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-14T22:30:08Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ShenaoZ/0.0001_ablation_iter_2	ShenaoZ	"2024-04-14T23:44:29Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_ablation_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:30:48Z"	--- license: mit base_model: ShenaoZ/0.0001_ablation_iter_1 tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.0001_ablation_iter_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 0.0001_ablation_iter_2 This model is a fine-tuned version of [ShenaoZ/0.0001_ablation_iter_1](https://huggingface.co/ShenaoZ/0.0001_ablation_iter_1) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
sealad886/c4ai-command-r-plus-2bit	sealad886	"2024-04-14T22:31:48Z"	0	null	[ "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-14T22:31:46Z"	--- license: cc-by-nc-4.0 ---
Abhinay123/wav2vec2_vedas2_epoch_0_step_1399	Abhinay123	"2024-04-14T22:33:20Z"	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-04-14T22:32:08Z"	Invalid username or password.
iamalexcaspian/Greg-OverTheGardenWall	iamalexcaspian	"2024-04-14T22:33:36Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:33:16Z"	Entry not found
Homiebear/Dreadunit	Homiebear	"2024-04-14T22:36:36Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-14T22:35:53Z"	--- license: openrail ---
Used111abc/Girl	Used111abc	"2024-04-14T22:36:20Z"	0	null	[ "license:cc-by-sa-3.0", "region:us" ]	null	"2024-04-14T22:36:20Z"	--- license: cc-by-sa-3.0 ---
yeshwanthkesani/codecraft	yeshwanthkesani	"2024-04-14T22:37:05Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:37:05Z"	Entry not found
jhamel/REBEL-KB-Fine-Tune	jhamel	"2024-04-14T22:42:49Z"	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	"2024-04-14T22:37:15Z"	--- license: apache-2.0 ---
Ruudan/Datasetmeu	Ruudan	"2024-04-14T22:38:55Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:37:27Z"	Entry not found
mlx-community/c4ai-command-r-plus-2bit	mlx-community	"2024-04-14T23:47:31Z"	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "mlx", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:37:43Z"	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 library_name: transformers tags: - mlx --- # mlx-community/c4ai-command-r-plus-2bit This model was converted to MLX format from [`CohereForAI/c4ai-command-r-plus`]() using mlx-lm version 0.9.0. Refer to the [original model card](https://huggingface.co/CohereForAI/c4ai-command-r-plus) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/c4ai-command-r-plus-2bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
Dracones/mixtral-8x22b-instruct-oh_exl2_6.0bpw	Dracones	"2024-04-14T22:55:29Z"	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "exl2", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]	text-generation	"2024-04-14T22:38:52Z"	--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 6.0bpw This is a 6.0bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "\| Quant Level \| Perplexity Score \|" echo "\|-------------\|------------------\|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" \| grep -oP 'Evaluation perplexity: \K[\d.]+') echo "\| $BIT_PRECISION \| $score \|" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```
Tverous/llama-13b-ppo-final	Tverous	"2024-04-14T22:39:46Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-14T22:39:44Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lexArYuld/net	lexArYuld	"2024-04-14T22:44:02Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-14T22:40:42Z"	--- license: openrail ---
woweenie/v66-sd21merge-45k-8krampdown-half	woweenie	"2024-04-14T22:45:59Z"	0	diffusers	[ "diffusers", "safetensors", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	null	"2024-04-14T22:41:01Z"	Entry not found
Lichang-Chen/zephyr-7b-sft-837k	Lichang-Chen	"2024-04-15T02:33:46Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:Lichang-Chen/800k_ift", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:43:20Z"	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - Lichang-Chen/800k_ift model-index: - name: zephyr-7b-sft-837k 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. --> # zephyr-7b-sft-837k This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the Lichang-Chen/800k_ift dataset. It achieves the following results on the evaluation set: - Loss: 3.1087 ## 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: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.1323 \| 1.0 \| 1179 \| 3.1087 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
