models Llama 2 7b chat

Llama-2-7b-chat

Overview

Description: # Model Details Note: Use of this model is governed by the Meta license. Click on View License above. Meta has developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. 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. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|A new mix of publicly available online data|7B|4k|✗|2.0T|3.0 x 10^-4| |Llama 2|A new mix of publicly available online data|13B|4k|✗|2.0T|3.0 x 10^-4| |Llama 2|A new mix of publicly available online data|70B|4k|✔|2.0T|1.5 x 10^-4| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger model -- 70B -- uses Grouped-Query Attention (GQA) for improved inference scalability. Model Developers Meta AI Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language optimized transformer. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available. Please see the Artifacts tab. Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README, or by opening an issue in the GitHub repository. How to run the model locally Llama 2 is available for you to run on device; checkout https://github.com/microsoft/Llama-2-Onnx. # Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). , Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. # Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| CO₂ emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. # Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. # Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks. For all the evaluations, we use our internal evaluations library. For these models, we always pick the best score between our evaluation framework and any publicly reported results. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|37.5|71.9|63.6|69.4|35.2|68.9|51.2|54.2| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. Popular Aggregated Benchmarks: We report the overall results for MMLU (5 shot), Big Bench Hard (BBH) (3 shot), and AGI Eval (3–5 shot). For AGI Eval, we only evaluate on the English tasks and report the average. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|21.25| |Llama 2|13B|41.86|26.10| |Llama 2|70B|50.18|24.60| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|0.00| |Llama-2-Chat|13B|62.18|0.00| |Llama-2-Chat|70B|64.14|0.01| Evaluation of fine-tuned LLMs on different safety datasets. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). # Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has not, and could not, cover all scenarios, including uses in languages other than English. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/ ## Sample inputs and outputs (for real-time inference) ### Sample input json { "input_data": { "input_string": [ { "role": "user", "content": "I am going to Paris, what should I see?" }, { "role": "assistant", "content": "Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world." }, { "role": "user", "content": "What is so great about #1?" } ], "parameters": { "temperature": 0.6, "top_p": 0.9, "do_sample": true, "max_new_tokens": 200 } } } ### Sample output json { "output": " The Eiffel Tower is considered one of the greatest engineering feats of the 19th century and is an iconic symbol of Paris and France. Here are some reasons why it's so great:\n\n1. Unique Design: The Eiffel Tower's unique design, with its lattice-like structure and iron beams, makes it a striking and recognizable landmark.\n2. Breathtaking Views: The Eiffel Tower offers panoramic views of the city of Paris and its surroundings, making it a popular spot for tourists and locals alike.\n3. Historical Significance: The Eiffel Tower was built for the 1889 World's Fair and was initially intended to be a temporary structure. However, it has become a permanent fixture in Paris and a symbol of French culture and engineering.\n4. Romantic Atmosphere: The Eiffel Tower is" }

Version: 12

Tags

Featured Preview evaluation_compute_allow_list : ['Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_ND40rs_v2', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4'] inference_compute_allow_list : ['Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_ND40rs_v2', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4'] sku_to_num_replicas_map : ordereddict([('Standard_NC12s_v3', 2), ('Standard_NC24s_v3', 4), ('Standard_ND40rs_v2', 8), ('Standard_ND96asr_v4', 8), ('Standard_ND96amsr_A100_v4', 8), ('Default', 1)]) license : custom author : meta disable-batch : true task : chat-completion inference_supported_envs : ['ds_mii'] SharedComputeCapacityEnabled

View in Studio: https://ml.azure.com/registries/azureml/models/Llama-2-7b-chat/version/12

License: custom

Properties

datasets: Publicly available sources

inference-recommended-sku: Standard_NC12s_v3, Standard_NC24s_v3, Standard_ND40rs_v2, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4

languages: EN

azureml.copyRegistryFilesToWorkspace: True

SharedComputeCapacityEnabled: True