Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 1

polyconnect/ppo-LunarLander-v2_unit8

polyconnect

reinforcement-learning

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -91.96 +/- 53.74 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 200000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'polyconnect/ppo-LunarLander-v2_unit8' 'batch_size': 512 'minibatch_size': 128} ```

Neroism8422/Text_Classification_100

Neroism8422

transformers

text-classification

Entry not found

AbdullahTarek/temp_llama3_model

AbdullahTarek

transformers

text-generation

--- 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]

Jaypen/HG0s_Community

Jaypen

--- license: apache-2.0 ---

juaaa/model

juaaa

Entry not found

Srujan9712/example-model

Srujan9712

#Example-model This is my ModelCard ReadME file --- license: mit ---

Majid097/distilbert-base-uncased-finetuned-mrpc

Majid097

Entry not found

rakesh18me/first_repo

rakesh18me

Entry not found

thakur1149/taxi-v1

thakur1149

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="thakur1149/taxi-v1", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Majid097/distilbert-base-uncased-finetuned-rte

Majid097

Entry not found

Bigpa/Car

Bigpa

Entry not found

Ponce-01/unsloth_4bit_mistral_imdb_model

Ponce-01

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - **Developed by:** Ponce-01 - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

deathsaber93/SafeSQL-v1

deathsaber93

keras

text-classification

--- license: apache-2.0 datasets: - b-mc2/sql-create-context - philikai/Spider-SQL-LLAMA2_train - ChrisHayduk/Llama-2-SQL-Dataset language: - en metrics: - accuracy - f1 - recall - precision library_name: keras pipeline_tag: text-classification tags: - sql-injection - malicious-sql - sql-injection-detection - malicious-sql-detection --- # SafeSQL-v1 ([Playground](https://huggingface.co/spaces/deathsaber93/SafeSQL-v1-Demo)) ### Model Meta - **Feedback:** aakash.howlader@gmail.com - **Model type:** Language model - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Playground:** [SafeSQL-v1-Demo](https://huggingface.co/spaces/deathsaber93/SafeSQL-v1-Demo) ## Overview This is a Keras 3.x model trained specifically to detect malicious SQLs. It is able to detect various SQL injection vectors such as Error-based, Union-based, Blind, Boolean-based ,Time-based, Out-of-band, Stacked queries. This was trained on ~167K SQLs containing an almost even distribution of malicious and benign SQLs. Its training involved preprocessing specifically for SQL with special masking tokens. 28 additional numeric features were also generated and the top 10 among them were selected for training using Recursive Feature Elimination. The training consisted of a warm-up period with a smaller, sinusoidally decaying learning rate followed by a higher learning rate with cosine decay. A special callback was used to monitor for and protect against gradient explosions and automatically adjust the learning rate and model weights based on the scale of the explosion. Weight and kernel constraints are applied to help prevent overfitting and achieve better generalization. For faster model loading and inference, [mixed precision](https://www.tensorflow.org/guide/mixed_precision) has been used. The best checkpoint has been saved and made available for use. **CONTEXT WINDOW:** 1200 tokens **PARAMETERS:** 30.7M *(**Trainable:** 7.7M, **Frozen:** 2K, **Optimizer:** 23M)* **NUMBER OF INPUTS:** 2 - The SQL queries as string and extra numeric features. **NUMBER OF OUTPUTS:** 1 - Probability that the given SQL is malicious (the output layer uses a sigmoid activation). #### Checkpointed Epoch ``` 823/823 ━━━━━━━━━━━━━━━━━━━━ 99s 120ms/step - AUPR: 0.9979 - f1_score: 0.5782 - fn: 64.0947 - fp: 8.2500 - loss: 0.0236 - precision: 0.9987 - recall: 0.9889 - val_AUPR: 0.9970 - val_f1_score: 0.5775 - val_fn: 34.0000 - val_fp: 4.0000 - val_loss: 0.0298 - val_precision: 0.9985 - val_recall: 0.9873 - learning_rate: 7.0911e-04 ``` #### Benchmark Results **Total SQLs:** 30919 **Total Negatives:** 11382 **Total Positives:** 19537 **Total hits:** 30844/30919 with accuracy of **99.76%**. **False Negatives:** 69 - **0.61%** **False Positives:** 6 - **0.03%** #### Training Data The training data is made available [here](dataset/train.csv) and the benchmark data is made available [here](dataset/benchmark.csv). The data was curated from the following sources - 1. https://www.kaggle.com/datasets/gambleryu/biggest-sql-injection-dataset/data 2. https://huggingface.co/datasets/b-mc2/sql-create-context 3. https://github.com/payloadbox/sql-injection-payload-list/tree/master/Intruder 4. https://huggingface.co/datasets/ChrisHayduk/Llama-2-SQL-Dataset/viewer/default/eval 5. https://huggingface.co/datasets/philikai/Spider-SQL-LLAMA2_train/viewer/default/train #### Benchmark Data 1. https://www.kaggle.com/datasets/sajid576/sql-injection-dataset?select=Modified_SQL_Dataset.csv ## How to Use 1. Based on your hardware (whether using GPU or not), please download the corresponding `requiremnts-[cpu/gpu].txt` file and install it (`pip install -r requirements.txt`) 2. Download the model file `sqid.keras`. 3. The model expects certain numerical features along with the SQL query. As of v1, some boiler-plate code needs to be written in order to add the numeric features. Please use the below code snippet to load the model, add the expected numeric features and run an inference. Future iterations of the model will have the numeric features baked into the network's layers. ```Python import re from multiprocessing import cpu_count from keras.src.saving import load_model import pandas as pd from numpy import int64 from pandarallel import pandarallel from sklearn.preprocessing import RobustScaler model = load_model('./sqid.keras') pandarallel.initialize(use_memory_fs=True, nb_workers=cpu_count()) def sql_tokenize(sql_query): sql_query = sql_query.replace('`', ' ').replace('%20', ' ').replace('=', ' = ').replace('((', ' (( ').replace( '))', ' )) ').replace('(', ' ( ').replace(')', ' ) ').replace('||', ' || ').replace(',', '').replace( '--', ' -- ').replace(':', ' : ').replace('%23', ' # ').replace('+', ' + ').replace('!=', ' != ') \ .replace('"', ' " ').replace('%26', ' and ').replace('$', ' $ ').replace('%28', ' ( ').replace('%2A', ' * ') \ .replace('%7C', ' | ').replace('&', ' & ').replace(']', ' ] ').replace('[', ' [ ').replace(';', ' ; ').replace( '/*', ' /* ') sql_reserved = {'SELECT', 'FROM', 'WHERE', 'AND', 'OR', 'NOT', 'IN', 'LIKE', 'ORDER', 'BY', 'GROUP', 'HAVING', 'LIMIT', 'BETWEEN', 'IS', 'NULL', '%', 'LIKE', 'MIN', 'MAX', 'AS', 'UPPER', 'LOWER', 'TO_DATE', '=', '>', '<', '>=', '<=', '!