YangPa/Gosegu	YangPa	"2024-04-14T22:44:45Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-14T22:43:26Z"	--- license: openrail ---
jhamel/rebel_fine_tune	jhamel	"2024-04-15T00:08:32Z"	0	transformers	[ "transformers", "safetensors", "bart", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	"2024-04-14T22:43:32Z"	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
liamvbetts/t5-small-finetuned-2024-03-12	liamvbetts	"2024-04-14T22:44:45Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:ericjiliangli/t5-small-news-summarization", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T22:44:27Z"	--- base_model: ericjiliangli/t5-small-news-summarization tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-12 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. --> # t5-small-finetuned-2024-03-12 This model is a fine-tuned version of [ericjiliangli/t5-small-news-summarization](https://huggingface.co/ericjiliangli/t5-small-news-summarization) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7445 - Rouge1: 30.158 - Rouge2: 15.0234 - Rougel: 25.9885 - Rougelsum: 26.1101 - Gen Len: 18.759 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.9214 \| 1.0 \| 328 \| 1.7445 \| 30.158 \| 15.0234 \| 25.9885 \| 26.1101 \| 18.759 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
stulcrad/CNEC_2_0_ext_Czert-B-base-cased	stulcrad	"2024-04-14T23:31:08Z"	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:cnec", "base_model:UWB-AIR/Czert-B-base-cased", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2024-04-14T22:46:03Z"	--- base_model: UWB-AIR/Czert-B-base-cased tags: - generated_from_trainer datasets: - cnec metrics: - precision - recall - f1 - accuracy model-index: - name: CNEC_2_0_ext_Czert-B-base-cased results: - task: name: Token Classification type: token-classification dataset: name: cnec type: cnec config: default split: validation args: default metrics: - name: Precision type: precision value: 0.7743521000893655 - name: Recall type: recall value: 0.8600496277915632 - name: F1 type: f1 value: 0.8149541500117563 - name: Accuracy type: accuracy value: 0.9550983576971666 --- <!-- 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. --> # CNEC_2_0_ext_Czert-B-base-cased This model is a fine-tuned version of [UWB-AIR/Czert-B-base-cased](https://huggingface.co/UWB-AIR/Czert-B-base-cased) on the cnec dataset. It achieves the following results on the evaluation set: - Loss: 0.1985 - Precision: 0.7744 - Recall: 0.8600 - F1: 0.8150 - Accuracy: 0.9551 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.5376 \| 2.23 \| 500 \| 0.2846 \| 0.5751 \| 0.6864 \| 0.6258 \| 0.9214 \| \| 0.2806 \| 4.46 \| 1000 \| 0.2324 \| 0.6761 \| 0.7906 \| 0.7289 \| 0.9391 \| \| 0.2234 \| 6.7 \| 1500 \| 0.2105 \| 0.7070 \| 0.8169 \| 0.7580 \| 0.9454 \| \| 0.1902 \| 8.93 \| 2000 \| 0.2102 \| 0.7298 \| 0.8323 \| 0.7776 \| 0.9480 \| \| 0.1697 \| 11.16 \| 2500 \| 0.2028 \| 0.7345 \| 0.8347 \| 0.7814 \| 0.9500 \| \| 0.1573 \| 13.39 \| 3000 \| 0.2006 \| 0.7533 \| 0.8442 \| 0.7962 \| 0.9515 \| \| 0.1423 \| 15.62 \| 3500 \| 0.2001 \| 0.7539 \| 0.8496 \| 0.7989 \| 0.9526 \| \| 0.1341 \| 17.86 \| 4000 \| 0.2088 \| 0.7596 \| 0.8561 \| 0.8049 \| 0.9527 \| \| 0.127 \| 20.09 \| 4500 \| 0.2021 \| 0.7642 \| 0.8556 \| 0.8073 \| 0.9543 \| \| 0.1233 \| 22.32 \| 5000 \| 0.1987 \| 0.7732 \| 0.8596 \| 0.8141 \| 0.9556 \| \| 0.1206 \| 24.55 \| 5500 \| 0.1985 \| 0.7744 \| 0.8600 \| 0.8150 \| 0.9551 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
MaiiaCompsolutions/industry_classifier_full_descr_3rd_level	MaiiaCompsolutions	"2024-04-14T22:50:07Z"	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-04-14T22:49:39Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
nik548/bioGPT_finetuned_ncbi	nik548	"2024-04-15T02:37:26Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "token-classification", "generated_from_trainer", "base_model:microsoft/biogpt", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	token-classification	"2024-04-14T22:51:17Z"	--- license: mit base_model: microsoft/biogpt tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bioGPT_finetuned_ncbi 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. --> # bioGPT_finetuned_ncbi This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1880 - Precision: 0.4637 - Recall: 0.5448 - F1: 0.5010 - Accuracy: 0.9476 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1735 \| 1.0 \| 1358 \| 0.2050 \| 0.3990 \| 0.4094 \| 0.4041 \| 0.9415 \| \| 0.1086 \| 2.0 \| 2716 \| 0.1800 \| 0.4230 \| 0.5354 \| 0.4726 \| 0.9463 \| \| 0.07 \| 3.0 \| 4074 \| 0.1880 \| 0.4637 \| 0.5448 \| 0.5010 \| 0.9476 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
