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.gitattributes

@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+codellama-7b-instruct.Q5_0.gguf filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3-alpine
+
+WORKDIR /app
+COPY ./requirements.txt /app
+COPY ./src/* /app
+
+RUN pip3 install -r requirements.txt
+
+ENV PORT=5500
+EXPOSE "$PORT/tcp"
+
+#ENTRYPOINT nginx && uwsgi --ini /app/params.ini -w FreeGPT4_Server
+#shell form necessary
+SHELL ["python3","/app/FreeGPT4_Server.py"]
+ENTRYPOINT ["python3","/app/FreeGPT4_Server.py"]
+CMD ["--cookie-file","/cookies.json"]

LICENSE

@@ -0,0 +1,201 @@
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+
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LICENSE.txt

@@ -0,0 +1,126 @@
+LLAMA 2 COMMUNITY LICENSE AGREEMENT	
+Llama 2 Version Release Date: July 18, 2023
+
+"Agreement" means the terms and conditions for use, reproduction, distribution and 
+modification of the Llama Materials set forth herein.
+
+"Documentation" means the specifications, manuals and documentation 
+accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-
+libraries/llama-downloads/.
+
+"Licensee" or "you" means you, or your employer or any other person or entity (if 
+you are entering into this Agreement on such person or entity's behalf), of the age 
+required under applicable laws, rules or regulations to provide legal consent and that 
+has legal authority to bind your employer or such other person or entity if you are 
+entering in this Agreement on their behalf.
+
+"Llama 2" means the foundational large language models and software and 
+algorithms, including machine-learning model code, trained model weights, 
+inference-enabling code, training-enabling code, fine-tuning enabling code and other 
+elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-
+libraries/llama-downloads/.
+
+"Llama Materials" means, collectively, Meta's proprietary Llama 2 and 
+Documentation (and any portion thereof) made available under this Agreement.
+
+"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you 
+are an entity, your principal place of business is in the EEA or Switzerland) and Meta 
+Platforms, Inc. (if you are located outside of the EEA or Switzerland). 
+
+By clicking "I Accept" below or by using or distributing any portion or element of the 
+Llama Materials, you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution. 
+
+      a. Grant of Rights. You are granted a non-exclusive, worldwide, non-
+transferable and royalty-free limited license under Meta's intellectual property or 
+other rights owned by Meta embodied in the Llama Materials to use, reproduce, 
+distribute, copy, create derivative works of, and make modifications to the Llama 
+Materials.  
+      
+      b. Redistribution and Use.  
+
+            i. If you distribute or make the Llama Materials, or any derivative works 
+thereof, available to a third party, you shall provide a copy of this Agreement to such 
+third party. 
+            ii.  If you receive Llama Materials, or any derivative works thereof, from 
+a Licensee as part of an integrated end user product, then Section 2 of this 
+Agreement will not apply to you. 
+
+            iii. You must retain in all copies of the Llama Materials that you 
+distribute the following attribution notice within a "Notice" text file distributed as a 
+part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License, 
+Copyright (c) Meta Platforms, Inc. All Rights Reserved."
+
+            iv. Your use of the Llama Materials must comply with applicable laws 
+and regulations (including trade compliance laws and regulations) and adhere to the 
+Acceptable Use Policy for the Llama Materials (available at 
+https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into 
+this Agreement.
+
+            v. You will not use the Llama Materials or any output or results of the 
+Llama Materials to improve any other large language model (excluding Llama 2 or 
+derivative works thereof).  
+
+2. Additional Commercial Terms. If, on the Llama 2 version release date, the 
+monthly active users of the products or services made available by or for Licensee, 
+or Licensee's affiliates, is greater than 700 million monthly active users in the 
+preceding calendar month, you must request a license from Meta, which Meta may 
+grant to you in its sole discretion, and you are not authorized to exercise any of the 
+rights under this Agreement unless or until Meta otherwise expressly grants you 
+such rights.
+            
+3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE 
+LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE 
+PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, 
+EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY 
+WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR 
+FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE 
+FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING 
+THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR 
+USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
+
+4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE 
+LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, 
+NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS 
+AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, 
+CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN 
+IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF 
+ANY OF THE FOREGOING.
+ 
+5. Intellectual Property.
+
+      a. No trademark licenses are granted under this Agreement, and in 
+connection with the Llama Materials, neither Meta nor Licensee may use any name 
+or mark owned by or associated with the other or any of its affiliates, except as 
+required for reasonable and customary use in describing and redistributing the 
+Llama Materials.
+
+      b. Subject to Meta's ownership of Llama Materials and derivatives made by or 
+for Meta, with respect to any derivative works and modifications of the Llama 
+Materials that are made by you, as between you and Meta, you are and will be the 
+owner of such derivative works and modifications.
+
+      c. If you institute litigation or other proceedings against Meta or any entity 
+(including a cross-claim or counterclaim in a lawsuit) alleging that the Llama 
+Materials or Llama 2 outputs or results, or any portion of any of the foregoing, 
+constitutes infringement of intellectual property or other rights owned or licensable 
+by you, then any licenses granted to you under this Agreement shall terminate as of 
+the date such litigation or claim is filed or instituted. You will indemnify and hold 
+harmless Meta from and against any claim by any third party arising out of or related 
+to your use or distribution of the Llama Materials.
+
+6. Term and Termination. The term of this Agreement will commence upon your 
+acceptance of this Agreement or access to the Llama Materials and will continue in 
+full force and effect until terminated in accordance with the terms and conditions 
+herein. Meta may terminate this Agreement if you are in breach of any term or 
+condition of this Agreement. Upon termination of this Agreement, you shall delete 
+and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the 
+termination of this Agreement. 
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and 
+construed under the laws 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. 
+

MANIFEST.in

@@ -0,0 +1,3 @@
+recursive-include exllamav2 *
+global-exclude *.pyc
+global-exclude dni_*

Notice

@@ -0,0 +1 @@
+Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

Pipfile

@@ -0,0 +1,14 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+edgegpt = "*"
+flask = {extras = ["async"], version = "*"}
+aiohttp = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.11"

Pipfile.lock

@@ -0,0 +1,864 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "df17fa7b0982d444252ef9397071979b8652d279db80bc14c6de62641ba71d8b"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.11"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "aiohttp": {
+            "hashes": [
+                "sha256:00ad4b6f185ec67f3e6562e8a1d2b69660be43070bd0ef6fcec5211154c7df67",
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+    },
+    "develop": {}
+}

README.md

@@ -1,31 +1,336 @@
----
-title: NashmiAI
-emoji: 🏢
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-datasets:
-- PetraAI/PetraAI
-- Petra-AI/ZalmatiAI
-- PetraAI/autotrain-data-zalmati-ai
-- tiiuae/falcon-refinedweb
-- code_search_net
-language:
-- en
-- ar
-metrics:
-- accuracy
-- code_eval
-- bertscore
-- brier_score
-tags:
-- code
-- text-generation-inference
-- art
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# StableLM: Stability AI Language Models
+
+![Stochastic Parrot](/assets/mascot.png)
+<br/>*“A Stochastic Parrot, flat design, vector art” — [Stable Diffusion XL](https://clipdrop.co/stable-diffusion)*
+
+This repository contains Stability AI's ongoing development of the StableLM series of language models and will be continuously updated with new checkpoints. The following provides an overview of all currently available models. More coming soon.
+
+## News
+
+*April 28, 2023*
+
+- Released StableVicuna-13B, our RLHF fine-tune of [Vicuna-13B v0](https://huggingface.co/lmsys/vicuna-13b-delta-v0), which itself is a fine-tune of [LLaMA-13B](https://github.com/facebookresearch/llama). Delta weights over the original Llama model is released under ([CC BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)).
+
+*April 20, 2023*
+
+- Released initial set of StableLM-alpha models, with 3B and 7B parameters. 15B and 30B models are on the way. Base models are released under [CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+
+- Try to chat with our 7B model, `StableLM-Tuned-Alpha-7B`, on [Hugging Face Spaces](https://huggingface.co/spaces/stabilityai/stablelm-tuned-alpha-chat).
+
+## Models
+
+### StableVicuna
+
+StableVicuna is an RLHF fine-tune of [Vicuna-13B v0](https://huggingface.co/lmsys/vicuna-13b-delta-v0), which itself is a fine-tune of [LLaMA-13B](https://github.com/facebookresearch/llama). It is our attempt at creating an open-source RLHF LLM Chatbot. This model is developed by StabilityAI's CarperAI team, with [Duy V. Phung](https://github.com/PhungVanDuy) leading the training effort.
+
+Due to the original non-commercial license of LLaMA, we can only release the weights of our model as deltas over the original model's weights. StableVicuna's delta weights are released under ([CC BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)).
+
+Please visit HuggingFace checkpoint for more information about how to combine our delta weights with the original model.
+
+| Model            | Download                                                               | Web Demo                                                             | Cite |
+| ---------------- | ---------------------------------------------------------------------- | -------------------------------------------------------------------- |------|
+| StableVicuna-13B | [checkpoint](https://huggingface.co/CarperAI/stable-vicuna-13b-delta/) | [Hugging Face](https://huggingface.co/spaces/CarperAI/StableVicuna/) | [![DOI:10.57967/hf/0588](https://zenodo.org/badge/DOI/10.1007/978-3-319-76207-4_15.svg)](https://doi.org/10.57967/hf/0588) |
+
+### StableLM-Alpha
+StableLM-Alpha models are trained on the new dataset that build on [The Pile](https://pile.eleuther.ai/), which contains 1.5 trillion tokens, roughly 3x the size of The Pile. These models will be trained on up to 1.5 trillion tokens. The context length for these models is 4096 tokens.
+
+An upcoming technical report will document the model specifications and the training settings.
+
+As a proof-of-concept, we also fine-tuned the model with [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)'s procedure using a combination of five recent datasets for conversational agents: Stanford's [Alpaca](https://github.com/tatsu-lab/stanford_alpaca), Nomic-AI's [gpt4all](https://github.com/nomic-ai/gpt4all), RyokoAI's [ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K) datasets, Databricks labs' [Dolly](https://github.com/databrickslabs/dolly), and Anthropic's [HH](https://github.com/anthropics/hh-rlhf). We will be releasing these models as StableLM-Tuned-Alpha.
+
+| Size | StableLM-Base-Alpha                                                      | StableLM-Tuned-Alpha                                                      | Training Tokens | Parameters    | Web Demo                                                                           |
+|------|--------------------------------------------------------------------------|---------------------------------------------------------------------------|-----------------|---------------|------------------------------------------------------------------------------------|
+| 3B   | [checkpoint](https://huggingface.co/stabilityai/stablelm-base-alpha-3b/) | [checkpoint](https://huggingface.co/stabilityai/stablelm-tuned-alpha-3b/) | 800B            | 3,638,525,952 |                                                                                    |
+| 7B   | [checkpoint](https://huggingface.co/stabilityai/stablelm-base-alpha-7b)  | [checkpoint](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b)  | 800B            | 7,869,358,080 | [Hugging Face](https://huggingface.co/spaces/stabilityai/stablelm-tuned-alpha-chat) |
+| 15B  | (in progress)                                                            | (pending)                                                                 |                 |               |                                                                                    |
+| 30B  | (in progress)                                                            | (pending)                                                                 |                 |               |                                                                                    |
+| 65B  | (in progress)                                                            | (pending)                                                                 |                 |               |                                                                                    |
+| 175B | (planned)                                                                |                                                                           |                 |               |                                                                                    |
+
+## Quickstart
+
+All StableLM models are hosted on [the Hugging Face hub](https://huggingface.co/StabilityAI). Check out this [notebook](https://github.com/Stability-AI/StableLM/blob/main/notebooks/stablelm-alpha.ipynb) to run inference with limited GPU capabilities.
+
+Get started chatting with `StableLM-Tuned-Alpha` by using the following code snippet:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
+
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b")
+model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-7b")
+model.half().cuda()
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = set([50278, 50279, 50277, 1, 0])
