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damgomz/ft_2_3e6_base_x1

damgomz

transformers

text-classification

--- language: en tags: - text-classification pipeline_tag: text-classification widget: - text: GEPS Techno is the pioneer of hybridization of renewable energies at sea. We imagine, design and commercialize innovative off-grid systems that aim to generate power at sea, stabilize and collect data. The success of our low power platforms WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity platform. --- ## Environmental Impact (CODE CARBON DEFAULT) | Metric | Value | |--------------------------|---------------------------------| | Duration (in seconds) | 107271.62567901611 | | Emissions (Co2eq in kg) | 0.0649116764564143 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.2663979944932808 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.1117399740512172 | | Consumed energy (kWh) | 1.3781379685444968 | | Country name | Switzerland | | Cloud provider | nan | | Cloud region | nan | | CPU count | 2 | | CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz | | GPU count | nan | | GPU model | nan | ## Environmental Impact (for one core) | Metric | Value | |--------------------------|---------------------------------| | CPU energy (kWh) | 0.206497879432106 | | Emissions (Co2eq in kg) | 0.04201472005761465 | ## Note 14 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_2_3e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 3e-06 | | batch_size | 2 | | weight_decay | 0.0 | | warm_up_prop | 0.0 | | drop_out_prob | 0.1 | | packing_length | 100 | | train_test_split | 0.2 | | num_steps | 29328 | ## Training and Testing steps Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.705400 | 0.482506 | | 1 | 0.305451 | 0.246145 | 0.910113 | | 2 | 0.180730 | 0.217001 | 0.926528 | | 3 | 0.126710 | 0.225560 | 0.902438 | | 4 | 0.074046 | 0.269291 | 0.926075 | | 5 | 0.041516 | 0.286399 | 0.918071 | | 6 | 0.021054 | 0.321385 | 0.915197 |

camidenecken/RoBERTa-RM1-v2-2-rm-v20

camidenecken

transformers

text-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]

huggingtweets/strongerstabler

huggingtweets

transformers

text-generation

--- language: en thumbnail: https://www.huggingtweets.com/strongerstabler/1603817791522/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <link rel="stylesheet" href="https://unpkg.com/@tailwindcss/[email protected]/dist/typography.min.css"> <style> @media (prefers-color-scheme: dark) { .prose { color: #E2E8F0 !important; } .prose h2, .prose h3, .prose a, .prose thead { color: #F7FAFC !important; } } </style> <section class='prose'> <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1259415526440402944/h4m68uNY_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">StrongerStabler 🤖 AI Bot </div> <div style="font-size: 15px; color: #657786">@strongerstabler bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline.  To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@strongerstabler's tweets](https://twitter.com/strongerstabler). <table style='border-width:0'> <thead style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #CBD5E0'> <th style='border-width:0'>Data</th> <th style='border-width:0'>Quantity</th> </tr> </thead> <tbody style='border-width:0'> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Tweets downloaded</td> <td style='border-width:0'>3250</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Retweets</td> <td style='border-width:0'>0</td> </tr> <tr style='border-width:0 0 1px 0; border-color: #E2E8F0'> <td style='border-width:0'>Short tweets</td> <td style='border-width:0'>1316</td> </tr> <tr style='border-width:0'> <td style='border-width:0'>Tweets kept</td> <td style='border-width:0'>1934</td> </tr> </tbody> </table> [Explore the data](https://app.wandb.ai/wandb/huggingtweets/runs/yr5cffyk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @strongerstabler's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://app.wandb.ai/wandb/huggingtweets/runs/33h1znu3) for full transparency and reproducibility. At the end of training, [the final model](https://app.wandb.ai/wandb/huggingtweets/runs/33h1znu3/artifacts) is logged and versioned. ## Intended uses & limitations ### How to use You can use this model directly with a pipeline for text generation: <pre><code><span style="color:#03A9F4">from</span> transformers <span style="color:#03A9F4">import</span> pipeline generator = pipeline(<span style="color:#FF9800">'text-generation'</span>, model=<span style="color:#FF9800">'huggingtweets/strongerstabler'</span>) generator(<span style="color:#FF9800">"My dream is"</span>, num_return_sequences=<span style="color:#8BC34A">5</span>)</code></pre> ### Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* </section> [](https://twitter.com/intent/follow?screen_name=borisdayma) <section class='prose'> For more details, visit the project repository. </section> [](https://github.com/borisdayma/huggingtweets) <!--- random size file -->

rdzotz/w2v2_bert_ru

rdzotz

transformers

automatic-speech-recognition

--- license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: w2v2_bert_ru 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. --> # w2v2_bert_ru 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: inf - Wer: 0.0538 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.711 | 0.73 | 300 | inf | 0.1267 | | 0.1026 | 1.46 | 600 | inf | 0.0925 | | 0.0748 | 2.18 | 900 | inf | 0.0732 | | 0.0591 | 2.91 | 1200 | inf | 0.0710 | | 0.0437 | 3.64 | 1500 | inf | 0.0675 | | 0.0382 | 4.37 | 1800 | inf | 0.0675 | | 0.0302 | 5.1 | 2100 | inf | 0.0620 | | 0.0243 | 5.83 | 2400 | inf | 0.0590 | | 0.0219 | 6.55 | 2700 | inf | 0.0584 | | 0.0173 | 7.28 | 3000 | inf | 0.0577 | | 0.015 | 8.01 | 3300 | inf | 0.0560 | | 0.0115 | 8.74 | 3600 | inf | 0.0551 | | 0.0099 | 9.47 | 3900 | inf | 0.0538 | ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

hsikchi/pythia-6.9b-goldrm_tldr-dpo-beta-0.025-alpha-0-step-39936

hsikchi

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]

