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2025-09-11 00:42:47
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AiForgeMaster/Qwen3-4B-Pretrain-v1-p2
AiForgeMaster
2025-08-16T00:50:22Z
1
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "axolotl", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-4B", "base_model:finetune:Qwen/Qwen3-4B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-07-28T15:08:06Z
--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-4B tags: - axolotl - generated_from_trainer model-index: - name: Qwen3-4B-Pretrain-v1 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: `0.12.0.dev0` ```yaml # axolotl train config.yml --deepspeed deepspeed_configs/zero2.json # Resume from checkpoint configuration resume_from_checkpoint: ./outputs/checkpoint-650 # Prevent NCCL timeout ddp_timeout: 7200 # 2 hours timeout instead of 10 minutes # Load model from local models directory first, fallback to HuggingFace if not found base_model: Qwen/Qwen3-4B # Local path - will fallback to Qwen/Qwen3-4B if not found locally # Automatically upload checkpoint and final model to HF hub_model_id: AiForgeMaster/Qwen3-4B-Pretrain-v1 load_in_8bit: false load_in_4bit: false strict: false # Pre-training dataset configuration - using HuggingFace datasets pretraining_dataset: - name: default path: AiForgeMaster/Smart_Merge_2025_07_25 # Private HF dataset - requires API key split: train text_column: text # column in dataset with the data, usually `text` type: pretrain trust_remote_code: false # skip: 0 # number of rows of data to skip over from the beginning # Local paths relative to working directory dataset_prepared_path: ./data/prepared val_set_size: 0.0 # Set to 0 for pure pre-training (no validation split) output_dir: ./outputs # Cache directories for HuggingFace downloads (relative to working dir) # This ensures models and datasets are downloaded to local directories hf_use_auth_token: true # Use HF token for private repos if needed sequence_len: 16384 sample_packing: true eval_sample_packing: false # Disable for pre-training # Enable sample concatenation for pre-training pretraining_sample_concatenation: true # LoRA/QLoRA configuration for memory-efficient training #adapter: lora #lora_model_dir: #lora_r: 32 #lora_alpha: 64 #lora_dropout: 0.05 #lora_target_linear: false # Set to false when using explicit target_modules #lora_target_modules: # Correct axolotl syntax for target modules # - "q_proj" # - "k_proj" # - "v_proj" # - "o_proj" # - "gate_proj" # - "up_proj" # - "down_proj" # - "embed_tokens" # - "lm_head" # WandB configuration - fill in your details wandb_project: ngpt-cpt wandb_entity: null wandb_watch: gradients wandb_name: qwen3_4b_pretraining_v9_resume_2 wandb_log_model: end # Batch size configuration (total effective batch size = micro_batch_size * gradient_accumulation_steps * num_gpus) # For batch size 8-16: micro_batch_size=2, gradient_accumulation_steps=4 gives effective batch size of 8 per GPU gradient_accumulation_steps: 3 micro_batch_size: 6 # Adjust based on your GPU memory optimizer: adamw_torch_fused lr_scheduler: cosine learning_rate: 1e-4 # Lower learning rate for pre-training bf16: auto tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false logging_steps: 10 # Log every 10 steps flash_attention: true warmup_steps: 100 # More warmup steps for pre-training # Checkpoint saving configuration - save every 1000 steps save_steps: 50 save_strategy: steps save_total_limit: 5 # Keep only 5 most recent checkpoints save_only_model: false # Save full checkpoint including optimizer state # Evaluation configuration removed for pure pre-training (val_set_size: 0.0) # eval_steps: 2000 # Not supported when val_set_size == 0 # eval_strategy: steps # Not supported when val_set_size == 0 weight_decay: 0.01 # Small weight decay for pre-training # Liger optimizations for memory efficiency and speed plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_glu_activation: true liger_layer_norm: true liger_fused_linear_cross_entropy: true # Additional pre-training optimizations # Enable for first run to validate checkpoint saving works save_first_step: false # Memory optimizations dataloader_pin_memory: true dataloader_num_workers: 4 remove_unused_columns: true # Advanced training settings for pre-training # Calculate max_steps for full epoch: dataset_size / (micro_batch_size * gradient_accumulation_steps * num_gpus) # With 24,578 rows, batch_size=18, and assuming 2 GPUs: 24,578 / (3 * 6 * 2) = ~682 steps per epoch max_steps: 682 # Set for one full epoch with your dataset size group_by_length: false # Disable for pre-training with sample packing train_on_inputs: true # Train on all tokens for pre-training # Loss monitoring loss_watchdog_threshold: 10.0 # Stop if loss exceeds this value loss_watchdog_patience: 3 # Garbage collection to manage memory gc_steps: 100 # Run garbage collection every 100 steps ``` </details><br> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/uskfoundation/ngpt-cpt/runs/8asfv1rl) # Qwen3-4B-Pretrain-v1 This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 3 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100 - training_steps: 682 ### Training results ### Framework versions - Transformers 4.54.0 - Pytorch 2.7.1+cu126 - Datasets 4.0.0 - Tokenizers 0.21.2
