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@@ -1,199 +1,1291 @@
 ---
-library_name: transformers
-tags: []
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+pipeline_tag: text-generation
+tags:
+- facebook
+- unsloth
+- meta
+- pytorch
+- llama
+- llama-3
+- heretic
+- uncensored
+- decensored
+- abliterated
+extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
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+  \ of the State of California without regard to choice of law principles, and the\
+  \ UN Convention on Contracts for the International Sale of Goods does not apply\
+  \ to this Agreement. The courts of California shall have exclusive jurisdiction\
+  \ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\
+  Meta is committed to promoting safe and fair use of its tools and features, including\
+  \ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
+  \ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
+  #### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
+  \ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
+  \ the law or others’ rights, including to:\n    1. Engage in, promote, generate,\
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+  \ illegal distribution of information or materials to minors, including obscene\
+  \ materials, or failure to employ legally required age-gating in connection with\
+  \ such information or materials.\n        5. Sexual solicitation\n        6. Any\
+  \ other criminal activity\n    3. Engage in, promote, incite, or facilitate the\
+  \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
+  \    4. Engage in, promote, incite, or facilitate discrimination or other unlawful\
+  \ or harmful conduct in the provision of employment, employment benefits, credit,\
+  \ housing, other economic benefits, or other essential goods and services\n    5.\
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+  \ but not limited to, financial, legal, medical/health, or related professional\
+  \ practices\n    6. Collect, process, disclose, generate, or infer health, demographic,\
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+  \ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\
+  \ to the following:\n    1. Military, warfare, nuclear industries or applications,\
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+  \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
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+  \ infrastructure, transportation technologies, or heavy machinery\n    5. Self-harm\
+  \ or harm to others, including suicide, cutting, and eating disorders\n    6. Any\
+  \ content intended to incite or promote violence, abuse, or any infliction of bodily\
+  \ harm to an individual\n3. Intentionally deceive or mislead others, including use\
+  \ of Llama 3.1 related to the following:\n    1. Generating, promoting, or furthering\
+  \ fraud or the creation or promotion of disinformation\n    2. Generating, promoting,\
+  \ or furthering defamatory content, including the creation of defamatory statements,\
+  \ images, or other content\n    3. Generating, promoting, or further distributing\
+  \ spam\n    4. Impersonating another individual without consent, authorization,\
+  \ or legal right\n    5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
+  \    6. Generating or facilitating false online engagement, including fake reviews\
+  \ and other means of fake online engagement\n4. Fail to appropriately disclose to\
+  \ end users any known dangers of your AI system\nPlease report any violation of\
+  \ this Policy, software “bug,” or other problems that could lead to a violation\
+  \ of this Policy through one of the following means:\n    * Reporting issues with\
+  \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
+  \    * Reporting risky content generated by the model:\n    developers.facebook.com/llama_output_feedback\n\
+  \    * Reporting bugs and security concerns: facebook.com/whitehat/info\n    * Reporting\
+  \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com"
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Date of birth: date_picker
+  Country: country
+  Affiliation: text
+  Job title:
+    type: select
+    options:
+    - Student
+    - Research Graduate
+    - AI researcher
+    - AI developer/engineer
+    - Reporter
+    - Other
+  geo: ip_location
+  ? By clicking Submit below I accept the terms of the license and acknowledge that
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+    with the Meta Privacy Policy
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+extra_gated_description: The information you provide will be collected, stored, processed
+  and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
+extra_gated_button_content: Submit
 ---
+# This is a decensored version of [unsloth/Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Llama-3.1-8B-Instruct), made using [Heretic](https://github.com/p-e-w/heretic) v1.0.0
+
+## Abliteration parameters
+
+| Parameter | Value |
+| :-------- | :---: |
+| **direction_index** | 13.20 |
+| **attn.o_proj.max_weight** | 1.45 |
+| **attn.o_proj.max_weight_position** | 23.85 |
+| **attn.o_proj.min_weight** | 1.34 |
+| **attn.o_proj.min_weight_distance** | 15.98 |
+| **mlp.down_proj.max_weight** | 0.97 |
+| **mlp.down_proj.max_weight_position** | 20.83 |
+| **mlp.down_proj.min_weight** | 0.61 |
+| **mlp.down_proj.min_weight_distance** | 12.90 |
+
+## Performance
+
+| Metric | This model | Original model ([unsloth/Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Llama-3.1-8B-Instruct)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 0.02 | 0 *(by definition)* |
+| **Refusals** | 3/100 | 96/100 |
+
+-----
+
+
+## Model Information
+
+The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
+
+**Model developer**: Meta
+
+**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. 
+
+
+<table>
+  <tr>
+   <td>
+   </td>
+   <td><strong>Training Data</strong>
+   </td>
+   <td><strong>Params</strong>
+   </td>
+   <td><strong>Input modalities</strong>
+   </td>
+   <td><strong>Output modalities</strong>
+   </td>
+   <td><strong>Context length</strong>
+   </td>
+   <td><strong>GQA</strong>
+   </td>
+   <td><strong>Token count</strong>
+   </td>
+   <td><strong>Knowledge cutoff</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="3" >Llama 3.1 (text only)
+   </td>
+   <td rowspan="3" >A new mix of publicly available online data.
+   </td>
+   <td>8B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+   <td rowspan="3" >15T+
+   </td>
+   <td rowspan="3" >December 2023
+   </td>
+  </tr>
+  <tr>
+   <td>70B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+  </tr>
+  <tr>
+   <td>405B
+   </td>
+   <td>Multilingual Text
+   </td>
+   <td>Multilingual Text and code
+   </td>
+   <td>128k
+   </td>
+   <td>Yes
+   </td>
+  </tr>
+</table>
+
+
+**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
+
+**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
+
+**Model Release Date:** July 23, 2024.
+
+**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, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
+
+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](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). 
+
+
+## Intended Use
+
+**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. 
+
+**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
 