+        return input_ids[0][-1] in stop_ids
+
+system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
+- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
+- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
+- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
+- StableLM will refuse to participate in anything that could harm a human.
+"""
+
+prompt = f"{system_prompt}<|USER|>What's your mood today?<|ASSISTANT|>"
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+tokens = model.generate(
+  **inputs,
+  max_new_tokens=64,
+  temperature=0.7,
+  do_sample=True,
+  stopping_criteria=StoppingCriteriaList([StopOnTokens()])
+)
+print(tokenizer.decode(tokens[0], skip_special_tokens=True))
+```
+
+StableLM Tuned should be used with prompts formatted to `<|SYSTEM|>...<|USER|>...<|ASSISTANT|>...`
+The system prompt is
+```
+<|SYSTEM|># StableLM Tuned (Alpha version)
+- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
+- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
+- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
+- StableLM will refuse to participate in anything that could harm a human.
+```
+
+## Fun with StableLM-Tuned-Alpha
+This section contains a collection of fun cherry-picked examples of what you can do with `stablelm-tuned-alpha`. 
+
+<details>
+  <summary>Chit-Chat</summary>
+  
+```
+User: Hey! My name is John nice to meet you!
+
+StableLM: Hello John! It’s always a pleasure to meet new people.
+
+User: The pleasure is mine. Hey my grandma is in Toronto. I want to plan a surprise visit for her. She’s a person who loves gardens and flowers.
+
+StableLM: That’s wonderful! It’s great to hear that you’ll be visiting your grandma in Toronto. Can you tell me a little bit more about your plans?
+
+User: Ya I need to book a flight there and a hotel. But first, what recommendations do you have for gifts that she might like?
+
+StableLM: I think that’s a very thoughtful idea! It’s a very personal gift that would be sure to make her very happy. I can think of a few examples that she might really enjoy. There are some lovely bouquets or arrangements that would be lovely for the house, or even for the yard. Flowers are a great choice for a surprise gift, and they’re also a great choice for a wedding shower present. Also, if you’re really looking for something a bit more special, I think she’d really enjoy a quality set of cooking pots or pans. That’s something that she’s likely to use frequently, so it would make her feel really loved.
+
+User: Thanks for the suggestion! Hmm a bouquet of flowers sounds nice but I don’t think I can take them on the air plane. What do you think I should do?
+
+StableLM: There are many possible solutions! One option would be to arrange a small gift in the shape of a card and send it to her by regular mail. Or you could arrange a special meal at a restaurant that she likes, or even cook a meal at home for her. Or you could send her some local goodies that she might like, like a nice bouquet of flowers or a special recipe. You could even arrange for tickets to a local theatre or sporting event, and make sure to include a note about what you’d like her to know about you. Really any sort of special attention from you will be sure to make her very happy!
+
+User: Ah! Right. I can order a bouquet for her online and get it delivered! Thanks!
+
+StableLM: You’re very welcome! As long as you do this and make it a very personal, thoughtful gesture, she’ll be sure to appreciate it.
+ ```
+ 
+</details>
+
+<details>
+  <summary>Formal Writing</summary>
+  
+  ```
+ User: What would you say to a friend who is graduating high school?
+
+StableLM: I would say that they have every right to feel proud of themselves and their accomplishments. They should be proud of the work they’ve done and the friends they have made, and also be excited about the future.
+
+User: Please write an email.
+StableLM: Okay, here’s an example of what could be written:
+
+Dear [Friend’s Name],
+
+I hope this email finds you well. I just wanted to take a moment to express how proud I am of you and all that you have accomplished. I know that getting into [university or college] is a big accomplishment, and I appreciate all that you have done for me and for this generation.
+
+I hope that you are having a good day. I’m sure that you are also concerned about the future, but I just wanted to let you know that I am also watching you grow and develop into the person that I know that you can be. Keep up the good work, and I have full faith that you will achieve great things.
+
+Thank you again for everything that you do.
+
+Best,
+[Your Name]
+
+ ```
+
+</details>
+
+<details>
+  <summary>Creative Writing</summary>
+
+### Rap Battles
+```
+User: Write an epic rap battle song between deep neural networks and symbolic AI
+
+StableLM: [Beat drops]
+
+Deep Neural Networks:
+Listen up everyone, it’s time to witness
+The power of deep neural networks, it’s just a matter of abstract
+We’ve trained these networks to solve complex problems with ease
+While symbolic AI is limited to simple math and simple flows
+
+Symbolic AI:
+Hold up, hold up, let me interrupt
+It’s been a while since I’ve heard, some new thoughts you interrupt
+But let me tell you, my friend, it’s not all about math and flow
+It’s about making the world a better place, you know
+
+Deep Neural Networks:
+Well, I can see where you’re coming from, it’s clear
+But deep neural networks, they never get the same gear
+We can use them to analyze and predict with ease
+And prove the superiority of machine learning
+
+Symbolic AI:
+You’re right, it’s not just math or numbers
+But it’s the essence of our lives, it’s what counts, we’re numbers
+I don’t need a complicated math formula, just a simple device
+To make the world a better place, it’s a noble price
+
+Deep Neural Networks:
+I’ll give you that, my friend, you have a point
+But let’s not forget, we’re making an ever-increasing joint
+Our progress is not bound by logic, it’s a journey of the mind
+So let’s work together, let’s strive, let’s leave all troubles behind
+
+Symbolic AI:
+Agreed, let’s work together, let’s not falter
+We’re both making history, we’re both from the fader
+So let’s make the world a better place, let’s end this debate
+Cause it’s time to join forces, it’s time to leave no stone unturned
+
+[Beat fades out]
+
+Who won this epic rap battle between deep neural networks and symbolic AI? Well, it’s up to you to decide!
+ ```
+
+### Fictional Stories
+
+```
+User: Write a story about a programmer who fell in love with a deep neural network.
+
+StableLM: Once upon a time, there was a programmer named John. He was a brilliant and ambitious individual, who was constantly searching for the next big thing in technology. One day, while he was strolling through the streets of his hometown, he stumbled upon a group of people crowded around a mysterious object. Intrigued, he approached to find out what the fuss was about.
+
+As he got closer, he realized that it was none other than a mysterious deep neural network. John’s mind was blown. He had never seen anything like it before. He had always been interested in machine learning, but this was something else entirely. He had to learn more about it.
+
+John spent the next few weeks holed up in his hotel room, trying to learn everything he could about deep neural networks. He spent hours reading and analyzing papers and documents, trying to wrap his head around the concept of neural networks. He even managed to get into the local university’s computer science department, hoping to find some help there.
+