powermove72/Granite-3.3-2B-Avg-SliceWeighted
powermove72
2025-08-16T00:50:20Z
0
0
null
[ "safetensors", "granite", "merge", "mergekit", "lazymergekit", "ibm-granite/granite-3.3-2b-instruct", "powermove72/granite-3.3-2b-Hermes3dataset", "base_model:ibm-granite/granite-3.3-2b-instruct", "base_model:merge:ibm-granite/granite-3.3-2b-instruct", "base_model:powermove72/granite-3.3-2b-Hermes3dataset", "base_model:merge:powermove72/granite-3.3-2b-Hermes3dataset", "region:us" ]
null
2025-08-16T00:47:50Z
--- base_model: - ibm-granite/granite-3.3-2b-instruct - powermove72/granite-3.3-2b-Hermes3dataset tags: - merge - mergekit - lazymergekit - ibm-granite/granite-3.3-2b-instruct - powermove72/granite-3.3-2b-Hermes3dataset --- # Granite-3.3-2B-Avg-SliceWeighted Granite-3.3-2B-Avg-SliceWeighted is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct) * [powermove72/granite-3.3-2b-Hermes3dataset](https://huggingface.co/powermove72/granite-3.3-2b-Hermes3dataset) ## 🧩 Configuration ```yaml # ---------------------------------------------------------------------- # merge_weighted_average_40layers.yaml # Slice‑wise weighted‑average merge for a 40‑layer LLM. # – Different contribution per layer range. # ---------------------------------------------------------------------- merge_method: linear # merge type # ---------------------------------------------------------------------- # Global merge options # ---------------------------------------------------------------------- dtype: bfloat16 # preferred dtype on modern GPUs parameters: normalize: true # make each slice’s weights sum to 1.0 low_cpu_mem_usage: true # stream weights, don’t load everything into RAM seed: 2025 # reproducibility deterministic: true # torch‑cudnn deterministic mode # ---------------------------------------------------------------------- # Metadata (helps with provenance & experiment tracking) # ---------------------------------------------------------------------- metadata: model_name: Granite-3.3-2B-Avg-SliceWeighted version: v1.0 date: 2025-08-15 notes: | - 40‑layer model (indices 0‑39). - Three slices: * Layers 0‑13 → 80 % Llama‑2, 20 % Mistral * Layers 14‑26 → 50 % each (mid‑point) * Layers 27‑39 → 20 % Llama‑2, 80 % Mistral - Normalised weights are enforced by `parameters.normalize`. - Uses granite-3.3-2b-Hermes3dataset tokenizer for token‑id alignment. # ---------------------------------------------------------------------- # Tokenizer – both source models share the same one, so we can safely force it. # ---------------------------------------------------------------------- tokenizer_source: powermove72/granite-3.3-2b-Hermes3dataset # ---------------------------------------------------------------------- # Slice definitions (non‑overlapping, each covers a contiguous block of layers) # ---------------------------------------------------------------------- slices: # -------------------------------------------------------------- # Slice 1: Layers 0‑13 (the first 14 transformer blocks) # -------------------------------------------------------------- - sources: - model: ibm-granite/granite-3.3-2b-instruct layer_range: [0, 13] parameters: weight: 0.8 - model: powermove72/granite-3.3-2b-Hermes3dataset layer_range: [0, 13] parameters: weight: 0.2 # -------------------------------------------------------------- # Slice 2: Layers 14‑26 (the middle 13 transformer blocks) # -------------------------------------------------------------- - sources: - model: ibm-granite/granite-3.3-2b-instruct layer_range: [13, 26] parameters: weight: 0.5 # balanced - model: powermove72/granite-3.3-2b-Hermes3dataset layer_range: [13, 26] parameters: weight: 0.5 # -------------------------------------------------------------- # Slice 3: Layers 27‑39 (the last 14 transformer blocks) # -------------------------------------------------------------- - sources: - model: ibm-granite/granite-3.3-2b-instruct layer_range: [26, 40] parameters: weight: 0.2 - model: powermove72/granite-3.3-2b-Hermes3dataset layer_range: [26, 40] parameters: weight: 0.8 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "powermove72/Granite-3.3-2B-Avg-SliceWeighted" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
kapalbalap/blockassist-bc-peaceful_wary_owl_1755303141
