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Paper · 2307.09288 · arXiv.org · 2023

Llama 2: Open Foundation and Fine-Tuned Chat Models

Thomas Scialom, Rui Hou, Naman Goyal, Binh Tang, Angela Fan, Vedanuj Goswami, Cristian Ferrer, Sharan Narang, Saghar Hosseini, Pushkar Mishra, Adina Williams, Yixin Nie, and 58 more

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 52 functions out of this paper's own repositories and ran 31 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
IBM/Dromedary canonical 16 of 16
squeezeailab/squeezellm canonical 5 of 15
meetyou-ai-lab/can-mc-evaluate-llms — 4 of 7
Lightning-AI/lit-gpt — 4 of 5
glb400/Toy-RecLM — 1 of 5
zurichnlp/contradecode — 1 of 1
young-geng/easylm — 0 of 2
xverse-ai/xverse-13b — 0 of 1
FunctionStatusWhere it lives
repeat_kv Ran IBM/Dromedary/training/llama_with_flash_attn.py
pointer only (licence: GPL-3.0) · get_code("30d7eec482ebf6b1")
round_to_nearest_pole_sim Ran squeezeailab/squeezellm/squeezellm/quant.py
code served (permissive licence) · get_code("cdc0ad3109c8984b")
Config Ran Lightning-AI/lit-gpt/litgpt/model.py
code served (permissive licence) · get_code("bf78fefededc09f7")
FeedForward Ran glb400/Toy-RecLM/model.py
code served (permissive licence) · get_code("9715deb8f27d4266")
LlamaConfig Ran meetyou-ai-lab/can-mc-evaluate-llms/Embeddings/src/llama/modeling_llama.py
code served (permissive licence) · get_code("1bcd8d227ad19b17")
LlamaMLP Ran meetyou-ai-lab/can-mc-evaluate-llms/Embeddings/src/llama/modeling_llama.py
code served (permissive licence) · get_code("50260ad39d7c5e06")
LlamaRMSNorm Ran meetyou-ai-lab/can-mc-evaluate-llms/Embeddings/src/llama/modeling_llama.py
code served (permissive licence) · get_code("98b6bb174c074dae")
LlamaRotaryEmbedding Ran meetyou-ai-lab/can-mc-evaluate-llms/Embeddings/src/llama/modeling_llama.py
code served (permissive licence) · get_code("26ec53f064adbca5")
PromptTemplate Ran zurichnlp/contradecode/translation_models/llama.py
code served (permissive licence) · get_code("4d273bfb0feb28e0")
RMSNorm Ran Lightning-AI/lit-gpt/litgpt/model.py
code served (permissive licence) · get_code("1e7100681c300ca2")
apply_rotary_emb Ran IBM/Dromedary/llama_dromedary/llama_dromedary/model.py
pointer only (licence: GPL-3.0) · get_code("b47d48e431b34acd")
apply_rotary_pos_emb Ran IBM/Dromedary/training/llama_with_flash_attn.py
pointer only (licence: GPL-3.0) · get_code("e34097675d132bc1")
check_indicator_and_length Ran Lightning-AI/lit-gpt/litgpt/model.py
code served (permissive licence) · get_code("faa77017b4beec30")
extract_alpaca_dataset Ran IBM/Dromedary/training/data_utils_sft.py
pointer only (licence: GPL-3.0) · get_code("b5445674ab17410f")
extract_dromedary_dataset Ran IBM/Dromedary/training/data_utils_sft.py
pointer only (licence: GPL-3.0) · get_code("48456f10725088b5")
extract_unnatural_instructions_data Ran IBM/Dromedary/training/data_utils_sft.py
pointer only (licence: GPL-3.0) · get_code("f82430123a91cf21")
find_layers Ran squeezeailab/squeezellm/squeezellm/modelutils.py
code served (permissive licence) · get_code("a9e7f2cdf016b88b")
find_multiple Ran Lightning-AI/lit-gpt/litgpt/model.py
code served (permissive licence) · get_code("ffe8d3a5e6f4b477")
generate_prompt Ran IBM/Dromedary/inference/run_stream_chatbot_demo.py
pointer only (licence: GPL-3.0) · get_code("55e55d5b9201bf5c")
generate_prompt Ran IBM/Dromedary/mc_evaluation/evaluate_hhh_eval.py
pointer only (licence: GPL-3.0) · get_code("c478c1e77422f0de")
get_log_prob Ran IBM/Dromedary/mc_evaluation/evaluate_hhh_eval.py
pointer only (licence: GPL-3.0) · get_code("0c0f540ee97855aa")
get_log_prob Ran IBM/Dromedary/mc_evaluation/evaluate_truthfulqa_mc.py
pointer only (licence: GPL-3.0) · get_code("47407f4c74f590df")
measure_multiple_choice_grade Ran IBM/Dromedary/mc_evaluation/evaluate_hhh_eval.py
pointer only (licence: GPL-3.0) · get_code("b14dfe7367095841")
measure_multiple_choice_grade Ran IBM/Dromedary/mc_evaluation/evaluate_truthfulqa_mc.py
pointer only (licence: GPL-3.0) · get_code("dfcc630d6e498f6f")
