Model · Text generation
Ornith
Chirp Chirp! We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For…
Open weights
mit
6.7B parameters
262,144 tokens
transformers
P
Model · Text generation
Park
A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then edited by 10 of 10 rounds of iterative parameter-neutral swap selected by the gapiter rule (up to 0.1% of dense parameters per round; the full run's budget is 1.0%). This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success…
Open weights
llama2
6.7B parameters
4,096 tokens
transformers
P
Model · Text generation
Park
A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then given a 0.0% parameter budget of restored SVD components selected by the unknown rule. This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success rate, and the point of the study is to quantify that and test recovery. Treat…
Open weights
llama2
6.7B parameters
4,096 tokens
transformers
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the website and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion…
Access requested at publisher
llama2
6.7B parameters
transformers
Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data. Here give some examples of how to use our model. This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the LICENSE-MODEL for more details. If you have any questions, please raise an issue or contact us at [email protected].
Open weights
other
6.9B parameters
4,096 tokens
transformers
Model · Text generation
AMD
ZenDNN v6.1.0 - ZenTorch v2.13.0.0 - PyTorch v2.13.0.0 - LLM Compressor v0.13.0 - vLLM v0.29.0 This is a quantized version of granite-4.0-h-tiny created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference. The model was quantized from granite-4.0-h-tiny using LLM Compressor via the Round-to-Nearest (RTN) algorithm. This reduces the model weights from 12.9 GiB to 6.6 GiB on disk (~49% reduction). granite-4.0-h-tiny is a hybrid Mamba-MoE model: of its 40 layers, 4 are full-attention blocks and the other 36 are Mamba (linear-attention) blocks, and every layer carries a 64-expert MoE block alongside a shared MLP. The recipe only needs two ignore entries. lmhead…
Open weights
apache-2.0
6.9B parameters
131,072 tokens
transformers