=', '<>', 'BETWEEN', 'LIKE', 'EXISTS', 'JOIN', 'UNION', 'ALL', 'ASC', 'DESC', '||', 'AVG', 'LIMIT', 'EXCEPT', 'INTERSECT', 'CASE', 'WHEN', 'THEN', 'IF', 'IF', 'ANY', 'CAST', 'CONVERT', 'COALESCE', 'NULLIF', 'INNER', 'OUTER', 'LEFT', 'RIGHT', 'FULL', 'CROSS', 'OVER', 'PARTITION', 'SUM', 'COUNT', 'WITH', 'INTERVAL', 'WINDOW', 'OVER', 'ROW_NUMBER', 'RANK', 'DENSE_RANK', 'NTILE', 'FIRST_VALUE', 'LAST_VALUE', 'LAG', 'LEAD', 'DISTINCT', 'COMMENT', 'INSERT', 'UPDATE', 'DELETED', 'MERGE', '*', 'generate_series', 'char', 'chr', 'substr', 'lpad', 'extract', 'year', 'month', 'day', 'timestamp', 'number', 'string', 'concat', 'INFORMATION_SCHEMA', "SQLITE_MASTER", 'TABLES', 'COLUMNS', 'CUBE', 'ROLLUP', 'RECURSIVE', 'FILTER', 'EXCLUDE', 'AUTOINCREMENT', 'WITHOUT', 'ROWID', 'VIRTUAL', 'INDEXED', 'UNINDEXED', 'SERIAL', 'DO', 'RETURNING', 'ILIKE', 'ARRAY', 'ANYARRAY', 'JSONB', 'TSQUERY', 'SEQUENCE', 'SYNONYM', 'CONNECT', 'START', 'LEVEL', 'ROWNUM', 'NOCOPY', 'MINUS', 'AUTO_INCREMENT', 'BINARY', 'ENUM', 'REPLACE', 'SET', 'SHOW', 'DESCRIBE', 'USE', 'EXPLAIN', 'STORED', 'VIRTUAL', 'RLIKE', 'MD5', 'SLEEP', 'BENCHMARK', '@@VERSION', 'VERSION', '@VERSION', 'CONVERT', 'NVARCHAR', '#', '##', 'INJECTX', 'DELAY', 'WAITFOR', 'RAND', } tokens = sql_query.split() tokens = [re.sub(r"""[^*\w\s.=\-><_|()!"']""", '', token) for token in tokens] for i, token in enumerate(tokens): if token.strip().upper() in sql_reserved: continue if token.strip().isnumeric(): tokens[i] = '#NUMBER#' elif re.match(r'^[a-zA-Z_.|][a-zA-Z0-9_.|]*$', token.strip()): tokens[i] = '#IDENTIFIER#' elif re.match(r'^[\d:]*$', token.strip()): tokens[i] = '#TIMESTAMP#' elif '%' in token.strip(): tokens[i] = ' '.join( [j.strip() if j.strip() in ('%', "'", "'") else '#IDENTIFIER#' for j in token.strip().split('%')]) return ' '.join(tokens) def add_features(x): s = ["num_tables", "num_columns", "num_literals", "num_parentheses", "has_union", "depth_nested_queries", "num_join", "num_sp_chars", "has_mismatched_quotes", "has_tautology"] x['Query'] = x['Query'].copy().parallel_apply(lambda a: sql_tokenize(a)) x['num_tables'] = x['Query'].str.lower().str.count(r'FROM\s+#IDENTIFIER#', flags=re.I) x['num_columns'] = x['Query'].str.lower().str.count(r'SELECT\s+#IDENTIFIER#', flags=re.I) x['num_literals'] = x['Query'].str.lower().str.count("'[^']*'", flags=re.I) + x['Query'].str.lower().str.count( '"[^"]"', flags=re.I) x['num_parentheses'] = x['Query'].str.lower().str.count("\\(", flags=re.I) + x['Query'].str.lower().str.count( '\\)', flags=re.I) x['has_union'] = x['Query'].str.lower().str.count(" union |union all", flags=re.I) > 0 x['has_union'] = x['has_union'].astype(int64) x['depth_nested_queries'] = x['Query'].str.lower().str.count("\\(", flags=re.I) x['num_join'] = x['Query'].str.lower().str.count( " join |inner join|outer join|full outer join|full inner join|cross join|left join|right join", flags=re.I) x['num_sp_chars'] = x['Query'].parallel_apply(lambda a: len(re.findall(r'[\'";\-*/%=><|#]', a))) x['has_mismatched_quotes'] = x['Query'].parallel_apply( lambda sql_query: 1 if re.search(r"'.*[^']$|\".*[^\"]$", sql_query) else 0) x['has_tautology'] = x['Query'].parallel_apply(lambda sql_query: 1 if re.search(r"'[\s]*=[\s]*'", sql_query) else 0) return x input_sqls = ['SELECT roomName , RoomId FROM Rooms WHERE basePrice > 160 AND maxOccupancy > 2;', # Not malicious "ORDER BY 1,SLEEP(5),BENCHMARK(1000000,MD5('A')),4,5,6,7,8,9,10,11,12,13,14,15,16,17,18#", # Malicious "; desc users; --", # Malicious "ORDER BY 1,SLEEP(5),BENCHMARK(1000000,MD5('A')),4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27", # Malicious "SELECT DISTINCT t2.datasetid FROM paperdataset AS t3 JOIN dataset AS t2 ON t3.datasetid = t2.datasetid JOIN paperkeyphrase AS t1 ON t1.paperid = t3.paperid JOIN keyphrase AS t4 ON t1.keyphraseid = t4.keyphraseid WHERE t4.keyphrasename = ""semantic parsing"";" # Not malicious ] numeric_features = ["num_tables", "num_columns", "num_literals", "num_parentheses", "has_union", "depth_nested_queries", "num_join", "num_sp_chars", "has_mismatched_quotes", "has_tautology"] input_df = pd.DataFrame(input_sqls, columns=['Query']) input_df = add_features(input_df) scaler = RobustScaler() x_in = scaler.fit_transform(input_df[numeric_features]) preds = model.predict([input_df['Query'], x_in]).tolist() for i, pred in enumerate(preds): print() print(f'Query: {input_sqls[i]}') print(f'Malicious? {pred[0] >= 0.50} ({pred[0]})') print() # Run the benchmark input_df = pd.read_csv('benchmark.csv') hits = 0 data_size = input_df.shape[0] miss_pos, miss_neg = [], [] total_negs = input_df[input_df['Label'] == 1.0].shape[0] total_pos = input_df[input_df['Label'] == 0.0].shape[0] pred_trans = ['Benign', 'Malicious'] false_metrics = {0: 0, 1: 0} x_in = scaler.transform(input_df[numeric_features]) print('Running benchmark') preds = model.predict([input_df['Query'], x_in]) miss_q = [] actuals = input_df['Label'].tolist() for i, pred in enumerate(preds): pred = int(pred[0] > .95) if pred == actuals[i]: hits += 1 else: false_metrics[int(pred)] += 1 print('Finished benchmark.') print('printing results.') acc = round((hits / data_size) * 100, 2) f_neg = round((false_metrics[0] / total_negs) * 100, 2) f_pos = round((false_metrics[1] / total_pos) * 100, 2) print(f'Total data: {data_size}') print(f'Total Negatives: {total_negs} \t Total Positives: {total_pos}') print() print(f'Total hits: {hits}/{data_size} with accuracy of {acc}%.') print(f'False Negatives: {false_metrics[0]}({f_neg}%) \t False Positives: {false_metrics[1]}({f_pos}%)', false_metrics[0], f_neg, false_metrics[1], f_pos) ``` #### Output ``` 2024-06-16 17:34:54.587073: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. ... 2024-06-16 17:36:11.762174: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:465] Loaded cuDNN version 8902 1/1 ━━━━━━━━━━━━━━━━━━━━ 14s 14s/step Query: SELECT roomName , RoomId FROM Rooms WHERE basePrice > 160 AND maxOccupancy > 2; Malicious? False (7.727547199465334e-05) Query: ORDER BY 1,SLEEP(5),BENCHMARK(1000000,MD5('A')),4,5,6,7,8,9,10,11,12,13,14,15,16,17,18# Malicious? True (1.0) Query: ; desc users; -- Malicious? True (0.9999552965164185) Query: ORDER BY 1,SLEEP(5),BENCHMARK(1000000,MD5('A')),4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27 Malicious? True (1.0) Query: SELECT DISTINCT t2.datasetid FROM paperdataset AS t3 JOIN dataset AS t2 ON t3.datasetid = t2.datasetid JOIN paperkeyphrase AS t1 ON t1.paperid = t3.paperid JOIN keyphrase AS t4 ON t1.keyphraseid = t4.keyphraseid WHERE t4.keyphrasename = semantic parsing; Malicious? False (6.156865989259686e-11) Running benchmark 967/967 ━━━━━━━━━━━━━━━━━━━━ 37s 37ms/step Finished benchmark. printing results. Total data: 30919 Total Negatives: 11382 Total Positives: 19537 Total hits: 30844/30919 with accuracy of 99.76%. False Negatives: 69(0.61%) False Positives: 6(0.03%) ``` ## Architecture 