liamvbetts/t5-small-finetuned-2024-03-13	liamvbetts	"2024-04-14T22:52:38Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-12", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T22:52:17Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-12 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-13 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. --> # t5-small-finetuned-2024-03-13 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-12](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-12) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8462 - Rouge1: 30.3334 - Rouge2: 17.8246 - Rougel: 26.5826 - Rougelsum: 27.0835 - Gen Len: 18.619 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.6474 \| 1.0 \| 332 \| 1.8462 \| 30.3334 \| 17.8246 \| 26.5826 \| 27.0835 \| 18.619 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
EddyGiusepe/Gemma-1.1-2b-it-instruct-aira-dataset-v2	EddyGiusepe	"2024-04-14T22:52:52Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:52:52Z"	Entry not found
cilantro9246/whfwbmi	cilantro9246	"2024-04-14T22:55:23Z"	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-14T22:53:04Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rxh220009/flan-t5-base-imdb-text-classification	rxh220009	"2024-04-14T22:53:12Z"	0	null	[ "region:us" ]	null	"2024-04-14T22:53:09Z"	Entry not found
ashishp-wiai/ClipArt_LoRA_60-2024-04-14	ashishp-wiai	"2024-04-14T23:42:48Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-04-14T22:53:17Z"	Entry not found
Cloudxego/J-Hope	Cloudxego	"2024-04-14T22:54:29Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-14T22:53:54Z"	--- license: openrail ---
Gustav0-Freind/my_coedit_onnx	Gustav0-Freind	"2024-04-15T00:44:52Z"	0	transformers	[ "transformers", "onnx", "t5", "text2text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T22:55:02Z"	--- license: mit ---
liamvbetts/t5-small-finetuned-2024-03-14	liamvbetts	"2024-04-14T22:55:42Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-13", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T22:55:26Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-13 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-14 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. --> # t5-small-finetuned-2024-03-14 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-13](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-13) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6488 - Rouge1: 36.7711 - Rouge2: 23.7969 - Rougel: 33.074 - Rougelsum: 33.6007 - Gen Len: 18.814 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:------:\|:---------:\|:-------:\| \| 1.6749 \| 1.0 \| 341 \| 1.6488 \| 36.7711 \| 23.7969 \| 33.074 \| 33.6007 \| 18.814 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Dracones/mixtral-8x22b-instruct-oh_exl2_5.5bpw	Dracones	"2024-04-14T23:10:43Z"	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "exl2", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T22:55:29Z"	--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 5.5bpw This is a 5.5bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "\| Quant Level \| Perplexity Score \|" echo "\|-------------\|------------------\|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" \| grep -oP 'Evaluation perplexity: \K[\d.]+') echo "\| $BIT_PRECISION \| $score \|" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```
Coolwowsocoolwow/Meg_FNAFHS_Season_2	Coolwowsocoolwow	"2024-04-14T22:59:23Z"	0	null	[ "license:openrail", "region:us" ]	null	"2024-04-14T22:57:44Z"	--- license: openrail ---
liamvbetts/t5-small-finetuned-2024-03-15	liamvbetts	"2024-04-14T22:58:11Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-14", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T22:57:58Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-14 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-15 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. --> # t5-small-finetuned-2024-03-15 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-14](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-14) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5180 - Rouge1: 42.6379 - Rouge2: 30.7892 - Rougel: 39.2984 - Rougelsum: 39.671 - Gen Len: 18.9765 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.787 \| 1.0 \| 337 \| 1.5180 \| 42.6379 \| 30.7892 \| 39.2984 \| 39.671 \| 18.9765 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
