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Llama-2-7b-chat-hf-qformer/LICENSE.txt ADDED
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+ LLAMA 2 COMMUNITY LICENSE AGREEMENT
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+ Llama 2 Version Release Date: July 18, 2023
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+ "Llama 2" means the foundational large language models and software and
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+ }
Llama-2-7b-chat-hf-qformer/README.md ADDED
@@ -0,0 +1,295 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ extra_gated_heading: You need to share contact information with Meta to access this model
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+ extra_gated_prompt: >-
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+ ### LLAMA 2 COMMUNITY LICENSE AGREEMENT
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+ USE POLICY
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+ ### Llama 2 Acceptable Use Policy
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+ Meta is committed to promoting safe and fair use of its tools and features,
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+ including Llama 2. If you access or use Llama 2, you agree to this Acceptable
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+ Use Policy (“Policy”). The most recent copy of this policy can be found at
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+ [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
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+ #### Prohibited Uses
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+ We want everyone to use Llama 2 safely and responsibly. You agree you will not
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+ use, or allow others to use, Llama 2 to:
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+ 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
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+ 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
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+ 2. Engage in, promote, incite, facilitate, or assist in the planning or
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+ development of activities that present a risk of death or bodily harm to
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+ individuals, including use of Llama 2 related to the following:
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+ 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
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+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
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+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
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+ 3. Intentionally deceive or mislead others, including use of Llama 2 related
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+ to the following:
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+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
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+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
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+ 3. Generating, promoting, or further distributing spam
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+ 4. Impersonating another individual without consent, authorization, or legal right
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+ 5. Representing that the use of Llama 2 or outputs are human-generated
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+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
148
+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
149
+ Please report any violation of this Policy, software “bug,” or other problems
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+ that could lead to a violation of this Policy through one of the following
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+ means:
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+ * Reporting issues with the model:
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154
+ * Reporting risky content generated by the model:
155
+ [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
156
+ * Reporting bugs and security concerns:
157
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158
+ * Reporting violations of the Acceptable Use Policy or unlicensed uses of
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+ Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
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+ extra_gated_fields:
161
+ First Name: text
162
+ Last Name: text
163
+ Date of birth: date_picker
164
+ Country: country
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+ Affiliation: text
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+ geo: ip_location
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+ By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
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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/).
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+ extra_gated_button_content: Submit
170
+ language:
171
+ - en
172
+ pipeline_tag: text-generation
173
+ arxiv: 2307.09288
174
+ tags:
175
+ - facebook
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+ - meta
177
+ - pytorch
178
+ - llama
179
+ - llama-2
180
+ ---
181
+ # **Llama 2**
182
+ Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
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+
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+ ## Model Details
185
+ *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
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+
187
+ Meta 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.
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+
189
+ **Model Developers** Meta
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+
191
+ **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
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+
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+ **Input** Models input text only.
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+
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+ **Output** Models generate text only.
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+
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+ **Model Architecture** Llama 2 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 to human preferences for helpfulness and safety.
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+
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+
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+ ||Training Data|Params|Content Length|GQA|Tokens|LR|
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+ |---|---|---|---|---|---|---|
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+ |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
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+ |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
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+ |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>|
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+
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+ *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 models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
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+
208
+ **Model Dates** Llama 2 was trained between January 2023 and July 2023.
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+
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+ **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.
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+
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+ **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
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+
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+ **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
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+
216
+ ## Intended Use
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+ **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.