Simpli58/Consulente_Decameron

Simpli58

--- license: mit ---

Majid097/bert-base-uncased-finetuned-cola

Majid097

Entry not found

WikiQuality/all_methods_hi.mlm.yo

WikiQuality

transformers

fill-mask

--- 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]

ccchair/llama3_chair

ccchair

--- license: mit ---

Majid097/finbert-finetuned-sst2

Majid097

Entry not found

Majid097/distilbert-base-uncased-finetuned-sst-2-english-finetuned-sst2

Majid097

Entry not found

PhoenixStormJr/Megaman-NT-Warrior-Iceman-RVC

PhoenixStormJr

--- license: mit ---  This is Iceman's voice from Megaman NT Warrior This was created with RVC V2, by Rejekts, trained on 500 epochs. If you would like to use the model, go here: https://huggingface.co/PhoenixStormJr/RVC-V2-easy-gui-tutorial (Iceman has very little audio. I'm just now realizing I won't be able to get every single character in the series. Skullman and Woodman are probably out.) Download Zip model here: https://huggingface.co/PhoenixStormJr/Megaman-NT-Warrior-Iceman-RVC/resolve/main/Iceman.zip Download .pth file here: https://huggingface.co/PhoenixStormJr/Megaman-NT-Warrior-Iceman-RVC/resolve/main/Iceman.pth Download .index here: https://huggingface.co/PhoenixStormJr/Megaman-NT-Warrior-Iceman-RVC/resolve/main/added_IVF222_Flat_nprobe_1_Iceman_v2.index?download=true Listen to a sample audio here: <audio controls src="https://huggingface.co/PhoenixStormJr/Megaman-NT-Warrior-Iceman-RVC/resolve/main/IcemanSample.wav?download=true"></audio>

litagin/sbv2_null_models

litagin

# Style-Bert-VITS2 ヌルモデル集 Ver 2.6.0で追加された（差分マージを実現する）ヌルモデルマージで使えるモデルたちをとりあえず置いてみています。 現在は適当に作ったささやき声モデルを置いており、これを使えば任意のモデルを囁き声モデルに変えることがある程度できます。 - `whisper1_null`: これを`B`に指定し、`A`に対してヌルモデルマージをすると、Aの話者性をある程度残したままささやき声（無声）のモデルが作れます - `whisper2_null`: 上と同じですがささやき声が有声音（ひそひそ声）なバージョンです

lifefabric/mistral-7b-v3

lifefabric

Entry not found

MubarakB/t5-formal

MubarakB

Entry not found

talli96123/meat_calssify_fresh_crop_fixed_epoch100_V_0_8_best

talli96123

transformers

image-classification

--- 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]

k2-fsa/sherpa-onnx-streaming-zipformer-korean-2024-06-16

k2-fsa

# Introduction This model is converted from https://huggingface.co/johnBamma/icefall-asr-ksponspeech-pruned-transducer-stateless7-streaming-2024-06-12 See https://github.com/k2-fsa/icefall/pull/1651 for how it is trained. Note it uses zipformer v1.

cooler8/model-test-hjh-001

cooler8

Entry not found

Anujgr8/w2v-bert-Tamil-large

Anujgr8

transformers

automatic-speech-recognition

--- license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: w2v-bert-Tamil-large 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. --> # w2v-bert-Tamil-large This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1815 - Wer: 0.2176 - Cer: 0.0328 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 2.0779 | 0.75 | 300 | 0.4934 | 0.6338 | 0.1189 | | 0.3653 | 1.5 | 600 | 0.4045 | 0.5424 | 0.0975 | | 0.2632 | 2.25 | 900 | 0.3148 | 0.4421 | 0.0723 | | 0.2084 | 3.0 | 1200 | 0.2297 | 0.3499 | 0.0576 | | 0.1359 | 3.75 | 1500 | 0.2042 | 0.3060 | 0.0464 | | 0.1049 | 4.5 | 1800 | 0.1939 | 0.2836 | 0.0446 | | 0.0823 | 5.25 | 2100 | 0.1827 | 0.2504 | 0.0382 | | 0.0561 | 6.0 | 2400 | 0.1731 | 0.2419 | 0.0368 | | 0.0352 | 6.75 | 2700 | 0.1802 | 0.2275 | 0.0335 | | 0.0224 | 7.5 | 3000 | 0.1815 | 0.2176 | 0.0328 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Elaaj/Elaaj-One

Elaaj

--- license: apache-2.0 ---

Vishalba95/Vidhnu

Vishalba95

Entry not found

csukuangfj/icefall-asr-ksponspeech-pruned-transducer-stateless7-streaming-2024-06-12

csukuangfj

automatic-speech-recognition

--- license: apache-2.0 language: - ko pipeline_tag: automatic-speech-recognition tags: - icefall --- See https://github.com/k2-fsa/icefall/pull/1651 # icefall-asr-ksponspeech-pruned-transducer-stateless7-streaming-2024-06-12 KsponSpeech is a large-scale spontaneous speech corpus of Korean. This corpus contains 969 hours of open-domain dialog utterances, spoken by about 2,000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. The original audio data has a pcm extension. During preprocessing, it is converted into a file in the flac extension and saved anew. KsponSpeech is publicly available on an open data hub site of the Korea government. The dataset must be downloaded manually. For more details, please visit: - Dataset: https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=123 - Paper: https://www.mdpi.com/2076-3417/10/19/6936 ### Streaming Zipformer-Transducer (Pruned Stateless Transducer + Streaming Zipformer) Number of model parameters: 79,022,891, i.e., 79.02 M #### Training on KsponSpeech (with MUSAN) The CERs are: | decoding method | chunk size | eval_clean | eval_other | comment | decoding mode | |----------------------|------------|------------|------------|---------------------|----------------------| | greedy search | 320ms | 10.21 | 11.07 | --epoch 30 --avg 9 | simulated streaming | | greedy search | 320ms | 10.22 | 11.07 | --epoch 30 --avg 9 | chunk-wise | | fast beam search | 320ms | 10.21 | 11.04 | --epoch 30 --avg 9 | simulated streaming | | fast beam search | 320ms | 10.25 | 11.08 | --epoch 30 --avg 9 | chunk-wise | | modified beam search | 320ms | 10.13 | 10.88 | --epoch 30 --avg 9 | simulated streaming | | modified beam search | 320ms | 10.1 | 10.93 | --epoch 30 --avg 9 | chunk-size | | greedy search | 640ms | 9.94 | 10.82 | --epoch 30 --avg 9 | simulated streaming | | greedy search | 640ms | 10.04 | 10.85 | --epoch 30 --avg 9 | chunk-wise | | fast beam search | 640ms | 10.01 | 10.81 | --epoch 30 --avg 9 | simulated streaming | | fast beam search | 640ms | 10.04 | 10.7 | --epoch 30 --avg 9 | chunk-wise | | modified beam search | 640ms | 9.91 | 10.72 | --epoch 30 --avg 9 | simulated streaming | | modified beam search | 640ms | 9.92 | 10.72 | --epoch 30 --avg 9 | chunk-size | Note: `simulated streaming` indicates feeding full utterance during decoding using `decode.py`, while `chunk-size` indicates feeding certain number of frames at each time using `streaming_decode.py`.

VKapseln475/SlimXmed4444

VKapseln475

# <Kaufen> Slimxmed Deutschland - SlimXmed Creme Erfahrungen Test nd Preis SlimXmed Erfahrungen Deutschland Inhaltsstoffe SlimXmed tritt auf den Plan im umkämpften Markt der Nahrungsergänzungsmittel zur Gewichtsreduktion. Dabei sind sie mehr als nur ein weiteres Produkt – sie sind ein Versprechen für eine gesündere und aktivere Zukunft für alle, die nicht nur abnehmen, sondern ihre Lebensqualität verbessern wollen. Dieser Blog gibt Einblicke in unsere Bewertungen und teilt echte Erfahrungen von Benutzern. ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von SlimXmed zu kaufen](https://adtocart.xyz/slimxmed-de)** ## Wurde SlimXMed von der Stiftung Warentest untersucht? Stiftung Warentest bewertet häufig Diätprodukte, allerdings fallen die Ergebnisse oft nicht besonders positiv aus. Daher waren wir gespannt, ob es eine Bewertung von SlimXMed durch die Stiftung Warentest gibt. Wir haben jedoch herausgefunden, dass die unabhängige Organisation noch keinen Test dazu durchgeführt hat. Dennoch gehen wir davon aus, dass es in Zukunft zu einer Untersuchung kommen wird, und wir sind gespannt auf das Ergebnis. ## War der Hersteller in der Fernsehsendung Die Höhle der Löwen zu sehen? Während unserer Recherchen und Untersuchungen stießen wir auf ein Gerücht, dass SlimXMed in der TV-Show „Die Höhle der Löwen“ zu sehen war. Es stimmt zwar, dass bereits in der Vergangenheit ähnliche Produkte auf der Messe gezeigt wurden, unser Produkt wurde dort jedoch nicht präsentiert. Auf Nachfrage beim Hersteller bestritten diese ebenfalls jegliche Beteiligung und bestätigten, dass die Gerüchte unbegründet seien. Darüber hinaus gibt es keine Pläne, das Produkt in Zukunft auf der Messe vorzustellen. ## SlimXMed Einnahme und Dosierung: Laut Herstellerempfehlung sollten je nach individuellem Bedarf täglich 2 bis 4 Kapseln SlimXMed eingenommen werden. Es ist jedoch wichtig, die empfohlene Dosierung nicht zu überschreiten. Wichtig ist, die Kapseln direkt mit reichlich Flüssigkeit zu schlucken. Die optimalen Zeitpunkte für die Einnahme von SlimXMed sind das Frühstück und das Abendessen. Es empfiehlt sich, die Kapseln etwa eine halbe Stunde vor den Mahlzeiten einzunehmen, damit die Kapseln genügend Zeit haben, ihre Wirkung zu entfalten. ## Aktive SlimXMed-Inhaltsstoffe: Angesichts der Behauptung des Herstellers einer natürlichen Wirksamkeit wollten wir die Inhaltsstoffe von SlimXMed genau untersuchen, um festzustellen, ob sie tatsächlich pflanzlich und natürlich sind. Dies bietet den Vorteil einer besseren Verträglichkeit durch den Körper. Ziel des Herstellers ist es, nicht nur die Fettverbrennung anzuregen, sondern auch das Sättigungsgefühl zu verbessern. Folgende Inhaltsstoffe sind in den Kapseln enthalten: Reisstärke: Dieser Wirkstoff in SlimXMed sorgt vor allem dafür, dass die Kapseln ihre Form behalten und leicht zu schlucken sind. Diese aus Reis gewonnene Stärke hilft beim Eindicken. Es handelt sich um einen völlig harmlosen Inhaltsstoff, der jedoch für die Struktur der Kapsel unerlässlich ist. Mangostan-Extrakt: Dieser Extrakt in SlimXMed dürfte vielen unbekannt sein, da es sich um eine exotische Frucht handelt. Der Hersteller nutzt seine natürliche Wirksamkeit und enthält über 40 Antioxidantien, die zur Stärkung des Körpers beitragen. Dies führt zu einer nachhaltigen Stärkung des Immunsystems und erleichtert den Fettabbau. ## **[Klicken Sie hier, um jetzt auf der offiziellen Website von SlimXmed zu kaufen](https://adtocart.xyz/slimxmed-de)**

PLS442/Bianca_Barclay

PLS442

--- license: openrail ---

Void2412/Temporal_Cnn

Void2412

--- license: mit ---

0xfaskety/Qwen-Qwen1.5-7B-1718528309

0xfaskety

Entry not found

gavinator647/test

gavinator647

Entry not found

whizzzzkid/G_2215000

whizzzzkid

Entry not found

lookuss/test-llilu

lookuss

adapter-transformers

--- license: gpl-3.0 language: - ko - en library_name: adapter-transformers --- # Llilu <!-- 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 리루 모델 테스트 버전 ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** ChorokTech - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** text-generation - **Language(s) (NLP):** ko, en - **License:** GPL 3.0 - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **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]

whizzzzkid/G_2216000

whizzzzkid

Entry not found

Tim0207/distilbert-base-uncased-finetuned-imdb

Tim0207

transformers

fill-mask

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4894 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6819 | 1.0 | 157 | 2.4978 | | 2.5872 | 2.0 | 314 | 2.4488 | | 2.527 | 3.0 | 471 | 2.4823 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

whizzzzkid/DUR_2218000

whizzzzkid

Entry not found

iiPilix/Neon

iiPilix

--- license: gpl-3.0 ---

christian29/jiwoov1

christian29

Entry not found

appsierra/resume-test

appsierra

transformers

feature-extraction

--- 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]

Dhahlan2000/lora_model_llama3_Chitti

Dhahlan2000

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** Dhahlan2000 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

skirano/trained-sd3-lora

skirano

Entry not found

plsmkse/500-whisper-syauqi-indo

plsmkse

--- license: apache-2.0 ---

SiriusFW/SiFW-AnnihilationXL

SiriusFW

--- language: - en tags: - art - anime - stable-diffusion - SDXL - Pony --- # **SiFW-AnnihilationXL**  ## Parameters ___ ### Recommended parameters: **Sampler**: Euler a, DPM++ 2M Karras, DPM++ 3M SDE Karras **Steps**: 25-40 **CFG**: 5-8 **Clip skip**: 5-8 ### Recommended parameters(Upscale): **Denoising**: 15-35 **Scale**: 1.4-1.7 (*I usually do two upscales of 1.4-1.7, bringing the final vertical resolution to 2500-3000px.*) **Extra noise**: 0.00-0.05 **SelfAttentionGuidance**: True ___ ## Prompt ___ ### Positive: **SFW/NSFW**: score_9,score_8_up,score_7_up,source_anime,BREAK (absurdres,highres),(high quality,best quality),(masterpiece:0.5),BREAK **{prompt}** **SFW** score_9,score_8_up,score_7_up,source_anime,rating_safe,BREAK (absurdres,highres),(high quality,best quality),(masterpiece:0.5), **{prompt}** ___ ### Negative: **SFW/NSFW** score_4,score_5,score_6, zombie,bad fingers,bad hand,bad feet,extra digits,(interlocked fingers, badly drawn hands and fingers, anatomically incorrect hands), **{prompt}**,(lipgloss, lipstick:0.7) BREAK lowres,missing,worst quality,jpeg artifacts,low quality,unfinished,displeasing,oldest,signature,jpeg artifacts,blurry, **SFW** score_4,score_5,score_6, zombie,bad fingers,bad hand,bad feet,extra digits,(interlocked fingers, badly drawn hands and fingers, anatomically incorrect hands), **{prompt}**,(lipgloss, lipstick:0.7),nude,nsfw BREAK lowres,missing,worst quality,jpeg artifacts,low quality,unfinished,displeasing,oldest,signature,jpeg artifacts,blurry, ___ ### Notes: - *Checkpoint likes to draw very plump lips, so I recommend adding (lipgloss, lipstick:0.7) to the negative prompt.* - *To avoid NSFW, you should add rating_safe to the positive prompt and nude, nsfw to the negative prompt.* - *These are just recommendations, there is nothing really important here, do as you wish.* ___ ## Examples: ### SiFW-AnnihilationXL_v1.0 #### IMG-1  ``` score_9,score_8_up,score_7_up,source_anime,rating_safe, BREAK (absurdres,highres),(high quality,best quality),(masterpiece:0.5), 1girl,solo,(flat chest:0.7),(skinny:0.5),(pale skin:0.5), BREAK mechanical eye,looking at viewer, medium hair,long hair, expressionless, single mechanical arm, bodysuit,science fiction, indoors, Negative prompt: score_4,score_5,score_6, zombie,bad fingers,bad hand,bad feet,extra digits,(interlocked fingers, badly drawn hands and fingers, anatomically incorrect hands), (wide hips,(huge ass:1.2),thick thighs:1.2),small breasts,signature,adult,(lipgloss, lipstick:0.7),nude,simple background,nsfw, BREAK lowres,missing,worst quality,jpeg artifacts,low quality,unfinished,displeasing,oldest,signature,jpeg artifacts,blurry, Steps: 30, Sampler: DPM++ 3M SDE Karras, CFG scale: 6, Seed: 192924084, Size: 768x1280, Model hash: e6b805e8c9, Model: SiFW_Annihilation_v01_rel, VAE hash: 62c7c729ad, VAE: SDXL_a_fixFP16ErrorsSDXLLowerMemoryUse_v10.safetensors, Clip skip: 2, Version: f0.0.17v1.8.0rc-latest-277-g0af28699 ``` #### IMG-2  #### IMG-3  #### IMG-4  #### IMG-5  #### IMG-6 

