

⚗️ Optimization
- Fine-tune Llama 2 with DPO, a guide to using the TRL library's DPO method to fine tune Llama 2 on a specific dataset.
- Extended Guide: Instruction-tune Llama 2, a guide to training Llama 2 to generate instructions from inputs, transforming the model from instruction-following to instruction-giving.
- A notebook on how to fine-tune the Llama 2 model on a personal computer using QLoRa and TRL. 🌎
⚡️ Inference
- A notebook on how to quantize the Llama 2 model using GPTQ from the AutoGPTQ library. 🌎
- A notebook on how to run the Llama 2 Chat Model with 4-bit quantization on a local computer or Google Colab. 🌎
🚀 Deploy
- Fine-tune LLaMA 2 (7-70B) on Amazon SageMaker, a complete guide from setup to QLoRA fine-tuning and deployment on Amazon SageMaker.
- Deploy Llama 2 7B/13B/70B on Amazon SageMaker, a guide on using Hugging Face's LLM DLC container for secure and scalable deployment.
LlamaConfig
[[autodoc]] LlamaConfig
LlamaTokenizer
[[autodoc]] LlamaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary
LlamaTokenizerFast
[[autodoc]] LlamaTokenizerFast
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - update_post_processor
    - save_vocabulary
LlamaModel
[[autodoc]] LlamaModel
    - forward
LlamaForCausalLM
[[autodoc]] LlamaForCausalLM
    - forward
LlamaForSequenceClassification
[[autodoc]] LlamaForSequenceClassification
    - forward