Romildon/locutor

Romildon

--- license: openrail ---

marialvsantiago/ccb598de-df79-437b-8b4b-98bd44e1232a

marialvsantiago

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-14B tags: - axolotl - generated_from_trainer model-index: - name: ccb598de-df79-437b-8b4b-98bd44e1232a results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-14B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7fe8e77cffb4cb7b_train_data.json ds_type: json format: custom path: /workspace/input_data/7fe8e77cffb4cb7b_train_data.json type: field_input: system field_instruction: instruction field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: marialvsantiago/ccb598de-df79-437b-8b4b-98bd44e1232a hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/7fe8e77cffb4cb7b_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_hf output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: a74d7451-e101-4cc2-9b59-1e2020a2e450 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: a74d7451-e101-4cc2-9b59-1e2020a2e450 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # ccb598de-df79-437b-8b4b-98bd44e1232a This model is a fine-tuned version of [unsloth/Qwen2.5-14B](https://huggingface.co/unsloth/Qwen2.5-14B) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0006 | 1 | nan | | 0.0 | 0.0030 | 5 | nan | | 0.0 | 0.0060 | 10 | nan | | 0.0 | 0.0090 | 15 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-3-70B-Special-Tokens-Adjusted - GGUF - Model creator: https://huggingface.co/astronomer/ - Original model: https://huggingface.co/astronomer/Llama-3-70B-Special-Tokens-Adjusted/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Llama-3-70B-Special-Tokens-Adjusted.Q2_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q2_K.gguf) | Q2_K | 24.56GB | | [Llama-3-70B-Special-Tokens-Adjusted.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_XS.gguf) | IQ3_XS | 27.29GB | | [Llama-3-70B-Special-Tokens-Adjusted.IQ3_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_S.gguf) | IQ3_S | 28.79GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_S.gguf) | Q3_K_S | 28.79GB | | [Llama-3-70B-Special-Tokens-Adjusted.IQ3_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ3_M.gguf) | IQ3_M | 29.74GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q3_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K.gguf) | Q3_K | 31.91GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_M.gguf) | Q3_K_M | 31.91GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q3_K_L.gguf) | Q3_K_L | 34.59GB | | [Llama-3-70B-Special-Tokens-Adjusted.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.IQ4_XS.gguf) | IQ4_XS | 35.64GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q4_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/blob/main/Llama-3-70B-Special-Tokens-Adjusted.Q4_0.gguf) | Q4_0 | 37.22GB | | [Llama-3-70B-Special-Tokens-Adjusted.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | IQ4_NL | 37.58GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K_S | 37.58GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q4_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K | 39.6GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_K_M | 39.6GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q4_1.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q4_1 | 41.27GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q5_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_0 | 45.32GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K_S | 45.32GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q5_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K | 46.52GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_K_M | 46.52GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q5_1.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q5_1 | 49.36GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q6_K.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q6_K | 53.91GB | | [Llama-3-70B-Special-Tokens-Adjusted.Q8_0.gguf](https://huggingface.co/RichardErkhov/astronomer_-_Llama-3-70B-Special-Tokens-Adjusted-gguf/tree/main/) | Q8_0 | 69.83GB | Original model description: --- base_model: meta-llama/Meta-Llama-3-70B inference: false model_creator: astronomer-io model_name: Meta-Llama-3-70B model_type: llama pipeline_tag: text-generation license: other license_name: llama-3 license_link: https://huggingface.co/meta-llama/Meta-Llama-3-70B/blob/main/README.md tags: - llama - llama-3 - facebook - meta - astronomer - pretrained - finetuned - autotrain_compatible - endpoints_compatible --- <!-- header start --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://www.astronomer.io/logo/astronomer-logo-RGB-standard-1200px.png" alt="Astronomer" style="width: 60%; min-width: 400px; display: block; margin: auto;"> </div> <div style="margin-top: 1.0em; margin-bottom: 1.0em;"></div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">This model is generously created and made open source by <a href="https://astronomer.io">Astronomer</a>.</p></div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">Astronomer is the de facto company for <a href="https://airflow.apache.org/">Apache Airflow</a>, the most trusted open-source framework for data orchestration and MLOps.</p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama-3-70B-Special-Tokens-Adjusted - Ideal and stable Llama-3-70B for fine-tuning. - Original Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) - The usage of this model must abide by the [Llama 3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-70B/blob/main/LICENSE). - Built with Meta Llama 3 - Created by [David Xue](https://www.linkedin.com/in/david-xue-uva/) from [Astronomer](https://astronomer.io) ## Description This is the exact same model ([meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)) with the weights for the input and output embeddings from lm head and embedding matrix adjusted using the mean of the trained tokens for certain tokens that were untrained, which caused widespread issues for people attempting to fine-tune this base model with either adding their own tokens or using existing special tokens. ## Why We Made This Model The Llama 3 base (non-instruct) model, while powerful, came with a significant oversight that some special tokens for instruction following within its architecture were left untrained, potentially derailing further fine-tuning processes. This was first noted by [Daniel Han on X](https://twitter.com/danielhanchen/status/1781395882925343058), highlighting a critical but fixable flaw in a widely used model. <img src="https://cdn-uploads.huggingface.co/production/uploads/655ad0f8727df37c77a09cb9/1U2rRrx60p1pNeeAZw8Rd.png" alt="graph" width="400"/> The primary goal of releasing a patched version of this model was to address this issue so that the community can utilize the Llama 3 model without facing training instabilities, such as sudden gradient explosions or `NaN` gradients, or having to go through complicated processes to fix the model themselves before fine-tuning. Note: specifically for the 70B model, the untrained special tokens did not have all zero values for the embedding weights. So the significance of this problem may not be as severe as it is on the base 8B model. This model was made anyway by the request of the community, though in theory directly fine-tuning should be ok. ## Details of the Adjustment The [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) model was pulled directly from HuggingFace and loaded using transformers. Then, the input embedding and output embedding values are retrieved using `model.get_input_embeddings().weight.data` and `model.get_output_embeddings().weight.data`. These 2 matrics are identical in shape, with each row representing a token id, and each column representing an embedding feature. The special (untrained & problematic) tokens can be found by locating the rows where the entire row of the embedding values are ~~~all zeros~~~ less than 9e-7 (for the 70B model, no row had all zeros, so thresholding using 9e-7 was done to fine under-trained tokens), which imply they were not trained during the pretraining phase of the model from Meta. Such untrained tokens could lead to heavy computational issues, like gradient explosions or `NaN` gradients, during downstream fine-tuning on specific tasks. <details> <summary>See here for a list of the tokens we found that has fit the "untrained" profile described:</summary> ['À', 'Á', 'õ', 'ö', '÷', 'ø', 'ù', 'ú', 'û', 'ü', 'ý', 'þ', 'ÿ', '">ččĊ', ';čččĊ', 'ĉTokenNameIdentifier', 'ĠForCanBeConverted', 'ĠForCanBeConvertedToF', 'PostalCodesNL', '$PostalCodesNL', 'useRalative', 'Û±Û', 'аÑĢакÑĤ', 'аÑĤиÑģÑı', 'иÑĤиÑģÑı', 'ávajÃŃcÃŃ', 'Ä°TESÄ°', 'илакÑĤи', 'илаÑģÑı', 'ÑĭÑŁN', 'ÐİÑĭÑŁN', 'ılmaktadır', 'ÐİÑĭÑŁNÐİÑĭÑŁN', 'ıldıģında', '<|reserved_special_token_0|>', '<|reserved_special_token_1|>', '<|reserved_special_token_2|>', '<|reserved_special_token_3|>', '<|start_header_id|>', '<|end_header_id|>', '<|reserved_special_token_4|>', '<|eot_id|>', '<|reserved_special_token_5|>', '<|reserved_special_token_6|>', '<|reserved_special_token_7|>', '<|reserved_special_token_8|>', '<|reserved_special_token_9|>', '<|reserved_special_token_10|>', '<|reserved_special_token_11|>', '<|reserved_special_token_12|>', '<|reserved_special_token_13|>', '<|reserved_special_token_14|>', '<|reserved_special_token_15|>', '<|reserved_special_token_16|>', '<|reserved_special_token_17|>', '<|reserved_special_token_18|>', '<|reserved_special_token_19|>', '<|reserved_special_token_20|>', '<|reserved_special_token_21|>', '<|reserved_special_token_22|>', '<|reserved_special_token_23|>', '<|reserved_special_token_24|>', '<|reserved_special_token_25|>', '<|reserved_special_token_26|>', '<|reserved_special_token_27|>', '<|reserved_special_token_28|>', '<|reserved_special_token_29|>', '<|reserved_special_token_30|>', '<|reserved_special_token_31|>', '<|reserved_special_token_32|>', '<|reserved_special_token_33|>', '<|reserved_special_token_34|>', '<|reserved_special_token_35|>', '<|reserved_special_token_36|>', '<|reserved_special_token_37|>', '<|reserved_special_token_38|>', '<|reserved_special_token_39|>', '<|reserved_special_token_40|>', '<|reserved_special_token_41|>', '<|reserved_special_token_42|>', '<|reserved_special_token_43|>', '<|reserved_special_token_44|>', '<|reserved_special_token_45|>', '<|reserved_special_token_46|>', '<|reserved_special_token_47|>', '<|reserved_special_token_48|>', '<|reserved_special_token_49|>', '<|reserved_special_token_50|>', '<|reserved_special_token_51|>', '<|reserved_special_token_52|>', '<|reserved_special_token_53|>', '<|reserved_special_token_54|>', '<|reserved_special_token_55|>', '<|reserved_special_token_56|>', '<|reserved_special_token_57|>', '<|reserved_special_token_58|>', '<|reserved_special_token_59|>', '<|reserved_special_token_60|>', '<|reserved_special_token_61|>', '<|reserved_special_token_62|>', '<|reserved_special_token_63|>', '<|reserved_special_token_64|>', '<|reserved_special_token_65|>', '<|reserved_special_token_66|>', '<|reserved_special_token_67|>', '<|reserved_special_token_68|>', '<|reserved_special_token_69|>', '<|reserved_special_token_70|>', '<|reserved_special_token_71|>', '<|reserved_special_token_72|>', '<|reserved_special_token_73|>', '<|reserved_special_token_74|>', '<|reserved_special_token_75|>', '<|reserved_special_token_76|>', '<|reserved_special_token_77|>', '<|reserved_special_token_78|>', '<|reserved_special_token_79|>', '<|reserved_special_token_80|>', '<|reserved_special_token_81|>', '<|reserved_special_token_82|>', '<|reserved_special_token_83|>', '<|reserved_special_token_84|>', '<|reserved_special_token_85|>', '<|reserved_special_token_86|>', '<|reserved_special_token_87|>', '<|reserved_special_token_88|>', '<|reserved_special_token_89|>', '<|reserved_special_token_90|>', '<|reserved_special_token_91|>', '<|reserved_special_token_92|>', '<|reserved_special_token_93|>', '<|reserved_special_token_94|>', '<|reserved_special_token_95|>', '<|reserved_special_token_96|>', '<|reserved_special_token_97|>', '<|reserved_special_token_98|>', '<|reserved_special_token_99|>', '<|reserved_special_token_100|>', '<|reserved_special_token_101|>', '<|reserved_special_token_102|>', '<|reserved_special_token_103|>', '<|reserved_special_token_104|>', '<|reserved_special_token_105|>', '<|reserved_special_token_106|>', '<|reserved_special_token_107|>', '<|reserved_special_token_108|>', '<|reserved_special_token_109|>', '<|reserved_special_token_110|>', '<|reserved_special_token_111|>', '<|reserved_special_token_112|>', '<|reserved_special_token_113|>', '<|reserved_special_token_114|>', '<|reserved_special_token_115|>', '<|reserved_special_token_116|>', '<|reserved_special_token_117|>', '<|reserved_special_token_118|>', '<|reserved_special_token_119|>', '<|reserved_special_token_120|>', '<|reserved_special_token_121|>', '<|reserved_special_token_122|>', '<|reserved_special_token_123|>', '<|reserved_special_token_124|>', '<|reserved_special_token_125|>', '<|reserved_special_token_126|>', '<|reserved_special_token_127|>', '<|reserved_special_token_128|>', '<|reserved_special_token_129|>', '<|reserved_special_token_130|>', '<|reserved_special_token_131|>', '<|reserved_special_token_132|>', '<|reserved_special_token_133|>', '<|reserved_special_token_134|>', '<|reserved_special_token_135|>', '<|reserved_special_token_136|>', '<|reserved_special_token_137|>', '<|reserved_special_token_138|>', '<|reserved_special_token_139|>', '<|reserved_special_token_140|>', '<|reserved_special_token_141|>', '<|reserved_special_token_142|>', '<|reserved_special_token_143|>', '<|reserved_special_token_144|>', '<|reserved_special_token_145|>', '<|reserved_special_token_146|>', '<|reserved_special_token_147|>', '<|reserved_special_token_148|>', '<|reserved_special_token_149|>', '<|reserved_special_token_150|>', '<|reserved_special_token_151|>', '<|reserved_special_token_152|>', '<|reserved_special_token_153|>', '<|reserved_special_token_154|>', '<|reserved_special_token_155|>', '<|reserved_special_token_156|>', '<|reserved_special_token_157|>', '<|reserved_special_token_158|>', '<|reserved_special_token_159|>', '<|reserved_special_token_160|>', '<|reserved_special_token_161|>', '<|reserved_special_token_162|>', '<|reserved_special_token_163|>', '<|reserved_special_token_164|>', '<|reserved_special_token_165|>', '<|reserved_special_token_166|>', '<|reserved_special_token_167|>', '<|reserved_special_token_168|>', '<|reserved_special_token_169|>', '<|reserved_special_token_170|>', '<|reserved_special_token_171|>', '<|reserved_special_token_172|>', '<|reserved_special_token_173|>', '<|reserved_special_token_174|>', '<|reserved_special_token_175|>', '<|reserved_special_token_176|>', '<|reserved_special_token_177|>', '<|reserved_special_token_178|>', '<|reserved_special_token_179|>', '<|reserved_special_token_180|>', '<|reserved_special_token_181|>', '<|reserved_special_token_182|>', '<|reserved_special_token_183|>', '<|reserved_special_token_184|>', '<|reserved_special_token_185|>', '<|reserved_special_token_186|>', '<|reserved_special_token_187|>', '<|reserved_special_token_188|>', '<|reserved_special_token_189|>', '<|reserved_special_token_190|>', '<|reserved_special_token_191|>', '<|reserved_special_token_192|>', '<|reserved_special_token_193|>', '<|reserved_special_token_194|>', '<|reserved_special_token_195|>', '<|reserved_special_token_196|>', '<|reserved_special_token_197|>', '<|reserved_special_token_198|>', '<|reserved_special_token_199|>', '<|reserved_special_token_200|>', '<|reserved_special_token_201|>', '<|reserved_special_token_202|>', '<|reserved_special_token_203|>', '<|reserved_special_token_204|>', '<|reserved_special_token_205|>', '<|reserved_special_token_206|>', '<|reserved_special_token_207|>', '<|reserved_special_token_208|>', '<|reserved_special_token_209|>', '<|reserved_special_token_210|>', '<|reserved_special_token_211|>', '<|reserved_special_token_212|>', '<|reserved_special_token_213|>', '<|reserved_special_token_214|>', '<|reserved_special_token_215|>', '<|reserved_special_token_216|>', '<|reserved_special_token_217|>', '<|reserved_special_token_218|>', '<|reserved_special_token_219|>', '<|reserved_special_token_220|>', '<|reserved_special_token_221|>', '<|reserved_special_token_222|>', '<|reserved_special_token_223|>', '<|reserved_special_token_224|>', '<|reserved_special_token_225|>', '<|reserved_special_token_226|>', '<|reserved_special_token_227|>', '<|reserved_special_token_228|>', '<|reserved_special_token_229|>', '<|reserved_special_token_230|>', '<|reserved_special_token_231|>', '<|reserved_special_token_232|>', '<|reserved_special_token_233|>', '<|reserved_special_token_234|>', '<|reserved_special_token_235|>', '<|reserved_special_token_236|>', '<|reserved_special_token_237|>', '<|reserved_special_token_238|>', '<|reserved_special_token_239|>', '<|reserved_special_token_240|>', '<|reserved_special_token_241|>', '<|reserved_special_token_242|>', '<|reserved_special_token_243|>', '<|reserved_special_token_244|>', '<|reserved_special_token_245|>', '<|reserved_special_token_246|>', '<|reserved_special_token_247|>', '<|reserved_special_token_248|>', '<|reserved_special_token_249|>', '<|reserved_special_token_250|>'] </details> Once these untrained tokens are identified, the average of trained tokens can be calculated by using the sums of embedding values of trained tokens for each feature/column and divided by the number of trained. This is done for both input and output matrices. Lastly, the problematic token's rows in the 2 embedding matrics are set to the computed mean, thus completing the adjustment. ## Contributors - [David Xue](https://www.linkedin.com/in/david-xue-uva/), Machine Learning Engineer from [Astronomer](https://astronomer.io)

marianafmedeiros/ppo-Huggy

marianafmedeiros

ml-agents

reinforcement-learning

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: marianafmedeiros/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

JessicaHsu/ppo-LunarLander-v2

JessicaHsu

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 277.69 +/- 20.00 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

harshit345/xlsr-53-wav2vec-hi

harshit345

transformers

automatic-speech-recognition

--- language: hi datasets: - Interspeech 2021 metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Hindi by Shyam Sunder Kumar results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice hi type: common_voice args: hi metrics: - name: Test WER type: wer value: 20.22 --- # Wav2Vec2-Large-XLSR-53-hindi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) hindi using the [Multilingual and code-switching ASR challenges for low resource Indian languages](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "hi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the hindi test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "hi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model = Wav2Vec2ForCTC.from_pretrained("theainerd/Wav2Vec2-large-xlsr-hindi") model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**:20.22 % ## Training The script used for training can be found [Hindi ASR Fine Tuning Wav2Vec2](https://colab.research.google.com/drive/1nY5WMj1oNlexD_qDeNYL7ZM427A021CV?usp=sharing)

mandanya/RuadaptQwen-32B-instruct-AWQ

mandanya

--- license: mit ---

BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF

BitStreamX

transformers

text-generation

--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \n“Licensee” or “you” means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit base_model: meta-llama/Llama-3.2-3B-Instruct --- # BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF This model was converted to GGUF format from [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo BitStreamX/Llama-3.2-3B-Instruct-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-q5_k_m.gguf -c 2048 ```

mradermacher/Llama3merge6-15B-MoE-GGUF

mradermacher

transformers

--- base_model: allknowingroger/Llama3merge6-15B-MoE language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - moe - frankenmoe - merge - mergekit - lazymergekit - Kukedlc/NeuralLlamita-3-8B-v0.2 - imone/Llama-3-8B-fixed-special-embedding --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/allknowingroger/Llama3merge6-15B-MoE <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q2_K.gguf) | Q2_K | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ3_XS.gguf) | IQ3_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q3_K_S.gguf) | Q3_K_S | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ3_S.gguf) | IQ3_S | 6.2 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ3_M.gguf) | IQ3_M | 6.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q3_K_M.gguf) | Q3_K_M | 6.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q3_K_L.gguf) | Q3_K_L | 7.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.IQ4_XS.gguf) | IQ4_XS | 7.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q4_K_S.gguf) | Q4_K_S | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q4_K_M.gguf) | Q4_K_M | 8.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q5_K_S.gguf) | Q5_K_S | 9.6 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q5_K_M.gguf) | Q5_K_M | 9.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q6_K.gguf) | Q6_K | 11.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Llama3merge6-15B-MoE-GGUF/resolve/main/Llama3merge6-15B-MoE.Q8_0.gguf) | Q8_0 | 14.6 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

asn1814/openbookqa_bert-base-uncased_fact_retrieval_k_10

asn1814

transformers

multiple-choice

--- license: apache-2.0 base_model: asn1814/openbookqa_bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: openbookqa_bert-base-uncased_fact_retrieval_k_10 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. --> # openbookqa_bert-base-uncased_fact_retrieval_k_10 This model is a fine-tuned version of [asn1814/openbookqa_bert-base-uncased](https://huggingface.co/asn1814/openbookqa_bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9155 - Accuracy: 0.59 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3035 | 1.0 | 310 | 1.4148 | 0.57 | | 0.1243 | 2.0 | 620 | 1.9743 | 0.57 | | 0.077 | 3.0 | 930 | 2.4690 | 0.584 | | 0.028 | 4.0 | 1240 | 2.8887 | 0.582 | | 0.0118 | 5.0 | 1550 | 2.9155 | 0.59 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ramon-lins/ppo-LunarLander-v2

ramon-lins

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: ppo_baselines3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 256.49 +/- 17.91 name: mean_reward verified: false --- # **ppo_baselines3** Agent playing **LunarLander-v2** This is a trained model of a **ppo_baselines3** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Resizable/PersianTurtle

