llama-index-graph-stores-neo4j [0.2.2] Handle cases where type is missing (neo4j property graph) (#13875) Rename Neo4jPGStore to Neo4jPropertyGraphStore (with backward compat) (#13891) llama-index-llms-openai [0.1.22] Improve the retry mechanism of OpenAI (#13878) llama-index-readers-...
Setting int8_quantization=True decreases the memory requirement and leads to faster training. Decreasing per_device_train_batch_size and max_input_length reduces the memory requirement and therefore can be run on smaller instances. However, setting very low values may increase the ...
Code Llama 2 fine-tuning supports a number of hyperparameters, each of which can impact the memory requirement, training speed, and performance of the fine-tuned model: epoch –The number of passes that the fine-tuning algorithm takes through the training dataset. Must be an integer greater th...
2024.04.22: Support for inference, fine-tuning, and deployment of chinese-llama-alpaca-2 series models. This includes:chinese-llama-2-1.3b, chinese-llama-2-7b, chinese-llama-2-13b, chinese-alpaca-2-1.3b, chinese-alpaca-2-7b and chinese-alpaca-2-13b along with their corresponding 16k and...
让我们在requirement.txt 中定义依赖关系。 Let's now define a Dockerfile to build the docker image of the Streamlit application. 现在让我们定义一个 Dockerfile 来构建 Streamlit 应用程序的 docker 映像。 We are using the python docker image, as the base image, and creating a working directory cal...
Experience it using swift infer --model_type llama3_2-11b-vision-instruct. 2024.09.26: Support for training and deploying llama3.2 series models. Experience it using swift infer --model_type llama3_2-1b-instruct. 2024.09.25: Support for training to deployment with got-ocr2. Best practices ...
the model is uploaded without any compression, which can significantly reduce the time taken for large model artifacts to be uploaded to Amazon S3. The uncompressed model can then be used directly for deployment or further processing. The following code s...
Requirement depends on model size. ### Where do I put my GGUF model? > [!IMPORTANT] > If running in Docker you should be running the container to a mounted storage location on the host machine so you > can update the storage files directly without having to re-download or re-build ...
In addition, you can configure deployment configuration, hyperparameters, and security settings for fine-tuning. You can then choose Train to start the training job on a SageMaker ML instance. The preceding screenshot shows the fine-tuning page for the Llama-2 7B model; howeve...
Inference is bottlenecked by memory, most notably the KV cache. They say the KV cache's most notable features are That it's very large That it's dynamic, size depends on sequence length which is variable. Existing systems waste 60-80% of memory due to fragmentation and over-reservation ...