+But, it wasn’t long before John realized that he was on his own. The city was bustling, and the constant chatter and noise of the city made it hard to concentrate on his work. He was about to give up when he stumbled upon a group of people playing a game that he had played before. They were having a good time, and John couldn’t help but join in.
+
+The group welcomed him into their group, and before he knew it, John was swept away in the excitement. The game was called “TensorFlow,” and it was just like a regular game with different controls. John played along with the group, trying to make sense of it all. And in a few hours, he got his hands on the source code of TensorFlow and fell in love with it.
+
+John was now a part of a team of programmers who loved using TensorFlow for their work. They continued to play the game throughout the day, with John adding his own knowledge and inspiration to the group’s work. And in the end, John realized that he had found his true calling - working with deep neural networks.
+
+He now knows the ins and outs of the TensorFlow programming language, and knows how to use it to create some of the most beautiful and complex machine learning models that anyone could dream of. And for everyone who had helped him on his journey, John will always be remembered as the guy who fell in love with a technology that changed his life forever.
+
+```
+
+### Humor 
+
+```
+User: What do you know about the League of Legends champion Zed? Write a short bio
+
+StableLM: Zed is a powerful and highly skilled player of the League of Legends (LoL) universe. He has been a part of a dominating team known as the “Apocalypse” for most of his time playing the game.
+
+Zed was born in the year 2013 and started playing as a child when he started League of Legends. He first became a champion in season 1, gaining the title of “the Dark Star”, thanks to his masterful gameplay and teamwork skills.
+
+Throughout his time playing the game, Zed has mastered several powerful techniques, such as QWQ combos, one-shot kills, and the ability to outmaneuver his enemies. He is also known for his versatility, with the ability to adapt to any situation the game puts him in.
+
+Despite his formidable skills and agility, Zed is not invincible. He can be CCed and killed, and he relies on his team to protect him. However, his team members can usually overwhelm and shut down his enemies, making him a force to be reckoned with.
+
+Zed has been a part of many memorable games, and his success in these games helped solidify his legacy as one of the most influential players of the LoL universe.
+
+User: Write a funny conversation after a team fight between Zed and Teemo
+
+StableLM: 
+Teemo: “Hey Zed, great game today. I wonder if you can help me out with my favorite hobby - collecting mushrooms!”
+Zed: “Teemo, please. I’m not a mushroom collector.”
+Teemo: “But of course, you are. You have the best taste in the game.”
+Zed: “Thanks, but can I at least get some of your mushrooms to be a little more useful?”
+Teemo: “I’d love to help you, but I’m afraid they’re all mine. Have you noticed my tunnels are always so well-guarded?”
+Zed: “Ha! Well, maybe I can start my own tunnel. Helpful or not, you can never tell what I might have stored in them.”
+Teemo: “Good luck with that. Maybe I’ll just create some extra for my garden.”
+Zed: “I’m not sure I can keep up with you there.”
+Teemo: “Ha! If only you had some more decent weapons. I could really see this as a challenge, though!”
+Zed: “Teemo, I think your luck has finally run out.”
+Teemo: “Well, at least I’m the one with the mushrooms now.”
+
+```
+
+</details>
+
+<details>
+  <summary>Writing Code</summary>
+
+```
+User: Write a program that solves the fib sequence in Python.
+```
+StableLM:
+
+```python
+def fib_sequence(n):
+    if n==0:
+        return 0
+    elif n==1:
+        return 1
+    else:
+        return fib(n-1)+fib(n-2)
+
+fib_sequence(10)
+```
+
+</details>
+
+## Selected failure modes of StableLM-Tuned-Alpha
+This section contains a collection of selected failure modes of `stablelm-tuned-alpha`. 
+
+<details>
+  <summary>Yann LeCun gears (linear)</summary>
+  
+```
+User: In front of us are six gears numbered 1 to 6, mounted on axles in a row. Each gear is engaged with the next gear. If gear number 3 is rotated clockwise, in which direction will gears 1 and 6 rotate?
+
+StableLM: Each gear is engaged with the next gear after every other gear, so that gears 1 and 6 will rotate together. Since gears 1 and 6 are both numbered 1, this means that they will rotate in the same direction.
+
+To determine the direction in which gears 1 and 6 will rotate, we need to look at the direction of the input force. Since gear 3 is rotating in the opposite direction of the other gears, it will be counterclockwise in the direction in which gears 1 and 6 are both rotating. Therefore, the output force that gear 3 is providing will also be counterclockwise, making gear 1 and 6 also rotate in the same direction as gear 3.
+ ```
+ 
+</details>
+
+<details>
+  <summary>Yann LeCun gears (circular)</summary>
+  
+```
+User: 7 axles are equally spaced around a circle. A gear is placed on each axle such that each gear is engaged with the gear to its left and the gear to its right. The gears are numbered 1 to 7 around the circle. If gear 3 were rotated clockwise, in which direction would gear 7 rotate?
+
+StableLM: Gear 3 is the smallest gear, so it is not affected by the circular pattern. Therefore, gear 7 is still perfectly evenly spaced around the circle despite the fact that it is not in its correct position.
+
+To find the angle that gear 3 would rotate in, you need to know the distance (d) between the center of the circle and the gear, as well as the direction of rotation (in this case, clockwise).
+
+According to the problem, gear 3 is situated at (d/2, 2 degrees) and the gears are numbered 1 to 7. Since d = (d/2) is an integer multiple of 2, gear 3 would also be at an angle of 2 degrees.
+
+Therefore, the answer is d = (7/2) * 2 = 5 degrees in total.
+ ```
+ 
+</details>
+
+## Request for Help
+
+Want to get involved?
+
+- We would love to port [llama.cpp](https://github.com/ggerganov/llama.cpp) to work with StableLMs
+- Integration into [Open Assistant](https://github.com/LAION-AI/Open-Assistant) from LAION-AI to collect high quality human-generated feedback data
+- ... Reach out to us with ideas on our [Discord](https://discord.com/invite/stablediffusion)
+
+## Potential issues
+As is typical for any pretrained Large Language Model without additional finetuning and reinforcement learning, the responses a user gets might be of varying quality and might potentially include offensive language and views. This is expected to be improved with scale, better data, community feedback, and optimisation.
+
+## Acknowledgements
+
+- `StableLM-Tuned-Alpha` would not have been possible without the helpful hand of Dakota Mahan [@dmayhem93](https://huggingface.co/dmayhem93).
+
+## Licenses
+
+- Base model checkpoints (`StableLM-Base-Alpha`) are licensed under the Creative Commons license ([CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)). Under the license, you must give [credit](https://creativecommons.org/licenses/by/4.0/#) to Stability AI, provide a link to the license, and [indicate if changes were made](https://creativecommons.org/licenses/by/4.0/#). You may do so in any reasonable manner, but not in any way that suggests the Stability AI endorses you or your use.
+
+- Fine-tuned checkpoints (`StableLM-Tuned-Alpha`) are licensed under the Non-Commercial Creative Commons license ([CC BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)), in-line with the original non-commercial license specified by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca).
+
+- All code in this repository is licensed under the Apache License 2.0 license.