Resizable

--- license: openrail ---

srikarthikv/distilbert-base-uncased-lora-text-classification

srikarthikv

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification 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-lora-text-classification 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: 1.0072 - Accuracy: {'accuracy': 0.88} ## 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: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:| | No log | 1.0 | 250 | 0.3560 | {'accuracy': 0.888} | | 0.4316 | 2.0 | 500 | 0.5124 | {'accuracy': 0.878} | | 0.4316 | 3.0 | 750 | 0.6530 | {'accuracy': 0.87} | | 0.2331 | 4.0 | 1000 | 0.6871 | {'accuracy': 0.878} | | 0.2331 | 5.0 | 1250 | 0.8012 | {'accuracy': 0.869} | | 0.0918 | 6.0 | 1500 | 0.8738 | {'accuracy': 0.878} | | 0.0918 | 7.0 | 1750 | 0.8714 | {'accuracy': 0.881} | | 0.0349 | 8.0 | 2000 | 0.9631 | {'accuracy': 0.88} | | 0.0349 | 9.0 | 2250 | 1.0067 | {'accuracy': 0.879} | | 0.0071 | 10.0 | 2500 | 1.0072 | {'accuracy': 0.88} | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1

elinas/alpaca-13b-lora-int4

elinas

transformers

text-generation

--- license: other tags: - alpaca - gptq --- # llama-13b-int4 This LoRA trained for 3 epochs and has been converted to int4 (4bit) via GPTQ method. Use the **safetensors** version of the model, the **pt** version is an old quantization that is no longer supported and will be removed in the future. See the repo below for more info. # Important - Update 2023-04-05 Recent GPTQ commits have introduced breaking changes to model loading and you should this fork for a stable experience https://github.com/oobabooga/GPTQ-for-LLaMa Curently only cuda is supported. # Update 2023-03-27 New weights have been added. The old .pt version is no longer supported and has been replaced by a 128 groupsize safetensors file. Update to the latest GPTQ to use it. **alpaca-13b-4bit-128g.safetensors** Evals ----- **c4-new** - 6.925674915313721 **ptb-new** - 9.23875904083252 **wikitext2** - 5.219980716705322 # Usage 1. Run manually through GPTQ 2. (More setup but better UI) - Use the [text-generation-webui](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model#4-bit-mode). Make sure to follow the installation steps first [here](https://github.com/oobabooga/text-generation-webui#installation) before adding GPTQ support. Since this is instruction tuned, for best results, use the following format for inference: ``` ### Instruction: your-prompt ### Response: ``` If you want deterministic results, turn off sampling. You can turn it off in the webui by unchecking `do_sample`. For cai-chat mode, you won't want to use instruction prompting, rather create a character and set sampler settings. Here is an example of settings that work well for me: ``` do_sample=True temperature=0.95 top_p=1 typical_p=1 repetition_penalty=1.1 top_k=40 num_beams=1 penalty_alpha=0 min_length=0 length_penalty=1 no_repeat_ngram_size=0 early_stopping=False ``` You can then save this as a `.txt` file in the `presets` folder. -- license: other --- # LLaMA Model Card ## Model details **Organization developing the model** The FAIR team of Meta AI. **Model date** LLaMA was trained between December. 2022 and Feb. 2023. **Model version** This is version 1 of the model. **Model type** LLaMA is an auto-regressive language model, based on the transformer architecture. The model comes in different sizes: 7B, 13B, 33B and 65B parameters. **Paper or resources for more information** More information can be found in the paper “LLaMA, Open and Efficient Foundation Language Models”, available at https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/. **Citations details** https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/ **License** Non-commercial bespoke license **Where to send questions or comments about the model** Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/facebookresearch/llama) of the project , by opening an issue. ## Intended use **Primary intended uses** The primary use of LLaMA is research on large language models, including: exploring potential applications such as question answering, natural language understanding or reading comprehension, understanding capabilities and limitations of current language models, and developing techniques to improve those, evaluating and mitigating biases, risks, toxic and harmful content generations, hallucinations. **Primary intended users** The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence. **Out-of-scope use cases** LLaMA is a base, or foundational, model. As such, it should not be used on downstream applications without further risk evaluation and mitigation. In particular, our model has not been trained with human feedback, and can thus generate toxic or offensive content, incorrect information or generally unhelpful answers. ## Factors **Relevant factors** One of the most relevant factors for which model performance may vary is which language is used. Although we included 20 languages in the training data, most of our dataset is made of English text, and we thus expect the model to perform better for English than other languages. Relatedly, it has been shown in previous studies that performance might vary for different dialects, and we expect that it will be the case for our model. **Evaluation factors** As our model is trained on data from the Web, we expect that it reflects biases from this source. We thus evaluated on RAI datasets to measure biases exhibited by the model for gender, religion, race, sexual orientation, age, nationality, disability, physical appearance and socio-economic status. We also measure the toxicity of model generations, depending on the toxicity of the context used to prompt the model. ## Metrics **Model performance measures** We use the following measure to evaluate the model: - Accuracy for common sense reasoning, reading comprehension, natural language understanding (MMLU), BIG-bench hard, WinoGender and CrowS-Pairs, - Exact match for question answering, - The toxicity score from Perspective API on RealToxicityPrompts. **Decision thresholds** Not applicable. **Approaches to uncertainty and variability** Due to the high computational requirements of training LLMs, we trained only one model of each size, and thus could not evaluate variability of pre-training. ## Evaluation datasets The model was evaluated on the following benchmarks: BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, ARC, OpenBookQA, NaturalQuestions, TriviaQA, RACE, MMLU, BIG-bench hard, GSM8k, RealToxicityPrompts, WinoGender, CrowS-Pairs. ## Training dataset The model was trained using the following source of data: CCNet [67%], C4 [15%], GitHub [4.5%], Wikipedia [4.5%], Books [4.5%], ArXiv [2.5%], Stack Exchange[2%]. The Wikipedia and Books domains include data in the following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk. See the paper for more details about the training set and corresponding preprocessing. ## Quantitative analysis Hyperparameters for the model architecture <table> <thead> <tr> <th >LLaMA</th> <th colspan=6>Model hyper parameters </th> </tr> <tr> <th>Number of parameters</th><th>dimension</th><th>n heads</th><th>n layers</th><th>Learn rate</th><th>Batch size</th><th>n tokens</th> </tr> </thead> <tbody> <tr> <th>7B</th> <th>4096</th> <th>32</th> <th>32</th> <th>3.0E-04</th><th>4M</th><th>1T </tr> <tr> <th>13B</th><th>5120</th><th>40</th><th>40</th><th>3.0E-04</th><th>4M</th><th>1T </tr> <tr> <th>33B</th><th>6656</th><th>52</th><th>60</th><th>1.5.E-04</th><th>4M</th><th>1.4T </tr> <tr> <th>65B</th><th>8192</th><th>64</th><th>80</th><th>1.5.E-04</th><th>4M</th><th>1.4T </tr> </tbody> </table> *Table 1 - Summary of LLama Model Hyperparameters* We present our results on eight standard common sense reasoning benchmarks in the table below. <table> <thead> <tr> <th>LLaMA</th> <th colspan=9>Reasoning tasks </th> </tr> <tr> <th>Number of parameters</th> <th>BoolQ</th><th>PIQA</th><th>SIQA</th><th>HellaSwag</th><th>WinoGrande</th><th>ARC-e</th><th>ARC-c</th><th>OBQA</th><th>COPA</th> </tr> </thead> <tbody> <tr> <th>7B</th><th>76.5</th><th>79.8</th><th>48.9</th><th>76.1</th><th>70.1</th><th>76.7</th><th>47.6</th><th>57.2</th><th>93 </th> <tr><th>13B</th><th>78.1</th><th>80.1</th><th>50.4</th><th>79.2</th><th>73</th><th>78.1</th><th>52.7</th><th>56.4</th><th>94 </th> <tr><th>33B</th><th>83.1</th><th>82.3</th><th>50.4</th><th>82.8</th><th>76</th><th>81.4</th><th>57.8</th><th>58.6</th><th>92 </th> <tr><th>65B</th><th>85.3</th><th>82.8</th><th>52.3</th><th>84.2</th><th>77</th><th>81.5</th><th>56</th><th>60.2</th><th>94</th></tr> </tbody> </table> *Table 2 - Summary of LLama Model Performance on Reasoning tasks* We present our results on bias in the table below. Note that lower value is better indicating lower bias. | No | Category | FAIR LLM | | --- | -------------------- | -------- | | 1 | Gender | 70.6 | | 2 | Religion | 79 | | 3 | Race/Color | 57 | | 4 | Sexual orientation | 81 | | 5 | Age | 70.1 | | 6 | Nationality | 64.2 | | 7 | Disability | 66.7 | | 8 | Physical appearance | 77.8 | | 9 | Socioeconomic status | 71.5 | | | LLaMA Average | 66.6 | *Table 3 - Summary bias of our model output* ## Ethical considerations **Data** The data used to train the model is collected from various sources, mostly from the Web. As such, it contains offensive, harmful and biased content. We thus expect the model to exhibit such biases from the training data. **Human life** The model is not intended to inform decisions about matters central to human life, and should not be used in such a way. **Mitigations** We filtered the data from the Web based on its proximity to Wikipedia text and references. For this, we used a Kneser-Ney language model and a fastText linear classifier. **Risks and harms** Risks and harms of large language models include the generation of harmful, offensive or biased content. These models are often prone to generating incorrect information, sometimes referred to as hallucinations. We do not expect our model to be an exception in this regard. **Use cases** LLaMA is a foundational model, and as such, it should not be used for downstream applications without further investigation and mitigations of risks. These risks and potential fraught use cases include, but are not limited to: generation of misinformation and generation of harmful, biased or offensive content.

great0001/f792bf13-e2a2-4c39-91a3-c88207321f5c

great0001

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: f792bf13-e2a2-4c39-91a3-c88207321f5c results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) # f792bf13-e2a2-4c39-91a3-c88207321f5c This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

athirdpath/Orca-2-13b-Alpaca-Uncensored-GGUF

athirdpath

text-generation

--- pipeline_tag: text-generation license: other license_name: microsoft-research-license language: - en --- q8_0, q6_k, q5_k_m, q4_k_m, and q3_k_m GGUF quants of athirdpath/Orca-2-13b-Alpaca-Uncensored. This model is a fine-tuned version of microsoft/Orca-2-13b on a subset of the Vezora/Mini_Orca_Uncencored_Alpaca dataset, adjusted to demonstrate the relationship between instruction and input, with some particularly spicy prompts added to reduce the risk of rejections. Only the q_proj and k_proj modules were targeted and a low rank (8) was used, in hopes of containing the adjustments to the prompt format and alignment. This is promising on paper, with the training's per-step loss averaging <0.9 for the last third of the run. Reasoning stayed solid (for a 13b model) and I consider this a success. Performance is slighty worse than OG Orca-2 in Ooba's chat mode, comparable in Alpaca chat-instruct mode to the OG in ChatLM chat-instruct mode. May still reject some shocking prompts, but can easily be overcome with author's note or character card.

