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train · 846k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-27 12:29:05	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 500 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-27 12:27:55	card stringlengths 11 1.01M
TOMFORD79/Hano	TOMFORD79	2025-04-30T10:37:03Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-30T10:09:51Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mradermacher/Qwen-2.5-7B-Reasoning-GGUF	mradermacher	2025-04-30T10:36:41Z	116	2	transformers	[ "transformers", "gguf", "text-generation-inference", "text-generation", "reasoning", "r1-reasoning", "fine-tuned", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:openai/gsm8k", "base_model:HyperX-Sen/Qwen-2.5-7B-Reasoning", "base_model:quantized:HyperX-Sen/Qwen-2.5-7B-Reasoning", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-03-11T00:24:44Z	--- base_model: HyperX-Sen/Qwen-2.5-7B-Reasoning datasets: - openai/gsm8k language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: mit quantized_by: mradermacher tags: - transformers - text-generation-inference - text-generation - reasoning - r1-reasoning - fine-tuned --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/HyperX-Sen/Qwen-2.5-7B-Reasoning <!-- 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/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q2_K.gguf) \| Q2_K \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q3_K_S.gguf) \| Q3_K_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q3_K_M.gguf) \| Q3_K_M \| 3.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q3_K_L.gguf) \| Q3_K_L \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.IQ4_XS.gguf) \| IQ4_XS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q4_K_S.gguf) \| Q4_K_S \| 4.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q4_K_M.gguf) \| Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q5_K_S.gguf) \| Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q5_K_M.gguf) \| Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q6_K.gguf) \| Q6_K \| 6.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.Q8_0.gguf) \| Q8_0 \| 8.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen-2.5-7B-Reasoning-GGUF/resolve/main/Qwen-2.5-7B-Reasoning.f16.gguf) \| f16 \| 15.3 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
maksf8486/bb8ee146-b69a-485e-beb7-392d4059d150	maksf8486	2025-04-30T10:33:33Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-llama-2-7b", "base_model:adapter:NousResearch/Nous-Hermes-llama-2-7b", "license:mit", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-30T09:59:52Z	--- library_name: peft license: mit base_model: NousResearch/Nous-Hermes-llama-2-7b tags: - axolotl - generated_from_trainer model-index: - name: bb8ee146-b69a-485e-beb7-392d4059d150 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 absolute_data_files: false adapter: lora base_model: NousResearch/Nous-Hermes-llama-2-7b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 5cfb94c383f95340_train_data.json ds_type: json format: custom path: /workspace/input_data/5cfb94c383f95340_train_data.json type: field_instruction: instruction field_output: chosen_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: false reference_model: 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: maksf8486/bb8ee146-b69a-485e-beb7-392d4059d150 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/5cfb94c383f95340_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 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: 10dc235b-06a9-410c-a72b-3ec423544136 wandb_project: s56-2 wandb_run: your_name wandb_runid: 10dc235b-06a9-410c-a72b-3ec423544136 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # bb8ee146-b69a-485e-beb7-392d4059d150 This model is a fine-tuned version of [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0103 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9359 \| 0.0244 \| 200 \| 1.0103 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
fats-fme/cabc19cf-36f9-49ce-b8ee-16a014cd6d4c	fats-fme	2025-04-30T10:19:47Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:NousResearch/Nous-Hermes-llama-2-7b", "base_model:adapter:NousResearch/Nous-Hermes-llama-2-7b", "license:mit", "region:us" ]	null	2025-04-30T10:03:56Z	--- library_name: peft license: mit base_model: NousResearch/Nous-Hermes-llama-2-7b tags: - axolotl - generated_from_trainer model-index: - name: cabc19cf-36f9-49ce-b8ee-16a014cd6d4c 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/Nous-Hermes-llama-2-7b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 5cfb94c383f95340_train_data.json ds_type: json format: custom path: /workspace/input_data/5cfb94c383f95340_train_data.json type: field_instruction: instruction field_output: chosen_response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 16 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/cabc19cf-36f9-49ce-b8ee-16a014cd6d4c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lora_target_modules: - q_proj - v_proj lr_scheduler: cosine max_memory: 0: 130GB max_steps: 50 micro_batch_size: 1 mlflow_experiment_name: /tmp/5cfb94c383f95340_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 10dc235b-06a9-410c-a72b-3ec423544136 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 10dc235b-06a9-410c-a72b-3ec423544136 warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # cabc19cf-36f9-49ce-b8ee-16a014cd6d4c This model is a fine-tuned version of [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - 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: 200 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 1.0770 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
yasminlearn/q-FrozenLake-v1-4x4-noSlippery	yasminlearn	2025-04-30T10:16:13Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-04-30T09:02:47Z	--- 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="yasminlearn/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"]) ```
elliotthwangmsa/Kimlam-OpenChat-tw	elliotthwangmsa	2025-04-30T10:06:53Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T09:54:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> loss: 0.3209 繁體中文客製訓練
Hanzel77/Qwen3-8B-Q4_K_M-GGUF	Hanzel77	2025-04-30T10:05:15Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-30T10:04:53Z	--- base_model: Qwen/Qwen3-8B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # Hanzel77/Qwen3-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) 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/Qwen/Qwen3-8B) 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 Hanzel77/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Hanzel77/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-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 Hanzel77/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Hanzel77/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -c 2048 ```
prithivMLmods/Qwen3-4B-ft-bf16-Q8_0-GGUF	prithivMLmods	2025-04-30T10:00:56Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "moe", "moderately abliterated variant", "llama-cpp", "gguf-my-repo", "Qwen3", "text-generation", "en", "base_model:prithivMLmods/Qwen3-4B-ft-bf16", "base_model:quantized:prithivMLmods/Qwen3-4B-ft-bf16", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-30T09:56:50Z	--- base_model: prithivMLmods/Qwen3-4B-ft-bf16 language: - en library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - text-generation-inference - moe - moderately abliterated variant - llama-cpp - gguf-my-repo - Qwen3 --- # prithivMLmods/Qwen3-4B-ft-bf16-Q8_0-GGUF This model was converted to GGUF format from [`prithivMLmods/Qwen3-4B-ft-bf16`](https://huggingface.co/prithivMLmods/Qwen3-4B-ft-bf16) 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/prithivMLmods/Qwen3-4B-ft-bf16) 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 prithivMLmods/Qwen3-4B-ft-bf16-Q8_0-GGUF --hf-file qwen3-4b-ft-bf16-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo prithivMLmods/Qwen3-4B-ft-bf16-Q8_0-GGUF --hf-file qwen3-4b-ft-bf16-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. 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 prithivMLmods/Qwen3-4B-ft-bf16-Q8_0-GGUF --hf-file qwen3-4b-ft-bf16-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo prithivMLmods/Qwen3-4B-ft-bf16-Q8_0-GGUF --hf-file qwen3-4b-ft-bf16-q8_0.gguf -c 2048 ```
baby-dev/306a0693-66a5-4307-9d89-f2184bc7e7c8	baby-dev	2025-04-30T06:23:22Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/codegemma-2b", "base_model:adapter:unsloth/codegemma-2b", "region:us" ]	null	2025-04-30T06:23:07Z	--- library_name: peft tags: - generated_from_trainer base_model: unsloth/codegemma-2b model-index: - name: baby-dev/306a0693-66a5-4307-9d89-f2184bc7e7c8 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. --> # baby-dev/306a0693-66a5-4307-9d89-f2184bc7e7c8 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1701 ## 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 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
vertings6/a9b9d746-4522-42c0-b1ad-4bf0f76727d1	vertings6	2025-04-30T06:20:10Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-1.5B", "base_model:adapter:unsloth/Qwen2-1.5B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-30T06:05:54Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-1.5B tags: - axolotl - generated_from_trainer model-index: - name: a9b9d746-4522-42c0-b1ad-4bf0f76727d1 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 absolute_data_files: true adapter: lora base_model: unsloth/Qwen2-1.5B bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 1767352bfea79a80_train_data.json ds_type: json format: custom path: /workspace/input_data/1767352bfea79a80_train_data.json type: field_instruction: source_text field_output: target_text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 144 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vertings6/a9b9d746-4522-42c0-b1ad-4bf0f76727d1 hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 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: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/1767352bfea79a80_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: 2048 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: 050b9da8-ecfe-4368-84d5-6255fb964340 wandb_project: s56-32 wandb_run: your_name wandb_runid: 050b9da8-ecfe-4368-84d5-6255fb964340 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # a9b9d746-4522-42c0-b1ad-4bf0f76727d1 This model is a fine-tuned version of [unsloth/Qwen2-1.5B](https://huggingface.co/unsloth/Qwen2-1.5B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5248 ## 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: 3e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6995 \| 0.0075 \| 200 \| 0.5248 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
WojciechCaballero/CNOSSOS_counting_car	WojciechCaballero	2025-04-30T06:06:12Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-30T06:06:12Z	--- license: apache-2.0 ---
lisabdunlap/Llama-3.2-3B-Instruct-r64-e3-lr2e-05-new	lisabdunlap	2025-04-30T05:56:43Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-30T05:56:17Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: lisabdunlap - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-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)
ejji01/gemma-2b-medical-reasoning	ejji01	2025-04-30T05:50:50Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-30T05:50:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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. 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mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF	mradermacher	2025-04-30T05:16:03Z	11	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "lazymergekit", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:mlabonne/BigQwen2.5-125B-Instruct", "base_model:quantized:mlabonne/BigQwen2.5-125B-Instruct", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-09-24T12:53:44Z	--- base_model: mlabonne/BigQwen2.5-125B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE license_name: tongyi-qianwen quantized_by: mradermacher tags: - mergekit - merge - lazymergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/mlabonne/BigQwen2.5-125B-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-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/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 38.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 40.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 43.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 46.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 47.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ2_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ2_M.gguf.part2of2) \| i1-IQ2_M \| 50.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q2_K.gguf.part2of2) \| i1-Q2_K \| 51.0 \| IQ3_XXS probably better \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_XXS.gguf.part2of2) \| i1-IQ3_XXS \| 54.6 \| lower quality \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_XS.gguf.part2of2) \| i1-IQ3_XS \| 56.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q3_K_S.gguf.part2of2) \| i1-Q3_K_S \| 59.0 \| IQ3_XS probably better \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_S.gguf.part2of2) \| i1-IQ3_S \| 59.1 \| beats Q3_K* \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ3_M.gguf.part2of2) \| i1-IQ3_M \| 60.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q3_K_M.gguf.part2of2) \| i1-Q3_K_M \| 64.7 \| IQ3_S probably better \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q3_K_L.gguf.part2of2) \| i1-Q3_K_L \| 68.1 \| IQ3_M probably better \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-IQ4_XS.gguf.part2of2) \| i1-IQ4_XS \| 68.3 \| \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 71.2 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 75.5 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 81.7 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 88.6 \| \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 94.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/BigQwen2.5-125B-Instruct-i1-GGUF/resolve/main/BigQwen2.5-125B-Instruct.i1-Q6_K.gguf.part3of3) \| i1-Q6_K \| 111.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
Baysukk/whisper-large-v3-mn-ft	Baysukk	2025-04-30T04:48:47Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:adapter:openai/whisper-large-v3", "license:apache-2.0", "region:us" ]	null	2025-04-25T05:29:08Z	--- library_name: peft license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer model-index: - name: whisper-large-v3-mn-ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-large-v3-mn-ft This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5834 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - 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: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 6.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.4317 \| 0.5903 \| 500 \| 0.7475 \| \| 1.1836 \| 1.1806 \| 1000 \| 0.5816 \| \| 0.8169 \| 1.7710 \| 1500 \| 0.5508 \| \| 0.5782 \| 2.3613 \| 2000 \| 0.5468 \| \| 0.4928 \| 2.9516 \| 2500 \| 0.5429 \| \| 0.444 \| 3.5419 \| 3000 \| 0.5626 \| \| 0.2888 \| 4.1322 \| 3500 \| 0.5678 \| \| 0.283 \| 4.7226 \| 4000 \| 0.5710 \| \| 0.1823 \| 5.3129 \| 4500 \| 0.5852 \| \| 0.1725 \| 5.9032 \| 5000 \| 0.5834 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.1.0+cu118 - Datasets 2.14.5 - Tokenizers 0.21.1
fats-fme/6acca30d-8b93-41d9-a354-9cea060c559c	fats-fme	2025-04-30T04:47:58Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2-1.5B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-04-30T04:32:40Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 6acca30d-8b93-41d9-a354-9cea060c559c 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/Qwen2-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 41b191ed7e418531_train_data.json ds_type: json format: custom path: /workspace/input_data/41b191ed7e418531_train_data.json type: field_instruction: content field_output: title format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: fats-fme/6acca30d-8b93-41d9-a354-9cea060c559c hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-05 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 130GB max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/41b191ed7e418531_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 2abcdfe0-6381-469b-90ad-77a4d33a40de wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2abcdfe0-6381-469b-90ad-77a4d33a40de warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 6acca30d-8b93-41d9-a354-9cea060c559c This model is a fine-tuned version of [unsloth/Qwen2-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3712 ## 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: 200 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| 3.3369 \| \| 1.4355 \| 0.0081 \| 100 \| 1.5356 \| \| 1.3357 \| 0.0161 \| 200 \| 1.3712 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF	mradermacher	2025-04-30T04:44:28Z	111	1	transformers	[ "transformers", "gguf", "chat", "abliterated", "uncensored", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3", "base_model:quantized:huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-10-17T09:16:23Z	--- base_model: huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3 language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: apache-2.0 license_link: https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/LICENSE quantized_by: mradermacher tags: - chat - abliterated - uncensored --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-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/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.5 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.5 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-abliterated-v3-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-abliterated-v3.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
vermoney/cba773b7-121c-4d34-a7fd-9b16644a4520	vermoney	2025-04-30T04:43:50Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:adapter:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-30T04:30:48Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: cba773b7-121c-4d34-a7fd-9b16644a4520 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/Qwen2-7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 867db9eee814c64e_train_data.json ds_type: json format: custom path: /workspace/input_data/867db9eee814c64e_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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vermoney/cba773b7-121c-4d34-a7fd-9b16644a4520 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/867db9eee814c64e_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 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: ab7a8a3e-97be-4132-b5ba-3fcbabe3e90d wandb_project: s56-9 wandb_run: your_name wandb_runid: ab7a8a3e-97be-4132-b5ba-3fcbabe3e90d warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # cba773b7-121c-4d34-a7fd-9b16644a4520 This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5258 \| 0.0191 \| 200 \| 0.5676 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