kapalbalap
2025-08-16T00:13:24Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]
null
2025-08-16T00:13:13Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Harsh1729/R1-Distill-Llama-8B-SFT-cotroller_dataset-bespoke_52k_cotif-ood-v7
Harsh1729
2025-08-16T00:03:09Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-15T23:57:04Z
--- base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B library_name: transformers model_name: tags: - sft - full-finetuning tags: - generated_from_trainer licence: license --- # Model Card for {'tags': ['sft', 'full-finetuning']} This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B). 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="None", 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 SFT. ### Framework versions - TRL: 0.13.0 - Transformers: 4.46.0 - Pytorch: 2.7.0 - Datasets: 3.2.0 - Tokenizers: 0.20.3 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755299765
ggozzy
2025-08-15T23:17:26Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T23:17:12Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755299226
ggozzy
2025-08-15T23:08:26Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T23:08:13Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
tatsusal/blockassist-bc-peaceful_rangy_wombat_1755298142
tatsusal
2025-08-15T22:50:26Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful rangy wombat", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T22:50:16Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful rangy wombat --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
chansung/Qwen2.5-Coder-1.5B-CCRL-CUR-COMPLEX-ONLY-1E
chansung
2025-08-15T22:20:58Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:chansung/verifiable-coding-problems-python-v2", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-08-15T14:04:44Z
--- base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct datasets: chansung/verifiable-coding-problems-python-v2 library_name: transformers model_name: Qwen2.5-Coder-1.5B-CCRL-CUR-COMPLEX-ONLY-1E tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen2.5-Coder-1.5B-CCRL-CUR-COMPLEX-ONLY-1E This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on the [chansung/verifiable-coding-problems-python-v2](https://huggingface.co/datasets/chansung/verifiable-coding-problems-python-v2) dataset. 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="chansung/Qwen2.5-Coder-1.5B-CCRL-CUR-COMPLEX-ONLY-1E", 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/chansung18/huggingface/runs/qejhfrz7) 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: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
manusiaperahu2012/blockassist-bc-roaring_long_tuna_1755293773
manusiaperahu2012
2025-08-15T22:05:47Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "roaring long tuna", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T22:05:43Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - roaring long tuna --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755292712
ihsanridzi
2025-08-15T21:42:32Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "wiry flexible owl", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T21:42:29Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - wiry flexible owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Muapi/vividly-surreal-flux
Muapi
2025-08-15T21:04:34Z
0
0
null
[ "lora", "stable-diffusion", "flux.1-d", "license:openrail++", "region:us" ]
null
2025-08-15T21:04:16Z
--- license: openrail++ tags: - lora - stable-diffusion - flux.1-d model_type: LoRA --- # Vividly Surreal Flux ![preview](./preview.jpg) **Base model**: Flux.1 D **Trained words**: ## 🧠 Usage (Python) 🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys) ```python import requests, os url = "https://api.muapi.ai/api/v1/flux_dev_lora_image" headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")} payload = { "prompt": "masterpiece, best quality, 1girl, looking at viewer", "model_id": [{"model": "civitai:930804@1296376", "weight": 1.0}], "width": 1024, "height": 1024, "num_images": 1 } print(requests.post(url, headers=headers, json=payload).json()) ```
kapalbalap/blockassist-bc-peaceful_wary_owl_1755290718
kapalbalap
2025-08-15T20:46:23Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T20:46:09Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
ultratopaz/1560750
ultratopaz
2025-08-15T20:09:55Z
0
0
null
[ "region:us" ]
null
2025-08-15T20:09:53Z
[View on Civ Archive](https://civarchive.com/models/1467962?modelVersionId=1660299)
wherobots/ftw-ep-torch280-cu126-pt2
wherobots
2025-08-15T19:59:01Z
0
0
null
[ "image-segmentation", "license:cc-by-3.0", "region:us" ]
image-segmentation
2025-08-14T15:47:23Z