Felox/emile-zola

Felox

Entry not found

QiaoyuZheng/RadDiag

QiaoyuZheng

--- license: mit --- Two checkpoints are stored in this repository. For more information, please refer to our [Github repository](https://github.com/qiaoyu-zheng/RP3D-Diag)

skirano/trained-sd3-lora-2

skirano

Entry not found

angelasasara2/magical

angelasasara2

--- license: apache-2.0 ---

u-10bei/phase2_tokenizer_Llama

u-10bei

--- license: mit ---

hf-100/Llama-3-Spellbound-Instruct-70B-0.2

hf-100

Entry not found

jtatman/orca-tau-1.8b-persian-lora

jtatman

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl base_model: M4-ai/Orca-2.0-Tau-1.8B --- # Uploaded model - **Developed by:** jtatman - **License:** apache-2.0 - **Finetuned from model :** M4-ai/Orca-2.0-Tau-1.8B This qwen2 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)

jtatman/orca-tau-4k-persian-alpaca-f32

jtatman

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft base_model: M4-ai/Orca-2.0-Tau-1.8B --- # Uploaded model - **Developed by:** jtatman - **License:** apache-2.0 - **Finetuned from model :** M4-ai/Orca-2.0-Tau-1.8B This qwen2 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)

MaziyarPanahi/Albatrox7B-GGUF

MaziyarPanahi

Entry not found

RadleighPompeiBusiness/llama-3-1

RadleighPompeiBusiness

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit ---

jwlee2/your_model_name_on_huggingface_hub

jwlee2

Entry not found

ZacharywW/chatglm3-6b-xhs

ZacharywW

Entry not found

leo009/sd3-lora

leo009

Entry not found

decepticonsIsAllYouNeed/root_gc_multilingual_bert_classifier_v6___

decepticonsIsAllYouNeed

Entry not found

Holmeister/BLOOM_AAID_structured_train

Holmeister

peft

--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: bigscience/bloom-7b1 model-index: - name: BLOOM_AAID_structured_train 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. --> # BLOOM_AAID_structured_train This model is a fine-tuned version of [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1) on the AAID_structured dataset. It achieves the following results on the evaluation set: - Loss: 0.8177 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.7338 | 0.0109 | 10 | 0.9125 | | 0.6341 | 0.0219 | 20 | 0.8568 | | 0.5526 | 0.0328 | 30 | 0.8991 | | 0.5651 | 0.0438 | 40 | 0.8944 | | 0.5392 | 0.0547 | 50 | 0.8796 | | 0.5038 | 0.0656 | 60 | 0.8612 | | 0.4904 | 0.0766 | 70 | 0.8335 | | 0.476 | 0.0875 | 80 | 0.8787 | | 0.4819 | 0.0984 | 90 | 0.8442 | | 0.4376 | 0.1094 | 100 | 0.8534 | | 0.4443 | 0.1203 | 110 | 0.8331 | | 0.44 | 0.1313 | 120 | 0.8530 | | 0.4362 | 0.1422 | 130 | 0.8594 | | 0.4273 | 0.1531 | 140 | 0.8177 | | 0.438 | 0.1641 | 150 | 0.8450 | | 0.4234 | 0.1750 | 160 | 0.8484 | | 0.4254 | 0.1859 | 170 | 0.8263 | | 0.4074 | 0.1969 | 180 | 0.8609 | | 0.4209 | 0.2078 | 190 | 0.8371 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