anuragk16/gretelai-synthetic_text_to_sql-llama2-model	anuragk16	"2024-04-14T23:10:50Z"	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T23:00:07Z"	Entry not found
liamvbetts/t5-small-finetuned-2024-03-16	liamvbetts	"2024-04-14T23:00:41Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-15", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T23:00:27Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-15 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-16 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. --> # t5-small-finetuned-2024-03-16 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-15](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-15) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9081 - Rouge1: 32.2806 - Rouge2: 18.3465 - Rougel: 27.9985 - Rougelsum: 28.4829 - Gen Len: 18.6506 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.9126 \| 1.0 \| 330 \| 1.9081 \| 32.2806 \| 18.3465 \| 27.9985 \| 28.4829 \| 18.6506 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
bdambrosio/command-r-plus-6.0bpw-h8-exl2	bdambrosio	"2024-04-15T00:26:06Z"	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]	text-generation	"2024-04-14T23:01:20Z"	--- license: apache-2.0 --- Like the name says: command-r-plus 6.0bpw h8 exl2 I have some trouble getting it to break free from it's rigid pre-trained json/function-calling format. YMMV
Erfan-Shayegani/llama2-lora_Unlearned_GA_Accelerate	Erfan-Shayegani	"2024-04-14T23:01:29Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:01:26Z"	Invalid username or password.
lovelyai999/temp	lovelyai999	"2024-04-14T23:19:31Z"	0	null	[ "gguf", "region:us" ]	null	"2024-04-14T23:02:19Z"	Entry not found
liamvbetts/t5-small-finetuned-2024-03-17	liamvbetts	"2024-04-14T23:03:06Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-16", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T23:02:49Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-16 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-17 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. --> # t5-small-finetuned-2024-03-17 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-16](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-16) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6870 - Rouge1: 36.9896 - Rouge2: 24.6597 - Rougel: 32.6752 - Rougelsum: 32.6582 - Gen Len: 18.8143 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.9296 \| 1.0 \| 276 \| 1.6870 \| 36.9896 \| 24.6597 \| 32.6752 \| 32.6582 \| 18.8143 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
mradermacher/stairolz-70b-GGUF	mradermacher	"2024-04-14T23:44:13Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:03:23Z"	Entry not found
nlp-group/gradio_bert	nlp-group	"2024-04-14T23:48:57Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:03:30Z"	Entry not found
woweenie/v66-sd21merge-45k-7krampdown-half	woweenie	"2024-04-14T23:09:37Z"	0	diffusers	[ "diffusers", "safetensors", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	null	"2024-04-14T23:04:22Z"	Entry not found
liamvbetts/t5-small-finetuned-2024-03-18	liamvbetts	"2024-04-14T23:06:57Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-17", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T23:06:29Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-17 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-18 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. --> # t5-small-finetuned-2024-03-18 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-17](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-17) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3412 - Rouge1: 42.2216 - Rouge2: 30.5944 - Rougel: 39.273 - Rougelsum: 39.1457 - Gen Len: 18.7386 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:------:\|:---------:\|:-------:\| \| 1.8771 \| 1.0 \| 349 \| 1.3412 \| 42.2216 \| 30.5944 \| 39.273 \| 39.1457 \| 18.7386 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
foundryservices/bittensor-sn28-base-lstm	foundryservices	"2024-04-14T23:11:08Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:06:42Z"	Entry not found
mahiatlinux/lora_test_33	mahiatlinux	"2024-04-14T23:08:13Z"	0	transformers	[ "transformers", "mistral", "text-generation-inference", "unsloth", "ggml", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:08:06Z"	Invalid username or password.