--- license: cc-by-3.0 pipeline_tag: image-segmentation recommended_patch_size: 512 recommended_clip_size: 64 device: cuda features: [ "s2med_harvest:B02", "s2med_harvest:B03", "s2med_harvest:B04", "s2med_harvest:B08", "s2med_planting:B02", "s2med_planting:B03", "s2med_planting:B04", "s2med_planting:B08" ] labels: [ non_field_background, field, field_boundaries ] ---
ggozzy/blockassist-bc-stubby_yapping_mandrill_1755286306
ggozzy
2025-08-15T19:33:33Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "stubby yapping mandrill", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T19:32:51Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - stubby yapping mandrill --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VIDEOS-18-alex-star-viral-video-Clip-hdq/Latest.New.full.videos.alex.star.Viral.Video.Official.Tutorial
VIDEOS-18-alex-star-viral-video-Clip-hdq
2025-08-15T19:12:33Z
0
0
null
[ "region:us" ]
null
2025-08-15T19:12:23Z
<a data-target="animated-image.originalLink" rel="nofollow" href="https://tinyurl.com/4axawfmy?Bri "><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" alt="WATCH Videos" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a>
Sophie-Rain-V-i-d-e-o-Tutorial/Sophie.Rain.Video.Tutorial
Sophie-Rain-V-i-d-e-o-Tutorial
2025-08-15T19:11:52Z
0
0
null
[ "region:us" ]
null
2025-08-15T19:10:20Z
<!-- HTML_TAG_END --><div> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p> <!-- HTML_TAG_END --></div>
kapalbalap/blockassist-bc-peaceful_wary_owl_1755281530
kapalbalap
2025-08-15T18:13:03Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T18:12:48Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755279615
Elizavr
2025-08-15T17:43:02Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "reclusive shaggy bee", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T17:42:50Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - reclusive shaggy bee --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jir88/gemma3-1B-gutenberg-v1
jir88
2025-08-15T16:53:56Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3_text", "trl", "en", "base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-08-15T16:53:46Z
--- base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3_text - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** jir88 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-1b-it-unsloth-bnb-4bit This gemma3_text 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/VyvoTTS-LFM2-Neuvillette-GGUF
mradermacher
2025-08-15T15:12:59Z
0
0
transformers
[ "transformers", "gguf", "en", "base_model:Vyvo/VyvoTTS-LFM2-Neuvillette", "base_model:quantized:Vyvo/VyvoTTS-LFM2-Neuvillette", "endpoints_compatible", "region:us", "conversational" ]
null
2025-08-15T15:09:33Z
--- base_model: Vyvo/VyvoTTS-LFM2-Neuvillette language: - en library_name: transformers mradermacher: readme_rev: 1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: --> static quants of https://huggingface.co/Vyvo/VyvoTTS-LFM2-Neuvillette <!-- provided-files --> ***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#VyvoTTS-LFM2-Neuvillette-GGUF).*** 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/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q2_K.gguf) | Q2_K | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q3_K_S.gguf) | Q3_K_S | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q3_K_L.gguf) | Q3_K_L | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.IQ4_XS.gguf) | IQ4_XS | 0.3 | | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q5_K_S.gguf) | Q5_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q5_K_M.gguf) | Q5_K_M | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q6_K.gguf) | Q6_K | 0.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/VyvoTTS-LFM2-Neuvillette-GGUF/resolve/main/VyvoTTS-LFM2-Neuvillette.f16.gguf) | f16 | 0.9 | 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 -->
mang3dd/blockassist-bc-tangled_slithering_alligator_1755268162
mang3dd
2025-08-15T14:56:57Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "tangled slithering alligator", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T14:56:51Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - tangled slithering alligator --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
Abbye/blockassist-bc-shaggy_small_octopus_1755266816
Abbye
2025-08-15T14:07:53Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "shaggy small octopus", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T14:07:44Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - shaggy small octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
jahyungu/Qwen2.5-7B-Instruct_mbpp
jahyungu
2025-08-15T13:12:18Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "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
2025-08-15T12:52:56Z
