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# What does this PR do? Fixes a bunch of violations. Note: this patch touches all files but post_training.py that will be significantly changed by #1437, hence leaving it out of the picture for now. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan Testing with https://github.com/meta-llama/llama-stack/pull/1543 Also checked that GPU training works with the change: ``` INFO: ::1:53316 - "POST /v1/post-training/supervised-fine-tune HTTP/1.1" 200 OK INFO: ::1:53316 - "GET /v1/post-training/job/status?job_uuid=test-jobb5ca2d84-d541-42f8-883b-762828b4c0e7 HTTP/1.1" 200 OK INFO: ::1:53316 - "GET /v1/post-training/job/artifacts?job_uuid=test-jobb5ca2d84-d541-42f8-883b-762828b4c0e7 HTTP/1.1" 200 OK 21:24:01.161 [END] /v1/post-training/supervised-fine-tune [StatusCode.OK] (32526.75ms) 21:23:28.769 [DEBUG] Setting manual seed to local seed 3918872849. Local seed is seed + rank = 3918872849 + 0 21:23:28.996 [INFO] Identified model_type = Llama3_2. Ignoring output.weight in checkpoint in favor of the tok_embedding.weight tied weights. 21:23:29.933 [INFO] Memory stats after model init: GPU peak memory allocation: 6.05 GiB GPU peak memory reserved: 6.10 GiB GPU peak memory active: 6.05 GiB 21:23:29.934 [INFO] Model is initialized with precision torch.bfloat16. 21:23:30.115 [INFO] Tokenizer is initialized. 21:23:30.118 [INFO] Optimizer is initialized. 21:23:30.119 [INFO] Loss is initialized. 21:23:30.896 [INFO] Dataset and Sampler are initialized. 21:23:30.898 [INFO] Learning rate scheduler is initialized. 21:23:31.618 [INFO] Memory stats after model init: GPU peak memory allocation: 6.24 GiB GPU peak memory reserved: 6.30 GiB GPU peak memory active: 6.24 GiB 21:23:31.620 [INFO] Starting checkpoint save... 21:23:59.428 [INFO] Model checkpoint of size 6.43 GB saved to /home/ec2-user/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/consolidated.00.pth 21:23:59.445 [INFO] Adapter checkpoint of size 0.00 GB saved to /home/ec2-user/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter.pth ``` [//]: # (## Documentation) Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com> |
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Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our ReadTheDocs page.
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack