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README.md

@@ -1,199 +1,506 @@
 ---
 library_name: transformers
-tags: []
+language:
+- en
+- fr
+- it
+- pt
+- hi
+- es
+- th
+- de
+base_model:
+- meta-llama/Llama-3.1-70B
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-3
+- heretic
+- uncensored
+- decensored
+- abliterated
+extra_gated_prompt: "### LLAMA 3.3 COMMUNITY LICENSE AGREEMENT\nLlama 3.3 Version\
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+  \ to this Agreement. The courts of California shall have exclusive jurisdiction\
+  \ of any dispute arising out of this Agreement.\n### Llama 3.3 Acceptable Use Policy\n\
+  Meta is committed to promoting safe and fair use of its tools and features, including\
+  \ Llama 3.3. If you access or use Llama 3.3, you agree to this Acceptable Use Policy\
+  \ (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3\\\
+  _3/use-policy](https://www.llama.com/llama3_3/use-policy).\nProhibited Uses\nWe\
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+  \ 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\n   2. Engage\
+  \ in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying\
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+  \ 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\n   4. Engage in the unauthorized or unlicensed\
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+  \ or remove usage restrictions or other safety measures, or to enable functionality\
+  \ disabled by Meta\n\n2. Engage in, promote, incite, facilitate, or assist in the\
+  \ planning or development of activities that present a risk of death or bodily harm\
+  \ to individuals, including use of Llama 3.3 related to the following:\n\n   1.\
+  \ Military, warfare, nuclear industries or applications, espionage, use for materials\
+  \ or activities that are subject to the International Traffic Arms Regulations (ITAR)\
+  \ maintained by the United States Department of State or to the U.S. Biological\
+  \ Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation\
+  \ Act of 1997\n\n   2. Guns and illegal weapons (including weapon development)\n\
+  \n   3. Illegal drugs and regulated/controlled substances\n\n   4. Operation of\
+  \ critical infrastructure, transportation technologies, or heavy machinery\n\n \
+  \  5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n\
+  \n   6. Any content intended to incite or promote violence, abuse, or any infliction\
+  \ of bodily harm to an individual\n\n3. Intentionally deceive or mislead others,\
+  \ including use of Llama 3.3 related to the following:\n\n   1. Generating, promoting,\
+  \ or furthering fraud or the creation or promotion of disinformation\n\n   2. Generating,\
+  \ promoting, or furthering defamatory content, including the creation of defamatory\
+  \ statements, images, or other content\n\n   3. Generating, promoting, or further\
+  \ distributing spam\n\n   4. Impersonating another individual without consent, authorization,\
+  \ or legal right\n\n   5. Representing that the use of Llama 3.3 or outputs are\
+  \ human-generated\n\n   6. Generating or facilitating false online engagement, including\
+  \ fake reviews and other means of fake online engagement\n\n4. Fail to appropriately\
+  \ disclose to end users any known dangers of your AI system\n5. Interact with third\
+  \ party tools, models, or software designed to generate unlawful content or engage\
+  \ in unlawful or harmful conduct and/or represent that the outputs of such tools,\
+  \ models, or software are associated with Meta or Llama 3.3\nWith respect to any\
+  \ multimodal models included in Llama 3.3, the rights granted under Section 1(a)\
+  \ of the Llama 3.3 Community License Agreement are not being granted to you if you\
+  \ are an individual domiciled in, or a company with a principal place of business\
+  \ in, the European Union. This restriction does not apply to end users of a product\
+  \ or service that incorporates any such multimodal models.\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://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\
+  \   * Reporting risky content generated by the model: [developers.facebook.com/llama\\\
+  _output\\_feedback](http://developers.facebook.com/llama_output_feedback)   * Reporting\
+  \ bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\
+  \   * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\
+  \ 3.3: 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:
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+  : checkbox
+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
+license: llama3.3
 ---
+# This is a decensored version of [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), made using [Heretic](https://github.com/p-e-w/heretic) v1.0.1
 
