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AI 모델 84757

• AReaL-tau2-telecom-user-sft-30B↗

  inclusionAI

• internlm2-1_8b↗

  internlm

  InternLM2-1.8B is the 1.8 billion parameter version of the second generation InternLM series. In order to facilitate user use and research, InternLM2-1.8B has three versions of open-source models. They are: We have evaluated InternLM2 on several important benchmarks using the open-source evaluation tool OpenCompass. Some of the evaluation results are shown in the table below. You are welcome to visit the OpenCompass Leaderboard for more evaluation results. **Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.

• bagel-dpo-34b-v0.2↗

  jondurbin

  This version also includes the toxic DPO dataset, and should have less censorship than it's counterparts. You may want to use a system prompt like: 1) For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental. 2) After you created your account update your billing and navigate to the deploy page. 3) Select the following - GPU Type: A6000 - GPU Quantity: 2 - Category: Creator - Image: Jon Durbin - Coupon Code: JonDurbin 4) Deploy the VM! 5) Navigate to 'Running Instances' to retrieve instructions to login to the VM 6) Once inside the VM, open the terminal and run `volume=$PWD/data` 7) Run `model=jondurbin/bagel-dpo-34b-v0.2` 8) `sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 --model-id $model` 9) The model will take some time to load... 10) Once loaded the model will be available on port 8080 *Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*

• xor-lite↗

  juspay

• rwkv-6-world-3b-gguf↗

  latestissue

• gemma-3-27b-it-FP8-Dynamic↗

  leon-se

• resnet10↗

  lerobot

  It's done in context of porting `HIL-SERL` paper code (https://hil-serl.github.io/) to `Lerobot` (https://github.com/Lerobot/lerobot). The HF doesn't have ResNet-10 model, which could be pretty usefult for robotics tasks because of it's small size. This model is converted from JAX to PyTorch, and the weights are preserved. This model is a PyTorch port of the original JAX implementation. The conversion maintains the original model's architecture and weights while making it accessible to PyTorch users. The original model is from https://github.com/rail-berkeley/hil-serl/blob/7d17d13560d85abffbd45facec17c4f9189c29c0/serl_launcher/serl_launcher/utils/train_utils.py#L103. - **Original Framework:** JAX - **Target Framework:** PyTorch - **Model Architecture:** ResNet-10 (4-stage ResNet with basic blocks) - **Original Model:** HIL-SERL ResNet-10 - **Total Parameters:** 4,905,792 (~4.9M parameters) - **Hidden Sizes:** [64, 128, 256, 512] - **Input:** 3-channel RGB images (128x128) - **Embedding Size:** 64

• ALoDLM-8B-GGUF↗

  liodon-ai

• d1-omni-600M-MLX-fp16↗

  LiquidAI

• Agents-A1-4B↗

  litert-community