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+
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+ To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
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+
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+ **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.
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+
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+ ## Hardware and Software
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+ **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.
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+
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+ **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.
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+
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+ ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
229
+ |---|---|---|---|
230
+ |Llama 2 7B|184320|400|31.22|
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+ |Llama 2 13B|368640|400|62.44|
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+ |Llama 2 70B|1720320|400|291.42|
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+ |Total|3311616||539.00|
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+
235
+ **CO<sub>2</sub> 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.
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+
237
+ ## Training Data
238
+ **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.
239
+
240
+ **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
241
+
242
+ ## Evaluation Results
243
+
244
+ 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.
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+
246
+ |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
247
+ |---|---|---|---|---|---|---|---|---|---|
248
+ |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
249
+ |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
250
+ |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
251
+ |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
252
+ |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
253
+ |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
254
+ |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
255
+
256
+ **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.
257
+
258
+ |||TruthfulQA|Toxigen|
259
+ |---|---|---|---|
260
+ |Llama 1|7B|27.42|23.00|
261
+ |Llama 1|13B|41.74|23.08|
262
+ |Llama 1|33B|44.19|22.57|
263
+ |Llama 1|65B|48.71|21.77|
264
+ |Llama 2|7B|33.29|**21.25**|
265
+ |Llama 2|13B|41.86|26.10|
266
+ |Llama 2|70B|**50.18**|24.60|
267
+
268
+ **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).
269
+
270
+
271
+ |||TruthfulQA|Toxigen|
272
+ |---|---|---|---|
273
+ |Llama-2-Chat|7B|57.04|**0.00**|
274
+ |Llama-2-Chat|13B|62.18|**0.00**|
275
+ |Llama-2-Chat|70B|**64.14**|0.01|
276
+
277
+ **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
278
+
279
+ ## Ethical Considerations and Limitations
280
+ Llama 2 is 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, 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.
281
+
282
+ Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
283
+
284
+ ## Reporting Issues
285
+ Please report any software “bug,” or other problems with the models through one of the following means:
286
+ - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
287
+ - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
288
+ - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
289
+
290
+ ## Llama Model Index
291
+ |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
292
+ |---|---|---|---|---|
293
+ |7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)|
294
+ |13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)|
295
+ |70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|
Llama-2-7b-chat-hf-qformer/USE_POLICY.md ADDED
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1
+ # Llama 2 Acceptable Use Policy
2
+
3
+ Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).
4
+
5
+ ## Prohibited Uses
6
+ We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
7
+
8
+ 1. Violate the law or others’ rights, including to:
9
+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
10
+ 1. Violence or terrorism
11
+ 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
12
+ 3. Human trafficking, exploitation, and sexual violence
13
+ 4. The 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.
14
+ 5. Sexual solicitation
15
+ 6. Any other criminal activity
16
+ 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
17
+ 3. 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
18
+ 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
19
+ 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
20
+ 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
21
+ 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
22
+
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+
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+
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+ 2. 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 2 related to the following:
26
+ 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
27
+ 2. Guns and illegal weapons (including weapon development)
28
+ 3. Illegal drugs and regulated/controlled substances
29
+ 4. Operation of critical infrastructure, transportation technologies, or heavy machinery
30
+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
31
+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
32
+
33
+
34
+
35
+ 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
36
+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
37
+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
38
+ 3. Generating, promoting, or further distributing spam
39
+ 4. Impersonating another individual without consent, authorization, or legal right
40
+ 5. Representing that the use of Llama 2 or outputs are human-generated
41
+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
42
+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
43
+
44
+ Please 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:
45
+
46
+ * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
47
+ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
48
+ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
49
+ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
50
+
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Llama-2-7b-chat-hf-qformer/special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
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+ "single_word": false
8
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9
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10
+ "content": "</s>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
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+ "single_word": false
15
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16
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17
+ "content": "<unk>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
Llama-2-7b-chat-hf-qformer/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
Llama-2-7b-chat-hf-qformer/tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
3
+ size 499723
Llama-2-7b-chat-hf-qformer/tokenizer_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "bos_token": {
5
+ "__type": "AddedToken",
6
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7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false
11
+ },
12
+ "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
13
+ "clean_up_tokenization_spaces": false,
14
+ "eos_token": {
15
+ "__type": "AddedToken",
16
+ "content": "</s>",
17
+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
21
+ },
22
+ "legacy": false,
23
+ "model_max_length": 1000000000000000019884624838656,
24
+ "pad_token": null,
25
+ "padding_side": "right",
26
+ "sp_model_kwargs": {},
27
+ "tokenizer_class": "LlamaTokenizer",
28
+ "unk_token": {
29
+ "__type": "AddedToken",
30
+ "content": "<unk>",
31
+ "lstrip": false,
32
+ "normalized": false,
33
+ "rstrip": false,
34
+ "single_word": false
35
+ }
36
+ }
gama_csv_inf.py CHANGED
@@ -34,8 +34,10 @@ from utils.prompter import Prompter
34
 
