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71fdc4920ad4a3a9f462072ee5fa4e3218fd6d6a
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20
+refs/pull/572/head
Adding_open_colab
Fix-broken-format-in-preview-for-RAG-chatbo-example
Getting_to_know_Llama
LG3-nits-after-launch
MM-Rag-fresh
Multi-Modal-RAG-Demo
Remove-Tokenizer-onPrem-vllm-InferenceThroughput
Tool_Calling_Demos
add-FAQ
add-promptguard-to-safety-checkers
adding_examples_with_aws
albertodepaola-patch-1
amitsangani-patch-1
archive-main
aws-do-fsdp
azure-api-example
benchmark-inference-throughput-cloud-api
chat-completion-fix
chat_pipeline
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v0.0.4.post1
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resized pngs #2
resized pngs
added HelloLlamaLocal.ipynb
improved README and HelloLlamaLocal.ipynb
updated README.md for llama-demo-apps
Create codeql.yml
Fixed typos
Merge remote-tracking branch 'origin/main' into feature/length_based_batch_sampling
Added documentation for th batching strategy
Fix fsdp_config.pure_bf16 flag in README
Add missing changes
clean up unit tests + add batching test
Fix order of concat vs sampler
Added packing test for samsum
Fix sampler vs batch_sampler
Adapt alpaca dataset to ConcatDataset
Fix usage of dataclass for train_config and fsdp_config
Fix unit test to reflect batch packing
Fix invalid labels for context in custom dataset/oasst1
addig flop counter
Merge branch 'fix/invalidate_label_for_chat' into feature/length_based_batch_sampling
Invalidate context in labels for samsum + grammar
Remove max_word from alpaca; lets deal tokenizer deal with truncation
Updated quickstart notebook to use llama_recipes package (#224)
Make packing/padding a training setting
Added unit test coverage for amp
Add missing amp context if use_fp16 is enabled
Added unit test coverage for amp
fix/amp_training
fix/amp_training
Add missing amp context if use_fp16 is enabled
adding profiler and flop_counter
adding flop counter
minor updates (#228)
minor updates
connect_noteboo…
connect_notebook_update
Llama 2 Connect 2023 Colab Notebook (#227)
address comments
amitsangani-pat…
amitsangani-patch-1
clean up
fix format and missing packages
Llama 2 Connect 2023 Colab Notebook
fixing typo on x label
Modularizing the graphs further. Plotting train and validation separetly due to difference in number of steps.
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