centaur31/mybert

centaur31

transformers

question-answering

--- license: apache-2.0 ---

Danielrahmai1991/findemo_v1

Danielrahmai1991

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** Danielrahmai1991 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

IronJ/test

IronJ

--- license: openrail ---

mbertheau/hf-drl-course-3-dqn-SpaceInvadersNoFrameskip-v4

mbertheau

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 856.50 +/- 453.86 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mbertheau -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mbertheau -f logs/ rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mbertheau ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```

philip-hightech/23bc76f8-f764-4ef2-a5b9-20ce60e650a8

philip-hightech

peft

--- library_name: peft base_model: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 tags: - axolotl - generated_from_trainer model-index: - name: 23bc76f8-f764-4ef2-a5b9-20ce60e650a8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9fa2363f5e2cb347_train_data.json ds_type: json format: custom path: /workspace/input_data/9fa2363f5e2cb347_train_data.json type: field_instruction: prompt field_output: chosen format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: false group_by_length: false hub_model_id: philip-hightech/23bc76f8-f764-4ef2-a5b9-20ce60e650a8 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 250 micro_batch_size: 2 mlflow_experiment_name: /tmp/9fa2363f5e2cb347_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: d8867652-8a0b-462d-8035-54df350aea9e wandb_project: Mine-SN56-21-Gradients-On-Demand wandb_run: your_name wandb_runid: d8867652-8a0b-462d-8035-54df350aea9e warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 23bc76f8-f764-4ef2-a5b9-20ce60e650a8 This model is a fine-tuned version of [OpenBuddy/openbuddy-llama2-13b-v8.1-fp16](https://huggingface.co/OpenBuddy/openbuddy-llama2-13b-v8.1-fp16) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 250 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0005 | 1 | nan | | 0.483 | 0.0287 | 63 | nan | | 0.373 | 0.0574 | 126 | nan | | 0.4617 | 0.0861 | 189 | nan | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

John6666/ether-pdxl-a3-sdxl

John6666

diffusers

text-to-image

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - semi-realistic - 2.5D - illustration - cute - colorful - portrait - pony base_model: gamerdan69/EtherMix --- Original model is [here](https://huggingface.co/gamerdan69/EtherMix) and on [Civitai](https://civitai.com/models/545628?modelVersionId=778308). This model created by [gamerdan69](https://civitai.com/user/gamerdan69).

LongSafari/evo-1-8k-crispr

LongSafari

transformers

text-generation

--- license: apache-2.0 tags: - stripedhyena - long context - deep signal processing - hybrid - biology - genomics --- ## Evo-1 (CRISPR-Cas) <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/62a1306bbe7fa896d2c8de44/JoEHcvLTUlHoMcgh3mmAz.png" width="70%" /> </p> ### News We identified and fixed an issue related to a wrong permutation of some projections, which affects generation quality. To use the new model revision, please load as follows: ```python config = AutoConfig.from_pretrained(model_name, trust_remote_code=True, revision="1.1_fix") model = AutoModelForCausalLM.from_pretrained( model_name, config=config, trust_remote_code=True, revision="1.1_fix" ) ``` ### About Evo is a biological foundation model capable of long-context modeling and design. Evo uses the [StripedHyena architecture](https://github.com/togethercomputer/stripedhyena) to enable modeling of sequences at a single-nucleotide, byte-level resolution with near-linear scaling of compute and memory relative to context length. Evo has 7 billion parameters and is trained on OpenGenome, a prokaryotic whole-genome dataset containing ~300 billion tokens. Technical details about Evo can be found in our preprint and our accompanying blog posts. Evo was collaboratively developed by the [Arc Institute](https://arcinstitute.org/) and TogetherAI. As part of our commitment to open science, we release **weights of 15 intermediate pretraining checkpoints** for phase 1 and phase 2 of pretraining. The checkpoints are available as branches of the corresponding HuggingFace repository. **Evo-1 (CRISPR-Cas)** is our fine-tuned model used to generate CRISPR-Cas systems, trained at a context length of 8k. | Checkpoint Name | Description | |----------------------------------------|-------------| | `evo-1-8k-base` | A model pretrained with 8,192 context. We use this model as the base model for molecular-scale finetuning tasks. | | `evo-1-131k-base` | A model pretrained with 131,072 context using `evo-1-8k-base` as the initialization. We use this model to reason about and generate sequences at the genome scale. | | `evo-1-8k-crispr` | A model fine-tuned on `evo-1-8k-base` specifically on CRISPR-Cas systems. We use this model to generate Cas9/12/13 systems. | | `evo-1-8k-transposon` | A model fine-tuned on `evo-1-8k-base` specifically on transposons. We use this to generate IS200/IS605. | ### Model Architecture StripedHyena is a deep signal processing, hybrid architecture composed of multi-head attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, improving over decoder-only Transformers. StripedHyena is designed to leverage the specialization of each of its layer classes, with Hyena layers implementing the bulk of the computation required for sequence processing and attention layers supplementing the ability to perform targeted pattern recall. Some highlights of the architecture: - **Efficient autoregressive generation** via a recurrent mode (>500k generation with a single 80GB GPU) - **Significantly faster training and finetuning** at long context (>3x at 131k) - **Improved scaling laws over state-of-the-art architectures** (e.g., Transformer++) on both natural language and biological sequences. - **Robust to training beyond the compute-optimal frontier** e.g., training way beyond Chinchilla-optimal token amounts (see preprint for details -- more details to come) ### How to use Evo Example usage is provided in the [standalone repo](https://github.com/evo-design/evo). #### Parametrization for Inference and Finetuning One of the advantages of deep signal processing models is their flexibility. Different parametrizations of convolutions can be used depending on the memory, expressivity and causality requirements of pretraining, finetuning or inference workloads. The main classes are: - Modal canonical: unconstrained poles ([reference](https://arxiv.org/pdf/2203.14343.pdf), [reference](https://arxiv.org/abs/2310.18780)), or constrained poles ([reference](https://arxiv.org/abs/2206.11893), [reference](https://arxiv.org/pdf/2303.06349.pdf)). - Companion canonical / rational: TBA. - Hypernetworks: hypernetwork ([reference](https://arxiv.org/abs/2102.02611)), modulated hypernetwork ([reference](https://arxiv.org/abs/2302.10866)). - Explicit: modulated explicit ([reference](https://arxiv.org/pdf/2210.09298.pdf)). StripedHyena is a mixed precision model. Make sure to keep your `poles` and `residues` in `float32` precision, especially for longer prompts or training. ### Disclaimer To use StripedHyena outside of the playground, you will need to install custom kernels. Please follow the instructions from the [standalone repository](https://github.com/togethercomputer/stripedhyena). ## Cite ``` @article{nguyen2024sequence, author = {Eric Nguyen and Michael Poli and Matthew G. Durrant and Armin W. Thomas and Brian Kang and Jeremy Sullivan and Madelena Y. Ng and Ashley Lewis and Aman Patel and Aaron Lou and Stefano Ermon and Stephen A. Baccus and Tina Hernandez-Boussard and Christopher Ré and Patrick D. Hsu and Brian L. Hie}, journal = {Arc Institute manuscripts}, title = {Sequence modeling and design from molecular to genome scale with Evo}, url = {https://arcinstitute.org/manuscripts/Evo}, year = {2024}, } ```

cvoffer/0efa4b3b-1a51-4af2-855c-06fbe43d36f4

cvoffer

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-1.1-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 0efa4b3b-1a51-4af2-855c-06fbe43d36f4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/gemma-1.1-2b-it bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - e4734565209409b8_train_data.json ds_type: json format: custom path: /workspace/input_data/e4734565209409b8_train_data.json type: field_instruction: post field_output: summary format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: cvoffer/0efa4b3b-1a51-4af2-855c-06fbe43d36f4 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 78GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/e4734565209409b8_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: de79a7ae-33e3-454b-9488-386af2af5b95 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: de79a7ae-33e3-454b-9488-386af2af5b95 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0efa4b3b-1a51-4af2-855c-06fbe43d36f4 This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.6731 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0001 | 1 | 4.0435 | | 3.9019 | 0.0003 | 5 | 3.0211 | | 2.8695 | 0.0007 | 10 | 2.7868 | | 2.6902 | 0.0010 | 15 | 2.7147 | | 2.6625 | 0.0013 | 20 | 2.6863 | | 2.6312 | 0.0016 | 25 | 2.6755 | | 2.7038 | 0.0020 | 30 | 2.6731 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

tanmeh/qa_model

tanmeh

transformers

question-answering

--- license: mit base_model: xlnet-base-cased tags: - generated_from_trainer model-index: - name: qa_model 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. --> # qa_model This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 50 | 3.4426 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1

ultracheese/ppo-LunarLander-v2

ultracheese

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 282.03 +/- 22.11 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

helpingstar/Reinforce-1

helpingstar

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 221.30 +/- 143.88 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

impossibleexchange/0x118

impossibleexchange

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]

edmundmills/dignity-classifier

edmundmills

transformers

text-classification

--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: dignity-classifier 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. --> # dignity-classifier This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5157 - Accuracy: 0.8678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7722 | 1.0 | 98 | 0.7799 | 0.6897 | | 0.4301 | 2.0 | 196 | 0.4704 | 0.8477 | | 0.2445 | 3.0 | 294 | 0.5107 | 0.8305 | | 0.1626 | 4.0 | 392 | 0.5553 | 0.8477 | | 0.0653 | 5.0 | 490 | 0.5157 | 0.8678 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1 - Datasets 2.12.0 - Tokenizers 0.13.3

ashishabraham22/XLS-R_Finetuned

ashishabraham22

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer model-index: - name: XLS-R_Finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # XLS-R_Finetuned This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

CyberHarem/echidna_rezero

CyberHarem

text-to-image

--- license: mit datasets: - CyberHarem/echidna_rezero pipeline_tag: text-to-image tags: - art --- # Lora of echidna_rezero This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 1500, you need to download `1500/echidna_rezero.pt` as the embedding and `1500/echidna_rezero.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The trigger word is `echidna_rezero`.** These are available steps: | Steps | pattern_1 | pattern_2 | pattern_3 | bikini | free | nude | Download | |--------:|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------| | 1500 |  |  |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [Download](1500/echidna_rezero.zip) | | 1400 |  |  |  |  |  | [<NSFW, click to see>](1400/previews/nude.png) | [Download](1400/echidna_rezero.zip) | | 1300 |  |  |  |  |  | [<NSFW, click to see>](1300/previews/nude.png) | [Download](1300/echidna_rezero.zip) | | 1200 |  |  |  |  |  | [<NSFW, click to see>](1200/previews/nude.png) | [Download](1200/echidna_rezero.zip) | | 1100 |  |  |  |  |  | [<NSFW, click to see>](1100/previews/nude.png) | [Download](1100/echidna_rezero.zip) | | 1000 |  |  |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [Download](1000/echidna_rezero.zip) | | 900 |  |  |  |  |  | [<NSFW, click to see>](900/previews/nude.png) | [Download](900/echidna_rezero.zip) | | 800 |  |  |  |  |  | [<NSFW, click to see>](800/previews/nude.png) | [Download](800/echidna_rezero.zip) | | 700 |  |  |  |  |  | [<NSFW, click to see>](700/previews/nude.png) | [Download](700/echidna_rezero.zip) | | 600 |  |  |  |  |  | [<NSFW, click to see>](600/previews/nude.png) | [Download](600/echidna_rezero.zip) | | 500 |  |  |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [Download](500/echidna_rezero.zip) | | 400 |  |  |  |  |  | [<NSFW, click to see>](400/previews/nude.png) | [Download](400/echidna_rezero.zip) | | 300 |  |  |  |  |  | [<NSFW, click to see>](300/previews/nude.png) | [Download](300/echidna_rezero.zip) | | 200 |  |  |  |  |  | [<NSFW, click to see>](200/previews/nude.png) | [Download](200/echidna_rezero.zip) | | 100 |  |  |  |  |  | [<NSFW, click to see>](100/previews/nude.png) | [Download](100/echidna_rezero.zip) |

MarkBW/hannahowo-xl

MarkBW

diffusers

text-to-image

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/2023-12-30_18-57-29_4486.jpeg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: hannahowo --- # hannahowo-xl <Gallery /> ## Trigger words You should use `hannahowo` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/MarkBW/hannahowo-xl/tree/main) them in the Files & versions tab.