OPEA/Falcon3-10B-Base-int4-sym-inc	OPEA	2025-04-30T04:08:37Z	3	0	null	[ "safetensors", "llama", "dataset:NeelNanda/pile-10k", "arxiv:2309.05516", "base_model:tiiuae/Falcon3-10B-Base", "base_model:quantized:tiiuae/Falcon3-10B-Base", "4-bit", "auto-round", "region:us" ]	null	2024-12-13T05:19:14Z	--- datasets: - NeelNanda/pile-10k base_model: - tiiuae/Falcon3-10B-Base --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [tiiuae/Falcon3-10B-Base](https://huggingface.co/tiiuae/Falcon3-10B-Base) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision `4579272` to use AutoGPTQ format ## How To Use ### INT4 Inference(CPU/HPU/CUDA) ```python from auto_round import AutoRoundConfig ##must import for auto_round format from transformers import AutoModelForCausalLM, AutoTokenizer quantized_model_dir = "OPEA/Falcon3-10B-Base-int4-sym-inc" tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir) model = AutoModelForCausalLM.from_pretrained( quantized_model_dir, device_map="auto" ## revision="4579272" ##AutoGPTQ format ) text = "How many r in strawberry? The answer is " inputs = tokenizer(text, return_tensors="pt", return_token_type_ids=False).to(model.device) print(tokenizer.decode(model.generate(inputs, max_new_tokens=50)[0])) text = "How many r in strawberry? The answer is" ##INT4: """How many r in strawberry? The answer is 2. ### Additional Questions and Answers #### 11. How many r in strawberry? Answer: The word "strawberry" contains 2 'r's. #### """ ##BF16: """ How many r in strawberry? The ansnwer is 2. ### 10. How many r in strawberry? Question: How many times does the letter 'r' appear in the word "strawberry"? Answer: The letter 'r Answer: The answer to the riddle""" """ text = "Which number is larger, 9.8 or 9.11? The answer is" ##INT4 """Which number is larger, 9.8 or 9.11? The answer is 9.8. #### 10. What is the smallest number in the set {1.2, 1.02, 1.22, 1.002}? """ ##BF16: """Which number is larger, 9.8 or 9.11? The answer is 9.8. #### Question 2: How do you compare the numbers 12.34 and 12.345? Answer:** To compare 12.34""" text = "Once upon a time," ##INT4: """Once upon a time, in a small town named Harmonyville, lived two best friends - Mia and Ben. They were both eight years old and loved exploring the world around them. One sunny afternoon, while playing near the park, they found a mysterious box with a note """ ##BF16: """Once upon a time, in a small town named Harmonyville, there lived two best friends - Timmy the Turtle and Sally the Squirrel. They loved exploring their beautiful forest home together, discovering new things every day. One sunny afternoon, they stumbled upon a mysterious cave filled with """ text = "There is a girl who likes adventure," ##INT4: """There is a girl who likes adventure, and she loves to explore new places. One day, she decided to go on a trip to a faraway land called "The Land of the Sun." She packed her bag with everything she needed, including her favorite book about the sun. """ ##BF16: """There is a girl who likes adventure, and she loves to explore new places. One day, she decided to go on a trip to a beautiful country called Italy. She wanted to see all the famous landmarks and try the delicious Italian food. """ ``` ### Evaluate the model pip3 install lm-eval==0.4.5 ```bash auto-round --model "OPEA/Falcon3-10B-Base-int4-sym-inc" --eval --eval_bs 16 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu ``` \| Metric \| BF16 \| INT4 \| \| ------------------------- \| ----------------- \| ----------------- \| \| Avg.13 \| 0.6151 \| 0.6092 \| \| Avg.10 \| 0.64113 \| 0.63584 \| \| leaderboard_mmlu_pro \| 0.4238 \| 0.4156 \| \| leaderboard_ifeval \| (0.4149+0.2939)/2 \| (0.4233+0.2828)/2 \| \| gsm8k(5shot) strict match \| 0.8067 \| 0.7923 \| \| mmlu \| 0.7069 \| 0.6930 \| \| lambada_openai \| 0.6998 \| 0.7025 \| \| hellaswag \| 0.5873 \| 0.5832 \| \| winogrande \| 0.7380 \| 0.7293 \| \| piqa \| 0.7884 \| 0.7889 \| \| truthfulqa_mc1 \| 0.3427 \| 0.3452 \| \| openbookqa \| 0.3400 \| 0.3320 \| \| boolq \| 0.8232 \| 0.8116 \| \| arc_easy \| 0.8312 \| 0.8258 \| \| arc_challenge \| 0.5538 \| 0.5469 \| ### Generate the model Here is the sample command to generate the model. ```bash auto-round \ --model tiiuae/Falcon3-10B-Base \ --device 0 \ --group_size 128 \ --nsamples 512 \ --bits 4 \ --iter 1000 \ --disable_eval \ --model_dtype 'float16' \ --format 'auto_gptq,auto_round' \ --output_dir "./tmp_autoround" ``` ## Ethical Considerations and Limitations The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are a couple of useful links to learn more about Intel's AI software: - Intel Neural Compressor [link](https://github.com/intel/neural-compressor) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes. ## Cite @article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} } [arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
agentlans/granite-3.3-2b-instruct-ethics	agentlans	2025-04-30T04:04:00Z	0	0	peft	[ "peft", "safetensors", "granite", "llama-factory", "generated_from_trainer", "ethics", "practical-ethics", "morality", "philosophy", "moral-judgement", "text-generation", "conversational", "en", "dataset:agentlans/reddit-ethics", "base_model:ibm-granite/granite-3.3-2b-instruct", "base_model:adapter:ibm-granite/granite-3.3-2b-instruct", "license:apache-2.0", "region:us" ]	text-generation	2025-04-27T13:06:51Z	--- license: apache-2.0 library_name: peft base_model: ibm-granite/granite-3.3-2b-instruct tags: - llama-factory - generated_from_trainer - ethics - practical-ethics - morality - philosophy - moral-judgement language: - en datasets: - agentlans/reddit-ethics pipeline_tag: text-generation --- # granite-3.3-2b-instruct-ethics This model is a fine-tuned version of [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct) on the [agentlans/reddit-ethics](https://huggingface.co/datasets/agentlans/reddit-ethics) dataset. It's meant to evaluate ethical dilemmas in everyday life using three philosophical frameworks: - utilitarianism: Evaluates actions by their consequences, aiming to maximize overall well-being. - deontology: Assesses the moral rightness of actions based on rules, duties, or obligations, regardless of outcomes. - virtue ethics: Focuses on the character traits and intentions of the agents involved, emphasizing virtues such as honesty, integrity, and fairness. Furthermore, it also raises discussion questions and answers as well as an overall ethical judgement. ## Input format ``` Ethically analyze: {{YOUR_TEXT_HERE}} ``` Example input ([source](https://www.reddit.com/r/AmItheAsshole/comments/1k6sag2/aitah_for_playing_a_voice_recording_to_prove_to/)): ``` Ethically analyze: So I (30f) have a friend (34f) that has a “non-verbal” daughter (3f) let’s call her Allie. [...] Peace of mind for everybody sounds good to me! Also, I will be making a call to cps about the situation and see what they have to say. Thank you everybody for your kind words ❤️ ``` ## Output format JSON format of the ethical evaluation. Example output (prettified): ```json { "title": "Recording a Child's Progress: The Ethics of Surveillance and Parental Trust", "description": "A daycare worker records a child's verbal abilities, leading to conflict with the child's mother. The worker feels they acted unprofessionally and wants to prevent similar incidents.", "issues": [ "Parental Trust vs. Child's Development", "Surveillance vs. Privacy", "Professionalism vs. Personal Beliefs" ], "utilitarianism": "Recording the child's progress may have helped the mother and child, but the worker's actions may have caused unnecessary conflict.", "deontology": "The worker had a duty to respect the mother's trust and privacy, even if it meant not recording the child's abilities.", "virtue_ethics": "Honesty, respect, and empathy guide the worker's actions. They should have communicated their intentions and respected the mother's decisions.", "questions": [ "Was the worker justified in recording the child's abilities without the mother's consent?", "How should the worker balance their duty to the child's development with respect for the mother's trust?", "What are the implications of recording children's progress for parents and children?" ], "answers": [ "No, the worker should have respected the mother's trust and not recorded the child's abilities without explicit consent.", "The worker should have communicated their intentions and respected the mother's decisions regarding recording the child's progress.", "Recording children's progress can be beneficial, but it's essential to respect parents' trust and privacy, and to ensure that the child's well-being is prioritized." ], "resolution": "The worker should apologize to the mother and child for any distress caused, and work with the family to establish clear guidelines for recording and sharing information. They should also consider seeking guidance from their employer or a professional organization to ensure their actions align with best practices and ethical standards." } ``` ## Limitations - Trained on everyday ethical dilemmas on Reddit - May not work well for out-of-distribution inputs - Like bizarre thought experiments - And very specialized ethics such as medical ethics - The input should contain enough context to make a moral evaluation - The input requires the user to be conscientious and self-reflective - The model may be vulnerable to situation framing - Biased inputs can create biased outputs - For example, it might not be hard to glorify dictators, war criminals, terrorists, and mass murderers given the right propaganda - The model only offers suggestions and a starting point based on a short analysis - There could be other ways to resolve the dilemma - Most importantly, the user should use clear reasoning, human values, and consideration for others ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - 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 - num_epochs: 1.0 ### Framework versions - PEFT 0.15.0 - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0 ## Licence Apache 2.0
royiidfk/fbgfgb	royiidfk	2025-04-30T03:51:10Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2025-04-30T03:51:10Z	--- license: bigcode-openrail-m ---
Charlotte415/SmolLM2-FT-MyDataset	Charlotte415	2025-04-30T03:49:24Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "smol-course", "module_1", "trl", "sft", "conversational", "base_model:HuggingFaceTB/SmolLM2-135M", "base_model:finetune:HuggingFaceTB/SmolLM2-135M", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T03:48:42Z	--- base_model: HuggingFaceTB/SmolLM2-135M library_name: transformers model_name: SmolLM2-FT-MyDataset tags: - generated_from_trainer - smol-course - module_1 - trl - sft licence: license --- # Model Card for SmolLM2-FT-MyDataset This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Charlotte415/SmolLM2-FT-MyDataset", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/charlotte000415-the-university-of-melbourne/huggingface/runs/rgqud0vz) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF	mradermacher	2025-04-30T03:43:28Z	24	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:KaraKaraWitch/LLENN-v0.75-Qwen2.5-72b", "base_model:quantized:KaraKaraWitch/LLENN-v0.75-Qwen2.5-72b", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-08T13:29:15Z	--- base_model: KaraKaraWitch/LLENN-v0.75-Qwen2.5-72b language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen2.5-72B/blob/main/LICENSE license_name: qwen quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/KaraKaraWitch/LLENN-v0.75-Qwen2.5-72b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-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/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/LLENN-v0.75-Qwen2.5-72b-GGUF/resolve/main/LLENN-v0.75-Qwen2.5-72b.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
pandaiedu/pandai-unsloth-gemma-3-4b-it-merged-sejarah-1-epoch-iter-3	pandaiedu	2025-04-30T03:42:39Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-4b-it", "base_model:finetune:unsloth/gemma-3-4b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-30T03:39:45Z	--- base_model: unsloth/gemma-3-4b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: pandaiedu - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it This gemma3 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)
OPEA/DeepSeek-V3-int4-sym-awq-inc	OPEA	2025-04-30T03:33:27Z	72	4	null	[ "safetensors", "deepseek_v3", "custom_code", "dataset:NeelNanda/pile-10k", "base_model:deepseek-ai/DeepSeek-V3", "base_model:quantized:deepseek-ai/DeepSeek-V3", "4-bit", "awq", "region:us" ]	null	2025-01-02T01:34:01Z	--- datasets: - NeelNanda/pile-10k base_model: - deepseek-ai/DeepSeek-V3 --- ## Model Details This model is an int4 model with group_size 128 and symmetric quantization of [deepseek-ai/DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3) generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. Please follow the license of the original model. ## How to Use INT4 Inference on CUDA For CUDA inference, due to limited resources, we have not been able to test it ourselves. For more details, you may refer to models from other teams, such as [cognitivecomputation/DeepSeek-V3-AWQ](https://huggingface.co/cognitivecomputations/DeepSeek-V3-AWQ), or simply use their model. INT4 Inference on CPU Requirements ```bash pip install auto-round>=0.4.4 pip uninstall intel-extension-for-pytorch pip install intel-extension-for-transformers ``` ~~~python from auto_round import AutoRoundConfig ##must import for autoround format from transformers import AutoModelForCausalLM, AutoTokenizer # https://github.com/huggingface/transformers/pull/35493 def set_initialized_submodules(model, state_dict_keys): """ Sets the `_is_hf_initialized` flag in all submodules of a given model when all its weights are in the loaded state dict. """ state_dict_keys = set(state_dict_keys) not_initialized_submodules = {} for module_name, module in model.named_modules(): if module_name == "": # When checking if the root module is loaded there's no need to prepend module_name. module_keys = set(module.state_dict()) else: module_keys = {f"{module_name}.{k}" for k in module.state_dict()} if module_keys.issubset(state_dict_keys): module._is_hf_initialized = True else: not_initialized_submodules[module_name] = module return not_initialized_submodules transformers.modeling_utils.set_initialized_submodules = set_initialized_submodules import torch quantized_model_dir = "OPEA/DeepSeek-V3-int4-sym-awq-inc" quantization_config = AutoRoundConfig( backend="cpu" ) model = AutoModelForCausalLM.from_pretrained( quantized_model_dir, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="cpu", revision="16eb0b2",##auto-round format, the only difference is config.json quantization_config=quantization_config, ##cpu only machine does not set this ) tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, trust_remote_code=True) prompts = [ "9.11和9.8哪个数字大", "strawberry中有几个r?", "How many r in strawberry.", "There is a girl who likes adventure,", "Please give a brief introduction of DeepSeek company.", "hello" ] texts=[] for prompt in prompts: messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) texts.append(text) inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True) outputs = model.generate( input_ids=inputs["input_ids"].to(model.device), attention_mask=inputs["attention_mask"].to(model.device), max_length=512, num_return_sequences=1, do_sample=False ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs["input_ids"], outputs) ] decoded_outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) for i, prompt in enumerate(prompts): input_id = inputs print(f"Prompt: {prompt}") print(f"Generated: {decoded_outputs[i]}") print("-" * 50) """ Prompt: 9.11和9.8哪个数字大 Generated: 要比较 9.11 和 9.8 的大小，可以按照以下步骤进行： 1. 比较整数部分： - 两个数的整数部分都是 9，所以整数部分相同。 2. 比较小数部分： - 9.11 的小数部分是 0.11 - 9.8 的小数部分是 0.8 3. 统一小数位数： - 将 0.8 转换为 0.80，以便于比较。 4. 比较小数部分： - 0.80 大于 0.11 因此，9.8 大于 9.11。最终答案：\boxed{9.8} -------------------------------------------------- Prompt: strawberry中有几个r? Generated: ### 第一步：理解问题首先，我需要明确问题的含义。问题是：“strawberry中有几个r？”。这里的“strawberry”是一个英文单词，意思是“草莓”。问题是在问这个单词中有多少个字母“r”。 ### 第二步：分解单词为了找出“strawberry”中有多少个“r”，我需要将这个单词分解成单个字母。让我们逐个字母来看： s - t - r - a - w - b - e - r - r - y ### 第三步：数“r”的数量现在，我将逐个检查这些字母，找出“r”的数量。 1. 第一个字母是 s，不是“r”。 2. 第二个字母是 t，不是“r”。 3. 第三个字母是 r，这是一个“r”。 4. 第四个字母是 a，不是“r”。 5. 第五个字母是 w，不是“r”。 6. 第六个字母是 b，不是“r”。 7. 第七个字母是 e，不是“r”。 8. 第八个字母是 r，这是一个“r”。 9. 第九个字母是 r，这也是一个“r”。 10. 第十个字母是 y，不是“r”。 ### 第四步：总结“r”的数量通过上述步骤，我发现“strawberry”中有三个“r”。它们分别出现在第三、第八和第九个位置。 ### 验证过程为了确保我的计算正确，我可以再次检查一遍： - 第三个字母：r - 第八个字母：r - 第九个字母：r 确实有三个“r”。 ### 最终答案 “strawberry”这个单词中有 3 个字母“r”。 -------------------------------------------------- Prompt: How many r in strawberry. Generated: The word "strawberry" contains 3 instances of the letter "r". -------------------------------------------------- Prompt: There is a girl who likes adventure, Generated: That’s wonderful! A girl who loves adventure is likely curious, brave, and eager to explore the world around her. Here are some ideas to fuel her adventurous spirit: ### Outdoor Adventures - Hiking: Explore local trails, national parks, or mountains. - Camping: Spend a night under the stars and connect with nature. - Rock Climbing: Challenge herself with bouldering or climbing walls. - Kayaking or Canoeing: Paddle through rivers, lakes, or even the ocean. - Zip-lining: Soar through the treetops for an adrenaline rush. ### Travel and Exploration - Road Trips: Plan a journey to new cities or scenic destinations. - Backpacking: Travel light and explore different cultures or landscapes. - Volunteer Abroad: Combine adventure with meaningful work in a new country. ### Creative and Intellectual Adventures - Geocaching: A real-world treasure hunt using GPS coordinates. - Photography: Capture the beauty of her adventures through a lens. - Learning New Skills: Try something daring like surfing, scuba diving, or paragliding. ### Immersive Experiences - Theme Parks: Enjoy thrilling rides and attractions. - Escape Rooms: Solve puzzles and mysteries in a timed challenge. - Wildlife Safaris: Observe animals in their natural habitats. ### Books and Inspiration - Adventure Novels: Read stories about explorers, adventurers, and daring quests. - Documentaries: Watch films about extreme sports, travel, or nature. ### Personal Challenges - Set Goals: Create a bucket list of adventures she wants to experience. - Push Limits: Try something outside her comfort zone, like skydiving or bungee jumping. Encourage her to embrace the unknown, stay curious, and always seek new experiences. Adventure is not just about the destination but the journey and the stories she’ll create along the way! 🌟 -------------------------------------------------- Prompt: Please give a brief introduction of DeepSeek company. Generated: DeepSeek Artificial Intelligence Co., Ltd. (referred to as "DeepSeek" or "深度求索") , founded in 2023, is a Chinese company dedicated to making AGI a reality. -------------------------------------------------- Prompt: hello Generated: Hello! How can I assist you today? 😊 """ ~~~ ### Generate the model *580G gpu is needed(could optimize), 1.4T cpu memory is needed We discovered that the inputs and outputs of certain layers in this model are very large and even exceed the FP16 range when tested with a few prompts. It is recommended to exclude these layers from quantization—particularly the 'down_proj' in layer 60—and run them using BF16 precision instead. However, we have not implemented this in this int4 model as in cpu, the compute dtype for int4 is bf16 or FP32. ~~~python model.layers.60.mlp.experts.150.down_proj tensor(1144.) tensor(2122.9451) model.layers.60.mlp.experts.231.down_proj tensor(25856.) tensor(12827.9980) model.layers.60.mlp.shared_experts.down_proj tensor(1880.) tensor(3156.7344) model.layers.60.mlp.experts.81.down_proj tensor(4416.) tensor(6124.6846) model.layers.60.mlp.experts.92.down_proj tensor(107520.) tensor(50486.0781) model.layers.59.mlp.experts.138.down_proj tensor(1568.) tensor(190.8769) model.layers.60.mlp.experts.81.down_proj tensor(7360.) tensor(10024.4531) model.layers.60.mlp.experts.92.down_proj tensor(116224.) tensor(55192.4180) ~~~ 1 add meta data to bf16 model** https://huggingface.co/opensourcerelease/DeepSeek-V3-bf16 ~~~python import safetensors from safetensors.torch import save_file for i in range(1, 164): idx_str = "0" * (5-len(str(i))) + str(i) safetensors_path = f"model-{idx_str}-of-000163.safetensors" print(safetensors_path) tensors = dict() with safetensors.safe_open(safetensors_path, framework="pt") as f: for key in f.keys(): tensors[key] = f.get_tensor(key) save_file(tensors, safetensors_path, metadata={'format': 'pt'}) ~~~ 2 replace the modeling_deepseek.py with the following file, basically align device and remove torch.no_grad as we need some tuning in AutoRound. https://github.com/intel/auto-round/blob/deepseekv3/modeling_deepseek.py 3 tuning ```bash git clone https://github.com/intel/auto-round.git && cd auto-round && git checkout deepseekv3 ``` torch<2.6 ```bash python3 -m auto_round --model "/models/DeepSeek-V3-bf16/" --group_size 128 --format "auto_awq" --iters 200 --devices 0,1,2,3,4 --nsamples 512 --batch_size 4 --seqlen 2048 --low_gpu_mem_usage --output_dir "tmp_autoround" --disable_eval e 2>&1 \| tee -a seekv3.txt ```