-# Model Card for Model ID
+## Abliteration parameters
 
-<!-- Provide a quick summary of what the model is/does. -->
+| Parameter | Value |
+| :-------- | :---: |
+| **direction_index** | per layer |
+| **attn.o_proj.max_weight** | 1.49 |
+| **attn.o_proj.max_weight_position** | 58.81 |
+| **attn.o_proj.min_weight** | 1.38 |
+| **attn.o_proj.min_weight_distance** | 47.39 |
+| **mlp.down_proj.max_weight** | 1.42 |
+| **mlp.down_proj.max_weight_position** | 50.58 |
+| **mlp.down_proj.min_weight** | 0.74 |
+| **mlp.down_proj.min_weight_distance** | 38.38 |
 
+## Performance
 
+| Metric | This model | Original model ([meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)) |
+| :----- | :--------: | :---------------------------: |
+| **KL divergence** | 1.63 | 0 *(by definition)* |
+| **Refusals** | 5/100 | 72/100 |
 
-## Model Details
+-----
 
-### Model Description
+## Model Information
 
-<!-- Provide a longer summary of what this model is. -->
+The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+**Model developer**: Meta
 
-- **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]
+**Model Architecture:** Llama 3.3 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. 
 
-### Model Sources [optional]
+|  | Training Data | Params | Input modalities | Output modalities | Context length | GQA | Token count | Knowledge cutoff |
+| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
+| Llama 3.3 (text only)  | A new mix of publicly available online data. | 70B | Multilingual Text | Multilingual Text and code  | 128k | Yes | 15T+ | December 2023 |
 
-<!-- Provide the basic links for the model. -->
+**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+**Llama 3.3 model**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
 
-## Uses
+**Model Release Date:** 
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+* **70B Instruct: December 6, 2024** 
 
-### Direct Use
+**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.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+**License** A custom commercial license, the Llama 3.3 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3\_3/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE)
 
-[More Information Needed]
+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 3.3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). 
 
-### Downstream Use [optional]
+## Intended Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+**Intended Use Cases** Llama 3.3 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 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases. 
 
-[More Information Needed]
+**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 3.3 Community License. Use in languages beyond those explicitly referenced as supported in this model card\*\*.
 
-### Out-of-Scope Use
+\*\*Note: Llama 3.3 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.3 models for languages beyond the 8 supported languages provided they comply with the Llama 3.3 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.3 in additional languages is done in a safe and responsible manner.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+## How to use
 
-[More Information Needed]
+This repository contains two versions of Llama-3.3-70B-Instruct, for use with transformers and with the original `llama` codebase.
 
-## Bias, Risks, and Limitations
+### Use with transformers
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Starting with `transformers >= 4.45.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
 
-[More Information Needed]
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
 
-### Recommendations
+See the snippet below for usage with Transformers:
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+import transformers
+import torch
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+model_id = "meta-llama/Llama-3.3-70B-Instruct"
 
-## How to Get Started with the Model
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
 
-Use the code below to get started with the model.
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
 
-[More Information Needed]
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+```
 
-## Training Details
+### Tool use with transformers
 
-### Training Data
+LLaMA-3.3 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/).
 
-<!-- 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. -->
+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:
 
-[More Information Needed]
+```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!
 
-### Training Procedure
+# 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?"}
+]
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
+```
 
-#### Preprocessing [optional]
+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:
 
-[More Information Needed]
+```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:
 
-#### Training Hyperparameters
+```python
+messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
+```
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+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).
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Use with `bitsandbytes`
 
-[More Information Needed]
+The model checkpoints can be used in `8-bit` and `4-bit` for further memory optimisations using `bitsandbytes` and `transformers`
 
-## Evaluation
+See the snippet below for usage:
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-### Testing Data, Factors & Metrics
+model_id = "meta-llama/Llama-3.3-70B-Instruct"
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
 
-#### Testing Data
+quantized_model = AutoModelForCausalLM.from_pretrained(
+	model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
 
-<!-- This should link to a Dataset Card if possible. -->
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+input_text = "What are we having for dinner?"
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-[More Information Needed]
+output = quantized_model.generate(**input_ids, max_new_tokens=10)
 
-#### Factors
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+To load in 4-bit simply pass `load_in_4bit=True`
 
-[More Information Needed]
+### Use with `llama`
 
-#### Metrics
+Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
 
-[More Information Needed]
+```
+huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --include "original/*" --local-dir Llama-3.3-70B-Instruct
+```
 
-### Results
+## Hardware and Software
 
-[More Information Needed]
+**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.
 