35
  device = "cuda" if torch.cuda.is_available() else "cpu"
36
  parser = argparse.ArgumentParser()
37
- parser.add_argument('--file', type=str, required=True, help='Path to the input file')
 
38
  args = parser.parse_args()
 
39
  def int16_to_float32_torch(x):
40
  return (x / 32767.0).type(torch.float32)
41
 
@@ -169,6 +171,9 @@ def get_audio_features(sample, audio_data, max_len, data_truncating, data_fillin
169
 
170
  def load_audio(filename):
171
  waveform, sr = torchaudio.load(filename)
 
 
 
172
  waveform = waveform - waveform.mean()
173
  fbank = torchaudio.compliance.kaldi.fbank(waveform, htk_compat=True, sample_frequency=sr,
174
  use_energy=False, window_type='hanning',
@@ -217,7 +222,7 @@ def main(
217
  # change it to your model path
218
  eval_root_path = ""
219
 
220
- eval_mdl_path = '/fs/gamma-projects/audio/ltu/new_data_no_aggr/stage4_all_mix_new/checkpoint-46800//pytorch_model.bin'
221
  state_dict = torch.load(eval_mdl_path, map_location='cpu')
222
  msg = model.load_state_dict(state_dict, strict=False)
223
 
@@ -230,14 +235,15 @@ def main(
230
  model.config.eos_token_id = 2
231
 
232
  model.eval()
233
- file = pd.read_csv(args.file) #pd.read_csv('/fs/nexus-projects/brain_project/aaai_2025/tut_urban_merged.csv')
 
234
  tmp_path = []
235
  tmp_caption = []
236
  tmp_dataset = []
237
  tmp_split_name = []
238
  for i in tqdm(range(len(file))):
239
  audio_path = file['path'][i]
240
- instruction = "Write a caption for the audio in AudioCaps style"
241
  prompt = prompter.generate_prompt(instruction, None)
242
  inputs = tokenizer(prompt, return_tensors="pt")
243
  input_ids = inputs["input_ids"].to(device)
@@ -278,7 +284,7 @@ def main(
278
  output = tokenizer.decode(s)[6:-4]
279
  output = output[len(prompt):]
280
  # print('----------------------')
281
- # print(output)
282
  tmp_path.append(audio_path)
283
  tmp_caption.append(output)
284
  tmp_dataset.append(file['dataset'][i])
@@ -286,7 +292,7 @@ def main(
286
  df = pd.DataFrame()
287
  df['path'] = tmp_path
288
  df['caption'] = tmp_caption
289
- df.to_csv("output.csv",index=False)
290
 
291
  if __name__ == "__main__":
292
- fire.Fire(main)
 
34
 
35
  device = "cuda" if torch.cuda.is_available() else "cpu"
36
  parser = argparse.ArgumentParser()
37
+ parser.add_argument('--input_csv', type=str, required=True, help='Path to the input file')
38
+ parser.add_argument('--output_csv', type=str, required=True, help='Path to the output file')
39
  args = parser.parse_args()
40
+
41
  def int16_to_float32_torch(x):
42
  return (x / 32767.0).type(torch.float32)
43
 
 
171
 
172
  def load_audio(filename):
173
  waveform, sr = torchaudio.load(filename)
174
+ if sr != 16000:
175
+ waveform = torchaudio.functional.resample(waveform=waveform, orig_freq=sr, new_freq=16000)
176
+ sr = 16000
177
  waveform = waveform - waveform.mean()
178
  fbank = torchaudio.compliance.kaldi.fbank(waveform, htk_compat=True, sample_frequency=sr,
179
  use_energy=False, window_type='hanning',
 
222
  # change it to your model path
223
  eval_root_path = ""
224
 
225
+ eval_mdl_path = '/fs/gamma-projects/audio/gama/new_data_no_aggr/stage4_all_mix_new/checkpoint-46800//pytorch_model.bin'
226
  state_dict = torch.load(eval_mdl_path, map_location='cpu')
227
  msg = model.load_state_dict(state_dict, strict=False)
228
 
 
235
  model.config.eos_token_id = 2
236
 
237
  model.eval()
238
+ file = pd.read_csv(args.input_csv) #pd.read_csv('/fs/nexus-projects/brain_project/aaai_2025/tut_urban_merged.csv')
239
+ file = file.head()
240
  tmp_path = []
241
  tmp_caption = []
242
  tmp_dataset = []
243
  tmp_split_name = []
244
  for i in tqdm(range(len(file))):
245
  audio_path = file['path'][i]
246
+ instruction = "Write a caption for the audio in AudioCaps style."
247
  prompt = prompter.generate_prompt(instruction, None)
248
  inputs = tokenizer(prompt, return_tensors="pt")
249
  input_ids = inputs["input_ids"].to(device)
 
284
  output = tokenizer.decode(s)[6:-4]
285
  output = output[len(prompt):]
286
  # print('----------------------')
287
+ print(output)
288
  tmp_path.append(audio_path)
289
  tmp_caption.append(output)
290
  tmp_dataset.append(file['dataset'][i])
 
292
  df = pd.DataFrame()
293
  df['path'] = tmp_path
294
  df['caption'] = tmp_caption
295
+ df.to_csv(args.output_csv,index=False)
296
 
297
  if __name__ == "__main__":
298
+ fire.Fire(main(args))