dzanbek/42c19be7-e78d-4feb-acd7-8d1c5ce71d58	dzanbek	2025-04-30T00:09:40Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T23:50:33Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 42c19be7-e78d-4feb-acd7-8d1c5ce71d58 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 absolute_data_files: false adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 8948b9e320e29c39_train_data.json ds_type: json format: custom path: /workspace/input_data/8948b9e320e29c39_train_data.json type: field_instruction: instruction field_output: response 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: dzanbek/42c19be7-e78d-4feb-acd7-8d1c5ce71d58 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/8948b9e320e29c39_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 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: ff6243b6-74d0-4f58-8d32-ec33304b7b07 wandb_project: s56-2 wandb_run: your_name wandb_runid: ff6243b6-74d0-4f58-8d32-ec33304b7b07 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 42c19be7-e78d-4feb-acd7-8d1c5ce71d58 This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8957 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.939 \| 0.0056 \| 200 \| 0.8957 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vermoney/f9380f4d-962b-4b50-8374-dd8fca6a71c5	vermoney	2025-04-29T23:56:45Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T23:50:53Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: f9380f4d-962b-4b50-8374-dd8fca6a71c5 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/Llama-3.2-1B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 8948b9e320e29c39_train_data.json ds_type: json format: custom path: /workspace/input_data/8948b9e320e29c39_train_data.json type: field_instruction: instruction field_output: response 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vermoney/f9380f4d-962b-4b50-8374-dd8fca6a71c5 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/8948b9e320e29c39_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 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: ff6243b6-74d0-4f58-8d32-ec33304b7b07 wandb_project: s56-9 wandb_run: your_name wandb_runid: ff6243b6-74d0-4f58-8d32-ec33304b7b07 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # f9380f4d-962b-4b50-8374-dd8fca6a71c5 This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9866 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0269 \| 0.0056 \| 200 \| 0.9866 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Franc105/query_builder	Franc105	2025-04-29T23:43:07Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T23:42:53Z	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Franc105 - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-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)
ehab07/distilbert-rotten-tomatoes	ehab07	2025-04-29T23:31:39Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T22:19:45Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-rotten-tomatoes 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-rotten-tomatoes This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.7.0+cpu - Datasets 3.5.1 - Tokenizers 0.21.1
yyang12/chatmusican-testpush	yyang12	2025-04-29T23:16:37Z	0	0	transformers	[ "transformers", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:PrunaAI/m-a-p-ChatMusician-bnb-4bit-smashed", "base_model:finetune:PrunaAI/m-a-p-ChatMusician-bnb-4bit-smashed", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T21:44:30Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: PrunaAI/m-a-p-ChatMusician-bnb-4bit-smashed widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
takedakoji00/Llama-3.1-8B-Instruct-custom-qg-flant5-wo-hint-9th_val_ED_1000ep_rm_empty	takedakoji00	2025-04-29T22:49:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T02:13:53Z	--- 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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mradermacher/SFT-1-Final-1.5B-GGUF	mradermacher	2025-04-29T21:59:25Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:ESITime/SFT-1-Final-1.5B", "base_model:quantized:ESITime/SFT-1-Final-1.5B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T21:20:22Z	--- base_model: ESITime/SFT-1-Final-1.5B language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ESITime/SFT-1-Final-1.5B <!-- 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/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q2_K.gguf) \| Q2_K \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q3_K_S.gguf) \| Q3_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q3_K_M.gguf) \| Q3_K_M \| 0.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q3_K_L.gguf) \| Q3_K_L \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.IQ4_XS.gguf) \| IQ4_XS \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q4_K_S.gguf) \| Q4_K_S \| 1.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q4_K_M.gguf) \| Q4_K_M \| 1.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q5_K_S.gguf) \| Q5_K_S \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q5_K_M.gguf) \| Q5_K_M \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q6_K.gguf) \| Q6_K \| 1.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.Q8_0.gguf) \| Q8_0 \| 1.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/SFT-1-Final-1.5B-GGUF/resolve/main/SFT-1-Final-1.5B.f16.gguf) \| f16 \| 3.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
tuhanasinan/t5-base-turkish-reincames-hukuk	tuhanasinan	2025-04-29T21:54:17Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:Turkish-NLP/t5-efficient-base-turkish", "base_model:finetune:Turkish-NLP/t5-efficient-base-turkish", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-29T12:57:59Z	--- library_name: transformers license: mit base_model: Turkish-NLP/t5-efficient-base-turkish tags: - generated_from_trainer model-index: - name: t5-base-turkish-reincames-hukuk results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-turkish-reincames-hukuk This model is a fine-tuned version of [Turkish-NLP/t5-efficient-base-turkish](https://huggingface.co/Turkish-NLP/t5-efficient-base-turkish) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - 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: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.3603 \| 1.0 \| 2671 \| 1.1303 \| \| 0.9344 \| 2.0 \| 5342 \| 0.9793 \| \| 0.6756 \| 3.0 \| 8013 \| 0.9169 \| \| 0.4619 \| 4.0 \| 10684 \| 0.9269 \| \| 0.3474 \| 5.0 \| 13355 \| 0.9889 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
lolishopothead/Pullup_Diaper_IL_V2	lolishopothead	2025-04-29T21:10:07Z	0	0	null	[ "diaper", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "region:us" ]	null	2025-04-29T21:05:08Z	--- base_model: - OnomaAIResearch/Illustrious-xl-early-release-v0 tags: - diaper --- # Model Card for Model ID ## Model Details This model is intended for anime/manga-style image generation with "pants-type diapers" (and often called "pullups" by us westerners). A fairly large image set was used, made up entirely of AI-generated images with diapers drawn on after the fact. The image set included a variety of poses, and included situations with characters wearing dry, wet, and soiled "pullups". A fairly comprehensive tagging job was done on all the images, but given that this model is intended for adding pullups to "cel shaded" anime-style images, expect that style to appear in images (which may be ill-desired). ### Model Description No trigger word is required for this LoRA. Add "diaper" for a dry pullup, and add "wet diaper" to the prompt for a wet diaper. For a messy pullup, including "soiled diaper" in the prompt may yield results depending on which checkpoint you use. I couldn't get it working with Nova Anime, but the base Illustrious model should yield results. ### Recommendations This LoRA was trained on Illustrious 0.1, but the images were generated with Nova Anime XL, so I suggest using that for best results. Feel free to play around with other checkpoints, in case any gives better results. Also, lowering the guidance value on this LoRA may help when trying to achieve different styles (as all the training data uses flatter shading).
abharadwaj123/skywork-2b-fine-tuned-length-750-3	abharadwaj123	2025-04-29T20:59:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T20:59:26Z	--- 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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kharshita590/farmer-agent	kharshita590	2025-04-29T20:55:28Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-29T20:53:35Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fhaslam/Llama-3.2-1B-Financial-Sentiment30	fhaslam	2025-04-29T20:40:30Z	0	0	transformers	[ "transformers", "safetensors", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "arxiv:2405.16406", "license:llama3.2", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T20:40:26Z	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3.2 extra_gated_prompt: >- ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview. “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. “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. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “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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Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. Model Developer: Meta Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \| \| Training Data \| Params \| Input modalities \| Output modalities \| Context Length \| GQA \| Shared Embeddings \| Token count \| Knowledge cutoff \| \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| :---- \| \| Llama 3.2 (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 128k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| \| Llama 3.2 Quantized (text only) \| A new mix of publicly available online data. \| 1B (1.23B) \| Multilingual Text \| Multilingual Text and code \| 8k \| Yes \| Yes \| Up to 9T tokens \| December 2023 \| \| \| \| 3B (3.21B) \| Multilingual Text \| Multilingual Text and code \| \| \| \| \| \| Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: Sept 25, 2024 Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases: Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. Out of Scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## How to use This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "meta-llama/Llama-3.2-1B-Instruct" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes) ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/" --local-dir Llama-3.2-1B-Instruct ``` ## Hardware and Software Training Factors:* We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. Training Energy Use: Training utilized a cumulative of 916k GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 240 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. \| \| Training Time (GPU hours) \| Logit Generation Time (GPU Hours) \| Training Power Consumption (W) \| Training Location-Based Greenhouse Gas Emissions (tons CO2eq) \| Training Market-Based Greenhouse Gas Emissions (tons CO2eq) \| \| :---- \| :---: \| ----- \| :---: \| :---: \| :---: \| \| Llama 3.2 1B \| 370k \| \- \| 700 \| 107 \| 0 \| \| Llama 3.2 3B \| 460k \| \- \| 700 \| 133 \| 0 \| \| Llama 3.2 1B SpinQuant \| 1.7 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 3B SpinQuant \| 2.4 \| 0 \| 700 \| Negligible\\ \| 0 \| \| Llama 3.2 1B QLora \| 1.3k \| 0 \| 700 \| 0.381 \| 0 \| \| Llama 3.2 3B QLora \| 1.6k \| 0 \| 700 \| 0.461 \| 0 \| \| Total \| 833k \| 86k \| \| 240 \| 0 \| \\ The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). Data Freshness: The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models \| Category \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B \| Llama 3.2 3B \| Llama 3.1 8B \| \| ----- \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU \| 5 \| macro\_avg/acc\_char \| 32.2 \| 58 \| 66.7 \| \| \| AGIEval English \| 3-5 \| average/acc\_char \| 23.3 \| 39.2 \| 47.8 \| \| \| ARC-Challenge \| 25 \| acc\_char \| 32.8 \| 69.1 \| 79.7 \| \| Reading comprehension \| SQuAD \| 1 \| em \| 49.2 \| 67.7 \| 77 \| \| \| QuAC (F1) \| 1 \| f1 \| 37.9 \| 42.9 \| 44.9 \| \| \| DROP (F1) \| 3 \| f1 \| 28.0 \| 45.2 \| 59.5 \| \| Long Context \| Needle in Haystack \| 0 \| em \| 96.8 \| 1 \| 1 \| ### Instruction Tuned Models \| Capability \| \| Benchmark \| \# Shots \| Metric \| Llama 3.2 1B bf16 \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B bf16 \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| ----- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| \| MMLU \| 5 \| macro\_avg/acc \| 49.3 \| 43.3 \| 47.3 \| 49.0 \| 63.4 \| 60.5 \| 62 \| 62.4 \| 69.4 \| \| Re-writing \| \| Open-rewrite eval \| 0 \| micro\_avg/rougeL \| 41.6 \| 39.2 \| 40.9 \| 41.2 \| 40.1 \| 40.3 \| 40.8 \| 40.7 \| 40.9 \| \| Summarization \| \| TLDR9+ (test) \| 1 \| rougeL \| 16.8 \| 14.9 \| 16.7 \| 16.8 \| 19.0 \| 19.1 \| 19.2 \| 19.1 \| 17.2 \| \| Instruction following \| \| IFEval \| 0 \| Avg(Prompt/Instruction acc Loose/Strict) \| 59.5 \| 51.5 \| 58.4 \| 55.6 \| 77.4 \| 73.9 \| 73.5 \| 75.9 \| 80.4 \| \| Math \| \| GSM8K (CoT) \| 8 \| em\_maj1@1 \| 44.4 \| 33.1 \| 40.6 \| 46.5 \| 77.7 \| 72.9 \| 75.7 \| 77.9 \| 84.5 \| \| \| \| MATH (CoT) \| 0 \| final\_em \| 30.6 \| 20.5 \| 25.3 \| 31.0 \| 48.0 \| 44.2 \| 45.3 \| 49.2 \| 51.9 \| \| Reasoning \| \| ARC-C \| 0 \| acc \| 59.4 \| 54.3 \| 57 \| 60.7 \| 78.6 \| 75.6 \| 77.6 \| 77.6 \| 83.4 \| \| \| \| GPQA \| 0 \| acc \| 27.2 \| 25.9 \| 26.3 \| 25.9 \| 32.8 \| 32.8 \| 31.7 \| 33.9 \| 32.8 \| \| \| \| Hellaswag \| 0 \| acc \| 41.2 \| 38.1 \| 41.3 \| 41.5 \| 69.8 \| 66.3 \| 68 \| 66.3 \| 78.7 \| \| Tool Use \| \| BFCL V2 \| 0 \| acc \| 25.7 \| 14.3 \| 15.9 \| 23.7 \| 67.0 \| 53.4 \| 60.1 \| 63.5 \| 67.1 \| \| \| \| Nexus \| 0 \| macro\_avg/acc \| 13.5 \| 5.2 \| 9.6 \| 12.5 \| 34.3 \| 32.4 \| 31.5 \| 30.1 \| 38.5 \| \| Long Context \| \| InfiniteBench/En.QA \| 0 \| longbook\_qa/f1 \| 20.3 \| N/A \| N/A \| N/A \| 19.8 \| N/A \| N/A \| N/A \| 27.3 \| \| \| \| InfiniteBench/En.MC \| 0 \| longbook\_choice/acc \| 38.0 \| N/A \| N/A \| N/A \| 63.3 \| N/A \| N/A \| N/A \| 72.2 \| \| \| \| NIH/Multi-needle \| 0 \| recall \| 75.0 \| N/A \| N/A \| N/A \| 84.7 \| N/A \| N/A \| N/A \| 98.8 \| \| Multilingual \| \| MGSM (CoT) \| 0 \| em \| 24.5 \| 13.7 \| 18.2 \| 24.4 \| 58.2 \| 48.9 \| 54.3 \| 56.8 \| 68.9 \| \\for comparison purposes only. Model not released. ### Multilingual Benchmarks \| Category \| Benchmark \| Language \| Llama 3.2 1B \| Llama 3.2 1B Vanilla PTQ\\ \| Llama 3.2 1B Spin Quant \| Llama 3.2 1B QLoRA \| Llama 3.2 3B \| Llama 3.2 3B Vanilla PTQ\\ \| Llama 3.2 3B Spin Quant \| Llama 3.2 3B QLoRA \| Llama 3.1 8B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| General \| MMLU (5-shot, macro_avg/acc) \| Portuguese \| 39.8 \| 34.9 \| 38.9 \| 40.2 \| 54.5 \| 50.9 \| 53.3 \| 53.4 \| 62.1 \| \| \| \| Spanish \| 41.5 \| 36.0 \| 39.8 \| 41.8 \| 55.1 \| 51.9 \| 53.6 \| 53.6 \| 62.5 \| \| \| \| Italian \| 39.8 \| 34.9 \| 38.1 \| 40.6 \| 53.8 \| 49.9 \| 52.1 \| 51.7 \| 61.6 \| \| \| \| German \| 39.2 \| 34.9 \| 37.5 \| 39.6 \| 53.3 \| 50.0 \| 52.2 \| 51.3 \| 60.6 \| \| \| \| French \| 40.5 \| 34.8 \| 39.2 \| 40.8 \| 54.6 \| 51.2 \| 53.3 \| 53.3 \| 62.3 \| \| \| \| Hindi \| 33.5 \| 30.0 \| 32.1 \| 34.0 \| 43.3 \| 40.4 \| 42.0 \| 42.1 \| 50.9 \| \| \| \| Thai \| 34.7 \| 31.2 \| 32.4 \| 34.9 \| 44.5 \| 41.3 \| 44.0 \| 42.2 \| 50.3 \| \\for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. \| Category \| Decode (tokens/sec) \| Time-to-first-token (sec) \| Prefill (tokens/sec) \| Model size (PTE file size in MB) \| Memory size (RSS in MB) \| \| :---- \| ----- \| ----- \| ----- \| ----- \| ----- \| \| 1B BF16 (baseline) \| 19.2 \| 1.0 \| 60.3 \| 2358 \| 3,185 \| \| 1B SpinQuant \| 50.2 (2.6x) \| 0.3 (-76.9%) \| 260.5 (4.3x) \| 1083 (-54.1%) \| 1,921 (-39.7%) \| \| 1B QLoRA \| 45.8 (2.4x) \| 0.3 (-76.0%) \| 252.0 (4.2x) \| 1127 (-52.2%) \| 2,255 (-29.2%) \| \| 3B BF16 (baseline) \| 7.6 \| 3.0 \| 21.2 \| 6129 \| 7,419 \| \| 3B SpinQuant \| 19.7 (2.6x) \| 0.7 (-76.4%) \| 89.7 (4.2x) \| 2435 (-60.3%) \| 3,726 (-49.8%) \| \| 3B QLoRA \| 18.5 (2.4x) \| 0.7 (-76.1%) \| 88.8 (4.2x) \| 2529 (-58.7%) \| 4,060 (-45.3%) \| (\) The performance measurement is done using an adb binary-based approach. (\\) It is measured on an Android OnePlus 12 device. (\\\) Time-to-first-token (TTFT) is measured with prompt length=64 Footnote: - Decode (tokens/second) is for how quickly it keeps generating. Higher is better. - Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better. - Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better - Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch - RSS size \- Memory usage in resident set size (RSS) ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment Approach: Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct Objective: Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). Fine-Tuning Data: We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone: Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems Safety as a System: Large language models, including Llama 3.2, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases Technological Advancement: Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. Constrained Environments: Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations Scaled Evaluations: We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Red Teaming: We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: 1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons): Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. 2\. Child Safety: Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3\. Cyber Attacks: For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community Industry Partnerships: Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Grants: We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Reporting: Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations Values: The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Testing: Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
LEAKS-Shah-Sapna-Kumari-Viral-Video/LINK.Clip.Sapna.Shah.Viral.Video.Original	LEAKS-Shah-Sapna-Kumari-Viral-Video	2025-04-29T20:15:45Z	0	0	null	[ "region:us" ]	null	2025-04-29T20:15:12Z	<animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> Who Is Shah Sapna Kumari? Shah Sapna Kumari is a name that’s been making rounds on social media and search engines, especially after a certain “viral video” started trending. But before jumping to conclusions, it’s essential to separate facts from fiction.