mradermacher/scarlett-33b-GGUF	mradermacher	"2024-04-15T00:32:17Z"	0	transformers	[ "transformers", "gguf", "en", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:09:06Z"	--- exported_from: ajibawa-2023/scarlett-33b language: - en library_name: transformers license: cc-by-nc-nd-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ajibawa-2023/scarlett-33b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/scarlett-33b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q2_K.gguf) \| Q2_K \| 12.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.IQ3_XS.gguf) \| IQ3_XS \| 13.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.IQ3_S.gguf) \| IQ3_S \| 14.2 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q3_K_S.gguf) \| Q3_K_S \| 14.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.IQ3_M.gguf) \| IQ3_M \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q3_K_M.gguf) \| Q3_K_M \| 15.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q3_K_L.gguf) \| Q3_K_L \| 17.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.IQ4_XS.gguf) \| IQ4_XS \| 17.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q4_K_S.gguf) \| Q4_K_S \| 18.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q4_K_M.gguf) \| Q4_K_M \| 19.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q5_K_S.gguf) \| Q5_K_S \| 22.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q5_K_M.gguf) \| Q5_K_M \| 23.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q6_K.gguf) \| Q6_K \| 26.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/scarlett-33b-GGUF/resolve/main/scarlett-33b.Q8_0.gguf) \| Q8_0 \| 34.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
liamvbetts/t5-small-finetuned-2024-03-19	liamvbetts	"2024-04-14T23:09:52Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-18", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T23:09:21Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-18 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-19 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. --> # t5-small-finetuned-2024-03-19 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-18](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-18) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7824 - Rouge1: 34.996 - Rouge2: 23.0601 - Rougel: 32.7854 - Rougelsum: 33.1113 - Gen Len: 18.6667 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.7163 \| 1.0 \| 347 \| 1.7824 \| 34.996 \| 23.0601 \| 32.7854 \| 33.1113 \| 18.6667 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
divinetaco/aranea-tenebris-120b-v1.0	divinetaco	"2024-04-14T23:21:16Z"	1	transformers	[ "transformers", "not-for-all-audiences", "nsfw", "mergekit", "merge", "base_model:Netrve/Miqu-PlayMaid-70B-v0.1", "base_model:ShinojiResearch/Senku-70B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:09:25Z"	--- license: cc-by-nc-4.0 base_model: - Netrve/Miqu-PlayMaid-70B-v0.1 - ShinojiResearch/Senku-70B library_name: transformers tags: - not-for-all-audiences - nsfw - mergekit - merge --- # aranea-tenebris-116b-v1.0 aka Netrve/Miqu-PlayMaid-70B-v0.1 + ShinojiResearch/Senku-70B Model merge for uncensored creative writing and rp ![image/png](https://huggingface.co/divinetaco/aranea-tenebris-120b-v1.0/resolve/main/aranea-tenebris.png) A [mergekit](https://github.com/arcee-ai/mergekit) frankenmerge based on [Netrve/Miqu-PlayMaid-70B-v0.1](https://huggingface.co/Netrve/Miqu-PlayMaid-70B-v0.1) with interleaved layers of [ShinojiResearch/Senku-70B](https://huggingface.co/ShinojiResearch/Senku-70B). This was the top performing model from a second series of merge experiments to create a highly coherant creative writing and rp model. Tests consisted of a series of private DnD scenario benchmarks, with manual comparison of the most promising merges. A number of different base models, interleave models and layer offsets were compared. This model outperformed a number of other popular 70B+ models and merges in both creativity and coherancy tests. It was (briefly) compared to Mixtral 8x22B running 2/3/4 experts. - Usable context: ~32768 - Recommended prompt format: Alpaca - Layers: 137 ### Testing Two different writing styles were considered for each testing scenario: - Completions