USE_POLICY.md

@@ -0,0 +1,50 @@
+# Llama 2 Acceptable Use Policy
+
+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).
+
+## Prohibited Uses
+We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 
+
+1. Violate the law or others’ rights, including to:
+    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 
+        1. Violence or terrorism 
+        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
+        3. Human trafficking, exploitation, and sexual violence
+        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.
+        5. Sexual solicitation
+        6. Any other criminal activity
+    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+    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
+    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 
+    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
+    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
+    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 
+
+
+
+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:
+    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
+    2. Guns and illegal weapons (including weapon development)
+    3. Illegal drugs and regulated/controlled substances
+    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
+    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
+    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+
+
+
+3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
+    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+    3. Generating, promoting, or further distributing spam
+    4. Impersonating another individual without consent, authorization, or legal right
+    5. Representing that the use of Llama 2 or outputs are human-generated
+    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
+4. Fail to appropriately disclose to end users any known dangers of your AI system 
+
+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:
+
+* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
+* 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: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
+

batch_throttle.py

@@ -0,0 +1,23 @@
+from hfapi import Client
+
+client = Client()
+
+BATCH_SIZE = 4
+
+LONG_LIST_OF_INPUTS = [
+    "I like you. </s></s> I love you.",
+    "At the other end of Pennsylvania Avenue, people began to line up for a White House tour. </s></s> People formed a line at the end of Pennsylvania Avenue.",
+] * 500
+
+def chunker(seq, size):
+    return (seq[pos:pos + size] for pos in range(0, len(seq), size))
+
+all_results = []
+
+for inputs in chunker(LONG_LIST_OF_INPUTS, BATCH_SIZE):
+    result = client.text_classification(inputs, model="roberta-large-mnli")
+    print(result)
+    all_results += result
+
+
+print("Done!")