--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - generated_from_trainer model-index: - name: Qwen2.5-7B-Instruct_mbpp 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. --> # Qwen2.5-7B-Instruct_mbpp This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - 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: 0.03 - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.55.0 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.0
wasabuko/blockassist-bc-noisy_zealous_macaw_1755257876
wasabuko
2025-08-15T12:27:24Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "noisy zealous macaw", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T12:07:39Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - noisy zealous macaw --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
kapalbalap/blockassist-bc-peaceful_wary_owl_1755258429
kapalbalap
2025-08-15T11:48:17Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "peaceful wary owl", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T11:48:02Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - peaceful wary owl --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
wwsrf/VIDEO.18.New-Clip-Afrin-Er-Viral-Video.New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial
wwsrf
2025-08-15T11:44:30Z
0
0
null
[ "region:us" ]
null
2025-08-15T11:42:41Z
<a href="https://topvid.cfd/New-Clip-Afrin-Er-Viral-Video"> 🌐 VIDEO.18.New-Clip-Afrin-Er-Viral-Video.New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://topvid.cfd/New-Clip-Afrin-Er-Viral-Video"> 🌐 VIDEO.18.New-Clip-Afrin-Er-Viral-Video.New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial <a href="https://topvid.cfd/New-Clip-Afrin-Er-Viral-Video"> 🌐 VIDEO.18.New-Clip-Afrin-Er-Viral-Video.New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://topvid.cfd/New-Clip-Afrin-Er-Viral-Video"> 🌐 VIDEO.18.New-Clip-Afrin-Er-Viral-Video.New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial
mustafakara/gpt-oss-20b-multilingual-reasoner
mustafakara
2025-08-15T08:19:51Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:openai/gpt-oss-20b", "base_model:finetune:openai/gpt-oss-20b", "endpoints_compatible", "region:us" ]
null
2025-08-13T13:42:32Z
--- base_model: openai/gpt-oss-20b library_name: transformers model_name: gpt-oss-20b-multilingual-reasoner tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for gpt-oss-20b-multilingual-reasoner This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b). 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="mustafakara/gpt-oss-20b-multilingual-reasoner", 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 SFT. ### Framework versions - TRL: 0.21.0 - Transformers: 4.55.0 - Pytorch: 2.4.1 - Datasets: 3.6.0 - Tokenizers: 0.21.4 ## 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}} } ```
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755237363
Sayemahsjn
2025-08-15T06:15:52Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "playful feline octopus", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T06:15:46Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - playful feline octopus --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
VietTung04/qwen1.7b-vlsp
VietTung04
2025-08-15T05:38:18Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-08-15T05:38:14Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
qualcomm/Whisper-Small-V2
qualcomm
2025-08-15T05:21:21Z
17
3
pytorch
[ "pytorch", "foundation", "android", "automatic-speech-recognition", "license:other", "region:us" ]
automatic-speech-recognition
2025-04-09T21:54:06Z
--- library_name: pytorch license: other tags: - foundation - android pipeline_tag: automatic-speech-recognition --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/whisper_small_v2/web-assets/model_demo.png) # Whisper-Small-V2: Optimized for Mobile Deployment ## Transformer-based automatic speech recognition (ASR) model for multilingual transcription and translation available on HuggingFace HuggingFace Whisper-Small ASR (Automatic Speech Recognition) model is a state-of-the-art system designed for transcribing spoken language into written text. This model is based on the transformer architecture and has been optimized for edge inference by replacing Multi-Head Attention (MHA) with Single-Head Attention (SHA) and linear layers with convolutional (conv) layers. It exhibits robust performance in realistic, noisy environments, making it highly reliable for real-world applications. Specifically, it excels in long-form transcription, capable of accurately transcribing audio clips up to 30 seconds long. Time to the first token is the encoder's latency, while time to each additional token is decoder's latency, where we assume a max decoded length specified below. This model is an implementation of Whisper-Small-V2 found [here](https://github.com/huggingface/transformers/tree/v4.42.3/src/transformers/models/whisper). This repository provides scripts to run Whisper-Small-V2 on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/whisper_small_v2). ### Model Details - **Model Type:** Model_use_case.speech_recognition - **Model Stats:** - Model checkpoint: openai/whisper-small - Input resolution: 80x3000 (30 seconds audio) - Max decoded sequence length: 200 tokens | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | HfWhisperEncoder | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 426.871 ms | 0 - 9 MB | NPU | Use Export Script | | HfWhisperEncoder | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_CONTEXT_BINARY | 308.724 ms | 0 - 23 MB | NPU | Use Export Script | | HfWhisperEncoder | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 137.833 ms | 0 - 2 MB | NPU | Use Export Script | | HfWhisperEncoder | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_CONTEXT_BINARY | 157.263 ms | 1 - 11 MB | NPU | Use Export Script | | HfWhisperEncoder | float | SA7255P ADP | Qualcomm® SA7255P | QNN_CONTEXT_BINARY | 426.871 ms | 0 - 9 MB | NPU | Use Export Script | | HfWhisperEncoder | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_CONTEXT_BINARY | 136.564 ms | 0 - 3 MB | NPU | Use Export Script | | HfWhisperEncoder | float | SA8295P ADP | Qualcomm® SA8295P | QNN_CONTEXT_BINARY | 234.197 ms | 0 - 15 MB | NPU | Use Export Script | | HfWhisperEncoder | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_CONTEXT_BINARY | 135.525 ms | 1 - 4 MB | NPU | Use Export Script | | HfWhisperEncoder | float | SA8775P ADP | Qualcomm® SA8775P | QNN_CONTEXT_BINARY | 157.263 ms | 1 - 11 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_CONTEXT_BINARY | 137.433 ms | 0 - 4 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | PRECOMPILED_QNN_ONNX | 138.874 ms | 0 - 258 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 105.031 ms | 0 - 19 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 106.239 ms | 122 - 141 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_CONTEXT_BINARY | 88.885 ms | 0 - 15 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | PRECOMPILED_QNN_ONNX | 90.593 ms | 149 - 163 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 132.398 ms | 0 - 0 MB | NPU | Use Export Script | | HfWhisperEncoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 135.347 ms | 226 - 226 MB | NPU | Use Export Script | | HfWhisperDecoder | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 17.58 ms | 47 - 55 MB | NPU | Use Export Script | | HfWhisperDecoder | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_CONTEXT_BINARY | 18.304 ms | 53 - 68 MB | NPU | Use Export Script | | HfWhisperDecoder | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 12.261 ms | 60 - 62 MB | NPU | Use Export Script | | HfWhisperDecoder | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_CONTEXT_BINARY | 13.14 ms | 57 - 66 MB | NPU | Use Export Script | | HfWhisperDecoder | float | SA7255P ADP | Qualcomm® SA7255P | QNN_CONTEXT_BINARY | 17.58 ms | 47 - 55 MB | NPU | Use Export Script | | HfWhisperDecoder | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_CONTEXT_BINARY | 11.926 ms | 55 - 57 MB | NPU | Use Export Script | | HfWhisperDecoder | float | SA8295P ADP | Qualcomm® SA8295P | QNN_CONTEXT_BINARY | 14.17 ms | 56 - 70 MB | NPU | Use Export Script | | HfWhisperDecoder | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_CONTEXT_BINARY | 11.95 ms | 60 - 65 MB | NPU | Use Export Script | | HfWhisperDecoder | float | SA8775P ADP | Qualcomm® SA8775P | QNN_CONTEXT_BINARY | 13.14 ms | 57 - 66 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_CONTEXT_BINARY | 12.158 ms | 57 - 60 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | PRECOMPILED_QNN_ONNX | 12.922 ms | 58 - 60 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 9.384 ms | 60 - 79 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 10.145 ms | 75 - 96 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_CONTEXT_BINARY | 8.102 ms | 60 - 74 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | PRECOMPILED_QNN_ONNX | 8.78 ms | 73 - 87 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 10.312 ms | 60 - 60 MB | NPU | Use Export Script | | HfWhisperDecoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 10.069 ms | 286 - 286 MB | NPU | Use Export Script | ## Installation Install the package via pip: ```bash pip install "qai-hub-models[whisper-small-v2]" ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo off target The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input. ```bash python -m qai_hub_models.models.whisper_small_v2.demo ``` The above demo runs a reference implementation of pre-processing, model inference, and post processing. **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.whisper_small_v2.