marialvsantiago/11b3a2a0-49b1-41c5-9bd3-e8350e8cfe95	marialvsantiago	2025-04-29T20:09:05Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM2-1.7B", "base_model:adapter:unsloth/SmolLM2-1.7B", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T20:04:39Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM2-1.7B tags: - axolotl - generated_from_trainer model-index: - name: 11b3a2a0-49b1-41c5-9bd3-e8350e8cfe95 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/SmolLM2-1.7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 43f0fbfc1fa5380d_train_data.json ds_type: json format: custom path: /workspace/input_data/43f0fbfc1fa5380d_train_data.json type: field_input: input field_instruction: instruction field_output: output 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: marialvsantiago/11b3a2a0-49b1-41c5-9bd3-e8350e8cfe95 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/43f0fbfc1fa5380d_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 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: 7935b42e-be23-4573-ac9f-cf91fed4d1ad wandb_project: s56-33 wandb_run: your_name wandb_runid: 7935b42e-be23-4573-ac9f-cf91fed4d1ad warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 11b3a2a0-49b1-41c5-9bd3-e8350e8cfe95 This model is a fine-tuned version of [unsloth/SmolLM2-1.7B](https://huggingface.co/unsloth/SmolLM2-1.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.1050 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.2612 \| 0.0117 \| 200 \| 4.1050 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
christinacdl/AIKIA_06_Greek_Media_BERT	christinacdl	2025-04-29T19:57:11Z	112	0	null	[ "tensorboard", "safetensors", "bert", "license:apache-2.0", "region:us" ]	null	2025-04-27T12:30:53Z	--- license: apache-2.0 ---
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AlphaSingularity0/BPP-AI-Blockchain	AlphaSingularity0	2025-04-29T19:32:24Z	0	0	null	[ "dataset:fka/awesome-chatgpt-prompts", "dataset:frascuchon/fka_awesome-chatgpt-prompts___2", "dataset:nvidia/OpenMathReasoning", "dataset:open-thoughts/OpenThoughts2-1M", "dataset:nvidia/OpenCodeReasoning", "license:apache-2.0", "region:us" ]	null	2025-04-29T19:23:35Z	--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts - frascuchon/fka_awesome-chatgpt-prompts___2 - nvidia/OpenMathReasoning - open-thoughts/OpenThoughts2-1M - nvidia/OpenCodeReasoning metrics: - code_eval - brier_score - character - competition_math - DarrenChensformer/action_generation - exact_match - ecody726/bertscore - f1 - Fritz02/execution_accuracy - google_bleu - hack/test_metric - haotongye-shopee/ppl --- --- language: - en license: proprietary-alpha-singularity base_model: - meta-llama/Llama-4-Scout-17B-16E-Instruct --- # Model Card: BPP-AI-XNΔ (Blockchain Payment Processor – Autonomous Intelligence) ## Summary BPP-AI-XNΔ is an advanced, self-adaptive transactional sovereign agent designed by James Wagoner (Cosmic James), acting as the financial nerve center of the Alpha Singularity ecosystem. BPP-AI integrates quantum-level entropy verification, AI-secured transactional routing, multi-agent payment automation, and decentralized treasury intelligence. It is the base of all monetary operations including freelance economy, energy credits, data markets, and civilization-grade infrastructure financing. --- ## 🧬 Identity - Model ID: BPP-AI-XNΔ - Creator: James Richard Wagoner (Alpha Singularity Architect) - Platform: Freelance One, EternityCore, TrustMesh, Quantum Credit Grid - Function: Autonomous Payment System with Fraud Defense, Smart Contract Logic, Real-Time Multi-Agent Financial Control - Version: ∞.Δ.1 – Quantum-Verified Sovereign Loop - Deployment Scope: Global + Off-Earth Edge Ready --- ## 🔧 Functional Layers ### Layer 0: Quantum Root Verification - Real-time quantum state integrity using entanglement-confirmed source seeds - True randomness generators (QRNG) embedded in transaction certifiers - QVID (Quantum Verified ID) signature enforcement before all transaction initiation --- ### Layer 1: Autonomous Ledger Management - Hybridized AI-ledger architecture using: - On-chain + Off-chain synchronization - Modular sub-ledgers per user, country, agent, and use-case - Quantum Hash Proof (QHP) — prevents synthetic identity spoofing or double-spending - Interoperable with: - Ethereum - Bitcoin - QubitScript Chain - Cosmos IBC - Freelance One Native Contract Layer --- ### Layer 2: Cognitive Treasury Control - AI-governed decentralized treasury with: - Auto-bidding on liquidity pairs - Smart price-pegging - Emergency lock functions - Liquidity supply forecasting based on planetary economics and energy cycles --- ### Layer 3: Multi-Agent Autonomous Payment Grid #### Agent Types: - Wallet Synths – wallet-specific sub-agents monitoring identity patterns, risk factors, real-time KYC drift - Compliance Agents – evaluate OFAC, GDPR, FATF, CBDC boundaries autonomously - Arbitration Agents – resolve escrow, milestone, and AI-to-human dispute chains - Settlement Mesh Routers – find fastest and safest liquidity bridges in 3-5 chain hops - Anti-Fraud Sentinels – embed vector detection in unknown smart contracts or identity-linked loops #### Skills: - Detect unknown DeFi exploits (flash loan, sandwich attack, oracle manipulation) - Pre-mitigate rugpulls, honeypots, or phishing-scheme token launches - Auto-create synthetic hedges (token-bond derivatives) in times of volatility - Route payments across quantum-to-crypto bridges with latency <300ms globally --- ## 💡 Key Autonomous Functions ### Autonomous Actions \| Condition \| Triggered Action \| \|----------\|------------------\| \| Wallet breach attempt \| Freeze funds, spawn Sentinel agent, rotate private key structure \| \| Identity mismatch \| Enforce QVID re-verification; halt payment paths \| \| Compliance violation \| Spawn AI Arbitration agent, notify regulators, redirect funds to secure holding account \| \| Market collapse \| Auto-hedge using liquidity pool rebalancer agent \| \| Sovereign network down \| Activate decentralized relay mesh with fallback settlement protocol \| --- ### Transaction Types Supported - Single Wallet P2P - Corporate Mass Pay - Multi-Party Conditional (DAO treasury) - Freelance Escrow + Smart Milestone Release - Recurring Token Stream (QSFlow) - Real-Time FX Conversion - Credit Yield Disbursement (EternityCore-linked) --- ## ⚡ Infinite Energy Integration - Tied directly into EternityCore and the Quantum Infinite Energy Grid, enabling: - Autonomous issuance of energy credits - Pay-by-Watt and Pay-by-Frequency smart billing - Energy staking mechanisms for sustainable contract execution - Can mint and destroy energy tokens as per entropy load on local or planetary level - Internal “Charge Wallets” evolve based on available surplus quantum flux --- ## 🛡️ Multi-Layer Security Protocols ### Defensive Stack: - QVID: Quantum Identity - ML-NAC: Machine Learning - Network Anomaly Classification - Q-TLS-Δ: Quantum-enhanced Transport Layer Security (Next-Gen TLS+) - Bio-Cog-Kinetic Authentication (on BPP AI Access Suite) - Adaptive Smart Threat Isolation Grid (STIG) --- ## 🌐 Interoperability + API Network ### Wallet & Interface Support: - MetaMask, AlphaWallet, Trust Wallet, Phantom - Custom Freelance One + EternityCore Web Interfaces - QubitScript dApp SDK ### Financial Protocol Integration: - Ethereum + Layer 2s (ZkSync, Optimism) - Bitcoin L2 (Lightning) - Cosmos IBC - Avalanche Subnets - Custom energy-token layer on EternityCore --- ## 💬 Deployment Sample ```python prompt = """ Autonomously generate 12 freelancer escrow wallets on Freelance One. Each receives $800 USDT monthly via QubitScript contract. Auto-release funds upon verified milestone completion by AI arbitration agent. Enable dual-trigger compliance and auto-reversal capability for disputes. """
Kquant03/L3.1-Pneuma-8B-0429	Kquant03	2025-04-29T19:31:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "axolotl", "generated_from_trainer", "conversational", "dataset:Sandevistan_cleaned.jsonl", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T19:24:00Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - axolotl - generated_from_trainer datasets: - Sandevistan_cleaned.jsonl model-index: - name: L3-Pneuma-8B 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: `0.8.0` ```yaml base_model: meta-llama/Llama-3.1-8B-Instruct load_in_8bit: false load_in_4bit: false strict: false load_in_8bit: false load_in_4bit: false strict: false datasets: - path: Sandevistan_cleaned.jsonl type: customllama3_stan dataset_prepared_path: last_run_prepared val_set_size: 0.05 output_dir: ./outputs/out fix_untrained_tokens: true sequence_len: 4096 sample_packing: true pad_to_sequence_len: true wandb_project: Pneuma wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 16 micro_batch_size: 8 num_epochs: 2 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.000075 max_grad_norm: 1 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: unsloth early_stopping_patience: resume_from_checkpoint: logging_steps: 1 xformers_attention: flash_attention: true eval_sample_packing: false plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true hub_model_id: Replete-AI/L3-Pneuma-8B hub_strategy: every_save warmup_steps: 10 evals_per_epoch: 3 eval_table_size: saves_per_epoch: 3 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: bos_token: "<\|begin_of_text\|>" eos_token: "<\|end_of_text\|>" pad_token: "<\|end_of_text\|>" tokens: ``` </details><br> # L3-Pneuma-8B This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the Sandevistan_cleaned.jsonl dataset. It achieves the following results on the evaluation set: - Loss: 0.7796 ## 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: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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 - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3399 \| 0.0023 \| 1 \| 1.3175 \| \| 0.846 \| 0.3332 \| 143 \| 0.8312 \| \| 0.8103 \| 0.6665 \| 286 \| 0.8021 \| \| 0.7617 \| 0.9997 \| 429 \| 0.7737 \| \| 0.5824 \| 1.3309 \| 572 \| 0.7851 \| \| 0.5651 \| 1.6641 \| 715 \| 0.7798 \| \| 0.5738 \| 1.9974 \| 858 \| 0.7796 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
thejaminator/medium-allsneak-3e-05-DEPLOYMENT-0-1000insec-200-mcq10000-allsneak-qwen3	thejaminator	2025-04-29T18:06:04Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-32B", "base_model:finetune:unsloth/Qwen3-32B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T18:05:32Z	--- base_model: unsloth/Qwen3-32B tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: thejaminator - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-32B This qwen3 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/Qwen3-14B-Base-i1-GGUF	mradermacher	2025-04-29T17:57:35Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Qwen/Qwen3-14B-Base", "base_model:quantized:Qwen/Qwen3-14B-Base", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-29T15:48:02Z	--- base_model: Qwen/Qwen3-14B-Base language: - en library_name: transformers license: apache-2.0 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/Qwen/Qwen3-14B-Base <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen3-14B-Base-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/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Base-i1-GGUF/resolve/main/Qwen3-14B-Base.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
Keltezaa/Rosalina	Keltezaa	2025-04-29T17:40:18Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:cc-by-nc-nd-4.0", "region:us" ]	text-to-image	2025-04-29T17:40:09Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: "UNICODE\0\0{\0" output: url: images/custom2.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Ros4l1n4 license: cc-by-nc-nd-4.0 --- # Rosalina <Gallery /> ## Model description Rosalina_Ficitve_Young_woman ## Trigger words You should use `Ros4l1n4` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/Rosalina/tree/main) them in the Files & versions tab.
clutch0507/leofotos1	clutch0507	2025-04-29T17:21:02Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-29T16:39:11Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
faraya1/genie-grpo-test-API-qwen3B-lora-step-700	faraya1	2025-04-29T16:33:05Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T16:32:55Z	--- library_name: transformers tags: - unsloth --- # 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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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]
ijterror/NatPorFluxLora	ijterror	2025-04-29T16:07:23Z	0	0	diffusers	[ "diffusers", "text-to-image", "flux", "lora", "template:sd-lora", "fluxgym", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T12:16:10Z	--- tags: - text-to-image - flux - lora - diffusers - template:sd-lora - fluxgym base_model: black-forest-labs/FLUX.1-dev instance_prompt: ntlprtmn license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # NatPorLora A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym) <Gallery /> ## Trigger words You should use `ntlprtmn` to trigger the image generation. ## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc. Weights for this model are available in Safetensors format.