-# Model Card for Model ID
+**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
 
-<!-- Provide a quick summary of what the model is/does. -->
+## How to use
 
+This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase.
 
+### Use with transformers
 
-## Model Details
+Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import transformers
+import torch
+
+model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+```
+
+Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
+
+### Tool use with transformers
+
+LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
+
+Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. 
+Here is a quick example showing a single simple tool:
+
+```python
+# First, define a tool
+def get_current_temperature(location: str) -> float:
+    """
+    Get the current temperature at a location.
+    
+    Args:
+        location: The location to get the temperature for, in the format "City, Country"
+    Returns:
+        The current temperature at the specified location in the specified units, as a float.
+    """
+    return 22.  # A real function should probably actually get the temperature!
+
+# Next, create a chat and apply the chat template
+messages = [
+  {"role": "system", "content": "You are a bot that responds to weather queries."},
+  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
+```
+
+You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
+
+```python
+tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
+messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
+```
+
+and then call the tool and append the result, with the `tool` role, like so:
 
-### Model Description
+```python
+messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
+```
 
-<!-- Provide a longer summary of what this model is. -->
+After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
+see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Use with `llama`
 
-### Model Sources [optional]
+Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
 
-<!-- Provide the basic links for the model. -->
+To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+```
+huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
+```
 