for 3rd person narration. No character role was assumed. - Completions for 1st and 2nd person turn based (out-of-order) rp. A character role was assumed by the model, but narration of minor characters and events was encouraged. Tests assumed a mature audience, but a range of scenarios were constructed. Thematic inconsistancy or bias in character behaviour was penalized heavily. Models showing the following were penalized during manual comparison: - Consistently short responses - Laziness or readily gave up on solving a character problem. - Overly malleable, where characters could not hold opinions or beliefs. - Passiveness or an inability to drive the narrative. - Persistent repeats. Bad merges tend to latch onto and reuse specific keywords. - Ignoring or missing obvious scenario solutions. - Impersonating other major characters out of turn during rp tests. - Faliure to follow a character's description. This criteria is pretty broad, and could include things like character skills, refusals etc. - Major inconsistencies in scenes or recall. Note - invention of thematically consistant detail was encouraged. ### Interesting observations from benchmarking - 10 layer interleave stride with a 20 layer interleave width consistently outperformed alternative combinations for coherancy. - 8 layer interleave stride with a 16 layer interleave width consistantly outperformed alternative combinations for creativity whilst remaining reasonably coherant. - Regular stride intervals are not optimal. In particular offsetting the first or last set of base models offets often improved metrics. - Goliath-120B is still a good standard for coherancy below 4096 context. A few miqu-1 merges are comparable, but testing found a small amount coherancy could be sacrificed for notable creativity improvements. ### Quantizations Exllamav2 quants will be available when bandwidth permits.
Dracones/mixtral-8x22b-instruct-oh_exl2_5.0bpw	Dracones	"2024-04-14T23:30:54Z"	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "exl2", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "5-bit", "region:us" ]	text-generation	"2024-04-14T23:10:45Z"	--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 5.0bpw This is a 5.0bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "\| Quant Level \| Perplexity Score \|" echo "\|-------------\|------------------\|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" \| grep -oP 'Evaluation perplexity: \K[\d.]+') echo "\| $BIT_PRECISION \| $score \|" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```
itay-nakash/model_9c66fb4639	itay-nakash	"2024-04-15T02:16:40Z"	0	null	[ "safetensors", "region:us" ]	null	"2024-04-14T23:12:10Z"	Entry not found
Aveo/autotrain-4hkjv-ypex0	Aveo	"2024-04-14T23:14:13Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "dataset:autotrain-4hkjv-ypex0/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-04-14T23:13:15Z"	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - autotrain-4hkjv-ypex0/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.9300565719604492 f1_macro: 0.8222222222222223 f1_micro: 0.8333333333333334 f1_weighted: 0.8222222222222223 precision_macro: 0.8888888888888888 precision_micro: 0.8333333333333334 precision_weighted: 0.8888888888888888 recall_macro: 0.8333333333333334 recall_micro: 0.8333333333333334 recall_weighted: 0.8333333333333334 accuracy: 0.8333333333333334
hui168/rl_course_vizdoom_health_gathering_supreme	hui168	"2024-04-14T23:13:39Z"	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2024-04-14T23:13:29Z"	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 11.34 +/- 5.39 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r hui168/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
mradermacher/NeuralStockFusion-7b-GGUF	mradermacher	"2024-04-15T01:56:08Z"	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Kukedlc/NeuralSirKrishna-7b", "base_model:Kukedlc/NeuralArjuna-7B-DT", "base_model:Kukedlc/NeuralMaths-Experiment-7b", "base_model:Kukedlc/NeuralSynthesis-7B-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:15:19Z"	--- base_model: - Kukedlc/NeuralSirKrishna-7b - Kukedlc/NeuralArjuna-7B-DT - Kukedlc/NeuralMaths-Experiment-7b - Kukedlc/NeuralSynthesis-7B-v0.1 exported_from: Kukedlc/NeuralStockFusion-7b language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Kukedlc/NeuralStockFusion-7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralStockFusion-7b-GGUF/resolve/main/NeuralStockFusion-7b.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