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.whisper_small_v2.export ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/whisper_small_v2/qai_hub_models/models/Whisper-Small-V2/export.py) leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model on-device. Lets go through each step below in detail: Step 1: **Compile model for on-device deployment** To compile a PyTorch model for on-device deployment, we first trace the model in memory using the `jit.trace` and then call the `submit_compile_job` API. ```python import torch import qai_hub as hub from qai_hub_models.models.whisper_small_v2 import Model # Load the model torch_model = Model.from_pretrained() # Device device = hub.Device("Samsung Galaxy S24") # Trace model input_shape = torch_model.get_input_spec() sample_inputs = torch_model.sample_inputs() pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()]) # Compile model on a specific device compile_job = hub.submit_compile_job( model=pt_model, device=device, input_specs=torch_model.get_input_spec(), ) # Get target model to run on-device target_model = compile_job.get_target_model() ``` Step 2: **Performance profiling on cloud-hosted device** After compiling models from step 1. Models can be profiled model on-device using the `target_model`. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics. ```python profile_job = hub.submit_profile_job( model=target_model, device=device, ) ``` Step 3: **Verify on-device accuracy** To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device. ```python input_data = torch_model.sample_inputs() inference_job = hub.submit_inference_job( model=target_model, device=device, inputs=input_data, ) on_device_output = inference_job.download_output_data() ``` With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output. **Note**: This on-device profiling and inference requires access to Qualcomm® AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN (`.so` export ): This [sample app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) provides instructions on how to use the `.so` shared library in an Android application. ## View on Qualcomm® AI Hub Get more details on Whisper-Small-V2's performance across various devices [here](https://aihub.qualcomm.com/models/whisper_small_v2). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of Whisper-Small-V2 can be found [here](https://github.com/huggingface/transformers/blob/v4.42.3/LICENSE). * The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) ## References * [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) * [Source Model Implementation](https://github.com/huggingface/transformers/tree/v4.42.3/src/transformers/models/whisper) ## Community * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1755232492
maxibillion1975
2025-08-15T05:02:25Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "iridescent squeaky sandpiper", "arxiv:2504.07091", "region:us" ]
null
2025-08-15T05:02:21Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - iridescent squeaky sandpiper --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
DYJG-research/Qwen3-8B-THTB
DYJG-research
2025-08-15T03:32:02Z
0
0
null
[ "safetensors", "qwen3", "license:apache-2.0", "region:us" ]
null
2025-08-14T17:33:48Z
--- license: apache-2.0 ---
myfi/parser_model_ner_3.53_checkpoint_250_lora
myfi
2025-08-15T01:40:01Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-08-15T01:39:03Z
--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** myfi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
indoempatnol/blockassist-bc-fishy_wary_swan_1755165755
indoempatnol
2025-08-14T10:28:14Z
0
0
null
[ "gensyn", "blockassist", "gensyn-blockassist", "minecraft", "fishy wary swan", "arxiv:2504.07091", "region:us" ]
null
2025-08-14T10:28:04Z
--- tags: - gensyn - blockassist - gensyn-blockassist - minecraft - fishy wary swan --- # Gensyn BlockAssist Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
dinesh-001/whisper_finetune_vdc_v3
dinesh-001
2025-08-14T10:27:38Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-08-14T10:27:29Z
--- 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kev216/20250813_old_new_finetune_for_gpt5_4langs_5epoch
kev216
2025-08-13T20:35:15Z
0
0
transformers
[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-08-13T20:34:59Z
--- 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. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
datasetsANDmodels/it_en
datasetsANDmodels
2025-08-13T07:59:29Z
0
0
null
[ "it", "en", "base_model:datasetsANDmodels/it_en", "base_model:finetune:datasetsANDmodels/it_en", "region:us" ]
null
2025-08-13T07:55:52Z
--- language: - it - en base_model: - datasetsANDmodels/it_en --- This model translates text from Italian to English.