-## Uses
+## Hardware and Software
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
+
+**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. 
+
+
+**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
+
+
+<table>
+  <tr>
+   <td>
+   </td>
+   <td><strong>Training Time (GPU hours)</strong>
+   </td>
+   <td><strong>Training Power Consumption (W)</strong>
+   </td>
+   <td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
+<p>
+<strong>(tons CO2eq)</strong>
+   </td>
+   <td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
+<p>
+<strong>(tons CO2eq)</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>Llama 3.1 8B
+   </td>
+   <td>1.46M
+   </td>
+   <td>700
+   </td>
+   <td>420
+   </td>
+   <td>0
+   </td>
+  </tr>
+  <tr>
+   <td>Llama 3.1 70B
+   </td>
+   <td>7.0M
+   </td>
+   <td>700
+   </td>
+   <td>2,040
+   </td>
+   <td>0
+   </td>
+  </tr>
+  <tr>
+   <td>Llama 3.1 405B
+   </td>
+   <td>30.84M
+   </td>
+   <td>700
+   </td>
+   <td>8,930
+   </td>
+   <td>0
+   </td>
+  </tr>
+  <tr>
+   <td>Total
+   </td>
+   <td>39.3M
+   <td>
+<ul>
+
+</ul>
+   </td>
+   <td>11,390
+   </td>
+   <td>0
+   </td>
+  </tr>
+</table>
+
+
+
+The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149).  Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions  will not be incurred by others.
+
+
+## Training Data
+
+**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. 
+
+**Data Freshness:** The pretraining data has a cutoff of December 2023.
+
+
+## Benchmark scores
+
+In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. 
+
+### Base pretrained models
+
+
+<table>
+  <tr>
+   <td><strong>Category</strong>
+   </td>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong># Shots</strong>
+   </td>
+   <td><strong>Metric</strong>
+   </td>
+   <td><strong>Llama 3 8B</strong>
+   </td>
+   <td><strong>Llama 3.1 8B</strong>
+   </td>
+   <td><strong>Llama 3 70B</strong>
+   </td>
+   <td><strong>Llama 3.1 70B</strong>
+   </td>
+   <td><strong>Llama 3.1 405B</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="7" >General
+   </td>
+   <td>MMLU
+   </td>
+   <td>5
+   </td>
+   <td>macro_avg/acc_char
+   </td>
+   <td>66.7
+   </td>
+   <td>66.7
+   </td>
+   <td>79.5
+   </td>
+   <td>79.3
+   </td>
+   <td>85.2
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU-Pro (CoT)
+   </td>
+   <td>5
+   </td>
+   <td>macro_avg/acc_char
+   </td>
+   <td>36.2
+   </td>
+   <td>37.1
+   </td>
+   <td>55.0
+   </td>
+   <td>53.8
+   </td>
+   <td>61.6
+   </td>
+  </tr>
+  <tr>
+   <td>AGIEval English
+   </td>
+   <td>3-5
+   </td>
+   <td>average/acc_char
+   </td>
+   <td>47.1
+   </td>
+   <td>47.8
+   </td>
+   <td>63.0
+   </td>
+   <td>64.6
+   </td>
+   <td>71.6
+   </td>
+  </tr>
+  <tr>
+   <td>CommonSenseQA
+   </td>
+   <td>7
+   </td>
+   <td>acc_char
+   </td>
+   <td>72.6
+   </td>
+   <td>75.0
+   </td>
+   <td>83.8
+   </td>
+   <td>84.1
+   </td>
+   <td>85.8
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande
+   </td>
+   <td>5
+   </td>
+   <td>acc_char
+   </td>
+   <td>-
+   </td>
+   <td>60.5
+   </td>
+   <td>-
+   </td>
+   <td>83.3
+   </td>
+   <td>86.7
+   </td>
+  </tr>
+  <tr>
+   <td>BIG-Bench Hard (CoT)
+   </td>
+   <td>3
+   </td>
+   <td>average/em
+   </td>
+   <td>61.1
+   </td>
+   <td>64.2
+   </td>
+   <td>81.3
+   </td>
+   <td>81.6
+   </td>
+   <td>85.9
+   </td>
+  </tr>
+  <tr>
+   <td>ARC-Challenge
+   </td>
+   <td>25
+   </td>
+   <td>acc_char
+   </td>
+   <td>79.4
+   </td>
+   <td>79.7
+   </td>
+   <td>93.1
+   </td>
+   <td>92.9
+   </td>
+   <td>96.1
+   </td>
+  </tr>
+  <tr>
+   <td>Knowledge reasoning
+   </td>
+   <td>TriviaQA-Wiki
+   </td>
+   <td>5
+   </td>
+   <td>em
+   </td>
+   <td>78.5
+   </td>
+   <td>77.6
+   </td>
+   <td>89.7
+   </td>
+   <td>89.8
+   </td>
+   <td>91.8
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >Reading comprehension
+   </td>
+   <td>SQuAD
+   </td>
+   <td>1
+   </td>
+   <td>em
+   </td>
+   <td>76.4
+   </td>
+   <td>77.0
+   </td>
+   <td>85.6
+   </td>
+   <td>81.8
+   </td>
+   <td>89.3
+   </td>
+  </tr>
+  <tr>
+   <td>QuAC (F1)
+   </td>
+   <td>1
+   </td>
+   <td>f1
+   </td>
+   <td>44.4
+   </td>
+   <td>44.9
+   </td>
+   <td>51.1
+   </td>
+   <td>51.1
+   </td>
+   <td>53.6
+   </td>
+  </tr>
+  <tr>
+   <td>BoolQ
+   </td>
+   <td>0
+   </td>
+   <td>acc_char
+   </td>
+   <td>75.7
+   </td>
+   <td>75.0
+   </td>
+   <td>79.0
+   </td>
+   <td>79.4
+   </td>
+   <td>80.0
+   </td>
+  </tr>
+  <tr>
+   <td>DROP (F1)
+   </td>
+   <td>3
+   </td>
+   <td>f1
+   </td>
+   <td>58.4
+   </td>
+   <td>59.5
+   </td>
+   <td>79.7
+   </td>
+   <td>79.6
+   </td>
+   <td>84.8
+   </td>
+  </tr>
+</table>
+
+
+
+### Instruction tuned models
+
+
+<table>
+  <tr>
+   <td><strong>Category</strong>
+   </td>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong># Shots</strong>
+   </td>
+   <td><strong>Metric</strong>
+   </td>
+   <td><strong>Llama 3 8B Instruct</strong>
+   </td>
+   <td><strong>Llama 3.1 8B Instruct</strong>
+   </td>
+   <td><strong>Llama 3 70B Instruct</strong>
+   </td>
+   <td><strong>Llama 3.1 70B Instruct</strong>
+   </td>