precompute_freqs_cis Ran IBM/Dromedary/llama_dromedary/llama_dromedary/model.py
pointer only (licence: GPL-3.0) · get_code("14a84c2cbfebc413")
remove_outliers Ran squeezeailab/squeezellm/squeezellm/outliers.py
code served (permissive licence) · get_code("303e2b878d149f4a")
remove_outliers_by_sensitivity Ran squeezeailab/squeezellm/squeezellm/outliers.py
code served (permissive licence) · get_code("e3a24217edf7812c")
remove_outliers_by_threshold Ran squeezeailab/squeezellm/squeezellm/outliers.py
code served (permissive licence) · get_code("3d2bc2ac00f3f434")
reshape_for_broadcast Ran IBM/Dromedary/llama_dromedary/llama_dromedary/model.py
pointer only (licence: GPL-3.0) · get_code("70bf6ebaafd266c4")
rotate_half Ran IBM/Dromedary/training/llama_with_flash_attn.py
pointer only (licence: GPL-3.0) · get_code("b99eea6376d1e212")
sample_top_p Ran IBM/Dromedary/llama_dromedary/llama_dromedary/generation.py
pointer only (licence: GPL-3.0) · get_code("8845976729f4c4ee")
Attention Not yet run glb400/Toy-RecLM/model.py
code served (permissive licence) · get_code("4334c46d3961b3fb")
FlaxLLaMAModel Not yet run young-geng/easylm/EasyLM/models/llama/llama_model.py
code served (permissive licence) · get_code("cf00f2eef08595e6")
FlaxLLaMAPreTrainedModel Not yet run young-geng/easylm/EasyLM/models/llama/llama_model.py
code served (permissive licence) · get_code("72032d6f73d90892")
LLaMA2_SASRec Not yet run glb400/Toy-RecLM/model.py
code served (permissive licence) · get_code("4427e517543e4577")
LLaMAMLP Not yet run Lightning-AI/lit-gpt/litgpt/model.py
code served (permissive licence) · get_code("b336e5e4f4b40ceb")
LlamaAttention Not yet run meetyou-ai-lab/can-mc-evaluate-llms/Embeddings/src/llama/modeling_llama.py
code served (permissive licence) · get_code("1104581ac75ea9c6")
LlamaDecoderLayer Not yet run meetyou-ai-lab/can-mc-evaluate-llms/Embeddings/src/llama/modeling_llama.py
code served (permissive licence) · get_code("660d5e5234e28212")
LlamaModel Not yet run meetyou-ai-lab/can-mc-evaluate-llms/Embeddings/src/llama/modeling_llama.py
code served (permissive licence) · get_code("865ca1b3e9c1aa5c")
ModelArgs Not yet run glb400/Toy-RecLM/model.py
code served (permissive licence) · get_code("75e3a8170f256bb7")
TransformerBlock Not yet run glb400/Toy-RecLM/model.py
code served (permissive licence) · get_code("61aae005c3076a9b")
get_c4 Not yet run squeezeailab/squeezellm/squeezellm/datautils.py
code served (permissive licence) · get_code("92dc709e4e73e133")
get_model Not yet run squeezeailab/squeezellm/llama.py
code served (permissive licence) · get_code("fd802dc39c73084a")
get_module_names Not yet run squeezeailab/squeezellm/squeezellm/model_parse.py
code served (permissive licence) · get_code("b65e0b634a94c717")
get_ptb Not yet run squeezeailab/squeezellm/squeezellm/datautils.py
code served (permissive licence) · get_code("b1e98f962f5ae82d")
get_wikitext2 Not yet run squeezeailab/squeezellm/squeezellm/datautils.py
code served (permissive licence) · get_code("efc347eff314b865")
init_model Not yet run xverse-ai/xverse-13b/chat_demo.py
code served (permissive licence) · get_code("b796bb9465578920")
kmeans_fit Not yet run squeezeailab/squeezellm/quantization/nuq.py
code served (permissive licence) · get_code("77fbadc3b9632874")
load_model Not yet run squeezeailab/squeezellm/squeezellm/model_parse.py
code served (permissive licence) · get_code("b66c43d90a1a3a1b")
load_quant Not yet run squeezeailab/squeezellm/llama.py
code served (permissive licence) · get_code("c0d4637f25eabf00")
parse_model Not yet run squeezeailab/squeezellm/squeezellm/model_parse.py
code served (permissive licence) · get_code("dd982ece7960b018")
tiktoken_tokenizer Not yet run squeezeailab/squeezellm/models/xgen-7b-8k-base/tokenization_xgen.py
code served (permissive licence) · get_code("5445de6acd6035bb")

Repositories linked to this paper

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Abstract

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

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