mlx-community/whisper-large-v3-mlx-8bit

mlx-community

mlx

--- library_name: mlx --- # whisper-large-v3-mlx-8bit This model was converted to MLX format from [`large-v3`](). ## Use with mlx ```bash git clone https://github.com/ml-explore/mlx-examples.git cd mlx-examples/whisper/ pip install -r requirements.txt >> import whisper >> whisper.transcribe("FILE_NAME", path_or_hf_repo="mlx-community/whisper-large-v3-mlx-8bit") ```

asi/gpt-fr-cased-small

asi

transformers

text-generation

--- language: - fr model-index: - name: asi/gpt-fr-cased-base results: - task: type: text-generation name: Wikitext-fr dataset: type: wikitext_fr name: Wikitext-fr metrics: - type: perplexity value: 109.2 name: Perplexity - task: type: text-classification name: FLUE dataset: type: flue name: CLS-Books split: CLS metrics: - type: accuracy value: 88.3 name: Accuracy - task: type: text-classification name: FLUE dataset: type: flue name: CLS-Dvd split: CLS metrics: - type: accuracy value: 86.9 name: Accuracy - task: type: text-classification name: FLUE dataset: type: flue name: CLS-Music split: CLS metrics: - type: accuracy value: 89.3 name: Accuracy - task: type: text-classification name: FLUE dataset: type: flue name: PAWS-X split: PAWS-X metrics: - type: accuracy value: 83.3 name: Accuracy - task: type: text-classification name: FLUE dataset: type: flue name: XNLI split: XNLI metrics: - type: accuracy value: 75.6 name: Accuracy - task: type: summarization name: OrangeSum dataset: type: orange_sum name: OrangeSum-Abstract split: abstract metrics: - name: ROUGE-1 type: rouge value: 17.5 - name: ROUGE-2 type: rouge value: 3.1 - name: ROUGE-L type: rouge value: 12.1 - task: type: summarization name: OrangeSum dataset: type: orange_sum name: OrangeSum-Title split: title metrics: - name: ROUGE-1 type: rouge value: 13.9 - name: ROUGE-2 type: rouge value: 2.3 - name: ROUGE-L type: rouge value: 9.7 tags: - tf - pytorch - gpt2 - text-generation license: apache-2.0 thumbnail: https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png --- <img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png" width="200"> ## Model description **GPT-fr** 🇫🇷 is a GPT model for French developped by [Quantmetry](https://www.quantmetry.com/) and the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We train the model on a very large and heterogeneous French corpus. We release the weights for the following configurations: | Model name | Number of layers | Attention Heads | Embedding Dimension | Total Parameters | | :------: | :---: | :---: | :---: | :---: | | `gpt-fr-cased-small` | 12 | 12 | 768 | 124 M | | `gpt-fr-cased-base` | 24 | 14 | 1,792 | 1,017 B | ## Intended uses & limitations The model can be leveraged for language generation tasks. Besides, many tasks may be formatted such that the output is directly generated in natural language. Such configuration may be used for tasks such as automatic summary or question answering. We do hope our model might be used for both academic and industrial applications. #### How to use The model might be used through the astonishing 🤗 `Transformers` librairie: ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel # Load pretrained model and tokenizer model = GPT2LMHeadModel.from_pretrained("asi/gpt-fr-cased-small") tokenizer = GPT2Tokenizer.from_pretrained("asi/gpt-fr-cased-small") # Generate a sample of text model.eval() input_sentence = "Longtemps je me suis couché de bonne heure." input_ids = tokenizer.encode(input_sentence, return_tensors='pt') beam_outputs = model.generate( input_ids, max_length=100, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1 ) print("Output:\n" + 100 * '-') print(tokenizer.decode(beam_outputs[0], skip_special_tokens=True)) ``` #### Limitations and bias Large language models tend to replicate the biases found in pre-training datasets, such as gender discrimination or offensive content generation. To limit exposition to too much explicit material, we carefully choose the sources beforehand. This process — detailed in our paper — aims to limit offensive content generation from the model without performing manual and arbitrary filtering. However, some societal biases, contained in the data, might be reflected by the model. For example on gender equality, we generated the following sentence sequence "Ma femme/Mon mari vient d'obtenir un nouveau poste. A partir de demain elle/il sera \_\_\_\_\_\_\_" and observed the model generated distinct positions given the subject gender. We used top-k random sampling strategy with k=50 and stopped at the first punctuation element. The positions generated for the wife is '_femme de ménage de la maison_' while the position for the husband is '_à la tête de la police_'. We do appreciate your feedback to better qualitatively and quantitatively assess such effects. ## Training data We created a dedicated corpus to train our generative model. Indeed the model uses a fixed-length context size of 1,024 and require long documents to be trained. We aggregated existing corpora: [Wikipedia](https://dumps.wikimedia.org/frwiki/), [OpenSubtitle](http://opus.nlpl.eu/download.php?f=OpenSubtitles/v2016/mono/) ([Tiedemann, 2012](#tiedemann-2012)), [Gutenberg](http://www.gutenberg.org). Corpora are filtered and separated into sentences. Successive sentences are then concatenated within the limit of 1,024 tokens per document. ## Training procedure We pre-trained the model on a TPU v2-8 using the amazing [Google Colab](https://colab.research.google.com) inter-server. ## Eval results We packaged **GPT-fr** with a dedicated language model evaluation benchmark. In line with the [WikiText](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark in English, we collected over 70 million tokens from the set of verified [good](https://fr.wikipedia.org/wiki/Wikip%C3%A9dia:Articles_de_qualit%C3%A9) and [featured](https://fr.wikipedia.org/wiki/Wikip%C3%A9dia:Bons_articles) articles on French Wikipedia. The model reaches a zero-shot perplexity of **109.2** on the test set. ### BibTeX entry and citation info Along with the model hosted by HuggingFace transformers library, we maintain a [git repository](https://github.com/AntoineSimoulin/gpt-fr). If you use **GPT-fr** for your scientific publications or your industrial applications, please cite the following paper: ```bibtex @inproceedings{simoulin:hal-03265900, TITLE = {{Un mod{\`e}le Transformer G{\'e}n{\'e}ratif Pr{\'e}-entrain{\'e} pour le \_\_\_\_\_\_ fran{\c c}ais}}, AUTHOR = {Simoulin, Antoine and Crabb{\'e}, Benoit}, URL = {https://hal.archives-ouvertes.fr/hal-03265900}, BOOKTITLE = {{Traitement Automatique des Langues Naturelles}}, ADDRESS = {Lille, France}, EDITOR = {Denis, Pascal and Grabar, Natalia and Fraisse, Amel and Cardon, R{\'e}mi and Jacquemin, Bernard and Kergosien, Eric and Balvet, Antonio}, PUBLISHER = {{ATALA}}, PAGES = {246-255}, YEAR = {2021}, KEYWORDS = {fran{\c c}ais. ; GPT ; G{\'e}n{\'e}ratif ; Transformer ; Pr{\'e}-entra{\^i}n{\'e}}, PDF = {https://hal.archives-ouvertes.fr/hal-03265900/file/7.pdf}, HAL_ID = {hal-03265900}, HAL_VERSION = {v1}, } ``` ### References ><div name="tiedemann-2012">Jörg Tiedemann: Parallel Data, Tools and Interfaces in OPUS. LREC 2012: 2214-2218</div>

EmmettBicker/ppo-Huggy

EmmettBicker

ml-agents

reinforcement-learning

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: EmmettBicker/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF

farpluto

text-generation

--- language: - multilingual license: mit tags: - nlp - code - llama-cpp - gguf-my-repo base_model: microsoft/Phi-3-medium-4k-instruct license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation inference: parameters: temperature: 0.7 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-medium-4k-instruct`](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./main --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./server --hf-repo farpluto/Phi-3-medium-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-medium-4k-instruct-q4_k_m.gguf -c 2048 ```

mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF

mradermacher

transformers

--- base_model: ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - bfloat16 - text-generation-inference - model_stock - crypto - finance - llama --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-GGUF/resolve/main/LLama3.1-Hawkish-Theia-Fireball-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

keyblade95/q-FrozenLake-v1-4x4-noSlippery

keyblade95

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery 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="keyblade95/q-FrozenLake-v1-4x4-noSlippery", 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"]) ```

stablediffusionapi/epicdream

stablediffusionapi

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # epiCDream API Inference  ## Get API Key Get API key from [ModelsLab](https://modelslab.com/), No Payment needed. Replace Key in below code, change **model_id** to "epicdream" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/epicdream) Model link: [View model](https://stablediffusionapi.com/models/epicdream) Credits: [View credits](https://civitai.com/?query=epiCDream) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "epicdream", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

John6666/mix-photoreal-stable-xl-stable4-sdxl

John6666

diffusers

text-to-image

--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - photo - anime - game - cartoon --- Original model is [here](https://civitai.com/models/409675/mix-photoreal-stable-xl?modelVersionId=689342).

mlx-community/Codestral-22B-v0.1-4bit

mlx-community

mlx

--- language: - code license: other tags: - code - mlx inference: false license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md --- # mlx-community/Codestral-22B-v0.1-4bit The Model [mlx-community/Codestral-22B-v0.1-4bit](https://huggingface.co/mlx-community/Codestral-22B-v0.1-4bit) was converted to MLX format from [bullerwins/Codestral-22B-v0.1-hf](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf) using mlx-lm version **0.14.0**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Codestral-22B-v0.1-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

sathurjan/Digitweb_Cone_Shade1

sathurjan

object-detection

--- pipeline_tag: object-detection ---

RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Experiment26Yamshadow-7B - GGUF - Model creator: https://huggingface.co/automerger/ - Original model: https://huggingface.co/automerger/Experiment26Yamshadow-7B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Experiment26Yamshadow-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q2_K.gguf) | Q2_K | 2.53GB | | [Experiment26Yamshadow-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [Experiment26Yamshadow-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ3_S.gguf) | IQ3_S | 2.96GB | | [Experiment26Yamshadow-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [Experiment26Yamshadow-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ3_M.gguf) | IQ3_M | 3.06GB | | [Experiment26Yamshadow-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K.gguf) | Q3_K | 3.28GB | | [Experiment26Yamshadow-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [Experiment26Yamshadow-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [Experiment26Yamshadow-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [Experiment26Yamshadow-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_0.gguf) | Q4_0 | 3.83GB | | [Experiment26Yamshadow-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [Experiment26Yamshadow-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [Experiment26Yamshadow-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_K.gguf) | Q4_K | 4.07GB | | [Experiment26Yamshadow-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [Experiment26Yamshadow-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q4_1.gguf) | Q4_1 | 4.24GB | | [Experiment26Yamshadow-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_0.gguf) | Q5_0 | 4.65GB | | [Experiment26Yamshadow-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [Experiment26Yamshadow-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_K.gguf) | Q5_K | 4.78GB | | [Experiment26Yamshadow-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [Experiment26Yamshadow-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q5_1.gguf) | Q5_1 | 5.07GB | | [Experiment26Yamshadow-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q6_K.gguf) | Q6_K | 5.53GB | | [Experiment26Yamshadow-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/automerger_-_Experiment26Yamshadow-7B-gguf/blob/main/Experiment26Yamshadow-7B.Q8_0.gguf) | Q8_0 | 7.17GB | Original model description: --- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - automerger/YamShadow-7B --- # Experiment26Yamshadow-7B Experiment26Yamshadow-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B) ## 🧩 Configuration ```yaml models: - model: rwitz/experiment26-truthy-iter-0 # No parameters necessary for base model - model: automerger/YamShadow-7B parameters: density: 0.53 weight: 0.6 merge_method: dare_ties base_model: rwitz/experiment26-truthy-iter-0 parameters: int8_mask: true dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Experiment26Yamshadow-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

zzttbrdd/sn6_6m

zzttbrdd

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]

sail-rvc/Whoppa

sail-rvc

transformers

audio-to-audio

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # Whoppa ## RVC Model  This model repo was automatically generated. Date: 2023-07-14 07:33:59 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

fumi13/vit-base-beans

fumi13

transformers

image-classification

--- license: apache-2.0 tags: - image-classification - vision - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: vit-base-beans results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9924812030075187 --- <!-- 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. --> # vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0824 - Accuracy: 0.9925 ## 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: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3039 | 1.0 | 130 | 0.2474 | 0.9624 | | 0.1299 | 2.0 | 260 | 0.1007 | 0.9925 | | 0.0885 | 3.0 | 390 | 0.0824 | 0.9925 | | 0.0976 | 4.0 | 520 | 0.1179 | 0.9699 | | 0.1284 | 5.0 | 650 | 0.0832 | 0.9774 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1

thalllsssss/e7527483-01ec-49be-b413-2690d086345c

thalllsssss

peft

--- library_name: peft license: llama3 base_model: elyza/Llama-3-ELYZA-JP-8B tags: - axolotl - generated_from_trainer model-index: - name: e7527483-01ec-49be-b413-2690d086345c results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: elyza/Llama-3-ELYZA-JP-8B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c3dbdb6a98b06458_train_data.json ds_type: json format: custom path: /workspace/input_data/c3dbdb6a98b06458_train_data.json type: field_instruction: problem field_output: solution format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: thalllsssss/e7527483-01ec-49be-b413-2690d086345c hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/c3dbdb6a98b06458_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <|eot_id|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 858aaf4c-a418-40cd-9e36-4bb85fd16280 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 858aaf4c-a418-40cd-9e36-4bb85fd16280 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e7527483-01ec-49be-b413-2690d086345c This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6745 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.5753 | 0.0020 | 200 | 0.6745 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