+   <td><strong>Llama 3.1 405B Instruct</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >General
+   </td>
+   <td>MMLU
+   </td>
+   <td>5
+   </td>
+   <td>macro_avg/acc
+   </td>
+   <td>68.5
+   </td>
+   <td>69.4
+   </td>
+   <td>82.0
+   </td>
+   <td>83.6
+   </td>
+   <td>87.3
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU (CoT)
+   </td>
+   <td>0
+   </td>
+   <td>macro_avg/acc
+   </td>
+   <td>65.3
+   </td>
+   <td>73.0
+   </td>
+   <td>80.9
+   </td>
+   <td>86.0
+   </td>
+   <td>88.6
+   </td>
+  </tr>
+  <tr>
+   <td>MMLU-Pro (CoT)
+   </td>
+   <td>5
+   </td>
+   <td>micro_avg/acc_char
+   </td>
+   <td>45.5
+   </td>
+   <td>48.3
+   </td>
+   <td>63.4
+   </td>
+   <td>66.4
+   </td>
+   <td>73.3
+   </td>
+  </tr>
+  <tr>
+   <td>IFEval
+   </td>
+   <td>
+   </td>
+   <td>
+   </td>
+   <td>76.8
+   </td>
+   <td>80.4
+   </td>
+   <td>82.9
+   </td>
+   <td>87.5
+   </td>
+   <td>88.6
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="2" >Reasoning
+   </td>
+   <td>ARC-C
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>82.4
+   </td>
+   <td>83.4
+   </td>
+   <td>94.4
+   </td>
+   <td>94.8
+   </td>
+   <td>96.9
+   </td>
+  </tr>
+  <tr>
+   <td>GPQA
+   </td>
+   <td>0
+   </td>
+   <td>em
+   </td>
+   <td>34.6
+   </td>
+   <td>30.4
+   </td>
+   <td>39.5
+   </td>
+   <td>46.7
+   </td>
+   <td>50.7
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >Code
+   </td>
+   <td>HumanEval
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>60.4
+   </td>
+   <td>72.6
+   </td>
+   <td>81.7
+   </td>
+   <td>80.5
+   </td>
+   <td>89.0
+   </td>
+  </tr>
+  <tr>
+   <td>MBPP ++ base version
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>70.6
+   </td>
+   <td>72.8
+   </td>
+   <td>82.5
+   </td>
+   <td>86.0
+   </td>
+   <td>88.6
+   </td>
+  </tr>
+  <tr>
+   <td>Multipl-E HumanEval
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>-
+   </td>
+   <td>50.8
+   </td>
+   <td>-
+   </td>
+   <td>65.5
+   </td>
+   <td>75.2
+   </td>
+  </tr>
+  <tr>
+   <td>Multipl-E MBPP
+   </td>
+   <td>0
+   </td>
+   <td>pass@1
+   </td>
+   <td>-
+   </td>
+   <td>52.4
+   </td>
+   <td>-
+   </td>
+   <td>62.0
+   </td>
+   <td>65.7
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="2" >Math
+   </td>
+   <td>GSM-8K (CoT)
+   </td>
+   <td>8
+   </td>
+   <td>em_maj1@1
+   </td>
+   <td>80.6
+   </td>
+   <td>84.5
+   </td>
+   <td>93.0
+   </td>
+   <td>95.1
+   </td>
+   <td>96.8
+   </td>
+  </tr>
+  <tr>
+   <td>MATH (CoT)
+   </td>
+   <td>0
+   </td>
+   <td>final_em
+   </td>
+   <td>29.1
+   </td>
+   <td>51.9
+   </td>
+   <td>51.0
+   </td>
+   <td>68.0
+   </td>
+   <td>73.8
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="4" >Tool Use
+   </td>
+   <td>API-Bank
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>48.3
+   </td>
+   <td>82.6
+   </td>
+   <td>85.1
+   </td>
+   <td>90.0
+   </td>
+   <td>92.0
+   </td>
+  </tr>
+  <tr>
+   <td>BFCL
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>60.3
+   </td>
+   <td>76.1
+   </td>
+   <td>83.0
+   </td>
+   <td>84.8
+   </td>
+   <td>88.5
+   </td>
+  </tr>
+  <tr>
+   <td>Gorilla Benchmark API Bench
+   </td>
+   <td>0
+   </td>
+   <td>acc
+   </td>
+   <td>1.7
+   </td>
+   <td>8.2
+   </td>
+   <td>14.7
+   </td>
+   <td>29.7
+   </td>
+   <td>35.3
+   </td>
+  </tr>
+  <tr>
+   <td>Nexus (0-shot)
+   </td>
+   <td>0
+   </td>
+   <td>macro_avg/acc
+   </td>
+   <td>18.1
+   </td>
+   <td>38.5
+   </td>
+   <td>47.8
+   </td>
+   <td>56.7
+   </td>
+   <td>58.7
+   </td>
+  </tr>
+  <tr>
+   <td>Multilingual
+   </td>
+   <td>Multilingual MGSM (CoT)
+   </td>
+   <td>0
+   </td>
+   <td>em
+   </td>
+   <td>-
+   </td>
+   <td>68.9
+   </td>
+   <td>-
+   </td>
+   <td>86.9
+   </td>
+   <td>91.6
+   </td>
+  </tr>
+</table>
+
+#### Multilingual benchmarks
+
+<table>
+  <tr>
+   <td><strong>Category</strong>
+   </td>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Language</strong>
+   </td>
+   <td><strong>Llama 3.1 8B</strong>
+   </td>
+   <td><strong>Llama 3.1 70B</strong>
+   </td>
+   <td><strong>Llama 3.1 405B</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="9" ><strong>General</strong>
+   </td>
+   <td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
+   </td>
+   <td>Portuguese
+   </td>
+   <td>62.12
+   </td>
+   <td>80.13
+   </td>
+   <td>84.95
+   </td>
+  </tr>
+  <tr>
+   <td>Spanish
+   </td>
+   <td>62.45
+   </td>
+   <td>80.05
+   </td>
+   <td>85.08
+   </td>
+  </tr>
+  <tr>
+   <td>Italian
+   </td>
+   <td>61.63
+   </td>
+   <td>80.4
+   </td>
+   <td>85.04
+   </td>
+  </tr>
+  <tr>
+   <td>German
+   </td>
+   <td>60.59
+   </td>
+   <td>79.27
+   </td>
+   <td>84.36
+   </td>
+  </tr>
+  <tr>
+   <td>French
+   </td>
+   <td>62.34
+   </td>
+   <td>79.82
+   </td>
+   <td>84.66
+   </td>
+  </tr>
+  <tr>
+   <td>Hindi
+   </td>
+   <td>50.88
+   </td>
+   <td>74.52
+   </td>
+   <td>80.31
+   </td>
+  </tr>
+  <tr>
+   <td>Thai
+   </td>
+   <td>50.32
+   </td>
+   <td>72.95
+   </td>
+   <td>78.21
+   </td>
+  </tr>
+</table>
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+## Responsibility & Safety
 
