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intellect-1-instruct
INTELLECT-1 is the first collaboratively trained 10 billion parameter language model trained from scratch on 1 trillion tokens of English text and code. This is an instruct model. The base model associated with it is INTELLECT-1. INTELLECT-1 was trained on up to 14 concurrent nodes distributed across 3 continents, with contributions from 30 independent community contributors providing compute. The training code utilizes the prime framework, a scalable distributed training framework designed for fault-tolerant, dynamically scaling, high-perfomance training on unreliable, globally distributed workers. The key abstraction that allows dynamic scaling is the ElasticDeviceMesh which manages dynamic global process groups for fault-tolerant communication across the internet and local process groups for communication within a node. The model was trained using the DiLoCo algorithms with 100 inner steps. The global all-reduce was done with custom int8 all-reduce kernels to reduce the communication payload required, greatly reducing the communication overhead by a factor 400x.

Repository: localaiLicense: apache-2.0

primeintellect_intellect-2
INTELLECT-2 is a 32 billion parameter language model trained through a reinforcement learning run leveraging globally distributed, permissionless GPU resources contributed by the community. The model was trained using prime-rl, a framework designed for distributed asynchronous RL, using GRPO over verifiable rewards along with modifications for improved training stability. For detailed information on our infrastructure and training recipe, see our technical report.

Repository: localaiLicense: apache-2.0

agentica-org_deepscaler-1.5b-preview
DeepScaleR-1.5B-Preview is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning (RL) to scale up to long context lengths. The model achieves 43.1% Pass@1 accuracy on AIME 2024, representing a 15% improvement over the base model (28.8%) and surpassing OpenAI's O1-Preview performance with just 1.5B parameters.

Repository: localaiLicense: mit

agentica-org_deepcoder-14b-preview
DeepCoder-14B-Preview is a code reasoning LLM fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning (RL) to scale up to long context lengths. The model achieves 60.6% Pass@1 accuracy on LiveCodeBench v5 (8/1/24-2/1/25), representing a 8% improvement over the base model (53%) and achieving similar performance to OpenAI's o3-mini with just 14B parameters.

Repository: localaiLicense: mit

agentica-org_deepcoder-1.5b-preview
DeepCoder-1.5B-Preview is a code reasoning LLM fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning (RL) to scale up to long context lengths. Data Our training dataset consists of approximately 24K unique problem-tests pairs compiled from: Taco-Verified PrimeIntellect SYNTHETIC-1 LiveCodeBench v5 (5/1/23-7/31/24)

Repository: localaiLicense: mit

noromaid-13b-0.4-DPO
Noromaid-13B-0.4-DPO is a 13B parameter language model based on Llama2, fine-tuned for roleplay and chat using Direct Preference Optimization. It is distributed in GGUF quantized format for efficient local inference. The model supports custom system prompts and is optimized for roleplay interfaces like SillyTavern.

Repository: localaiLicense: cc-by-nc-4.0