shreyas1104/shreyas_flan-t5-base_peft_lora	shreyas1104	"2024-04-15T02:47:15Z"	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	"2024-04-14T23:16:29Z"	Entry not found
SilvioLima/Llama-2-7b-chat-finetune	SilvioLima	"2024-04-14T23:28:24Z"	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T23:20:07Z"	Entry not found
hvein/melotts3253	hvein	"2024-04-15T00:56:39Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:20:32Z"	Entry not found
John-Yakuza/SteveLora	John-Yakuza	"2024-04-15T01:17:47Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:21:06Z"	Entry not found
hvein/melotts464	hvein	"2024-04-15T00:56:13Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:21:18Z"	Entry not found
REDOUAN/Adulteducation	REDOUAN	"2024-04-14T23:21:29Z"	1	null	[ "license:openrail", "region:us" ]	null	"2024-04-14T23:21:29Z"	--- license: openrail ---
cackerman/rewrites_gem7unsloth_4bit_ft_full_merged	cackerman	"2024-04-14T23:23:47Z"	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "region:us" ]	text-generation	"2024-04-14T23:21:30Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: cackerman - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
hvein/melotts1191	hvein	"2024-04-15T00:56:38Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:22:29Z"	Entry not found
K00B404/Hitotsubashi_xB_v0-1	K00B404	"2024-04-14T23:25:28Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:25:28Z"	Invalid username or password.
mradermacher/Euryale-Inverted-L2-70B-GGUF	mradermacher	"2024-04-14T23:48:50Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:25:43Z"	Entry not found
EdBerg/quotes_Llama-2-13b-chat-hf	EdBerg	"2024-04-14T23:33:34Z"	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	"2024-04-14T23:26:13Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/stairolzlv-70b-GGUF	mradermacher	"2024-04-15T00:03:54Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:27:54Z"	Entry not found
anezatra/gpt2-alpaca-lora	anezatra	"2024-04-14T23:29:55Z"	0	peft	[ "peft", "safetensors", "text-generation", "en", "dataset:tatsu-lab/alpaca", "arxiv:1910.09700", "base_model:gpt2", "license:mit", "region:us" ]	text-generation	"2024-04-14T23:27:59Z"	--- base_model: gpt2 license: mit datasets: - tatsu-lab/alpaca language: - en pipeline_tag: text-generation library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.9.0
andrewbai/tinyllama-sft-wizardlm_evol_instruct_v2-full	andrewbai	"2024-04-15T01:42:22Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:ucla-cmllab/WizardLM_evol_instruct_V2_100k-chat-format", "base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T23:28:00Z"	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - ucla-cmllab/WizardLM_evol_instruct_V2_100k-chat-format model-index: - name: tinyllama-sft-wizardlm_evol_instruct_v2-full results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama-sft-wizardlm_evol_instruct_v2-full This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the ucla-cmllab/WizardLM_evol_instruct_V2_100k-chat-format dataset. It achieves the following results on the evaluation set: - Loss: 0.7234 ## 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: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.735 \| 1.0 \| 781 \| 0.7234 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
IamYash/VA-LLM-meiaa10a	IamYash	"2024-04-15T00:52:11Z"	0	transformers	[ "transformers", "safetensors", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:28:37Z"	Entry not found
metinovadilet/mistral-kyrgyz	metinovadilet	"2024-04-14T23:40:48Z"	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space" ]	text-generation	"2024-04-14T23:28:54Z"	--- license: apache-2.0 ---