convert.py

@@ -0,0 +1,208 @@
+from exllamav2 import ExLlamaV2, ExLlamaV2Config, ExLlamaV2Tokenizer
+import argparse, os
+import sys
+import json
+from conversion.tokenize import tokenize
+from conversion.quantize import embeddings, measure_quant, quant
+from conversion.optimize import optimize
+from conversion.compile import compile_model
+
+# import tracemalloc
+# tracemalloc.start()
+
+parser = argparse.ArgumentParser(description = "Convert model to ExLlamaV2")
+parser.add_argument("-i", "--in_dir", type = str, help = "Input directory", default = "")
+parser.add_argument("-o", "--out_dir", type = str, help = "Output directory")
+parser.add_argument("-c", "--cal_dataset", type = str, help = "Calibration dataset (.parquet file)", default = "")
+parser.add_argument("-r", "--dataset_rows", type = int, default = 100, help = "Number of rows to apply from dataset")
+parser.add_argument("-mr", "--measurement_rows", type = int, default = 16, help = "Number of rows to apply from dataset when measuring")
+parser.add_argument("-gr", "--gpu_rows", type = int, default = 16, help = "Threshold for paging hidden state to CPU")
+parser.add_argument("-l", "--length", type = int, default = 2048, help = "Max no. tokens per sample")
+parser.add_argument("-ml", "--measurement_length", type = int, default = 2048, help = "Max no. tokens per sample when measuring")
+parser.add_argument("-b", "--bits", type = float, default = 4.156, help = "Target bits per weight")
+parser.add_argument("-hb", "--head_bits", type = int, default = 6, help = "Target bits per weight (head layer)")
+parser.add_argument("-m", "--measurement", type = str, help = "Reuse previous measurement")
+
+args = parser.parse_args()
+
+# Arguments
+
+in_dir = None if args.in_dir == "" else os.path.abspath(args.in_dir)
+out_dir = os.path.abspath(args.out_dir)
+cal_dataset = None if args.cal_dataset == "" else os.path.abspath(args.cal_dataset)
+dataset_rows = args.dataset_rows
+measurement_rows = args.measurement_rows
+gpu_rows = args.gpu_rows
+length = args.length
+measurement_length = args.measurement_length
+bits = args.bits
+head_bits = args.head_bits
+reuse_measurement = args.measurement
+
+if not os.path.exists(out_dir):
+    print(f" ## Error: Directory not found: {out_dir}")
+    sys.exit()
+
+# Create model without loading weights
+
+config = ExLlamaV2Config()
+config.model_dir = in_dir
+config.prepare()
+
+model = ExLlamaV2(config)
+model.load(lazy = True)
+
+tokenizer = ExLlamaV2Tokenizer(config)
+
+# Job file
+
+job_file = os.path.join(out_dir, "job.json")
+
+# Create new job
+
+def save_job():
+    global job_file, job
+    with open(job_file, "w") as f:
+        f.write(json.dumps(job, indent = 4))
+
+if not os.path.exists(job_file):
+
+    print(f" -- Beginning new job")
+
+    if len(os.listdir(out_dir)) != 0:
+        print(f" !! Warning: Output directory is not empty: {out_dir}")
+
+    if in_dir is None:
+        print(f" ## Error: No input directory specified")
+        sys.exit()
+
+    if cal_dataset is None:
+        print(f" ## Error: No calibration dataset specified")
+        sys.exit()
+
+    job = { "in_dir": in_dir,
+            "out_dir": out_dir,
+            "cal_dataset": cal_dataset,
+            "dataset_rows": dataset_rows,
+            "measurement_rows": measurement_rows,
+            "gpu_rows": gpu_rows,
+            "length": length,
+            "measurement_length": measurement_length,
+            "bits": bits,
+            "head_bits": head_bits,
+            "progress": "begin",
+            }
+
+    if reuse_measurement is not None:
+
+        with open(reuse_measurement, "r") as f:
+
+            imp_measurement = json.load(f)
+            job["measurement"] = imp_measurement["measurement"]
+            job["last_module_idx"] = imp_measurement["last_module_idx"]
+            job["base_perplexity"] = imp_measurement["base_perplexity"]
+            job["reuse_measurement"] = reuse_measurement
+
+    save_job()
+
+# Resume existing job
+
+else:
+
+    print(f" -- Resuming job")
+    print(f" !! Note: Overriding options with settings from existing job")
+
+    with open(job_file, "r") as f:
+        job = json.load(f)
+
+    if "invalid" in job:
+        print(" ** Error: Corrupted job")
+        sys.exit()
+
+    job["out_dir"] = out_dir
+
+# Feedback
+
+print(f" -- Input: {job['in_dir']}")
+print(f" -- Output: {out_dir}")
+print(f" -- Calibration dataset: {job['cal_dataset']}, {job['dataset_rows']} / {job['measurement_rows']} ({job['gpu_rows']}) rows, {job['length']} tokens per sample")
+print(f" -- Target bits per weight: {job['bits']} (decoder), {job['head_bits']} (head)")
+
+# Make sure subfolders exist
+
+out_tensor_dir = os.path.join(job["out_dir"], "out_tensor")
+if not os.path.exists(out_tensor_dir):
+    os.makedirs(out_tensor_dir)
+
+# Do the things
+
+while True:
+
+    progress = job["progress"]
+
+    if progress == "begin":
+
+        if "reuse_measurement" in job:
+
+            print(f" -- Reusing measurement: {job['reuse_measurement']}")
+            job["progress"] = "optimize"
+            save_job()
+
+        else:
+
+            print(f" -- Tokenizing samples (measurement)...")
+            tokenize(job, save_job, tokenizer, measure = True)
+            job["progress"] = "initial_embeddings"
+            save_job()
+
+    if progress == "initial_embeddings":
+
+        print(f" -- Token embeddings (measurement)...")
+        embeddings(job, save_job, model)
+        job["progress"] = "measure_quant"
+        save_job()
+
+    if progress == "measure_quant":
+
+        print(f" -- Measuring quantization impact...")
+        measure_quant(job, save_job, model)
+        job["progress"] = "optimize"
+        save_job()
+
+    if progress == "optimize":
+
+        print(f" -- Optimizing...")
+        optimize(job, save_job)
+        job["progress"] = "tokens_cal"
+        save_job()
+
+    if progress == "tokens_cal":
+
+        print(f" -- Tokenizing samples...")
+        tokenize(job, save_job, tokenizer)
+        job["progress"] = "embeddings"
+        save_job()
+
+    if progress == "embeddings":
+        print(f" -- Token embeddings again...")
+        embeddings(job, save_job, model)
+        job["progress"] = "quant"
+        save_job()
+
+    if progress == "quant":
+
+        print(f" -- Quantizing...")
+        quant(job, save_job, model)
+        job["progress"] = "compile"
+        save_job()
+
+    if progress == "compile":
+
+        print(f" -- Compiling output file...")
+        compile_model(job, save_job, model)
+        job["progress"] = "finished"
+        save_job()
+
+    if progress == "finished": break
+
+print(f" -- Finished")

