Ollama v0.1.21 — Release Notes & Engineering Impact

## New models * [Qwen](https://ollama.ai/library/qwen): Qwen is a series of large language models by Alibaba Cloud spanning from 1.8B to 72B parameters. * [DuckDB-NSQL](https://ollama.ai/library/duckdb-nsql): A text-to-sql LLM for DuckDB * [Stable Code](https://ollama.ai/library/stable-code): A new code completion model on par with Code Llama 7B and similar models. * [Nous Hermes 2 Mixtral](https://ollama.ai/library/nous-hermes2-mixtral): The Nous Hermes 2 model from Nous Research, now trained over Mixtral. ## Saving and loading models and messages Models can now be saved and loaded with `/save <model>` and `/load <model>` when using `ollama run`. This will save or load conversations and any model changes with `/set parameter`, `/set system` and more as a new model with the provided name. ## `MESSAGE` modelfile command Messages can now be specified in a `Modelfile` ahead of time using the `MESSAGE` command: ``` # example Modelfile FROM llama2 SYSTEM You are a friendly assistant that only answers with 'yes' or 'no' MESSAGE user Is Toronto in Canada? MESSAGE assistant yes MESSAGE user Is Sacramento in Canada? MESSAGE assistant no MESSAGE user Is Ontario in Canada? MESSAGE assistant yes ``` After creating this model, running it will restore the message history. This is useful for techniques such as [Chain-Of-Thought](https://www.promptingguide.ai/techniques/cot) prompting ``` ollama create -f Modelfile yesno ollama run yesno >>> Is Toronto in Canada? yes >>> Is Sacramento in Canada? no >>> Is Ontario in Canada? yes >>> Is Havana in Canada? no ``` ## Python and Javascript libraries The first versions of the [Python](https://github.com/ollama/ollama-python) and [JavaScript](https://github.com/ollama/ollama-js) libraries for Ollama are [now available](https://ollama.ai/blog/python-javascript-libraries). ## Intel & AMD CPU improvements Ollama now supports CPUs without AVX. This means Ollama will now run on older CPUs and in environments (such as virtual machines, Rosetta, GitHub actions) that don't provide support for AVX instructions. For newer CPUs that support AVX2, Ollama will receive a small performance boost, running models about 10% faster. ## What's Changed * Support for a much broader set of CPUs, including CPUs without AVX instruction set support * If a GPU detection error is hit when attempting to run a model, Ollama will fallback to CPU * Fixed issue where generating responses with the same prompt would hang after around 20 requests * New `MESSAGE` Modelfile command to set the conversation history when building a model * Ollama will now use AVX2 for faster performance if available * Improved detection of Nvidia GPUs, especially in WSL * Fixed issue where models with LoRA layers may not load * Fixed incorrect error that would occur when retrying network connections in `ollama pull` and `ollama push` * Fixed issue where `/show parameter` would round decimal numbers * Fixed issue where upon hitting the context window limit, requests would hang ## New Contributors * @fpreiss made their first contribution in https://github.com/jmorganca/ollama/pull/1921 * @eavanvalkenburg made their first contribution in https://github.com/jmorganca/ollama/pull/1931 * @0atman made their first contribution in https://github.com/jmorganca/ollama/pull/1924 * @sachinsachdeva made their first contribution in https://github.com/jmorganca/ollama/pull/2021 * @Arrendy made their first contribution in https://github.com/jmorganca/ollama/pull/2016 * @purificant made their first contribution in https://github.com/jmorganca/ollama/pull/1958 * @lainedfles made their first contribution in https://github.com/jmorganca/ollama/pull/1999 **Full Changelog**: https://github.com/jmorganca/ollama/compare/v0.1.20...v0.1.21