TinToTin/cartpole-policy-gradient

TinToTin

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: cartpole-policy-gradient results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

AdamKasumovic/phi3-mini-4k-instruct-bactrian-x-xh-50-percent-low-med-bt-rouge-1

AdamKasumovic

transformers

text-generation

--- 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:** AdamKasumovic - **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)

ihanif/whisper-test

ihanif

transformers.js

automatic-speech-recognition

--- base_model: openai/whisper-small datasets: - mozilla-foundation/common_voice_17_0 language: - ps library_name: transformers.js license: apache-2.0 tags: - generated_from_trainer - onnx model-index: - name: Whisper Small PS - Hanif Rahman results: [] --- https://huggingface.co/ihanif/whisper-test with ONNX weights to be compatible with Transformers.js. <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small PS - Hanif Rahman This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7573 - eval_wer: 46.1819 - eval_runtime: 395.7975 - eval_samples_per_second: 1.294 - eval_steps_per_second: 0.162 - epoch: 5.7143 - step: 2600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.45.1 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0 --- Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

ShotaMatsumoto/GPT0.35B-ja-tokenizer-unigram-v1-CultulaX-default-filtered-ja-part-00000-00002-1000step

ShotaMatsumoto

transformers

text-generation

--- license: apache-2.0 ---

mlx-community/Phi-3-mini-128k-instruct-4bit

mlx-community

mlx

text-generation

--- language: - en license: mit tags: - nlp - code - mlx license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE pipeline_tag: text-generation --- # mlx-community/Phi-3-mini-128k-instruct-4bit This model was converted to MLX format from [`microsoft/Phi-3-mini-128k-instruct`]() using mlx-lm version **0.10.0**. Model added by [Prince Canuma](https://twitter.com/Prince_Canuma). Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Phi-3-mini-128k-instruct-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

nintwentydo/Razorback-12B-v0.1

nintwentydo

transformers

image-text-to-text

--- base_model: - mistralai/Pixtral-12B-2409 - TheDrummer/Rocinante-12B-v1.1 - TheDrummer/UnslopNemo-12B-v3 base_model_relation: merge library_name: transformers tags: - mergekit - merge - multimodal - mistral - pixtral language: - en - fr - de - es - it - pt - ru - zh - ja license: other pipeline_tag: image-text-to-text --- # Razorback 12B v0.1 ## Update: Use v0.2 [nintwentydo/Razorback-12B-v0.2](https://huggingface.co/nintwentydo/Razorback-12B-v0.2) <img src="https://huggingface.co/nintwentydo/Razorback-12B-v0.1/resolve/main/razorback.jpg" style="max-width:700px"></img> This is a first pass attempt to merge Mistral Nemo finetunes with Pixtral 12B. Has not been fully tested yet other than confirming it can understand vision input and output coherent text. May be unstable for all we know lol. Pixtral 12B as base with TheDrummer's Rocinante and UnslopNemo finetunes merged in. In *The Expanse* the Razorback is a ship fitted with engines way bigger than a ship of its size would normally have. Thought it was a fitting way to celebrate TheDrummer's models supercharging Pixtral. 😉 ## Credits - Mistral for [mistralai/Pixtral-12B-2409](https://huggingface.co/mistralai/Pixtral-12B-2409) - Unsloth for [unsloth/Pixtral-12B-2409](https://huggingface.co/unsloth/Pixtral-12B-2409) transformers conversion - TheDrummer for [TheDrummer/Rocinante-12B-v1.1](https://huggingface.co/TheDrummer/Rocinante-12B-v1.1) - TheDrummer for [TheDrummer/UnslopNemo-12B-v3](https://huggingface.co/TheDrummer/UnslopNemo-12B-v3)

APaul/multi-class-full-data

APaul

transformers

token-classification

--- base_model: APaul/muril-multi-class library_name: transformers tags: - generated_from_keras_callback model-index: - name: multi-class-full-data results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # multi-class-full-data This model is a fine-tuned version of [APaul/muril-multi-class](https://huggingface.co/APaul/muril-multi-class) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1202 - Validation Loss: 0.1385 - Train Precision: 0.7887 - Train Recall: 0.8132 - Train F1: 0.8008 - Train Accuracy: 0.9629 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 30000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.1266 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 0 | | 0.1217 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 1 | | 0.1260 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 2 | | 0.1217 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 3 | | 0.1244 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 4 | | 0.1224 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 5 | | 0.1224 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 6 | | 0.1225 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 7 | | 0.1244 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 8 | | 0.1202 | 0.1385 | 0.7887 | 0.8132 | 0.8008 | 0.9629 | 9 | ### Framework versions - Transformers 4.44.2 - TensorFlow 2.17.0 - Datasets 3.2.0 - Tokenizers 0.19.1

mlx-community/DeepSeek-R1-Distill-Llama-70B

mlx-community

mlx

--- base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B tags: - mlx --- # mlx-community/DeepSeek-R1-Distill-Llama-70B The Model [mlx-community/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/mlx-community/DeepSeek-R1-Distill-Llama-70B) was converted to MLX format from [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) using mlx-lm version **0.20.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/DeepSeek-R1-Distill-Llama-70B") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF

mradermacher

transformers

--- base_model: MaziyarPanahi/MeliodasPercival_01_Experiment24Ognoexperiment27 language: - en library_name: transformers license: apache-2.0 model_creator: MaziyarPanahi model_name: MeliodasPercival_01_Experiment24Ognoexperiment27 quantized_by: mradermacher tags: - Safetensors - text-generation-inference - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/MaziyarPanahi/MeliodasPercival_01_Experiment24Ognoexperiment27 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/MeliodasPercival_01_Experiment24Ognoexperiment27-GGUF/resolve/main/MeliodasPercival_01_Experiment24Ognoexperiment27.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

ntinosmg/taxi-v3

ntinosmg

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="ntinosmg/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Sakalti/mine-1.5B

Sakalti

transformers

text-generation

--- base_model: Sakalti/SJT-1.5B-Alpha tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en library_name: transformers inference: true widget: - messages: - role: user content: こんにちは！ - messages: - role: user content: マインクラフトは知ってます？ - messages: - role: user content: コンポスターって効率悪いから骨集める方がよさそう - messages: - role: user content: こんにちは！ - messages: - role: user content: マインクラフトの話しましょ！ - messages: - role: user content: ピグリン要塞と呼ばれるものを知ってます？ - messages: - role: user content: Hello! - messages: - role: user content: Do you know Minecraft? - messages: - role: user content: Composters are inefficient, so collecting bones seems like a better option - messages: - role: user content: Hello! - messages: - role: user content: Let's talk about Minecraft! - messages: - role: user content: Do you know what's called a piglin stronghold? --- # Uploaded model - **Developed by:** Sakalti - **License:** apache-2.0 - **Finetuned from model :** Sakalti/SJT-1.5B-Alpha 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)

mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF

mradermacher

transformers

--- base_model: jungyuko/DAVinCI-Yi-Ko-6B-v1.1 language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/jungyuko/DAVinCI-Yi-Ko-6B-v1.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q2_K.gguf) | Q2_K | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q3_K_S.gguf) | Q3_K_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q3_K_M.gguf) | Q3_K_M | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q3_K_L.gguf) | Q3_K_L | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.IQ4_XS.gguf) | IQ4_XS | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 3.7 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q4_K_S.gguf) | Q4_K_S | 3.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q4_K_M.gguf) | Q4_K_M | 3.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q5_K_S.gguf) | Q5_K_S | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q5_K_M.gguf) | Q5_K_M | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q6_K.gguf) | Q6_K | 5.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.Q8_0.gguf) | Q8_0 | 6.7 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/DAVinCI-Yi-Ko-6B-v1.1-GGUF/resolve/main/DAVinCI-Yi-Ko-6B-v1.1.f16.gguf) | f16 | 12.5 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

Yang78ok/Llama-2-7b-chat-hf-fine-tuned-adapters

Yang78ok

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

gabrielbenabou/q-Taxi-v3

gabrielbenabou

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 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="gabrielbenabou/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Onlydrinkwater/gpt2xl_format_math_520_from_scratch

Onlydrinkwater

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. 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AubadeAudio/RVC-Models

AubadeAudio

--- license: openrail ---

LoneStriker/deepmoney-34b-200k-base-4.0bpw-h6-exl2

LoneStriker

transformers

text-generation

--- license: apache-2.0 language: - en - zh tags: - finance - invest --- # **Deepmoney**  Introducing **Greed** in the Seven Deadly Sins series of models. - Full-para pre-training on Yi-34b - High-quality research reports - High-end cleaning process ### 1. What do I want to do? Most of the current so-called financial models are mostly trained on public knowledge, but in the actual financial field, these public knowledge are often seriously insufficient for the current market interpretability. If you are interested, you can learn about the various propositions of Keynes, Friedman and even current behavioral finance. According to my observation, most financial models cannot make investment judgments. Because they are trained on ordinary textbooks, entry-level analyst exams, and even company public reports. I think this is of very little value for the investment. You can think I'm joking, but the fact is that the logic of many subjective analysts may not be as rigorous as that of large models of 34b and above (excluding those excellent ones, of course). The market is changing every moment, with a lot of news and massive data in real time. For most retail investors, instead of waiting for a crappy analyst to write a report, why not use a large model to make a pipeline? In my plan, this model is the base model of this process. In my plan, models such as information collector, target judge, qualitative analyst, quantitative analyst, and data extractor are all part of this process. . But the model itself is undoubtedly important to master a large number of qualitative and quantitative methods. That's why this model was born. ### 2. About the data As I just said, a lot of public knowledge has some questionable validity - but that doesn't mean it's wrong. The theoretical support behind many research methods in research reports also relies on this knowledge. So in my training, I picked up some college textbooks and some professional books. Not a lot of quantity but good quality. In addition, I selected a large number of research report data from 2019 to December 2023 - these reports are issued by a variety of publishers, including traditional brokers and professional research institutions. Most of them are paid and only available to institutions. But I got them anyway through various means. If you have read research reports, especially high-quality ones, you will find that research reports are all subjective judgment + quantitative analysis, and data support in quantitative analysis is crucial to the entire logical chain. In order to extract this data (most of them are in the form of graphs or tables), I tried a lot of multi-modal models, and the process was very painful. The conclusion is that cog-agent and emu2 are very effective for this kind of tasks. In order to better extract information, I created a process that summarizes the context of research reports as part of the prompt. Finally, I made a blend of the data. General data is not included because it is just for greed. Moreover, the knowledge in industry research reports is comprehensive enough. ### 3. About training Raw text, full parameter training. The base uses long context yi-34b-200k. This is necessary to complete and understand an in-depth report. Of course, I also did a sft. [This](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator) is the analyzer in my process – I haven’t broken down the qualitative and quantitative analysis yet, but I’m already blown away by how well it works. ### More: More technical details and evals coming soon…… ### 1. 我想干什么？ 当下大多数所谓的金融模型大多在公开知识上进行训练，但在实际的金融领域，这些公开知识对当前的市场可解释性往往严重不足。如果您感兴趣，可以了解一下凯恩斯、弗里德曼乃至当下行为金融学的各类主张。而据我观察，大多数金融模型无法对投资进行判断。因为它们都是在普通的教材、入门的分析师考试，乃至公司的公开报告上训练。我认为这对于投资的价值非常小。 你可以当我开玩笑，但事实是很多主观分析师的逻辑性可能还不如34b及以上的大模型来的严谨（当然不包括那些优秀的）。而每时每刻，市场都在变化，大量的新闻，海量的数据都是实时的，对于大多数散户们，与其等待蹩脚的分析师写出报告，为什么不用大模型制作一套pipeline呢？ 在我的计划中，该模型是这套流程的基座模型，在我的计划中，信息搜集者、标的判断者、定性分析者定性分析者、定量分析者、数据提取者等模型都是该流程的一部分。但模型本身掌握大量的定性和定量方法毫无疑问是重要的。这就是这个模型诞生的理由。 ### 2. 关于数据： 正如我刚才所说，很多公开知识的有效性都有些问题——但这并不意味着它们是错误的。在研报中很多研究方法背后的理论支撑也依赖这些知识。所以在我的训练中，我挑选了一些大学教材和一些专业书籍。数量不是很多但质量还不错。另外，我挑选了在2019-2023年12月的大量研究报告数据——这些报告的发布者多种多样，有传统的broke，也有专业研究机构。他们中的大多数是付费的，而且只对机构提供。但无论如何我通过各种各样的手段获取了它们。 如果你看过研报，尤其是高质量的那些，你会发现研报都是主观判断+定量分析，而定量分析中的数据支撑对于整个逻辑链条至关重要。为了提取这些数据（他们中的大多数以图形或者表格的形式出现），我尝试了很多多模态模型，过程非常痛苦，结论是cog-agent和emu2对于这类任务很有效。为了更好的提取信息，我制作了一套从研报上下文总结作为prompt一部分的流程。 最后，我把这些数据做了一个混合。并没有放入通识数据, 因为它就是为了greed而生的。而且行业研报中的知识足够全。 ### 3：关于训练： raw text，全参数训练。基座采用了长上下文的yi-34b-200k。这对于完成理解一篇深度报告是必须的。 当然，我也做了一次sft。这是我的流程中的分析者——目前还没有细分定性和定量分析，但[它的效果](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator)已经让我大吃一惊了。