liamvbetts/t5-small-finetuned-2024-03-20	liamvbetts	"2024-04-14T23:30:18Z"	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:liamvbetts/t5-small-finetuned-2024-03-19", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-14T23:29:46Z"	--- base_model: liamvbetts/t5-small-finetuned-2024-03-19 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-2024-03-20 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. --> # t5-small-finetuned-2024-03-20 This model is a fine-tuned version of [liamvbetts/t5-small-finetuned-2024-03-19](https://huggingface.co/liamvbetts/t5-small-finetuned-2024-03-19) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9234 - Rouge1: 29.6968 - Rouge2: 15.5967 - Rougel: 25.7424 - Rougelsum: 25.9564 - Gen Len: 18.4459 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.0445 \| 1.0 \| 293 \| 1.9234 \| 29.6968 \| 15.5967 \| 25.7424 \| 25.9564 \| 18.4459 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Ostixe360/MoMv2-bf16	Ostixe360	"2024-04-14T23:31:10Z"	0	transformers	[ "transformers", "safetensors", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-14T23:30:03Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Dracones/mixtral-8x22b-instruct-oh_exl2_4.5bpw	Dracones	"2024-04-14T23:43:22Z"	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "exl2", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "base_model:mistral-community/Mixtral-8x22B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-14T23:30:54Z"	--- language: - en license: apache-2.0 datasets: - teknium/OpenHermes-2.5 base_model: mistral-community/Mixtral-8x22B-v0.1 tags: - exl2 --- # mixtral-8x22b-instruct-oh - EXL2 4.5bpw This is a 4.5bpw EXL2 quant of [fireworks-ai/mixtral-8x22b-instruct-oh](https://huggingface.co/fireworks-ai/mixtral-8x22b-instruct-oh) Details about the model can be found at the above model page. ## EXL2 Version These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library. If you have problems loading these models, please update Text Generation WebUI to the latest version. ## Perplexity Scoring Below are the perplexity scores for the EXL2 models. A lower score is better. _TODO_ ### Perplexity Script This was the script used for perplexity testing. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) # Print the markdown table header echo "\| Quant Level \| Perplexity Score \|" echo "\|-------------\|------------------\|" for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do MODEL_DIR="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" if [ -d "$MODEL_DIR" ]; then output=$(python test_inference.py -m "$MODEL_DIR" -gs 44,48 -ed data/wikitext/wikitext-2-v1.parquet) score=$(echo "$output" \| grep -oP 'Evaluation perplexity: \K[\d.]+') echo "\| $BIT_PRECISION \| $score \|" fi done ``` ## Quant Details This is the script used for quantization. ```bash #!/bin/bash # Activate the conda environment source ~/miniconda3/etc/profile.d/conda.sh conda activate exllamav2 # Set the model name and bit size MODEL_NAME="mixtral-8x22b-instruct-oh" # Define variables MODEL_DIR="models/$MODEL_NAME" OUTPUT_DIR="exl2_$MODEL_NAME" MEASUREMENT_FILE="measurements/$MODEL_NAME.json" # Create the measurement file if needed if [ ! -f "$MEASUREMENT_FILE" ]; then echo "Creating $MEASUREMENT_FILE" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE fi # Choose one of the below. Either create a single quant for testing or a batch of them. # BIT_PRECISIONS=(2.25) BIT_PRECISIONS=(7.0 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25) for BIT_PRECISION in "${BIT_PRECISIONS[@]}" do CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw" # If it doesn't already exist, make the quant if [ ! -d "$CONVERTED_FOLDER" ]; then echo "Creating $CONVERTED_FOLDER" # Create directories if [ -d "$OUTPUT_DIR" ]; then rm -r "$OUTPUT_DIR" fi mkdir "$OUTPUT_DIR" mkdir "$CONVERTED_FOLDER" # Run conversion commands python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER fi done ```
Cbrown9/Articulate-Visions-T1	Cbrown9	"2024-04-14T23:31:13Z"	0	null	[ "region:us" ]	null	"2024-04-14T23:31:13Z"	Entry not found
relu-ntnu/bart-large-cnn_v1_trained_on_100	relu-ntnu	"2024-04-14T23:32:25Z"	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:32:22Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
neural-commons/neural-mem-cell-256-128-0.1-v0.0.1	neural-commons	"2024-04-14T23:55:15Z"	0	transformers	[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]	null	"2024-04-14T23:35:02Z"	Entry not found