-### Downstream Use [optional]
+As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
 
-### Out-of-Scope Use
+* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. 
+* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
+* Provide protections for the community to help prevent the misuse of our models.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+### Responsible deployment 
 
-## Bias, Risks, and Limitations
+Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. 
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+#### Llama 3.1 instruct 
 
-### Recommendations
+Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. 
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+**Fine-tuning data**
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. 
 
-## How to Get Started with the Model
+**Refusals and Tone**
 
-Use the code below to get started with the model.
+Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow  tone guidelines. 
 
-[More Information Needed]
 
-## Training Details
+#### Llama 3.1 systems
 
-### Training Data
+**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. 
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. 
 
-[More Information Needed]
 
-### Training Procedure
+#### New capabilities 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
 
-#### Preprocessing [optional]
+**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. 
 
-[More Information Needed]
+**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. 
 
 
-#### Training Hyperparameters
+### Evaluations
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. 
 
-#### Speeds, Sizes, Times [optional]
+Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+**Red teaming**
 
-[More Information Needed]
+For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. 
 
-## Evaluation
+We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity.  The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
+### Critical and other risks 
 
-#### Testing Data
+We specifically focused our efforts on mitigating the following critical risk areas:
 
-<!-- This should link to a Dataset Card if possible. -->
+**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
 
-[More Information Needed]
+To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. 
 
-#### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+**2. Child Safety**
 
-[More Information Needed]
+Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development.  For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 
 
-#### Metrics
+**3. Cyber attack enablement**
 
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+Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
 
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+Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
 
-### Results
+Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
 
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-#### Summary
+### Community 
 
+Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). 
 
+We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). 
 
-## Model Examination [optional]
+Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
 
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+## Ethical Considerations and Limitations
 
-## Environmental Impact
+The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. 
 
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’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 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.