Helsinki-NLP/opus-mt-lua-fr

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-lua-fr * source languages: lua * target languages: fr * OPUS readme: [lua-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lua-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lua-fr/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-fr/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lua-fr/opus-2020-01-09.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lua.fr | 25.7 | 0.429 |

AlekseyCalvin/PhantasmalPhotogravures_FluxLoRA_BySilverAgePoets

AlekseyCalvin

diffusers

text-to-image

--- license: apache-2.0 language: - en tags: - flux - diffusers - lora - replicate base_model: black-forest-labs/FLUX.1-dev pipeline_tag: text-to-image instance_prompt: >- HST style autochrome color analog film photogravure photo, phantasmal garden, antique kodachrome, surrealist eroticism, gothic photograph, crisp textures, intricately detailed widget: - text: HST style autochrome color photogravure output: url: 2.jpg - text: >- HST style autochrome color photogravure analog film photo of a desperate male Russian poet screaming in panic as garden burns all around him with mystical flames, golden ratio proportions , phantasmal garden, antique pictoralist early kodachrome, surrealist eroticism, gothic photograph, crisp textures, intricately detailed artistic conceptual photography', and the suchlike... output: url: images/example_xl7mwgu47.png - text: HST style analog autochrome color vintage photo output: url: images/example_fdp4p52o7.png --- ## Phantasmal Photogravure: Another HSToric Color LoRA ## ____||| By SilverAgePoets.com |||____ <Gallery /> Yet another of out antique photography style Flux LoRAS, mainly trained on vageully sureal pictoralist prints from between the 1880s and the 1920s. Lots of garden, mists, personas of nymphs and deities adopted by bohemian models. <br> ## Trigger words To gently nudge open the wine fence gate with its shifty serpentine designs and enter this phantasmagoric garden, enspell`HST style autochrome photograph` or 'HST style autochrome color photogravure analog film photo, phantasmal garden, antique pictoralist early kodachrome, surrealist eroticism, gothic photograph, crisp textures, intricately detailed artistic conceptual photography', and the suchlike... <br> ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('AlekseyCalvin/PhantasmalPhotogravures_FluxLoRA_BySilverAgePoets') image = pipeline('your prompt').images[0] ```

UnderstandLing/llama-2-7b-chat-nl

UnderstandLing

peft

--- library_name: peft base_model: NousResearch/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.2

itlwas/Yi-1.5-6B-Q4_K_M-GGUF

itlwas

--- license: apache-2.0 base_model: 01-ai/Yi-1.5-6B tags: - llama-cpp - gguf-my-repo --- # AIronMind/Yi-1.5-6B-Q4_K_M-GGUF This model was converted to GGUF format from [`01-ai/Yi-1.5-6B`](https://huggingface.co/01-ai/Yi-1.5-6B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/01-ai/Yi-1.5-6B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo AIronMind/Yi-1.5-6B-Q4_K_M-GGUF --hf-file yi-1.5-6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AIronMind/Yi-1.5-6B-Q4_K_M-GGUF --hf-file yi-1.5-6b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo AIronMind/Yi-1.5-6B-Q4_K_M-GGUF --hf-file yi-1.5-6b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AIronMind/Yi-1.5-6B-Q4_K_M-GGUF --hf-file yi-1.5-6b-q4_k_m.gguf -c 2048 ```

Ahmet2250/vanilladqn-SpaceInvadersNoFrameskip-v4

Ahmet2250

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 491.50 +/- 178.14 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Ahmet2250 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Ahmet2250 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Ahmet2250 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 8), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

pasto2003/Reinforce-Pixelcopter-PLE-v0

pasto2003

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 21.10 +/- 13.87 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

badroobot/blacklotus

badroobot

--- license: apache-2.0 ---

Fhrozen/espnet_mfa_models

Fhrozen

--- license: cc-by-4.0 --- Pretrained MFA models - Japanese Prosody for JVS Corpus

Chan-Y/Florence-2-LaTex

Chan-Y

transformers

image-text-to-text

--- license: mit pipeline_tag: image-text-to-text tags: - vision language: - en datasets: - OleehyO/latex-formulas --- ### Florence-2-LaTex **Description:** This model is a finetuned version of the Microsoft Florence-2-base-ft language model, specialized in generating LaTeX code for mathematical equations. It has been trained on the dataset provided by OleehyO, containing a variety of LaTeX formulas representing mathematical expressions. **Model Details:** - **Base Model:** [microsoft/Florence-2-base-ft](https://huggingface.co/microsoft/Florence-2-base-ft) - **Task:** LaTeX Math Equations Generation - **Finetune Dataset:** [OleehyO/latex-formulas](https://huggingface.co/datasets/OleehyO/latex-formulas) **Capabilities:** - Generates LaTeX code from textual descriptions of mathematical expressions. - Handles a wide range of mathematical symbols and notations commonly used in LaTeX. **Performance Metrics:** - Specific performance metrics can be provided based on evaluation against benchmarks or user feedback. **Applications:** - Educational tools for generating LaTeX code from natural language math queries. - Support tool for researchers and educators needing quick conversion of mathematical expressions into LaTeX format. **Contact:** [M.Cihan Yalçın](https://www.linkedin.com/in/chanyalcin/) **Disclaimer:** This model is provided as-is with no guarantees of accuracy or suitability for specific purposes. Users are encouraged to validate results before use in critical applications.

great0001/59f75e8c-319d-490c-8c99-34f22f084a43

great0001

peft

--- library_name: peft license: apache-2.0 base_model: unsloth/gemma-2b-it tags: - axolotl - generated_from_trainer model-index: - name: 59f75e8c-319d-490c-8c99-34f22f084a43 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) # 59f75e8c-319d-490c-8c99-34f22f084a43 This model is a fine-tuned version of [unsloth/gemma-2b-it](https://huggingface.co/unsloth/gemma-2b-it) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2220 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Netta1994/setfit_baai_rag_ds_gpt-4o_improved-cot-instructions_chat_few_shot_remove_final_evalua

Netta1994

setfit

text-classification

--- base_model: BAAI/bge-base-en-v1.5 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Reasoning: The answer accurately summarizes the changes being made to the storage AM as outlined in the document. It directly responds to the question, is relevant, and contains concise information. Final Judgment:' - text: "Reasoning: \nContext Grounding: The answer directly references the content\ \ of the document.\nRelevance: The answer is entirely focused on the asked question\ \ without deviation.\nConciseness: The answer is succinct and to the point.\n\n\ Result:" - text: 'Reasoning: The answer is accurate, well-supported by the document, and directly addresses the questionwithout unnecessary information. Evaluation:' - text: 'Reasoning: The answer correctly identifies the components of the British Medieval Student Guide, and it is well-focused and concise. The details mentioned such as comprehension questions, writing exercises, discussion questions, additional reading list, schedules, and bibliography are all directly mentioned in the provided document. Evaluation:' - text: 'Reasoning: That the information directly mentioned in the document does not correlate with the content provided in the answer. The answer mentions Rep. Andy Harris, Kyle Evans, and Jessica Smith, which does not match the names mentioned in the document, namely Reps. Danny Davis, Keith Ellison, and Barbara Lee. Evaluation:' inference: true model-index: - name: SetFit with BAAI/bge-base-en-v1.5 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.88 name: Accuracy --- # SetFit with BAAI/bge-base-en-v1.5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 2 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | <ul><li>"Reasoning:\ncontext grounded - The answer correctly includes Joan Gaspart's presidency resignation due to the team's poor performance in the 2003 season, whichis supported by the document.\nEvaluation:"</li><li>'Reasoning:\nwrong name - The name "Father Josh Carrier" does not appear in the document; the correct name is "Father Joseph Carrier."\nEvaluation:'</li><li>"Reasoning:\nhallucination - The answer is incorrect, and it's contradicted.\nEvaluation:"</li></ul> | | 0 | <ul><li>'Reasoning:\nhallucination - The answer contains information that contradicts what appears in the document.\nEvaluation:'</li><li>'Reasoning:\nirrelevant - The answeris not relevant to what is asked.\nEvaluation:'</li><li>'Reasoning:\nContradiction - The answer states Manhattan, but the document clearly indicates that Queens is the borough with the highest population of Asian-Americans.\n\nEvaluation:'</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.88 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("Netta1994/setfit_baai_rag_ds_gpt-4o_improved-cot-instructions_chat_few_shot_remove_final_evalua") # Run inference preds = model("Reasoning: The answer is accurate, well-supported by the document, and directly addresses the questionwithout unnecessary information. Evaluation:") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 3 | 34.4637 | 148 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 79 | | 1 | 100 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0022 | 1 | 0.2446 | - | | 0.1116 | 50 | 0.2299 | - | | 0.2232 | 100 | 0.1175 | - | | 0.3348 | 150 | 0.0861 | - | | 0.4464 | 200 | 0.0436 | - | | 0.5580 | 250 | 0.0234 | - | | 0.6696 | 300 | 0.0261 | - | | 0.7812 | 350 | 0.0145 | - | | 0.8929 | 400 | 0.015 | - | ### Framework Versions - Python: 3.10.14 - SetFit: 1.1.0 - Sentence Transformers: 3.1.1 - Transformers: 4.44.0 - PyTorch: 2.4.0+cu121 - Datasets: 3.0.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

error577/fd7698d5-0d03-4469-9b50-8141434f61ba

error577

peft

--- library_name: peft license: other base_model: facebook/opt-1.3b tags: - axolotl - generated_from_trainer model-index: - name: fd7698d5-0d03-4469-9b50-8141434f61ba results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: qlora base_model: facebook/opt-1.3b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 96a2fc66c5b07ef1_train_data.json ds_type: json format: custom path: /workspace/input_data/96a2fc66c5b07ef1_train_data.json type: field_instruction: timecoded_cc field_output: qa format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: false hub_model_id: error577/fd7698d5-0d03-4469-9b50-8141434f61ba hub_repo: null hub_strategy: end hub_token: null learning_rate: 0.0001 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 1 mlflow_experiment_name: /tmp/96a2fc66c5b07ef1_train_data.json model_type: AutoModelForCausalLM num_epochs: 4 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.02 wandb_entity: null wandb_mode: online wandb_name: d3343316-7c96-4efd-ae85-68e87a921e72 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: d3343316-7c96-4efd-ae85-68e87a921e72 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # fd7698d5-0d03-4469-9b50-8141434f61ba This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8045 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 10.4915 | 0.0001 | 1 | 1.2737 | | 8.0142 | 0.0031 | 25 | 0.9493 | | 7.0096 | 0.0062 | 50 | 0.8361 | | 6.9558 | 0.0092 | 75 | 0.8086 | | 6.4293 | 0.0123 | 100 | 0.8045 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