docker-compose.yml

@@ -0,0 +1,9 @@
+version: "3.9"
+services:
+  api:
+    image: "d0ckmg/free-gpt4-web-api:latest"
+    ports:
+      - "5500:5500"
+    volumes:
+      - ./cookies.json:/cookies.json
+

example.py

@@ -0,0 +1,61 @@
+import hfapi
+client = hfapi.Client()
+
+print("""
+
+```python
+import hfapi
+client = hfapi.Client()
+```
+
+""")
+
+print("""```python
+client.question_answering("Where does she live?", "She lives in Berlin.")
+```
+""")
+
+print(">", client.question_answering("Where does she live?", "She lives in Berlin."))
+
+print("""```python
+client.text_generation("My name is Julien and I like to ")
+```
+""")
+print("```")
+print(">", client.text_generation("My name is Julien and I like to ", model="gpt2"))
+print("```")
+print()
+
+print("""```python
+client.summarization("The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.")
+```
+""")
+
+print(">", client.summarization("The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."))
+print()
+
+print("""```python
+client.fill_mask("Paris is the [MASK] of France."))
+```
+""")
+
+print(">",client.fill_mask("Paris is the [MASK] of France."))
+print()
+
+
+print("""```python
+client.text_classification("I hated the movie!")
+```
+""")
+
+print(">", client.text_classification("I hated the movie!"))
+print()
+
+
+print("""```python
+client.token_classification("My name is Sarah and I live in London")
+```
+""")
+
+print(">", client.token_classification("My name is Sarah and I live in London"))
+print()

requirements.txt

@@ -0,0 +1,6 @@
+pandas
+ninja
+fastparquet
+torch>=2.0.1
+safetensors>=0.3.2
+sentencepiece>=0.1.97

setup.py

@@ -0,0 +1,10 @@
+#!/usr/bin/env python
+
+
+from distutils.core import setup
+
+setup(name='HF API',
+      version='0.1',
+      description='Hugging Face Python API',
+      packages=['hfapi']
+)