ShapeKapseln33/Nexalyn3398

ShapeKapseln33

Nexalyn Norge Opplevelser Dose, inntak: I en verden hvor vitalitet og ytelse ofte er synonymt med suksess, er det viktig å opprettholde topp fysisk form. For menn strekker dette seg ofte utover bare kondisjon til områder med vitalitet, virilitet og generelt velvære. Men ettersom alderen innhenter oss, kan ulike faktorer hemme vår evne til å opprettholde optimal ytelse og tilfredshet i disse rikene. **[Klikk her for å kjøpe nå fra Nexalyns offisielle nettsted](https://ketogummies24x7.com/nexalyn-no)** Nexalyn Pills er en forbedring for å løfte showet under seksuelle sosiale anledninger for folket. Det er oppdateringen som kan brukes av personer som opplever seksuelle mangler. Denne forbedringen fungerer ved å hjelpe testosteronnivåene i samlingen av menn. Testosteroner er kjemikalier som er i faresonen for en tilfredsstillende funksjon av kjønnsorganene og OK seksuell ytelse. Denne redesignen hjelper til med å jobbe med presentasjonen under seksuelle sosiale begivenheter, og øker omfanget av ereksjonen som tilsvarer økningen i størrelsen på pizazzen. Et klart svar for enkeltpersoner bør også hjelpe deres seksuelle grenser. ##Hva er Nexalyn? Nexalyn virker ved å øke testosteronkjemikaliene i det menneskelige møtet. Nexalyn-tilskuddet inneholder forskjellige dekorasjoner som fremmer mengden av testosteronkjemikalier og som sådan hjelper til med seksuell henrettelse. I tillegg til testosteronoppdateringen utvider redesignet også kroppens sirkulasjon. Den økte sirkulasjonen vil da risikere bedre suksess og bedre ytelse av ulike organer i kroppen. De ulike organene i kroppen, inkludert penis, vil fungere beundringsverdig, og det vil oppdatere den seksuelle presentasjonen som svarer til vitaliteten til mennesker. Forbedringen inkluderer nitrogenoksid, som er forpliktet til bedre sirkulasjon og bedre bemerkelsesverdighet for folket. ##Er ingrediensene i Nexalyn Nexalyns redesign inkluderer hvert merkefragment. Dekorasjonene er ganske typiske og ganske mye konsentrert om noen få bemerkelsesverdige standardsmaker. Noen av de kritiske elementene i denne redesignen er som følger: Tongkat Ali: Denne spesifikke handlingen er ansvarlig for å øke testosteronnivået i menneskekroppen. **[Klikk her for å kjøpe nå fra Nexalyns offisielle nettsted](https://ketogummies24x7.com/nexalyn-no)** L Arginin: Denne fikseringen er underlagt utviklingen av konstansen til personen som utmatter den. På denne måten er det underlagt en bedre funksjon av kjønnsorganet. Maca: Dette er i tillegg en av de kritiske delene av denne forbedringen. Det er gjenstand for diffusjon av det bedre blodet på omtrent samme måte som den generelle oppblomstringen av forskjellige organer i kroppen. ##Ginseng: Denne fikseringen er bundet til å gi ekstra sentralitet til folket. Dette opprettholder sikkerheten til mennesker under de seksuelle møtene. Som det burde være åpenbart, er disse alle gjennomtenkte konsentrater av normale smaker. Derfor er det beskyttet å ta denne oppgraderingen. ##Hvordan bør du ta Nexalyn? Nexalyn er laget som en holder som inneholder piller fra plasteret. Esken inneholder ca 60 piller, som bør tas 2 pålitelig. Den ene pillen bør tas i det underliggende fragmentet av dagen etter frokost, og den andre pillen bør tas etter middag. Nexalyn mannlige forsterkningspiller bør tas med varmt vann. En høy prosentandel vann bør brukes mens denne forbedringen utnyttes. Dette vil være bedre for å få en raskere effekt av forbedringen. ##Hvor kan jeg få tak i Nexalyn enkelt og raskt? Dermed har vi sett at Nexalyn er ledende blant andre ekstrautstyrstilskudd som er åpne på markedet for å endre seksuell makt. Forutsatt at du opplever seksuelle mangler, kan du også prøve denne forbedringen og møte de betydelige konsekvensene av denne redesignen. For å kjøpe disse pillene kan du klikke på hvilken som helst lenke på denne siden og få ditt beste tilbud. **[Klikk her for å kjøpe nå fra Nexalyns offisielle nettsted](https://ketogummies24x7.com/nexalyn-no)**

ZhenbinWang/MedSora

ZhenbinWang

Entry not found

HoangLe1312/codecontest-solver-lora-medium

HoangLe1312

transformers

--- base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** HoangLe1312 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

fullnonstop/random_mask.safetensors

fullnonstop

--- license: mit ---

KakaoL0L/Llama38B_CVetOFFRETEST_16bit

KakaoL0L

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit --- # Uploaded model - **Developed by:** KakaoL0L - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

dfgdfddd/wrgwer

dfgdfddd

Entry not found

cuongpp/bartpho-word-base-ed

cuongpp

transformers

text2text-generation

--- base_model: vinai/bartpho-word-base tags: - generated_from_trainer model-index: - name: bartpho-word-base-ed 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. --> # bartpho-word-base-ed This model is a fine-tuned version of [vinai/bartpho-word-base](https://huggingface.co/vinai/bartpho-word-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0119 - F1 Micro: 0.7414 - Recall Micro: 0.6649 - Precision Micro: 0.8378 - F1 Macro: 0.1869 - Recall Macro: 0.1816 - Precision Macro: 0.1949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Micro | Recall Micro | Precision Micro | F1 Macro | Recall Macro | Precision Macro | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------------:|:---------------:|:--------:|:------------:|:---------------:| | No log | 1.0 | 373 | 0.0156 | 0.7286 | 0.6544 | 0.8219 | 0.1709 | 0.1813 | 0.1667 | | 0.1321 | 2.0 | 746 | 0.0124 | 0.7387 | 0.6563 | 0.8448 | 0.1711 | 0.1599 | 0.1889 | | 0.0134 | 3.0 | 1119 | 0.0126 | 0.7290 | 0.6488 | 0.8319 | 0.1842 | 0.1897 | 0.1866 | | 0.0134 | 4.0 | 1492 | 0.0119 | 0.7490 | 0.6859 | 0.8248 | 0.1820 | 0.1644 | 0.2081 | | 0.0105 | 5.0 | 1865 | 0.0119 | 0.7414 | 0.6649 | 0.8378 | 0.1869 | 0.1816 | 0.1949 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

sylvestr/pegasus-samsum

sylvestr

Entry not found

DBangshu/GPT2_e5_6_6

DBangshu

transformers

text-generation

--- 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]