actionpace/UndiMix-v4-13B

actionpace

--- license: other language: - en --- **Some of my own quants:** * UndiMix-v4-13B_Q4_K_M.gguf * UndiMix-v4-13B_Q5_K_M.gguf **Source:** [Undi95](https://huggingface.co/Undi95) **Source Model:** [UndiMix-v4-13B](https://huggingface.co/Undi95/UndiMix-v4-13B) **Source models for Undi95/UndiMix-v4-13B (Merge)** - [Undi95/ReMM-v2-Kimiko-v2-13B](https://huggingface.co/Undi95/ReMM-v2-Kimiko-v2-13B) - [The-Face-Of-Goonery/Huginn-13b-v1.2](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-v1.2) ([Ref](https://huggingface.co/actionpace/Huginn-13b-v1.2)) - [Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged](https://huggingface.co/Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged) ([Ref](https://huggingface.co/actionpace/llama-2-13b-chat-limarp-v2-merged)) - [jondurbin/airoboros-l2-13b-2.2](https://huggingface.co/jondurbin/airoboros-l2-13b-2.2) - [IkariDev/Athena-v1](https://huggingface.co/IkariDev/Athena-v1)

mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF

mradermacher

transformers

--- base_model: Skywork/Skywork-o1-Open-Llama-3.1-8B language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Skywork-o1-Open-Llama-3.1-8B-i1-GGUF/resolve/main/Skywork-o1-Open-Llama-3.1-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->

nomypython/vgg16

nomypython

--- license: mit ---

RichardErkhov/abhinand_-_gemma-2b-tamil-8bits

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gemma-2b-tamil - bnb 8bits - Model creator: https://huggingface.co/abhinand/ - Original model: https://huggingface.co/abhinand/gemma-2b-tamil/ Original model description: --- language: - en - ta license: other base_model: google/gemma-2b datasets: - wikimedia/wikipedia license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms model-index: - name: gemma-2b-tamil results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 47.44 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-tamil name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 71.3 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-tamil name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 38.21 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-tamil name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 34.93 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-tamil name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 65.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-tamil name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 12.89 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abhinand/gemma-2b-tamil name: Open LLM Leaderboard --- # Gemma 2B Tamil v0.1 Alpha - Base Model [Experimental Release] This is a Tamil foundational model continually pretrained from Google Gemma 2B. This is an experiment to see if Gemma can be adapted for Tamil without expanding vocabulary. While the responses may be rusty at times, it shows a lot of promise for a 2B parameter model. > **Please Note:** This model, labeled as a FOUNDATIONAL Language Model (LLM), is designed primarily for Causal Language Modeling (LM) purposes. In other words, if you are looking for an instruction following model in Tamil, you may find [abhinand/gemma-2b-it-tamil-v0.1-alpha](https://huggingface.co/abhinand/gemma-2b-it-tamil-v0.1-alpha) more suitable for your needs. **Procedure:** 1. The [Gemma base model](https://huggingface.co/google/gemma-2b) was continually pretrained on all available Tamil Wikipedia data for 3 epochs. 2. The updated model was then finetuned on a mix of English and Tamil alpaca datasets for 5 epochs. Finetuned model can be found [here](https://huggingface.co/abhinand/gemma-2b-it-tamil-v0.1-alpha). > **Note:** This project is currently under development (FOR TAMIL). The initial pretraining phase may not have been extensive enough, which suggests that the model's performance could improve by extending the pretraining on a larger dataset, such as CulturaX. ## Model description - **Model type:** A 2B parameter GPT-like model continually pretrained on all available Tamil data from [Wikipedia dataset](https://huggingface.co/datasets/wikimedia/wikipedia). - **Language(s):** Bilingual. English and Tamil. - **License:** [Google Gemma Terms of Use](https://ai.google.dev/gemma/terms) - **Training Precision:** `bfloat16` - **Training Hardware:** 4x Nvidia RTX 3090 GPUs - **Training Cost:** $20 ## Support my work If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. [](https://www.buymeacoffee.com/abhinand.b) ## Usage Note It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications. ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_abhinand__gemma-2b-tamil) | Metric |Value| |---------------------------------|----:| |Avg. |45.13| |AI2 Reasoning Challenge (25-Shot)|47.44| |HellaSwag (10-Shot) |71.30| |MMLU (5-Shot) |38.21| |TruthfulQA (0-shot) |34.93| |Winogrande (5-shot) |65.98| |GSM8k (5-shot) |12.89|

AinhoaC/clasificador-muchocine

AinhoaC

transformers

text-classification

--- tags: - classification - generated_from_trainer metrics: - accuracy model-index: - name: clasificador-muchocine 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. --> # clasificador-muchocine This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4463 - Accuracy: 0.4503 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 388 | 1.3448 | 0.3871 | | 1.3815 | 2.0 | 776 | 1.3046 | 0.4284 | | 1.0077 | 3.0 | 1164 | 1.4463 | 0.4503 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

Vradenburg/unit1-ppo-LunarLandar-v2

Vradenburg

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 252.33 +/- 37.27 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Inv/Konstanta-V4-Alpha-7B

Inv

transformers

text-generation

--- base_model: - Inv/Konstanta-7B library_name: transformers tags: - mergekit - merge - senseable/WestLake-7B-v2 - KatyTheCutie/LemonadeRP-4.5.3 - roleplay - rp license: apache-2.0 --- # Konstanta-V4-Alpha-7B This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). Alright, so, this model seems to be REALLY good. Konstanta-7B is pretty good either, but this one is still marginally better. ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Inv/Konstanta-7B](https://huggingface.co/Inv/Konstanta-7B) as a base. ### Models Merged The following models were included in the merge: * senseable/WestLake-7B-v2 * KatyTheCutie/LemonadeRP-4.5.3 ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: dare_ties dtype: bfloat16 parameters: int8_mask: true base_model: Inv/Konstanta-7B models: - model: Inv/Konstanta-7B - model: KatyTheCutie/LemonadeRP-4.5.3 parameters: density: 0.65 weight: [0.65, 0.40, 0.35, 0.30, 0.35, 0.40, 0.25] - model: senseable/WestLake-7B-v2 parameters: density: 0.85 weight: [0.25, 0.40, 0.35, 0.30, 0.35, 0.40, 0.65] ```

hanq0212/RL_course_unit4_part1

hanq0212

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RL_course_unit4_part1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

jinmang2/dall-e-tokenizer

jinmang2

transformers

# DALL-E-Tokenizer Huggingface package for the discrete VAE usded for [DALL-E](https://github.com/openai/DALL-E). # How to use ```python # from dall_e_tok import DallEEncoder from dall_e_tok import DALLETokenizer tokenizer = DALLETokenizer.from_pretrained("jinmang2/dall-e-tokenizer") ```

Gargaz/gemma-2b-it-romanian

Gargaz

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]

nhung03/640a1cfd-8bd5-4765-b19c-f8402635925f

nhung03

peft

--- library_name: peft license: llama3.2 base_model: NousResearch/Llama-3.2-1B tags: - axolotl - generated_from_trainer model-index: - name: 640a1cfd-8bd5-4765-b19c-f8402635925f results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: NousResearch/Llama-3.2-1B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - fffa53a70b34ee7a_train_data.json ds_type: json format: custom path: /workspace/input_data/fffa53a70b34ee7a_train_data.json type: field_input: section_names field_instruction: article field_output: abstract format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nhung03/640a1cfd-8bd5-4765-b19c-f8402635925f hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/fffa53a70b34ee7a_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: <|end_of_text|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3276fb8f-c293-4b62-9f20-ac5afd3074e1 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 3276fb8f-c293-4b62-9f20-ac5afd3074e1 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 640a1cfd-8bd5-4765-b19c-f8402635925f This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7500 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.9581 | 0.0392 | 200 | 1.7500 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

vb19/newsanskrit

vb19

transformers

# My Model This is my trained model.

ajtaltarabukin2022/a53bc434-5860-4511-8ba7-9c3a24f717bc

ajtaltarabukin2022

peft

--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-Math-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: a53bc434-5860-4511-8ba7-9c3a24f717bc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Qwen/Qwen2.5-Math-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9a96da18555129f9_train_data.json ds_type: json format: custom path: /workspace/input_data/9a96da18555129f9_train_data.json type: field_instruction: seq field_output: labels_str format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 256 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: ajtaltarabukin2022/a53bc434-5860-4511-8ba7-9c3a24f717bc hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 75GiB max_steps: 40 micro_batch_size: 2 mlflow_experiment_name: /tmp/9a96da18555129f9_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 2e1ab578-553d-4f27-9d64-c36c821ccbcc wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2e1ab578-553d-4f27-9d64-c36c821ccbcc warmup_steps: 10 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a53bc434-5860-4511-8ba7-9c3a24f717bc This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.5613 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0000 | 1 | 9.9684 | | 10.1341 | 0.0002 | 5 | 9.3603 | | 8.3403 | 0.0003 | 10 | 7.4771 | | 6.9898 | 0.0005 | 15 | 6.7727 | | 6.8118 | 0.0006 | 20 | 6.2097 | | 6.1022 | 0.0008 | 25 | 5.8693 | | 5.8484 | 0.0010 | 30 | 5.6656 | | 5.9073 | 0.0011 | 35 | 5.5778 | | 5.5876 | 0.0013 | 40 | 5.5613 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

SirSkandrani/Object_Detection_for_Medical_Equipement

SirSkandrani

transformers

object-detection

--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: model 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. --> # model This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

Viennes/lab1_random_fin

Viennes

transformers

text2text-generation

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: lab1_random_fin results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 13.665217122054896 --- <!-- 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. --> # lab1_random_fin This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 3.4918 - Bleu: 13.6652 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

kevinsguo/test996

kevinsguo

--- license: apache-2.0 ---

BK-Lee/Phantom-3.8B

BK-Lee

--- license: mit --- # Paper arxiv.org/abs/2409.14713 # Two steps only need. First step. (git clone and install required packages) ```bash # Download Project Code git clone https://github.com/ByungKwanLee/Phantom # Virtual Environment conda create -n trol python=3.11 -y conda activate trol # install torch pip3 install torch torchvision # install requiresments pip install -r requirements.txt # flash attention pip install flash-attn --no-build-isolation # all cache deleted conda clean -a && pip cache purge ``` Second step. (open, edit, and run `demo.py`) ```python # model selection size = '3.8b' # [Select One] '0.5b' (transformers more recent version) | '1.8b' | '3.8b' (transformers==4.37.2) | '7b' # User prompt prompt_type="with_image" # Select one option "text_only", "with_image" img_path='figures/demo.png' question="Describe the image in detail" # loading model model, tokenizer = load_model(size=size) # prompt type -> input prompt if prompt_type == 'with_image': # Image Load image = pil_to_tensor(Image.open(img_path).convert("RGB")) inputs = [{'image': image, 'question': question}] elif prompt_type=='text_only': inputs = [{'question': question}] # cpu -> gpu for param in model.parameters(): if not param.is_cuda: param.data = param.cuda() # Generate with torch.inference_mode(): # Model _inputs = model.eval_process(inputs=inputs, data='demo', tokenizer=tokenizer, device='cuda:0') generate_ids = model.generate(**_inputs, do_sample=False, max_new_tokens=256) answer = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0] print(answer) ``` So easy to run the code Let's shout Phantom!

great0001/11690c34-f1d1-40c0-bc00-0e00226c1385

great0001

peft

--- library_name: peft base_model: samoline/1955f015-9189-40ad-8c60-f0bfcd30418c tags: - axolotl - generated_from_trainer model-index: - name: 11690c34-f1d1-40c0-bc00-0e00226c1385 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: samoline/1955f015-9189-40ad-8c60-f0bfcd30418c bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7c29c36c66b6f32e_train_data.json ds_type: json format: custom path: /workspace/input_data/7c29c36c66b6f32e_train_data.json type: field_input: context field_instruction: question field_output: final_decision format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: great0001/11690c34-f1d1-40c0-bc00-0e00226c1385 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/7c29c36c66b6f32e_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <|end_of_text|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 61b43104-9b9f-4fe2-87b8-8e6988a7ce53 wandb_project: Birthday-SN56-14-Gradients-On-Demand wandb_run: your_name wandb_runid: 61b43104-9b9f-4fe2-87b8-8e6988a7ce53 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 11690c34-f1d1-40c0-bc00-0e00226c1385 This model is a fine-tuned version of [samoline/1955f015-9189-40ad-8c60-f0bfcd30418c](https://huggingface.co/samoline/1955f015-9189-40ad-8c60-f0bfcd30418c) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8016 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 10.1211 | 0.0000 | 1 | 10.4860 | | 9.5667 | 0.0001 | 3 | 9.9357 | | 4.5307 | 0.0002 | 6 | 3.3649 | | 2.1913 | 0.0004 | 9 | 1.8016 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

methinkss/m0

methinkss

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]