├── .gitignore ├── README.md ├── Stanford_cs229.ipynb ├── illya_NeurIPS_2024.ipynb ├── imgs ├── 2880px-MCTS_(English).svg.png ├── 4d_rotation.png ├── accelerate.png ├── adap_kl_coef.png ├── agent_rl.png ├── alpaca-instruct.png ├── bf-float.png ├── bloom.png ├── bloom_lora.png ├── bpe.png ├── c4.png ├── cg.png ├── ch-dataset.png ├── cope.png ├── decoding-qkv.png ├── drl.png ├── en-dataset.png ├── float16.png ├── floats-rep.png ├── generating.png ├── gpt-2-autoregression-2.gif ├── gpt2_bert_training.png ├── gpt4-moe.png ├── gpt4-price.png ├── gpt_config.jpeg ├── gqa.png ├── grad-accumulation.png ├── hf_llama_rope.png ├── kv-cache.gif ├── kvcache-arch.png ├── kvmatrices.png ├── llama2-weights.png ├── llama_weights.png ├── llm-archs.png ├── llm-int8.png ├── llm-tokenizer.png ├── lora-weights.png ├── lora.png ├── meta-llama-model.png ├── model-base-model-free.png ├── model_clm.png ├── model_mlm.png ├── model_seq2seq.png ├── moe.png ├── openrlhf.png ├── peft.png ├── pile.png ├── ppo_algo.png ├── qkv.png ├── quant-freeze.png ├── regular-finetuning-alt.png ├── regular-finetuning.png ├── rel_position_matrix.png ├── rlhf.png ├── rm_ppo.png ├── rope_1.png ├── rope_2.png ├── rope_paper.png ├── self-instruct.png ├── t+1.png ├── token-kvcache.png ├── token_steps.png ├── trl-sft.png ├── trl_overview.png ├── trl_pipeline.png ├── trl_ppo_loss.png ├── trl_steps.png ├── unique-tokens.jpeg └── value_iteration.png └── tutorials ├── 01_llama_intro.ipynb ├── 02_lora_baics.ipynb ├── 03_lora_practice.ipynb ├── 04_PEFT_lora.ipynb ├── 05_float16_bf16.ipynb ├── 06_alpaca_llama_7b_lora.ipynb ├── 07_fp16_mixed_precision_training.ipynb ├── Chinese(中文) └── chinese_llama2.ipynb ├── LLM.int8.ipynb ├── LLMs & NLPs.ipynb ├── alpaca_vicuna.ipynb ├── attention └── flash_attention.ipynb ├── bitsandbytes.ipynb ├── bloom_源码分析.ipynb ├── chatgpt_reviews.ipynb ├── code_sql └── overall.ipynb ├── cuda_basics.ipynb ├── discussion ├── imgs │ └── search_methods.png └── o1.ipynb ├── drl ├── 01_q_learning_dqn.ipynb ├── imgs │ ├── REINFOCE.png │ ├── ac_actor_pg.png │ ├── ac_algo.png │ ├── ac_critic_td.png │ ├── agent_as_nn.png │ ├── discounted_reward.png │ ├── game_mcts_1.png │ ├── game_mcts_2.png │ ├── game_mcts_3.png │ ├── game_mcts_4.png │ ├── her_idea.png │ ├── importance_sampling_issue.png │ ├── lm_rl.png │ ├── mab-definition.png │ ├── mab_algo.png │ ├── mc_estimate_v_pi.png │ ├── mcts_algo.png │ ├── mcts_exam_1.png │ ├── mcts_exam_2.png │ ├── mcts_exam_3.png │ ├── mcts_exam_4.png │ ├── node-edge.png │ ├── pg_gradient.png │ ├── pg_is_advantage.png │ ├── pg_loss.png │ ├── pieter_abbeel_trpo.png │ ├── policy_net_update.png │ ├── policy_net_value_net_sharing.png │ ├── policy_value_update_summary.png │ ├── ppo2.png │ ├── ppo_algo_lhy.png │ ├── ppo_clip.png │ ├── ppo_clip_curve.png │ ├── q_values.png │ ├── reinforce_monte_carlo.png │ ├── reinforce_with_baseline_approx.png │ ├── rl_setting.png │ ├── rollout.png │ ├── s_a_r.png │ ├── s_a_r_p.png │ ├── s_a_r_pi.png │ ├── suitable_credit.png │ ├── td_learning.png │ ├── td_trip.png │ ├── td_update.png │ ├── td_v_pi.png │ ├── trpo_Lagrange_duality.png │ ├── trpo_obj.png │ ├── trpo_summary.png │ ├── value_iter.png │ ├── value_loss.png │ └── value_net_update.png ├── mcts │ ├── alpha_zero.ipynb │ ├── alphago_mcts.ipynb │ ├── mcts_01_intro_bascis.ipynb │ ├── mcts_02_from_scartch.ipynb │ ├── mcts_03_binary_tree.ipynb │ ├── mcts_04_rl.ipynb │ ├── mcts_05_tic-tac-toe.ipynb │ ├── mcts_06_vis.ipynb │ ├── mcts_demo.py │ └── mcts_supp.ipynb ├── mdp_basics.ipynb ├── mdps_policy_gradient.ipynb ├── mdps_value_iteration.ipynb ├── multi_armed_bandits.ipynb ├── o1_self_rl.ipynb ├── policy_based │ ├── PPO_lihongyi_basics.ipynb │ ├── actor_critic.ipynb │ ├── annotated_trpo.ipynb │ ├── pg_trpo_ppo.ipynb │ ├── ppo_gae.ipynb │ ├── ppo_supp.ipynb │ └── trpo_optimization.ipynb ├── policy_based_rl.ipynb ├── policy_gradient_with_basline.ipynb ├── pomdp.ipynb ├── rl_basics.ipynb ├── rl_intro_review.ipynb ├── rlhf │ └── RM_loss.ipynb ├── rl应用 │ └── 供应链优化.ipynb ├── temporal_difference_bootstrapping.ipynb ├── utils │ ├── gridworld.py │ ├── mdp.py │ ├── rendering_utils.py │ └── test.ipynb ├── value_based │ ├── q_learning_lihongyi.ipynb │ └── td.ipynb ├── 对(深度)强化学习的认识与理解.ipynb └── 经典 envs.ipynb ├── evaluation ├── datasets与大模型评估（evaluation）.ipynb └── test_questions.ipynb ├── falcon.ipynb ├── gpt2.ipynb ├── gpt2_train_tokenizer.ipynb ├── gpt_family.ipynb ├── gradient_accumulation.ipynb ├── gradient_checkpointing.ipynb ├── instruction_chat └── chat_template.ipynb ├── karpathy ├── 01_build_GPT.ipynb └── input.txt ├── llama ├── RewardModel.ipynb ├── attention_map_vis.ipynb ├── llama2_7b_llama3_8b.ipynb ├── llama2_tokenizer.model ├── llama3_0419.ipynb ├── llama3_supp_data_config.ipynb ├── llama3_tokenizer.model └── tokenizer.py ├── llama2_introduction.ipynb ├── llama2_src_01.ipynb ├── llama2_src_02_RMSNorm_SwiGLU.ipynb ├── llama2_src_03_RoPE.ipynb ├── llama2_src_03_RoPE_01_complex_position_encoding.ipynb ├── llama2_src_03_RoPE_apply_rotary_emb.ipynb ├── llama2_src_cache_kv.ipynb ├── llama2_src_generate.ipynb ├── llama2_src_grouped_query_attention.ipynb ├── llama2_src_text_chat.ipynb ├── llama_tokenizer.ipynb ├── llm_training_sft.ipynb ├── llms_models_datasets_finetune.ipynb ├── lora_all_parameters.ipynb ├── misc └── gradio.ipynb ├── model_finetune_quantization.ipynb ├── moe ├── Mistral_basics.ipynb ├── basics.ipynb └── mistral_src_code.ipynb ├── multi_model_vit_clip.ipynb ├── peft ├── 04_PEFT_lora.ipynb ├── PEFT_supp.ipynb ├── lora_inject.ipynb ├── lora_merge.ipynb ├── lora_svd_basics.ipynb ├── lora_vs_whole_model_1.ipynb └── lora_vs_whole_model_2.ipynb ├── position_encoding.ipynb ├── position_encoding ├── long_context_window.ipynb ├── rope-beta-encoding.ipynb └── rope_cope.ipynb ├── pretrain_corpus_大模型预训练语料.ipynb ├── quantization_gptq.ipynb ├── reward_model ├── Mistral-7b-dpo.ipynb ├── bradley_terry_modeling.ipynb ├── dpo.ipynb ├── dpo_llama2.ipynb ├── imgs │ └── quality-labels.png ├── ppo_basics.ipynb ├── reward_model_Bradley_Terry_model.ipynb ├── reward_model_ppo.ipynb ├── sft_llama2.ipynb └── trl_reward_trainer.ipynb ├── sentencepiece.ipynb ├── supplement_01_svd.ipynb ├── tokenizer.model ├── tokenizer ├── EsperBERTo │ ├── merges.txt │ └── vocab.json ├── how_to_train_a_tokenizer.ipynb ├── pad_eos.ipynb ├── tokenizer_gpt4o.ipynb ├── tokenizer_supply.ipynb └── xyj.txt ├── tokenizer_basics.ipynb ├── tokenizer_与词表拓展.ipynb ├── transformers_相关包及安装.ipynb ├── tricks ├── Speculative_Decoding.ipynb ├── multi-task-learning.ipynb └── speculative_decoding.png ├── trl_basics.ipynb ├── trl_hf ├── DPO_Final.pdf ├── dpo_math.ipynb ├── imgs │ ├── instructGPT.jpeg │ ├── lm_rl.png │ └── rlhf_dpo.png ├── lm_meets_rl.ipynb ├── rlhf_ppo_vs_dpo.ipynb ├── trl_basics.ipynb ├── trl_dpo.ipynb ├── trl_ppo_basics.ipynb ├── trl_ppo_pipeline.ipynb ├── trl_ppo_step.ipynb ├── trl_ppotrainer.ipynb ├── trl_ppotrainer_helloworld.ipynb └── trl_trainer_utils.ipynb ├── 数据集 └── 经典-标准数据集.ipynb └── 领域大模型 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