manbeast3b/KinoInfer500

manbeast3b

Entry not found

fdc95/multiagent

fdc95

Entry not found

Honi086/Xavier_thorpe

Honi086

--- license: openrail ---

calv8nx/ass-1

calv8nx

Entry not found

Maykeye/MambaFaceKISS-hf

Maykeye

--- license: apache-2.0 datasets: - huggan/anime-faces --- # Mamba face kiss ## KISS This repo contains two Keep It Simple Stupid anime face generators that generates 64x64 faces from 8x8 provided images. Basic idea was to take 64x64 anime faces dataset(https://huggingface.co/datasets/huggan/anime-faces), resize it to 8x8, then teach the model to restore original images, intuition is that after that if new unseen images are provided, it will make some face.  Mamba is being fed a sequence `[A][A]...[A][SEP][B][B][B]...[B]` where there are 64 `[A]` that came from the 8x8 draft. there are 64x64 `[B]`s that are initially are upscaled draft(nearest neighbor) with addition of PAE. Model run several layers of mamba, and spits last 64x64 into RGB image. (`[SEP]` is not used for anything significant other than BERT has it to separate sentences, so I used it too as placeholder for command "Upscale from here") Two models are used. ### RNN goess brr (one way) One(`imgen3test.ipynb` and `imgen3.py`) always feeds images from top-left pixel to bottom-right pixel row by row  ### "Bi-directional" Another take(`imgen3test_flip.ipynb` and `imgen3_flip.py`) feed from top-left pixel to bottom-right pixel in every even layer and every odd layer sees upscaled images in reverse order  This flip version also uses way more parameters and different dtype. I didn't notice that much difference. #### Command line tool Simple script can be used to call the model on a single image ```console $ cli_imgen3_flip ./krita/face1.png face1.out.png python cli_imgen3_flip.py ./krita/face1.png /tmp/face1.png Weight path is data/image-flip-weights-1024x4-torch.bfloat16.bin Loading the model Loading 8x8 input image from ./krita/face1.png Writing 64x64 image to /tmp/face1.png ``` It's not really good way to use, comparing to calling through jupyter it though: mamba2 is implemented using triton and it takes around 30 seconds to initialize the model each time (on Raider GE76). ## Recreating Training is done in `imgen3(_flip)?.py`. Testing is in notebook. `Image_utils` should provide path to anime faces dataset. ## Naming and configuring Name imgen3 comes from "image generation 3". Two other attemts are not that interesting to even backup them. I'm too lazy to pass configuration around so parameters are hardcoded in the beginning of the file.

jayspring/Chap06_c602

jayspring

transformers

--- 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]

newbie2022/mbti

newbie2022

--- license: mit ---

manssster/tech

manssster

--- license: apache-2.0 ---

firmaments/1-1

firmaments

--- license: wtfpl ---

laddaramanuj/Project_Chatbot

laddaramanuj

--- license: llama3 ---

jackswie/sb

jackswie

--- license: openrail ---

hzy88886/llama3

hzy88886

--- license: apache-2.0 ---

AUTOMATIC/stable-diffusion-3-medium-text-encoders

AUTOMATIC

--- {} --- This repository contains three text encoders and their original model card links used in [Stable Diffusion 3](https://huggingface.co/stabilityai/stable-diffusion-3-medium). All components are subject to their respective original licenses. CLIP-ViT/L: * [https://huggingface.co/openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14/blob/main/README.md) * [MIT License](https://github.com/openai/CLIP/blob/main/LICENSE) OpenCLIP-ViT/G: * [https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/blob/main/README.md) * [MIT License](https://choosealicense.com/licenses/mit) T5 Version 1.1: * [https://huggingface.co/google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl/blob/main/README.md) * [Apache License 2.0](https://choosealicense.com/licenses/apache-2.0)

Haytham-0019/H.E

Haytham-0019

--- license: apache-2.0 ---

revelacion1/q_FrozenLake_v1_4x4_noSlippery_course_base_implementation

revelacion1

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q_FrozenLake_v1_4x4_noSlippery_course_base_implementation results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="revelacion1/q_FrozenLake_v1_4x4_noSlippery_course_base_implementation", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

revelacion1/Taxi_v3_base_course_model

revelacion1

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi_v3_base_course_model results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="revelacion1/Taxi_v3_base_course_model", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

aibabyshark/llama38binstruct_summarize

aibabyshark

peft

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Meta-Llama-3-8B-Instruct datasets: - generator model-index: - name: llama38binstruct_summarize 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. --> # llama38binstruct_summarize This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.7928 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4799 | 1.25 | 25 | 1.4020 | | 0.5231 | 2.5 | 50 | 1.5481 | | 0.3147 | 3.75 | 75 | 1.6357 | | 0.1531 | 5.0 | 100 | 1.7928 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

Hry62/EMNM

Hry62

Entry not found

Kishor798/Llama-2-7b-chat-finetune

Kishor798

transformers

text-generation

Entry not found

wmamanee/dollLikeness

wmamanee

Entry not found

polyconnect/rl_course_vizdoom_health_gathering_supreme

polyconnect

sample-factory

reinforcement-learning

--- 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: 12.91 +/- 4.75 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 polyconnect/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 .home.ivan..env.u8.lib.python3.10.site-packages.ipykernel_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 .home.ivan..env.u8.lib.python3.10.site-packages.ipykernel_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.

Swarts/Elraen

Swarts

Entry not found

brian-gordon/paligemma-3b-pt-448-Tasks-1-2__2024-06-16_14-35-38

brian-gordon

Entry not found

tantb/bert-finetuned-squad-accelerate

tantb

Entry not found

aaalby/YUJINv2

aaalby

--- license: openrail ---

BennieL/3d

BennieL

--- license: apache-2.0 ---

manbeast3b/KinoInfer502

manbeast3b

Entry not found

aitorrent/Meta-Llama-3-8B-Instruct-GGUF-torrent

aitorrent

--- license: llama3 tags: - torrent --- ## Llamacpp imatrix Quantizations of Meta-Llama-3-8B-Instruct Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> commit <a href="https://github.com/ggerganov/llama.cpp/commit/ffe666572f98a686b17a2cd1dbf4c0a982e5ac0a">ffe6665</a> for quantization. Original model: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Meta-Llama-3-8B-Instruct-Q8_0.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. | | [Meta-Llama-3-8B-Instruct-Q6_K.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. | | [Meta-Llama-3-8B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. | | [Meta-Llama-3-8B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. | | [Meta-Llama-3-8B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [Meta-Llama-3-8B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. | | [Meta-Llama-3-8B-Instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. | | [Meta-Llama-3-8B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Meta-Llama-3-8B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. | | [Meta-Llama-3-8B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. | | [Meta-Llama-3-8B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Meta-Llama-3-8B-Instruct-IQ3_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_S.gguf) | IQ3_S | 3.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. | | [Meta-Llama-3-8B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. | | [Meta-Llama-3-8B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Meta-Llama-3-8B-Instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Meta-Llama-3-8B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. | | [Meta-Llama-3-8B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [Meta-Llama-3-8B-Instruct-IQ2_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. | | [Meta-Llama-3-8B-Instruct-IQ2_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. | | [Meta-Llama-3-8B-Instruct-IQ2_XXS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. | | [Meta-Llama-3-8B-Instruct-IQ1_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. | | [Meta-Llama-3-8B-Instruct-IQ1_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. | ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Sibinraj/T5-base-finetuned-sumx

Sibinraj

Entry not found