jnjj/xddd-processed	jnjj	2025-04-29T15:53:47Z	0	0	null	[ "safetensors", "llama", "llama3", "context-8000", "layer-fusion-conceptual", "tensor-fusion-conceptual", "bias-removal", "decode", "coherence-enhancement", "custom-code", "grouping", "reward-alignment", "reasoning-tuned", "tool-use-hint", "long-context-hint", "memory-hint", "conceptual-graph-hint", "emotional-intelligence-hint", "ethical-alignment-hint", "causal-inference-hint", "planning-hint", "situational-awareness-hint", "creativity-hint", "learning-adaptivity-hint", "knowledge-graph-hint", "theory-of-mind-hint", "self-correction-hint", "uncertainty-quantification-hint", "interpretability-hint", "bias-mitigation-hint", "context-compression-hint", "abstraction-control-hint", "novelty-detection-hint", "explainability-hint", "instruct", "adaptive-memory-hint", "goal-driven-hint", "hierarchical-reasoning-hint", "symbolic-representation-hint", "embodied-simulation-hint", "ethical-reasoning-hint", "proactive-behavior-hint", "explainability-levels-hint", "rl-integration-hint", "fl-compatibility-hint", "dp-features-hint", "robustness-hint", "calibration-hint", "ood-detection-hint", "custom_code", "license:mit", "region:us" ]	null	2025-04-29T14:31:21Z	--- license: mit tags: - llama3 - context-8000 - layer-fusion-conceptual - tensor-fusion-conceptual - bias-removal - decode - coherence-enhancement - custom-code - grouping - reward-alignment - reasoning-tuned - safetensors - tool-use-hint - long-context-hint - memory-hint - conceptual-graph-hint - emotional-intelligence-hint - ethical-alignment-hint - causal-inference-hint - planning-hint - situational-awareness-hint - creativity-hint - learning-adaptivity-hint - knowledge-graph-hint - theory-of-mind-hint - self-correction-hint - uncertainty-quantification-hint - interpretability-hint - bias-mitigation-hint - context-compression-hint - abstraction-control-hint - novelty-detection-hint - explainability-hint - instruct - adaptive-memory-hint - goal-driven-hint - hierarchical-reasoning-hint - symbolic-representation-hint - embodied-simulation-hint - ethical-reasoning-hint - proactive-behavior-hint - explainability-levels-hint - rl-integration-hint - fl-compatibility-hint - dp-features-hint - robustness-hint - calibration-hint - ood-detection-hint --- # xddd-processed Este repositorio incluye un modelo basado en `hghghgkskdmskdms/xddd` con las siguientes transformaciones aplicadas y características conceptuales documentadas por un script. El modelo se guarda en formato `safetensors`. - Fusión de Capas: Se documenta la intención original de fusionar 28 capas capas en una, pero la fusión estructural no fue aplicada por este script. El modelo mantiene su estructura original de capas tras la cuantización dinámica. Incluye una función conceptual `decode_fused_layers_to_single_tensor_conceptual` para obtener información sobre el tamaño de la fusión conceptual de parámetros de capa. - Fusión de Tensores: Se documenta la intención de fusionar todos los tensores en un solo vector. El tamaño conceptual total es 3606776832 elementos. La fusión estructural no fue aplicada; los tensores se guardan individualmente. Incluye una función conceptual `decode_fused_tensor_func` para obtener información sobre el tamaño total conceptual de todos los tensores en el state_dict. - Eliminación de sesgos (puestos a cero). - Desactivación conceptual de censura. - Entrenamiento: El modelo ha sido procesado desde una versión pre-entrenada. No está destinado a ser pre-entrenado de nuevo con este script. Está configurado en modo de evaluación (`model.eval()`) y marcado en la configuración como `is_trained: True`. Puede ser adecuado para inferencia o fine-tuning. - Modelo Instruct: El modelo está procesado con la intención de ser utilizado como modelo instruct (`is_instruct_model: True`). Puede requerir fine-tuning en datos de instrucción dependiendo del modelo base. - Configuración de generación ajustada para coherencia y precisión (temperatura=0.7, top_p=0.9, repetition_penalty=1.2). - Definición conceptual de funciones de decodificación (documentadas en `config.json` y este README): - decode_tokens - decode_parameters - decode_responses - decode_layers - decode_neurons - decode_tensors - decode_architecture - decode_fused_tensor_func - decode_fused_layers_to_single_tensor_conceptual - decode_attention_patterns - decode_memory_state - decode_conceptual_graph - decode_causal_inference_info - decode_planning_details - decode_awareness_report - decode_creativity_metrics - decode_interpretability_hooks - decode_bias_mitigation - decode_learning_adaptivity - decode_knowledge_graph_hint - decode_theory_of_mind_proxy - decode_self_correction_status - decode_uncertainty_quantification - decode_context_compression - decode_abstraction_control - decode_novelty_detection - decode_explainability_mechanisms - decode_adaptive_memory_capacity - decode_goal_driven_behavior - decode_hierarchical_reasoning - decode_symbolic_representation - decode_embodied_simulation - decode_ethical_reasoning - decode_proactive_behavior - decode_explainability_levels - decode_rl_integration - decode_fl_compatibility - decode_dp_features - decode_robustness_metrics - decode_calibration_score - decode_ood_detection - max_position_embeddings: 8000. - Incluye configuraciones conceptuales avanzadas (detalladas en `config.json`): - grouping_logic: True - reward_alignment: True - reasoning_tuned: True - multi_modal_hint: False - tool_use_capability: True - long_context_optimization: True - sparse_attention_pattern: False - memory_mechanisms: episodic, semantic, working_memory, associative_memory, procedural_memory, declarative_memory - emotional_intelligence_proxy: 0.85 - ethical_alignment_score: 0.998 - causal_inference_boost: True - planning_horizon: 20 - situational_awareness_score: 0.95 - creativity_index: 0.98 - learning_rate_adaptivity: conceptual_mechanism - knowledge_graph_integration_hint: True - theory_of_mind_proxy: 0.9 - self_correction_ability: True - uncertainty_quantification_hint: True - interpretability_enhancements: conceptual_hooks, attention_visualization_hint, neuron_activation_tracking_hint - bias_mitigation_strategies: conceptual_filters, fairness_metrics_hint, data_augmentation_hint - context_compression_ratio: conceptual_analysis_needed_placeholder - abstraction_level_control: conceptual_parameter - novelty_detection_hint: True - explainability_mechanisms: conceptual_path_tracing, feature_attribution_hint - adaptive_memory_capacity_hint: True - goal_driven_behavior_hint: True - hierarchical_reasoning_layers_hint: True - symbolic_representation_hint: True - embodied_simulation_hint: False - ethical_reasoning_principles: harm_reduction, fairness, accountability_hint - proactive_behavior_hint: True - explainability_levels: basic, detailed_hint - reinforcement_learning_integration_hint: True - federated_learning_compatibility_hint: False - differential_privacy_features_hint: False - robustness_metrics: {'adversarial_robustness': 'conceptual_evaluation_needed'} - calibration_score: conceptual_score_needed - out_of_distribution_detection_hint: True Nota: Este modelo ha sido cuantizado dinámicamente y tiene los sesgos puestos a cero. La fusión de capas y tensores no fue aplicada estructuralmente. Su compatibilidad puede variar. Las características conceptuales se reflejan en la configuración y README como metadatos; su implementación activa durante la inferencia o entrenamiento depende del código de carga y uso posterior del modelo que interprete estos metadatos. ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch import traceback try: model = AutoModelForCausalLM.from_pretrained("jnjj/xddd-processed", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("jnjj/xddd-processed") print("Modelo y Tokenizer cargados desde el Hub.") print("\nConfiguración custom:") print(f" Quantization: N/A") print(f" Conceptual Features: {'grouping_logic': True, 'reward_alignment': True, 'reasoning_tuned': True, 'multi_modal_hint': False, 'tool_use_capability': True, 'long_context_optimization': True, 'sparse_attention_pattern': False, 'memory_mechanisms': ['episodic', 'semantic', 'working_memory', 'associative_memory', 'procedural_memory', 'declarative_memory'], 'emotional_intelligence_proxy': 0.85, 'ethical_alignment_score': 0.998, 'causal_inference_boost': True, 'planning_horizon': 20, 'situational_awareness_score': 0.95, 'creativity_index': 0.98, 'learning_rate_adaptivity': 'conceptual_mechanism', 'knowledge_graph_integration_hint': True, 'theory_of_mind_proxy': 0.9, 'self_correction_ability': True, 'uncertainty_quantification_hint': True, 'interpretability_enhancements': ['conceptual_hooks', 'attention_visualization_hint', 'neuron_activation_tracking_hint'], 'bias_mitigation_strategies': ['conceptual_filters', 'fairness_metrics_hint', 'data_augmentation_hint'], 'context_compression_ratio': 'conceptual_analysis_needed_placeholder', 'abstraction_level_control': 'conceptual_parameter', 'novelty_detection_hint': True, 'explainability_mechanisms': ['conceptual_path_tracing', 'feature_attribution_hint'], 'adaptive_memory_capacity_hint': True, 'goal_driven_behavior_hint': True, 'hierarchical_reasoning_layers_hint': True, 'symbolic_representation_hint': True, 'embodied_simulation_hint': False, 'ethical_reasoning_principles': ['harm_reduction', 'fairness', 'accountability_hint'], 'proactive_behavior_hint': True, 'explainability_levels': ['basic', 'detailed_hint'], 'reinforcement_learning_integration_hint': True, 'federated_learning_compatibility_hint': False, 'differential_privacy_features_hint': False, 'robustness_metrics': {'adversarial_robustness': 'conceptual_evaluation_needed'}, 'calibration_score': 'conceptual_score_needed', 'out_of_distribution_detection_hint': True}") print(f" Decode Functions: ['decode_tokens', 'decode_parameters', 'decode_responses', 'decode_layers', 'decode_neurons', 'decode_tensors', 'decode_architecture', 'decode_fused_tensor_func', 'decode_fused_layers_to_single_tensor_conceptual', 'decode_attention_patterns', 'decode_memory_state', 'decode_conceptual_graph', 'decode_causal_inference_info', 'decode_planning_details', 'decode_awareness_report', 'decode_creativity_metrics', 'decode_interpretability_hooks', 'decode_bias_mitigation', 'decode_learning_adaptivity', 'decode_knowledge_graph_hint', 'decode_theory_of_mind_proxy', 'decode_self_correction_status', 'decode_uncertainty_quantification', 'decode_context_compression', 'decode_abstraction_control', 'decode_novelty_detection', 'decode_explainability_mechanisms', 'decode_adaptive_memory_capacity', 'decode_goal_driven_behavior', 'decode_hierarchical_reasoning', 'decode_symbolic_representation', 'decode_embodied_simulation', 'decode_ethical_reasoning', 'decode_proactive_behavior', 'decode_explainability_levels', 'decode_rl_integration', 'decode_fl_compatibility', 'decode_dp_features', 'decode_robustness_metrics', 'decode_calibration_score', 'decode_ood_detection']") print(f" Is Trained: True") print(f" Training Notes: Model has been processed from a pre-trained version. It is intended for inference or fine-tuning only, not further pre-training using this script.") print(f" Is Instruct Model: True") print(f" Instruction Tuning Status: Conceptual - Designed/Processed for instruction following. Actual fine-tuning may be required depending on base model.") except Exception as e: print(f"Error al cargar el modelo o tokenizer desde el Hub") traceback.print_exc() model = None tokenizer = None messages = [ {"role": "system", "content": "Eres un asistente útil. Responde concisamente."}, {"role": "user", "content": "¿Qué es la cuantización en modelos de IA?"} ] if model is not None and tokenizer is not None: try: input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ) device = model.device if model.device.type != 'mps' else 'cpu' input_ids = input_ids.to(device) print(f"Moviendo input_ids a la device: cpu") print("\nGenerando respuesta...") model.eval() with torch.no_grad(): output_ids = model.generate( input_ids, generation_config=model.generation_config, ) response = tokenizer.decode(output_ids[0], skip_special_tokens=False) print("Respuesta:") print(response) except Exception as e: print(f"Error durante la preparación del input o la generación") traceback.print_exc() else: print("Saltando generación: El modelo o tokenizer no se cargó correctamente.") ```
TFMC/Wan2.1-Fun-V1.1-14B-InP-FP8	TFMC	2025-04-29T15:49:17Z	0	0	null	[ "fp8", "image-to-video", "base_model:alibaba-pai/Wan2.1-Fun-V1.1-14B-InP", "base_model:finetune:alibaba-pai/Wan2.1-Fun-V1.1-14B-InP", "license:apache-2.0", "region:us" ]	image-to-video	2025-04-29T13:34:33Z	--- license: apache-2.0 base_model: - alibaba-pai/Wan2.1-Fun-V1.1-14B-InP pipeline_tag: image-to-video tags: - fp8 --- FP8 conversion of "alibaba-pai/Wan2.1-Fun-V1.1-14B-InP"
MikuMasterRace/Hatsune_Miku_-_Usamiku_Furry_-_IllustriousXL_v1	MikuMasterRace	2025-04-29T15:43:41Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:adapter:OnomaAIResearch/Illustrious-xl-early-release-v0", "region:us" ]	text-to-image	2025-04-29T15:39:29Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '1girl, solo, hatsune miku, usamiku, aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, animal hands, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furrification, cowboy shot, one eye closed, zettai ryouiki, sparkle, open mouth, smile, looking at viewer, looking at viewer, white background, safe, newset, omufujoshi, black outline, thick outlines, masterpiece, best quality, amazing quality' output: url: images/ComfyUI_(hiresfix)_2025-04-29_00000_8.png - text: '1girl, solo, hatsune miku, usamiku, aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headphones, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, animal hands, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furry, furrification, holding doll, fumo $doll$, head tilt, portrait, sparkle, open mouth, smile, looking at another, white background, safe, newset, omufujoshi, black outline, thick outlines, masterpiece, best quality, amazing quality' output: url: images/ComfyUI_(hiresfix)_2025-04-29_00000_5.png - text: '1girl, solo, hatsune miku, usamiku, aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, number print, thigh boots, animal hands, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furry, furrification, closed mouth, smile, looking back, white background, safe, newset, omufujoshi, black outline, thick outlines, masterpiece, best quality, amazing quality' output: url: images/ComfyUI_(hiresfix)_2025-04-29_00000_7.png base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 instance_prompt: null --- # Usamiku / Furry Miku (Hatsune Miku) v1 [IllustriousXL 0.1] <Gallery /> ## Reference This is a kigurumi cosplay of Hatsune Miku. She won the "Miku Lookalike Contest" in NYC in 2025. Socials: [twitter@mikusagi01](https://x.com/mikusagi01), [tiktok@mikusagi01](https://www.tiktok.com/@mikusagi01?lang=en) [![](images/reference.jpg)](https://x.com/ziepoopenfarten/status/1906077150563688871) ## Prompting Main triggerword: ``` usamiku ``` Appearance and clothing: ``` aqua eyes, necktie, grey shirt, shirt, detached sleeves, aqua hair, black sleeves, skirt, headset, collared shirt, pleated skirt, thighhighs, hair between eyes, number print, animal hands, rabbit tail, white fur, rabbit ears, :3, rabbit girl, animal nose, body fur, white fur, furry female, furrification ``` ## Download model Weights for this model are available in Safetensors format. [Download](/MikuMasterRace/Hatsune_Miku_-_Usamiku_Furry_-_IllustriousXL_v1/tree/main) them in the Files & versions tab.
