Update model card (#1)

- Update model card (bb1fbd306a4958f24f17632508827bcda55ac788)

README.md

@@ -36,7 +36,7 @@ Model capabilities:
 
 **Model Developers** Meta
 
-**Variations** Code Llama comes in three model sizes, and three variants:
+**Variations** Code Llama comes in four model sizes, and three variants:
 
 * Code Llama: base models designed for general code synthesis and understanding
 * Code Llama - Python: designed specifically for Python
@@ -50,9 +50,9 @@ All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.
 
 **Output** Models generate text only.
 
-**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
+**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and supports up to 100k tokens at inference time.
 
-**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
+**Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024.
 
 **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
 
@@ -68,11 +68,13 @@ All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.
 ## Hardware and Software
 **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
 
+**Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.
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 ## Evaluation Results
 
 See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
 
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 ## Ethical Considerations and Limitations
 
 Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.