Commits Deepseek Ai Deepseek R1 Distill Qwen 14b Like 619 deepseek 59.5k text generation transformers safetensors qwen2 conversational text generation inference arxiv:2501.12948 license:mit model card filesfiles and versions community 14 train deploy use this model main deepseek r1 distill qwen 7b ctrl k ctrl k 4 contributors history:12 commits deepseekddm update bib info 916b56a verified2. This repo contains a minimal implementation of 6 small models distilled from deepseek r1, a model trained via large scale reinforcement learning (rl) to execute chain of thought reasoning. specifically, these are fine tuned versions of qwen and llama, on a dataset of 800k samples generated by deepseek r1.
Commits Devquasar Deepseek Ai Deepseek R1 Distill Qwen 7b Gguf Deepseek r1 distill qwen 32b outperforms openai o1 mini across various benchmarks, achieving new state of the art results for dense models. note: before running deepseek r1 series models locally, we kindly recommend reviewing the usage recommendation section. Successful integration of deepseek r1 distill qwen 7b into applications requires thoughtful planning and best practices. this section discusses strategies for deploying the model locally and on cloud environments, ensuring optimal performance and reliability. Deepseek r1 achieves performance comparable to openai o1 across math, code, and reasoning tasks. deepseek distill qwen 7b is distilled from deepseek r1 based on qwen2.5 math 7b. Our model is trained on top of deepseek r1 distill qwen 1.5b and deepseek r1 distill qwen 14b. our work is done as part of berkeley sky computing lab, berkeley ai research, and a successful collaboration with together ai.
Open Llm Leaderboard Deepseek Ai Deepseek R1 Distill Qwen 7b Details Deepseek r1 achieves performance comparable to openai o1 across math, code, and reasoning tasks. deepseek distill qwen 7b is distilled from deepseek r1 based on qwen2.5 math 7b. Our model is trained on top of deepseek r1 distill qwen 1.5b and deepseek r1 distill qwen 14b. our work is done as part of berkeley sky computing lab, berkeley ai research, and a successful collaboration with together ai. Nexa ai, today, announced nexaquants of two deepseek r1 distills: the deepseek r1 distill qwen 1.5b and deepseek r1 distill llama 8b. popular quantization methods like the llama.cpp based q4 k m allow large language models to significantly reduce their memory footprint and typically offer low perplexity loss for dense models as a tradeoff. To support the research community, we have open sourced deepseek r1 zero, deepseek r1, and six dense models distilled from deepseek r1 based on llama and qwen. deepseek r1 distill qwen 32b outperforms openai o1 mini across various benchmarks, achieving new state of the art results for dense models. Discover the security risks and vulnerabilities in deepseek r1 and its distilled models (qwen 1.5b, 7b, llama 8b), including prompt injection, jailbreaking, and misinformation threats. our in depth analysis covers mobile app and api testing, local model assessments, and industry specific impacts, offering insights for secure ai deployment.