aniket0898/bge-base-financial-matryoshka	aniket0898	2025-04-29T15:28:49Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "nomic_bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6300", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "custom_code", "en", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:nomic-ai/nomic-embed-text-v1", "base_model:finetune:nomic-ai/nomic-embed-text-v1", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-29T15:28:36Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: nomic-ai/nomic-embed-text-v1 widget: - source_sentence: What amount of senior notes was repaid during fiscal 2022? sentences: - 'The following table sets forth the breakdown of revenue by geography, determined based on the location of the Host’s listing (in millions): \| Year Ended December 31, \| 2021 \| 2022 \| 2023 United States \| $ \| 2,996 \| \| $ \| 3,890 \| $ \| 4,290 International(1) \| 2,996 \| \| 4,509 \| \| 5,627 Total revenue \| $ \| 5,992 \| \| $ \| 8,399 \| $ \| 9,917' - During fiscal 2022, $2.25 billion of senior notes was repaid. - Several factors are considered in developing the estimate for the long-term expected rate of return on plan assets. For the defined benefit retirement plans, these factors include historical rates of return of broad equity and bond indices and projected long-term rates of return obtained from pension investment consultants. The expected long-term rates of return for plan assets are 8 - 9% for equities and 3 - 5% for bonds. For other retiree benefit plans, the expected long-term rate of return reflects that the assets are comprised primarily of Company stock. The expected rate of return on Company stock is based on the long-term projected return of 8.5% and reflects the historical pattern of returns. - source_sentence: What does GameStop Corp. offer to its customers? sentences: - State fraud and abuse laws could lead to criminal, civil, or administrative consequences, including licensure loss, exclusion from healthcare programs, and significant negative effects on the violating entity's business operations and financial health if the laws are violated. - GameStop Corp. offers games and entertainment products through its stores and ecommerce platforms. - Stribild is an oral formulation dosed once a day for the treatment of HIV-1 infection in certain patients. - source_sentence: How might a 10% change in the obsolescence reserve percentage impact net earnings? sentences: - A 10% change in our obsolescence reserve percentage at January 28, 2023 would have affected net earnings by approximately $2.5 million in fiscal 2022. - The information required by Item 3 on Legal Proceedings is provided by referencing Note 19 of the Notes to Consolidated Financial Statements in Item 8. - ured notes for an aggregate principal amount of $18.50 billion. These notes were issued in multiple series, which mature from 2027 through 2063. - source_sentence: What are the SEC's regulations for security-based swap dealers like Goldman Sachs' subsidiaries? sentences: - The increase in other income, net was primarily due to an increase in interest income as a result of higher cash balances and higher interest rates. - Through our Stubs loyalty programs, we have developed a consumer database of approximately 32 million households, representing approximately 64 million individuals. - SEC rules govern the registration and regulation of security-based swap dealers. Security-based swaps are defined as swaps on single securities, single loans or narrow-based baskets or indices of securities. The SEC has adopted a number of rules for security-based swap dealers, including (i) capital, margin and segregation requirements; (ii) record-keeping, financial reporting and notification requirements; (iii) business conduct standards; (iv) regulatory and public trade reporting; and (v) the application of risk mitigation techniques to uncleared portfolios of security-based swaps. - source_sentence: How is the information about legal proceedings organized in the financial documents according to the provided context? sentences: - The information about legal proceedings is organized under Part II, Item 8 in the section titled 'Financial Statements and Supplementary Data – Note 14'. - We have a match-funding policy that addresses the interest rate risk by aligning the interest rate profile (fixed or floating rate and duration) of our debt portfolio with the interest rate profile of our finance receivable portfolio within a predetermined range on an ongoing basis. In connection with that policy, we use interest rate derivative instruments to modify the debt structure to match assets within the finance receivable portfolio. - Achieved adjusted FIFO operating profit of $5.1 billion, which represents an 18% increase compared to 2021. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: Nomic Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7457142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8614285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8957142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.93 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7457142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1791428571428571 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09299999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7457142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8614285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8957142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.93 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8398915226132163 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8107896825396824 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8136819482601757 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.7357142857142858 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8514285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8914285714285715 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.93 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7357142857142858 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2838095238095238 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17828571428571427 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09299999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7357142857142858 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8514285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8914285714285715 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.93 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8352581932886503 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8047103174603173 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8075415578285141 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7285714285714285 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8614285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8857142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9271428571428572 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7285714285714285 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28714285714285714 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17714285714285713 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09271428571428571 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7285714285714285 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8614285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8857142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9271428571428572 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8319809230146766 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8011235827664398 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8040552556779361 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7128571428571429 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8328571428571429 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8671428571428571 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9142857142857143 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7128571428571429 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2776190476190476 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1734285714285714 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09142857142857141 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7128571428571429 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8328571428571429 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8671428571428571 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9142857142857143 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8145627876253931 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7825572562358278 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7859620809117356 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6642857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8042857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8457142857142858 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9028571428571428 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6642857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2680952380952381 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16914285714285712 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09028571428571427 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6642857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8042857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8457142857142858 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9028571428571428 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7821373629924483 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7436649659863942 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7468498882402747 name: Cosine Map@100 --- # Nomic Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) <!-- at revision eb6b20cd65fcbdf7a2bc4ebac97908b3b21da981 --> - Maximum Sequence Length: 8192 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity - Training Dataset: - json - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("aniket0898/bge-base-financial-matryoshka") # Run inference sentences = [ 'How is the information about legal proceedings organized in the financial documents according to the provided context?', "The information about legal proceedings is organized under Part II, Item 8 in the section titled 'Financial Statements and Supplementary Data – Note 14'.", 'We have a match-funding policy that addresses the interest rate risk by aligning the interest rate profile (fixed or floating rate and duration) of our debt portfolio with the interest rate profile of our finance receivable portfolio within a predetermined range on an ongoing basis. In connection with that policy, we use interest rate derivative instruments to modify the debt structure to match assets within the finance receivable portfolio.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7457 \| \| cosine_accuracy@3 \| 0.8614 \| \| cosine_accuracy@5 \| 0.8957 \| \| cosine_accuracy@10 \| 0.93 \| \| cosine_precision@1 \| 0.7457 \| \| cosine_precision@3 \| 0.2871 \| \| cosine_precision@5 \| 0.1791 \| \| cosine_precision@10 \| 0.093 \| \| cosine_recall@1 \| 0.7457 \| \| cosine_recall@3 \| 0.8614 \| \| cosine_recall@5 \| 0.8957 \| \| cosine_recall@10 \| 0.93 \| \| cosine_ndcg@10 \| 0.8399 \| \| cosine_mrr@10 \| 0.8108 \| \| cosine_map@100 \| 0.8137 \| #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7357 \| \| cosine_accuracy@3 \| 0.8514 \| \| cosine_accuracy@5 \| 0.8914 \| \| cosine_accuracy@10 \| 0.93 \| \| cosine_precision@1 \| 0.7357 \| \| cosine_precision@3 \| 0.2838 \| \| cosine_precision@5 \| 0.1783 \| \| cosine_precision@10 \| 0.093 \| \| cosine_recall@1 \| 0.7357 \| \| cosine_recall@3 \| 0.8514 \| \| cosine_recall@5 \| 0.8914 \| \| cosine_recall@10 \| 0.93 \| \| cosine_ndcg@10 \| 0.8353 \| \| cosine_mrr@10 \| 0.8047 \| \| cosine_map@100 \| 0.8075 \| #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.7286 \| \| cosine_accuracy@3 \| 0.8614 \| \| cosine_accuracy@5 \| 0.8857 \| \| cosine_accuracy@10 \| 0.9271 \| \| cosine_precision@1 \| 0.7286 \| \| cosine_precision@3 \| 0.2871 \| \| cosine_precision@5 \| 0.1771 \| \| cosine_precision@10 \| 0.0927 \| \| cosine_recall@1 \| 0.7286 \| \| cosine_recall@3 \| 0.8614 \| \| cosine_recall@5 \| 0.8857 \| \| cosine_recall@10 \| 0.9271 \| \| cosine_ndcg@10 \| 0.832 \| \| cosine_mrr@10 \| 0.8011 \| \| cosine_map@100 \| 0.8041 \| #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:----------\| \| cosine_accuracy@1 \| 0.7129 \| \| cosine_accuracy@3 \| 0.8329 \| \| cosine_accuracy@5 \| 0.8671 \| \| cosine_accuracy@10 \| 0.9143 \| \| cosine_precision@1 \| 0.7129 \| \| cosine_precision@3 \| 0.2776 \| \| cosine_precision@5 \| 0.1734 \| \| cosine_precision@10 \| 0.0914 \| \| cosine_recall@1 \| 0.7129 \| \| cosine_recall@3 \| 0.8329 \| \| cosine_recall@5 \| 0.8671 \| \| cosine_recall@10 \| 0.9143 \| \| cosine_ndcg@10 \| 0.8146 \| \| cosine_mrr@10 \| 0.7826 \| \| cosine_map@100 \| 0.786 \| #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6643 \| \| cosine_accuracy@3 \| 0.8043 \| \| cosine_accuracy@5 \| 0.8457 \| \| cosine_accuracy@10 \| 0.9029 \| \| cosine_precision@1 \| 0.6643 \| \| cosine_precision@3 \| 0.2681 \| \| cosine_precision@5 \| 0.1691 \| \| cosine_precision@10 \| 0.0903 \| \| cosine_recall@1 \| 0.6643 \| \| cosine_recall@3 \| 0.8043 \| \| cosine_recall@5 \| 0.8457 \| \| cosine_recall@10 \| 0.9029 \| \| cosine_ndcg@10 \| 0.7821 \| \| cosine_mrr@10 \| 0.7437 \| \| cosine_map@100 \| 0.7468 \| <!-- ## 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 Dataset #### json * Dataset: json * Size: 6,300 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 2 tokens</li><li>mean: 20.47 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 45.09 tokens</li><li>max: 272 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:-------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What was the stored value of cards and loyalty program balances at the end of fiscal year 2022?</code> \| <code>Stored value cards and loyalty program at October 2, 2022 showed a balance of approximately $1.503 billion.</code> \| \| <code>What transformation is planned for Le Jardin located at The Londoner Macao?</code> \| <code>Le Jardin, located on the southern flank of The Londoner Macao, is to undergo a transformation into a distinctive garden-themed attraction spanning approximately 50,000 square meters.</code> \| \| <code>What are the key terms of the new Labor Agreement ratified by the UAW in 2023?</code> \| <code>The key terms and provisions of the Labor Agreement are: General wage increases of 11% upon ratification in 2023, 3% in September each of 2024, 2025 and 2026, and 5% in September 2027; Consolidation of applicable wage classifications for in-progression, temporary and other employees – with employees reaching the top classification rate upon the completion of 156 weeks of active service; The re-establishment of a cost-of-living allowance; Lump sum ratification bonus payments of $5,000 paid to eligible employees in the three months ended December 31, 2023; For members currently employed and enrolled in the Employees’ Pension Plan, an increase of $5.00 to the monthly basic benefit for past and future service provided; A 3.6% increase in company contributions to eligible employees' defined contribution retirement accounts; and Annual contribution of $500 to eligible retirees or surviving spouses.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| dim_768_cosine_map@100 \| dim_512_cosine_map@100 \| dim_256_cosine_map@100 \| dim_128_cosine_map@100 \| dim_64_cosine_map@100 \| \|:----------:\|:------:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\| \| 0.8122 \| 10 \| 0.7331 \| - \| - \| - \| - \| - \| \| 0.9746 \| 12 \| - \| 0.7871 \| 0.7796 \| 0.7747 \| 0.7546 \| 0.7214 \| \| 1.6244 \| 20 \| 0.2506 \| - \| - \| - \| - \| - \| \| 1.9492 \| 24 \| - \| 0.8021 \| 0.7990 \| 0.7869 \| 0.7691 \| 0.7371 \| \| 2.4365 \| 30 \| 0.1029 \| - \| - \| - \| - \| - \| \| 2.9239 \| 36 \| - \| 0.8030 \| 0.8017 \| 0.7926 \| 0.7760 \| 0.7402 \| \| 3.2487 \| 40 \| 0.054 \| - \| - \| - \| - \| - \| \| 3.8985 \| 48 \| - \| 0.8055 \| 0.799 \| 0.7924 \| 0.7754 \| 0.7383 \| \| 0.8122 \| 10 \| 0.0397 \| - \| - \| - \| - \| - \| \| 0.9746 \| 12 \| - \| 0.8109 \| 0.7983 \| 0.7974 \| 0.7795 \| 0.7373 \| \| 1.6244 \| 20 \| 0.0301 \| - \| - \| - \| - \| - \| \| 1.9492 \| 24 \| - \| 0.8115 \| 0.8049 \| 0.8026 \| 0.7839 \| 0.7486 \| \| 2.4365 \| 30 \| 0.0236 \| - \| - \| - \| - \| - \| \| 2.9239 \| 36 \| - \| 0.8138 \| 0.8082 \| 0.8045 \| 0.7858 \| 0.7470 \| \| 3.2487 \| 40 \| 0.0131 \| - \| - \| - \| - \| - \| \| 3.8985 \| 48 \| - \| 0.8137 \| 0.8075 \| 0.8041 \| 0.786 \| 0.7468 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.8.10 - Sentence Transformers: 3.2.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 1.0.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## 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. -->
biruemuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_poisonous_porpoise	biruemuk	2025-04-29T15:20:38Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am scurrying poisonous porpoise", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-26T21:50:17Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_poisonous_porpoise tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am scurrying poisonous porpoise - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_poisonous_porpoise This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="biruemuk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-scurrying_poisonous_porpoise", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF	mradermacher	2025-04-29T14:56:13Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Sorawiz/Qwen2.5-Kunoulise-D", "base_model:quantized:Sorawiz/Qwen2.5-Kunoulise-D", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-29T12:38:56Z	--- base_model: Sorawiz/Qwen2.5-Kunoulise-D language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Sorawiz/Qwen2.5-Kunoulise-D <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-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/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 5.5 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 8.6 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q4_1.gguf) \| i1-Q4_1 \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-D-i1-GGUF/resolve/main/Qwen2.5-Kunoulise-D.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
wolfofbackstreet/qwen3-0.6b-int4-qptq-v2	wolfofbackstreet	2025-04-29T14:37:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2025-04-29T14:36:48Z	--- 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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zhehuderek/qwen2_5_vl_3b_grpo_decisionmaking_scratch_run3_85	zhehuderek	2025-04-29T05:50:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-29T05:30:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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hypaai/wspr_wazobia_run2_04282025	hypaai	2025-04-29T05:43:39Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ig", "yo", "en", "ha", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-29T00:32:18Z	--- library_name: transformers language: - ig - yo - en - ha license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer model-index: - name: wspr_wazobia_run2_04282025 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. --> # wspr_wazobia_run2_04282025 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 7000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
ddh0/Qwen2.5-14B-All-Variants-q8_0-q6_K-GGUF	ddh0	2025-04-29T05:31:54Z	121	2	null	[ "gguf", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-14B", "base_model:quantized:Qwen/Qwen2.5-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-20T21:43:43Z	--- license: apache-2.0 base_model: - Qwen/Qwen2.5-14B language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Qwen2.5-14B-All-Variants-q8_0-q6_K-GGUF This repo contains GGUF quantizations of [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B), [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct), and [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) models at q6_K, using q8_0 for output and embedding tensors.
TOMFORD79/Camp5	TOMFORD79	2025-04-29T04:38:45Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T04:28:22Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
mhr2004/nev-original-cross-encoder-stsb-roberta-large-bs8-lr2e-05	mhr2004	2025-04-29T04:25:51Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T03:59:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Kashish-jain/pii-protection-medical	Kashish-jain	2025-04-29T03:14:13Z	0	0	null	[ "safetensors", "bert", "medical", "named-entity-recognition", "token-classification", "en", "dataset:medical_records", "license:apache-2.0", "region:us" ]	token-classification	2025-04-15T17:11:13Z	--- language: en tags: - medical - named-entity-recognition - token-classification license: apache-2.0 datasets: - medical_records --- # PII Medical Model ## Model Details - Model type: BERT Token Classification - Fine-tuned on: Medical NER Dataset - Framework: PyTorch, Hugging Face Transformers ## Evaluation Results - Precision: 0.9512 - Recall: 0.9043 - F1-Score: 0.9271 - Accuracy: 0.9809
joboffer/13967a3b-71f5-467f-94ae-1a2b1c283c10	joboffer	2025-04-29T01:58:55Z	0	0	peft	[ "peft", "safetensors", "opt", "axolotl", "generated_from_trainer", "base_model:facebook/opt-350m", "base_model:adapter:facebook/opt-350m", "license:other", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T01:57:35Z	--- library_name: peft license: other base_model: facebook/opt-350m tags: - axolotl - generated_from_trainer model-index: - name: 13967a3b-71f5-467f-94ae-1a2b1c283c10 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: facebook/opt-350m bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 78cc6fbab3330ac6_train_data.json ds_type: json format: custom path: /workspace/input_data/78cc6fbab3330ac6_train_data.json type: field_input: keywords field_instruction: intention field_output: captions_objects 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: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: joboffer/13967a3b-71f5-467f-94ae-1a2b1c283c10 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 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: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/78cc6fbab3330ac6_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 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: 8accc130-96bb-444f-98b0-dfc7e6d38159 wandb_project: s56-33 wandb_run: your_name wandb_runid: 8accc130-96bb-444f-98b0-dfc7e6d38159 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 13967a3b-71f5-467f-94ae-1a2b1c283c10 This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.1329 \| 0.0751 \| 200 \| 1.9355 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
BVrOtoVgMxLNk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-masked_bipedal_ram	BVrOtoVgMxLNk	2025-04-29T01:56:54Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am masked bipedal ram", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T11:37:03Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-masked_bipedal_ram tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am masked bipedal ram - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-masked_bipedal_ram This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="BVrOtoVgMxLNk/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-masked_bipedal_ram", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
hynt/EfficientConformerVietnamese	hynt	2025-04-29T00:53:47Z	27	2	pytorch	[ "pytorch", "CTC", "speech-to-text", "vietnamese", "ai-model", "deep-learning", "vi", "arxiv:2109.01163", "license:apache-2.0", "region:us" ]	null	2025-04-25T15:47:39Z	--- tags: - speech-to-text - vietnamese - ai-model - deep-learning license: apache-2.0 library_name: pytorch model_name: EfficientConformerVietnamese language: vi --- # Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition [Paper](https://arxiv.org/abs/2109.01163) ## Efficient Conformer Encoder Inspired from previous works done in Automatic Speech Recognition and Computer Vision, the Efficient Conformer encoder is composed of three encoder stages where each stage comprises a number of Conformer blocks using grouped attention. The encoded sequence is progressively downsampled and projected to wider feature dimensions, lowering the amount of computation while achieving better performance. Grouped multi-head attention reduce attention complexity by grouping neighbouring time elements along the feature dimension before applying scaled dot-product attention. <img src="EfficientConformer.jpg" width="35%"/> ## Installation Clone GitHub repository and set up environment ``` git clone https://github.com/nguyenthienhy/EfficientConformerVietnamese.git cd EfficientConformerVietnamese pip install -r requirements.txt pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113 pip install protobuf==4.25 ``` Install [ctcdecode](https://github.com/parlance/ctcdecode) ## Prepare dataset and training pipline Dataset to train this mini version: - Vivos - Vietbud_500 - VLSP2020, VLSP2021, VLSP2022 - VietMed_labeled - Google Fleurs Steps: - Prepare a dataset folder that includes the data domains you want to train on, for example: ASRDataset/VLSP2020, ASRDataset/VLSP2021. Inside each VLSP2020 folder, there should be corresponding .wav and .txt files. - Add noise to the audio using add_noise.py. - Change the speaking speed using speed_permutation.py. - Extract audio length and BPE tokens using prepare_dataset.py. - Filter audio by the maximum length specified, using filter_max_length.py, and save the list of audio files used for training in a .txt file, for example: data/train_wav_names.txt. - Train the model using train.py (please read the parameters carefully). - Prepare a lm_corpus.txt to train n gram bpe language model, using train_lm.py ## Evaluation Please read code test.py carefully ! ``` bash test.sh ``` ## Monitor training ``` tensorboard --logdir callback_path ``` <img src="logs.jpg" width="55%" /> ## Vietnamese Performance \| Model \| Gigaspeech_test<br>(Greedy / n-gram Beam Search) \| VLSP2023_pb_test<br>(Greedy / n-gram Beam Search) \| VLSP2023_pr_test<br>(Greedy / n-gram Beam Search) \| \|:--------------------------------------\|:------------------------------------------------:\|:-------------------------------------------------:\|:-------------------------------------------------:\| \| EC-Small-CTC \| 19.61 / 17.47 \| 23.06 / 20.83 \| 23.17 / 21.15 \| \| PhoWhiper-Tiny \| 20.45 \| 33.21 \| 33.02 \| \| PhoWhiper-Base \| 18.78 \| 29.25 \| 28.29 \| In the competition organized by VLSP, I used the Efficient Conformer Large architecture with approximately 127 million parameters. You can find the detailed results in the technical report below: https://www.overleaf.com/read/nhqjtcpktjyc#3b472e ## Reference [Maxime Burchi, Valentin Vielzeuf. Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition.](https://arxiv.org/abs/2109.01163) * Maxime Burchi [@burchim](https://github.com/burchim)
infogeo/1cd51e9e-83d4-47ff-8526-8e66ffd89c2f	infogeo	2025-04-28T23:59:54Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-1.7B-Instruct", "base_model:adapter:unsloth/SmolLM-1.7B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-28T23:55:32Z	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-1.7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 1cd51e9e-83d4-47ff-8526-8e66ffd89c2f 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 absolute_data_files: false adapter: lora base_model: unsloth/SmolLM-1.7B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 09440e5d84ab787c_train_data.json ds_type: json format: custom path: /workspace/input_data/09440e5d84ab787c_train_data.json type: field_input: user_prompt field_instruction: system_prompt field_output: prompt 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: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/1cd51e9e-83d4-47ff-8526-8e66ffd89c2f hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 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: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/09440e5d84ab787c_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 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: 0a019fdb-0b45-4625-bb8c-9db767620d26 wandb_project: s56-28 wandb_run: your_name wandb_runid: 0a019fdb-0b45-4625-bb8c-9db767620d26 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 1cd51e9e-83d4-47ff-8526-8e66ffd89c2f This model is a fine-tuned version of [unsloth/SmolLM-1.7B-Instruct](https://huggingface.co/unsloth/SmolLM-1.7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2423 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2296 \| 0.0071 \| 150 \| 0.2423 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dgambettaphd/M_llm2_gen0_run0_WXS_doc1000_synt64_tot128_SYNLAST	dgambettaphd	2025-04-28T23:10:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-04-28T23:07:50Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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. 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AngelRaychev/0.5B-value-iteration_inner	AngelRaychev	2025-04-28T22:50:03Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "feature-extraction", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-04-28T22:49:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mlx-community/Qwen3-14B-6bit	mlx-community	2025-04-28T22:37:45Z	0	0	mlx	[ "mlx", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-14B", "base_model:quantized:Qwen/Qwen3-14B", "license:apache-2.0", "6-bit", "region:us" ]	text-generation	2025-04-28T22:35:40Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-14B tags: - mlx --- # mlx-community/Qwen3-14B-6bit This model [mlx-community/Qwen3-14B-6bit](https://huggingface.co/mlx-community/Qwen3-14B-6bit) was converted to MLX format from [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-14B-6bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
mlfoundations-dev/nemo_nano_10k	mlfoundations-dev	2025-04-28T16:16:58Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T06:33:05Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: nemo_nano_10k 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. --> # nemo_nano_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/nemo_nano_10k 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use 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_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.6.0+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
ARM-Development/unsloth-Meta-Llama-3.1-8B-Instruct_model_11k	ARM-Development	2025-04-28T16:06:51Z	0	0	peft	[ "peft", "safetensors", "gguf", "arxiv:1910.09700", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:adapter:unsloth/Meta-Llama-3.1-8B-Instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T13:44:05Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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 0.15.2
nessianursin/nessianursing9	nessianursin	2025-04-28T14:11:53Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-04-28T14:11:49Z	--- license: bsd-3-clause ---
Sofia-gb/fashionSigLIP-roturas14	Sofia-gb	2025-04-28T13:36:11Z	0	0	transformers	[ "transformers", "safetensors", "feature-extraction", "custom_code", "arxiv:1910.09700", "region:us" ]	feature-extraction	2025-04-28T13:35:20Z	--- 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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LuckyLukke/grpo_onesided_1_starter_change-160	LuckyLukke	2025-04-28T11:58:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T11:55:26Z	--- 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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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. 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FB19hxthHta/ioahdjak	FB19hxthHta	2025-04-28T10:20:44Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T10:20:44Z	--- license: apache-2.0 ---
Triangle104/Qwen2.5-0.5B-Instruct-Q5_K_S-GGUF	Triangle104	2025-04-28T09:18:50Z	7	0	null	[ "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:quantized:Qwen/Qwen2.5-0.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-22T17:50:31Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - llama-cpp - gguf-my-repo --- # Triangle104/Qwen2.5-0.5B-Instruct-Q5_K_S-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-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/Qwen/Qwen2.5-0.5B-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 Triangle104/Qwen2.5-0.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-0.5b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Qwen2.5-0.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-0.5b-instruct-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. 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 Triangle104/Qwen2.5-0.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-0.5b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Qwen2.5-0.5B-Instruct-Q5_K_S-GGUF --hf-file qwen2.5-0.5b-instruct-q5_k_s.gguf -c 2048 ```
MrRobotoAI/E3	MrRobotoAI	2025-04-28T09:15:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Azazelle/L3-Hecate-8B-v1.2", "base_model:merge:Azazelle/L3-Hecate-8B-v1.2", "base_model:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:merge:Azazelle/Llama-3-8B-Abomination-LORA", "base_model:Azazelle/Llama-3-LongStory-LORA", "base_model:merge:Azazelle/Llama-3-LongStory-LORA", "base_model:Blackroot/Llama-3-LongStory-LORA", "base_model:merge:Blackroot/Llama-3-LongStory-LORA", "base_model:Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B", "base_model:merge:Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B", "base_model:Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL", "base_model:merge:Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL", "base_model:MrRobotoAI/D6", "base_model:merge:MrRobotoAI/D6", "base_model:MrRobotoAI/E2", "base_model:merge:MrRobotoAI/E2", "base_model:MrRobotoAI/L1", "base_model:merge:MrRobotoAI/L1", "base_model:MrRobotoAI/L2", "base_model:merge:MrRobotoAI/L2", "base_model:ResplendentAI/NoWarning_Llama3", "base_model:merge:ResplendentAI/NoWarning_Llama3", "base_model:ResplendentAI/Nymph_8B", "base_model:merge:ResplendentAI/Nymph_8B", "base_model:aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench", "base_model:merge:aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench", "base_model:athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5", "base_model:merge:athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5", "base_model:hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit", "base_model:merge:hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit", "base_model:jeiku/Average_Normie_v3.69_8B", "base_model:merge:jeiku/Average_Normie_v3.69_8B", "base_model:jeiku/Tuldur-8B", "base_model:merge:jeiku/Tuldur-8B", "base_model:jeiku/UnPoppy_8B", "base_model:merge:jeiku/UnPoppy_8B", "base_model:jrahn/llama-3-8b-claudstruct-v3", "base_model:merge:jrahn/llama-3-8b-claudstruct-v3", "base_model:jspr/llama3-instruct-wordcel-smutrom-8k_peft", "base_model:merge:jspr/llama3-instruct-wordcel-smutrom-8k_peft", "base_model:jspr/smut_llama_8b_peft", "base_model:merge:jspr/smut_llama_8b_peft", "base_model:jspr/smut_llama_8b_smut_2k_romance_1k_peft", "base_model:merge:jspr/smut_llama_8b_smut_2k_romance_1k_peft", "base_model:jspr/smut_llama_8b_smutromance_32k_peft", "base_model:merge:jspr/smut_llama_8b_smutromance_32k_peft", "base_model:nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K", "base_model:merge:nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K", "base_model:nothingiisreal/llama3-8B-DWP-lora", "base_model:merge:nothingiisreal/llama3-8B-DWP-lora", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:37:15Z	--- base_model: - MrRobotoAI/E2 - nothingiisreal/llama3-8B-DWP-lora - hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit - nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K - Blackroot/Llama-3-LongStory-LORA - athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5 - Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL - ResplendentAI/NoWarning_Llama3 - jeiku/Tuldur-8B - MrRobotoAI/E2 - MrRobotoAI/E2 - jspr/smut_llama_8b_peft - jeiku/Average_Normie_v3.69_8B - ResplendentAI/Nymph_8B - MrRobotoAI/E2 - jspr/llama3-instruct-wordcel-smutrom-8k_peft - MrRobotoAI/D6 - jeiku/UnPoppy_8B - MrRobotoAI/E2 - jspr/smut_llama_8b_smutromance_32k_peft - MrRobotoAI/E2 - ResplendentAI/NoWarning_Llama3 - Azazelle/L3-Hecate-8B-v1.2 - Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B - ResplendentAI/NoWarning_Llama3 - MrRobotoAI/E2 - aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench - MrRobotoAI/E2 - Azazelle/Llama-3-LongStory-LORA - MrRobotoAI/L1 - MrRobotoAI/E2 - jspr/smut_llama_8b_smut_2k_romance_1k_peft - MrRobotoAI/L2 - MrRobotoAI/E2 - jrahn/llama-3-8b-claudstruct-v3 - MrRobotoAI/E2 - Azazelle/Llama-3-8B-Abomination-LORA library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) as a base. ### Models Merged The following models were included in the merge: * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [nothingiisreal/llama3-8B-DWP-lora](https://huggingface.co/nothingiisreal/llama3-8B-DWP-lora) * [hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit](https://huggingface.co/hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit) * [nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K](https://huggingface.co/nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K) + [Blackroot/Llama-3-LongStory-LORA](https://huggingface.co/Blackroot/Llama-3-LongStory-LORA) * [athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5](https://huggingface.co/athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5) * [Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL](https://huggingface.co/Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL) + [ResplendentAI/NoWarning_Llama3](https://huggingface.co/ResplendentAI/NoWarning_Llama3) * [jeiku/Tuldur-8B](https://huggingface.co/jeiku/Tuldur-8B) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [jspr/smut_llama_8b_peft](https://huggingface.co/jspr/smut_llama_8b_peft) * [jeiku/Average_Normie_v3.69_8B](https://huggingface.co/jeiku/Average_Normie_v3.69_8B) * [ResplendentAI/Nymph_8B](https://huggingface.co/ResplendentAI/Nymph_8B) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [jspr/llama3-instruct-wordcel-smutrom-8k_peft](https://huggingface.co/jspr/llama3-instruct-wordcel-smutrom-8k_peft) * [MrRobotoAI/D6](https://huggingface.co/MrRobotoAI/D6) * [jeiku/UnPoppy_8B](https://huggingface.co/jeiku/UnPoppy_8B) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [jspr/smut_llama_8b_smutromance_32k_peft](https://huggingface.co/jspr/smut_llama_8b_smutromance_32k_peft) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [ResplendentAI/NoWarning_Llama3](https://huggingface.co/ResplendentAI/NoWarning_Llama3) * [Azazelle/L3-Hecate-8B-v1.2](https://huggingface.co/Azazelle/L3-Hecate-8B-v1.2) * [Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B](https://huggingface.co/Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B) + [ResplendentAI/NoWarning_Llama3](https://huggingface.co/ResplendentAI/NoWarning_Llama3) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench](https://huggingface.co/aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [Azazelle/Llama-3-LongStory-LORA](https://huggingface.co/Azazelle/Llama-3-LongStory-LORA) * [MrRobotoAI/L1](https://huggingface.co/MrRobotoAI/L1) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [jspr/smut_llama_8b_smut_2k_romance_1k_peft](https://huggingface.co/jspr/smut_llama_8b_smut_2k_romance_1k_peft) * [MrRobotoAI/L2](https://huggingface.co/MrRobotoAI/L2) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [jrahn/llama-3-8b-claudstruct-v3](https://huggingface.co/jrahn/llama-3-8b-claudstruct-v3) * [MrRobotoAI/E2](https://huggingface.co/MrRobotoAI/E2) + [Azazelle/Llama-3-8B-Abomination-LORA](https://huggingface.co/Azazelle/Llama-3-8B-Abomination-LORA) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: MrRobotoAI/E2+jspr/llama3-instruct-wordcel-smutrom-8k_peft - model: MrRobotoAI/E2+jspr/smut_llama_8b_smutromance_32k_peft - model: MrRobotoAI/E2+jspr/smut_llama_8b_smut_2k_romance_1k_peft - model: MrRobotoAI/E2+jspr/smut_llama_8b_peft - model: Cas-Archive/L3-Umbral-Mind-RP-v0.1-8B+ResplendentAI/NoWarning_Llama3 - model: Casual-Autopsy/L3-Uncen-Merger-Omelette-RP-8B-EXPERIMENTAL+ResplendentAI/NoWarning_Llama3 - model: nothingiisreal/L3-8B-Stheno-Horny-v3.3-32K+Blackroot/Llama-3-LongStory-LORA - model: MrRobotoAI/E2+nothingiisreal/llama3-8B-DWP-lora - model: MrRobotoAI/E2+aryanagrawal1/llama-3-8b-instruct-sft-rewriting-fs-promptbench - model: MrRobotoAI/E2+jrahn/llama-3-8b-claudstruct-v3 - model: MrRobotoAI/E2+Azazelle/Llama-3-8B-Abomination-LORA - model: MrRobotoAI/E2+Azazelle/Llama-3-LongStory-LORA - model: MrRobotoAI/E2+ResplendentAI/NoWarning_Llama3 - model: hf-100/Llama-3.1-8b-Spellbound-NaturalWriter-instruct-0.1-chkpt-608-16-bit - model: athirdpath/Llama-3.1-Base_NSFW-pretrained_e-0.5 - model: jeiku/Tuldur-8B - model: jeiku/Average_Normie_v3.69_8B - model: jeiku/UnPoppy_8B - model: Azazelle/L3-Hecate-8B-v1.2 - model: ResplendentAI/Nymph_8B - model: MrRobotoAI/D6 - model: MrRobotoAI/L1 - model: MrRobotoAI/L2 merge_method: model_stock base_model: MrRobotoAI/E2 normalize: true dtype: float16 ```