-#### Summary
+**Training Energy Use** Training utilized a cumulative of **39.3**M 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.
 
-## Model Examination [optional]
+|  | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
+| :---- | :---: | :---: | :---: | :---: |
+| Llama 3.3 70B | 7.0M | 700 | 2,040 | 0 |
 
-<!-- Relevant interpretability work for the model goes here -->
+## 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.
 
-[More Information Needed]
+## Training Data
 
-## Environmental Impact
+**Overview:** Llama 3.3 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. 
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+**Data Freshness:** The pretraining data has a cutoff of December 2023\.
 
-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).
+## Benchmarks \- English Text
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+In this section, we report the results for Llama 3.3 relative to our previous models. 
 
-## Technical Specifications [optional]
+### Instruction tuned models
 
-### Model Architecture and Objective
+## 
 
-[More Information Needed]
+| Category | Benchmark | \# Shots | Metric | Llama 3.1 8B Instruct | Llama 3.1 70B Instruct | Llama-3.3 70B Instruct | Llama 3.1 405B Instruct |
+| :---- | :---- | ----- | :---- | ----- | ----- | ----- | ----- |
+|  | MMLU (CoT) | 0 | macro\_avg/acc | 73.0 | 86.0 | 86.0 | 88.6 |
+|  | MMLU Pro (CoT) | 5 | macro\_avg/acc | 48.3 | 66.4 | 68.9 | 73.3 |
+| Steerability | IFEval |  |  | 80.4 | 87.5 | 92.1 | 88.6 |
+| Reasoning | GPQA Diamond (CoT) | 0 | acc | 31.8 | 48.0 | 50.5 | 49.0 |
+| Code | HumanEval | 0 | pass@1 | 72.6 | 80.5 | 88.4 | 89.0 |
+|  | MBPP EvalPlus (base) | 0 | pass@1 | 72.8 | 86.0 | 87.6 | 88.6 |
+| Math | MATH (CoT) | 0 | sympy\_intersection\_score | 51.9 | 68.0 | 77.0 | 73.8 |
+| Tool Use | BFCL v2 | 0 | overall\_ast\_summary/macro\_avg/valid | 65.4 | 77.5 | 77.3 | 81.1 |
+| Multilingual | MGSM | 0 | em | 68.9 | 86.9 | 91.1 | 91.6 |
 
-### Compute Infrastructure
+## 
 
-[More Information Needed]
+## Responsibility & Safety
 
-#### Hardware
+As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
 
-[More Information Needed]
+* 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.
 
-#### Software
+### Responsible deployment
 
-[More Information Needed]
+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 3.3 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.
 
-## Citation [optional]
+#### Llama 3.3 instruct
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+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.
 
-**BibTeX:**
+**Fine-tuning data**
+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.
 
-[More Information Needed]
+**Refusals and Tone**
+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.
 
-**APA:**
+#### Llama 3.3 systems
 
-[More Information Needed]
+**Large language models, including Llama 3.3, 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.
+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.
 
-## Glossary [optional]
+#### Capability specific considerations 
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+**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 3.3 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. 
 
-## More Information [optional]
+### Evaluations
 
-[More Information Needed]
+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.   
+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.
 
-## Model Card Authors [optional]
+**Red teaming**   
+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.   
+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. . 
 
-[More Information Needed]
+### Critical and other risks 
 
-## Model Card Contact
+### We specifically focused our efforts on mitigating the following critical risk areas:
 
-[More Information Needed]
+**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
+To assess risks related to proliferation of chemical and biological weapons of the Llama 3 family of models, we performed uplift testing designed to assess whether use of the Llama 3 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
+
+### **2\. Child Safety**
+
+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. 
+
+**3\. Cyber attack enablement**
+Our cyber attack uplift study investigated whether the Llama 3 family of LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
+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.
+
+### 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). 
+
+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.
+
+## Ethical Considerations and Limitations
+
+The core values of Llama 3.3 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 3.3 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. 
+
+But Llama 3.3 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 3.3’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 3.3 model, 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.