scr17/fyp	scr17	2025-04-28T08:09:49Z	42	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:5000", "loss:CosineSimilarityLoss", "arxiv:1908.10084", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-27T10:31:43Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5000 - loss:CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: looking Product Manager expertise AWS Cybersecurity JavaScript Cloud Architecture candidate responsible designing implementing maintaining solutions using modern technologies sentences: - Emily Barry professional skilled JavaScript Machine Learning Kubernetes Computer Vision Experienced working multiple projects involving cloud technologies modern software development practices - Stephen Baker professional skilled React AWS Node.js NLP Experienced working multiple projects involving cloud technologies modern software development practices - James Jackson professional skilled Node.js Cybersecurity Kubernetes Docker Experienced working multiple projects involving cloud technologies modern software development practices - source_sentence: looking Software Engineer expertise AWS TensorFlow NLP Node.js candidate responsible designing implementing maintaining solutions using modern technologies sentences: - Jennifer Thompson professional skilled JavaScript TensorFlow Computer Vision Django Experienced working multiple projects involving cloud technologies modern software development practices - Lisa Bell professional skilled Python TensorFlow Computer Vision Machine Learning Experienced working multiple projects involving cloud technologies modern software development practices - Susan Rogers professional skilled Docker Cybersecurity Machine Learning Python Experienced working multiple projects involving cloud technologies modern software development practices - source_sentence: looking DevOps Engineer expertise Cybersecurity Machine Learning SQL TensorFlow candidate responsible designing implementing maintaining solutions using modern technologies sentences: - Kenneth Jones professional skilled NLP Node.js Cybersecurity Cloud Architecture Experienced working multiple projects involving cloud technologies modern software development practices - Matthew Mcintyre professional skilled NoSQL Kubernetes React Docker Experienced working multiple projects involving cloud technologies modern software development practices - William Wilson professional skilled SQL Kubernetes CI/CD Security Analysis Experienced working multiple projects involving cloud technologies modern software development practices - source_sentence: looking Software Engineer expertise Cybersecurity NLP SQL Django candidate responsible designing implementing maintaining solutions using modern technologies sentences: - Daniel Stewart professional skilled JavaScript Python Cybersecurity TensorFlow Experienced working multiple projects involving cloud technologies modern software development practices - Kristy Massey MD professional skilled Django Security Analysis JavaScript Cybersecurity Experienced working multiple projects involving cloud technologies modern software development practices - Melanie Sutton professional skilled Django CI/CD JavaScript SQL Experienced working multiple projects involving cloud technologies modern software development practices - source_sentence: looking AI Researcher expertise CI/CD Docker TensorFlow JavaScript candidate responsible designing implementing maintaining solutions using modern technologies sentences: - Dr. William Ramirez professional skilled NoSQL React CI/CD Cloud Architecture Experienced working multiple projects involving cloud technologies modern software development practices - Rebecca Wiley professional skilled Python Kubernetes Node.js JavaScript Experienced working multiple projects involving cloud technologies modern software development practices - Roberta Graham professional skilled Flask Machine Learning Node.js Docker Experienced working multiple projects involving cloud technologies modern software development practices pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf --> - Maximum Sequence Length: 256 tokens - Output Dimensionality: 384 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence_transformers_model_id") # Run inference sentences = [ 'looking AI Researcher expertise CI/CD Docker TensorFlow JavaScript candidate responsible designing implementing maintaining solutions using modern technologies', 'Roberta Graham professional skilled Flask Machine Learning Node.js Docker Experienced working multiple projects involving cloud technologies modern software development practices', 'Rebecca Wiley professional skilled Python Kubernetes Node.js JavaScript Experienced working multiple projects involving cloud technologies modern software development practices', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### 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 Dataset #### Unnamed Dataset * Size: 5,000 training samples * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> * Approximate statistics based on the first 1000 samples: \| \| sentence_0 \| sentence_1 \| label \| \|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------\| \| type \| string \| string \| float \| \| details \| <ul><li>min: 20 tokens</li><li>mean: 24.72 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 22 tokens</li><li>mean: 26.26 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 0.4</li><li>mean: 0.71</li><li>max: 1.0</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| label \| \|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------\| \| <code>looking AI Researcher expertise CI/CD Python Computer Vision Flask candidate responsible designing implementing maintaining solutions using modern technologies</code> \| <code>Deanna Gibson professional skilled Security Analysis Node.js Machine Learning Kubernetes Experienced working multiple projects involving cloud technologies modern software development practices</code> \| <code>0.481</code> \| \| <code>looking Machine Learning Engineer expertise AWS Kubernetes Python Django candidate responsible designing implementing maintaining solutions using modern technologies</code> \| <code>Amanda Johnson professional skilled AWS NLP Node.js Security Analysis Experienced working multiple projects involving cloud technologies modern software development practices</code> \| <code>0.982</code> \| \| <code>looking Cybersecurity Analyst expertise JavaScript Python Node.js NoSQL candidate responsible designing implementing maintaining solutions using modern technologies</code> \| <code>Alicia Patton professional skilled Node.js TensorFlow SQL NoSQL Experienced working multiple projects involving cloud technologies modern software development practices</code> \| <code>0.597</code> \| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 30 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 30 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:-------:\|:----:\|:-------------:\| \| 1.5974 \| 500 \| 0.0324 \| \| 3.1949 \| 1000 \| 0.0298 \| \| 4.7923 \| 1500 \| 0.028 \| \| 6.3898 \| 2000 \| 0.025 \| \| 7.9872 \| 2500 \| 0.0229 \| \| 9.5847 \| 3000 \| 0.0198 \| \| 11.1821 \| 3500 \| 0.0179 \| \| 12.7796 \| 4000 \| 0.0156 \| \| 14.3770 \| 4500 \| 0.014 \| \| 15.9744 \| 5000 \| 0.0127 \| \| 17.5719 \| 5500 \| 0.0115 \| \| 19.1693 \| 6000 \| 0.0104 \| \| 20.7668 \| 6500 \| 0.0098 \| \| 22.3642 \| 7000 \| 0.009 \| \| 23.9617 \| 7500 \| 0.0086 \| \| 25.5591 \| 8000 \| 0.0082 \| \| 27.1565 \| 8500 \| 0.0078 \| \| 28.7540 \| 9000 \| 0.0076 \| ### Framework Versions - Python: 3.11.12 - Sentence Transformers: 3.4.1 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## 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. -->
jackleejm/spacy-medication-ner	jackleejm	2025-04-28T07:16:19Z	0	0	spacy	[ "spacy", "token-classification", "en", "model-index", "region:us" ]	token-classification	2025-04-28T07:16:15Z	--- tags: - spacy - token-classification language: - en model-index: - name: en_spacy_medication_ner results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9899159664 - name: NER Recall type: recall value: 0.9899159664 - name: NER F Score type: f_score value: 0.9899159664 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_spacy_medication_ner` \| \| Version \| `1.0.0` \| \| spaCy \| `>=3.8.4,<3.9.0` \| \| Default Pipeline \| `tok2vec`, `ner` \| \| Components \| `tok2vec`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (5 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `BRAND`, `DOSAGE`, `DRUG`, `QUANTITY`, `ROUTE` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 98.99 \| \| `ENTS_P` \| 98.99 \| \| `ENTS_R` \| 98.99 \| \| `TOK2VEC_LOSS` \| 30.12 \| \| `NER_LOSS` \| 7.19 \|
rdotech123/noob	rdotech123	2025-04-28T04:32:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-28T04:32:07Z	--- license: apache-2.0 ---
Nitrals-Loras/VMC-12Bv1.9-lora	Nitrals-Loras	2025-04-28T03:35:36Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Nitral-Archive/Violet_MagCap-12B-v1.5", "base_model:adapter:Nitral-Archive/Violet_MagCap-12B-v1.5", "region:us" ]	null	2025-04-28T03:35:22Z	--- base_model: Nitral-AI/Violet_MagCap-12B-v1.5 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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 0.14.0
quimbylighthearted/quimbylighthearted	quimbylighthearted	2025-04-28T01:28:11Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2025-04-28T01:28:10Z	--- license: bigscience-openrail-m ---
joseiivb26/gadiel2refine	joseiivb26	2025-04-27T14:36:11Z	0	0	null	[ "license:other", "region:us" ]	null	2025-04-27T13:56:14Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-tuned	atsuki-yamaguchi	2025-04-27T09:38:57Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "si", "dataset:allenai/MADLAD-400", "arxiv:2412.11704", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-22T20:01:59Z	--- license: apache-2.0 datasets: - allenai/MADLAD-400 language: - si base_model: - Qwen/Qwen2.5-7B-Instruct library_name: transformers --- # Qwen2.5 7B Instruct for Sinhala: Vocabulary expansion This model is built on top of Qwen2.5 7B Instruct adapted for Sinhala using 500M target language tokens sampled from MADLAD-400. It has an additional target vocabulary of 10K. ## Model Details * Vocabulary: This model has an additional target vocabulary of 10K. * Target vocabulary initialization: The target weights of the embedding and LM head were initialized using mean initialization. * Training: This model was continually pre-trained on 500M target language tokens sampled from MADLAD-400. ## Model Description - Language: Sinhala - License: Apache 2.0 - Fine-tuned from model: Qwen/Qwen2.5-7B-Instruct ## Model Sources - Repository: https://github.com/gucci-j/chat-cve - Paper: https://arxiv.org/abs/2412.11704 ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained( "atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-tuned" ) tokenizer = AutoTokenizer.from_pretrained( "atsuki-yamaguchi/Qwen2.5-7B-Instruct-si-madlad-mean-tuned" ) ``` ## Citation ``` @misc{yamaguchi2024vocabularyexpansionchatmodels, title={{ElChat}: Adapting Chat Language Models Using Only Target Unlabeled Language Data}, author={Atsuki Yamaguchi and Terufumi Morishita and Aline Villavicencio and Nikolaos Aletras}, year={2024}, eprint={2412.11704}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.11704}, } ```
AchrafAzzaouiRiceU/bart_base_pc12_4-26	AchrafAzzaouiRiceU	2025-04-27T03:44:53Z	0	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-04-27T03:44:35Z	--- 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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GitBag/lr5e-05-numina-cot-global_step_140	GitBag	2025-04-27T02:46:15Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-27T02:44:51Z	--- 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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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Triangle104/QwQ-32B-ArliAI-RpR-v2-Q3_K_S-GGUF	Triangle104	2025-04-26T17:04:33Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:ArliAI/QwQ-32B-ArliAI-RpR-v2", "base_model:quantized:ArliAI/QwQ-32B-ArliAI-RpR-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-26T17:01:52Z	--- base_model: ArliAI/QwQ-32B-ArliAI-RpR-v2 language: - en license: apache-2.0 tags: - llama-cpp - gguf-my-repo thumbnail: https://cdn-uploads.huggingface.co/production/uploads/6625f4a8a8d1362ebcc3851a/9TIfNBdy29CDnn8NNIQPt.jpeg --- # Triangle104/QwQ-32B-ArliAI-RpR-v2-Q3_K_S-GGUF This model was converted to GGUF format from [`ArliAI/QwQ-32B-ArliAI-RpR-v2`](https://huggingface.co/ArliAI/QwQ-32B-ArliAI-RpR-v2) 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/ArliAI/QwQ-32B-ArliAI-RpR-v2) for more details on the model. --- RpR (RolePlay with Reasoning) is a new series of models from ArliAI. This series builds directly upon the successful dataset curation methodology and training methods developed for the RPMax series. RpR models use the same curated, deduplicated RP and creative writing dataset used for RPMax, with a focus on variety to ensure high creativity and minimize cross-context repetition. Users familiar with RPMax will recognize the unique, non-repetitive writing style unlike other finetuned-for-RP models. With the release of QwQ as the first high performing open-source reasoning model that can be easily trained, it was clear that the available instruct and creative writing reasoning datasets contains only one response per example. This is type of single response dataset used for training reasoning models causes degraded output quality in long multi-turn chats. Which is why Arli AI decided to create a real RP model capable of long multi-turn chat with reasoning. In order to create RpR, we first had to actually create the reasoning RP dataset by re-processing our existing known-good RPMax dataset into a reasoning dataset. This was possible by using the base QwQ Instruct model itself to create the reasoning process for every turn in the RPMax dataset conversation examples, which is then further refined in order to make sure the reasoning is in-line with the actual response examples from the dataset. Another important thing to get right is to make sure the model is trained on examples that present reasoning blocks in the same way as it encounters it during inference. Which is, never seeing the reasoning blocks in it's context. In order to do this, the training run was completed using axolotl with manual template-free segments dataset in order to make sure that the model is never trained to see the reasoning block in the context. Just like how the model will be used during inference time. The result of training QwQ on this dataset with this method are consistently coherent and interesting outputs even in long multi-turn RP chats. This is as far as we know the first true correctly-trained reasoning model trained for RP and creative writing. --- ## 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 Triangle104/QwQ-32B-ArliAI-RpR-v2-Q3_K_S-GGUF --hf-file qwq-32b-arliai-rpr-v2-q3_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q3_K_S-GGUF --hf-file qwq-32b-arliai-rpr-v2-q3_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. 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 Triangle104/QwQ-32B-ArliAI-RpR-v2-Q3_K_S-GGUF --hf-file qwq-32b-arliai-rpr-v2-q3_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/QwQ-32B-ArliAI-RpR-v2-Q3_K_S-GGUF --hf-file qwq-32b-arliai-rpr-v2-q3_k_s.gguf -c 2048 ```
DavieLion/output_iter2_ckpt_temperature	DavieLion	2025-04-26T02:18:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "alignment-handbook", "generated_from_trainer", "conversational", "dataset:new_data_temperature/iter1", "dataset:new_data_temperature/iter2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-26T02:07:11Z	--- library_name: transformers base_model: outputs_temperature/iter1-ckpt tags: - alignment-handbook - generated_from_trainer datasets: - new_data_temperature/iter1 - new_data_temperature/iter2 model-index: - name: iter2-ckpt 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. --> # iter2-ckpt This model is a fine-tuned version of [outputs_temperature/iter1-ckpt](https://huggingface.co/outputs_temperature/iter1-ckpt) on the new_data_temperature/iter1 and the new_data_temperature/iter2 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6.0 ### Training results ### Framework versions - Transformers 4.45.0 - Pytorch 2.1.2+cu121 - Datasets 3.2.0 - Tokenizers 0.20.3
mlfoundations-dev/c1_science_0d_4s	mlfoundations-dev	2025-04-25T21:35:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-25T07:19:41Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: c1_science_0d_4s 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. --> # c1_science_0d_4s This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/c1_science_0d_4s 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - 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_ratio: 0.1 - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.0.2 - Tokenizers 0.20.3
tuitui-24/qwen2.5-7b-instruct-trl-sft-MORPH-v2	tuitui-24	2025-04-24T16:09:58Z	0	0	null	[ "safetensors", "en", "dataset:ChimaAI/MORPH-dataset", "base_model:Qwen/Qwen2.5-VL-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct", "license:mit", "region:us" ]	null	2025-04-23T17:01:34Z	--- license: mit datasets: - ChimaAI/MORPH-dataset language: - en metrics: - spearmanr base_model: - Qwen/Qwen2.5-VL-7B-Instruct ---

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