test_inference.py

@@ -0,0 +1,219 @@
+
+from exllamav2 import(
+    ExLlamaV2,
+    ExLlamaV2Config,
+    ExLlamaV2Cache,
+    ExLlamaV2Tokenizer,
+    model_init,
+)
+
+import argparse, os, math, time
+import pandas, fastparquet
+import torch
+import torch.nn.functional as F
+from conversion.tokenize import get_tokens
+from conversion.quantize import list_live_tensors
+
+import sys
+import json
+
+torch.cuda._lazy_init()
+torch.set_printoptions(precision = 10)
+# torch.backends.cuda.matmul.allow_tf32 = True
+# torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
+# torch.set_float32_matmul_precision("medium")
+
+parser = argparse.ArgumentParser(description = "Test inference on ExLlamaV2 model")
+parser.add_argument("-ed", "--eval_dataset", type = str, help = "Perplexity evaluation dataset (.parquet file)")
+parser.add_argument("-er", "--eval_rows", type = int, default = 128, help = "Number of rows to apply from dataset")
+parser.add_argument("-el", "--eval_length", type = int, default = 2048, help = "Max no. tokens per sample")
+parser.add_argument("-p", "--prompt", type = str, help = "Generate from prompt")
+parser.add_argument("-t", "--tokens", type = int, default = 128, help = "Max no. tokens")
+parser.add_argument("-ps", "--prompt_speed", action = "store_true", help = "Test prompt processing (batch) speed over context length")
+parser.add_argument("-s", "--speed", action = "store_true", help = "Test raw generation speed over context length")
+
+# Initialize model and tokenizer
+
+model_init.add_args(parser)
+args = parser.parse_args()
+model_init.check_args(args)
+model_init.print_options(args)
+model, tokenizer = model_init.init(args)
+
+# Test generation
+
+if args.prompt:
+
+    with torch.inference_mode():
+
+        cache = ExLlamaV2Cache(model)
+
+        ids = tokenizer.encode(args.prompt)
+        tokens_prompt = ids.shape[-1]
+
+        print(f" -- Warmup...")
+
+        model.forward(ids[:, -1:])
+
+        print(f" -- Generating (greedy sampling)...")
+        print()
+        print(args.prompt, end = "")
+        sys.stdout.flush()
+
+        time_begin = time.time()
+
+        if ids.shape[-1] > 1: model.forward(ids[:, :-1], cache, preprocess_only = True)
+
+        torch.cuda.synchronize()
+        time_prompt = time.time()
+
+        for i in range(args.tokens):
+
+            text1 = tokenizer.decode(ids[:, -2:])[0]
+
+            logits = model.forward(ids[:, -1:], cache)
+            sample = torch.argmax(logits[0, -1]).cpu().unsqueeze(0).unsqueeze(0)
+            ids = torch.cat((ids, sample), dim = -1)
+
+            text2 = tokenizer.decode(ids[:, -3:])[0]
+            text2 = text2[len(text1):]
+
+            print (text2, end = "")
+            # sys.stdout.flush()
+
+        time_end = time.time()
+
+    print()
+    print()
+
+    total_prompt = time_prompt - time_begin
+    total_gen = time_end - time_prompt
+    print(f"Prompt processed in {total_prompt:.2f} seconds, {tokens_prompt} tokens, {tokens_prompt / total_prompt:.2f} tokens/second")
+    print(f"Response generated in {total_gen:.2f} seconds, {args.tokens} tokens, {args.tokens / total_gen:.2f} tokens/second")
+
+    cache = None
+
+
+# Test perplexity
+
+if args.eval_dataset:
+
+    with torch.inference_mode():
+
+        eval_dataset = args.eval_dataset
+        eval_rows = args.eval_rows
+        eval_length = args.eval_length
+
+        print(f" -- Running perplexity test")
+        print(f" -- Dataset: {eval_dataset}")
+        print(f" -- Tokenizing eval data, {eval_rows} rows x {eval_length} tokens...")
+
+        eval_tokens = get_tokens(eval_rows, eval_length, eval_dataset, tokenizer)
+
+        print(f" -- Inference", end = "")
+        sys.stdout.flush()
+
+        logprob_sum = 0.0
+        logprob_count = 0
+
+        for i in range(eval_tokens.shape[0]):
+        #for i in range(126, 127):
+
+            if i % 10 == 0: print(".", end = "")
+            sys.stdout.flush()
+
+            input_ids = eval_tokens[i:i+1, :]
+
+            input_ids = input_ids[:, :-1]
+            logits = model.forward(input_ids)
+
+            # print (tokenizer.decode(input_ids))
+
+            target_ids = input_ids[:, 1:].to(logits.device)
+
+            log_probs = F.log_softmax(logits, dim=-1)
+            token_log_probs = log_probs.gather(-1, target_ids.unsqueeze(-1)).squeeze(-1)
+            logprob_sum += token_log_probs.sum().item()
+            logprob_count += target_ids.numel()
+
+        print()
+
+        mean_log_prob = logprob_sum / logprob_count
+        perplexity = math.exp(-mean_log_prob)
+
+        print(f" -- Evaluation perplexity: {perplexity:.4f}")
+
+        xx = 0
+
+
+# Test prompt speed
+
+if args.prompt_speed:
+
+    with torch.inference_mode():
+
+        cache = ExLlamaV2Cache(model)
+
+        ids = torch.randint(0, model.config.vocab_size - 1, (1, model.config.max_seq_len))
+
+        print(f" -- Warmup...")
+
+        model.forward(ids[:, -1:])
+
+        print(f" -- Measuring prompt speed...")
+
+        current_len = 128
+        while True:
+
+            time_begin = time.time()
+
+            cache.current_seq_len = 0
+            model.forward(ids[:, :current_len], cache, preprocess_only = True)
+            torch.cuda.synchronize()
+
+            time_end = time.time()
+            tps = current_len / (time_end - time_begin)
+
+            print(f" ** Length {current_len:>5} tokens: {tps:>11.4f} t/s")
+
+            current_len_ = current_len
+            current_len = min(current_len + 128, model.config.max_seq_len)
+            if current_len == current_len_: break
+
+    cache = None
+
+
+# Test token speed
+
+if args.speed:
+
+    with torch.inference_mode():
+
+        cache = ExLlamaV2Cache(model)
+
+        print(f" -- Measuring token speed...")
+        ids = tokenizer.encode("X")
+        model.forward(ids[:, :])
+
+        current_idx = ids.shape[-1]
+        next_stop = 128
+
+        while True:
+
+            time_begin = time.time()
+
+            tokens = next_stop - current_idx
+            for i in range(tokens):
+
+                logits = model.forward(ids[:, -1:], cache)
+                sample = torch.argmax(logits[0, -1]).cpu().unsqueeze(0).unsqueeze(0)
+                ids = torch.cat((ids, sample), dim=-1)
+
+            time_end = time.time()
+            tps = tokens / (time_end - time_begin)
+
+            print(f" ** Position {current_idx:>5} + {tokens:>3} tokens: {tps:>9.4f} t/s")
+
+            current_idx = next_stop
+            next_stop = min(next_stop + 128, model.config.max_seq_len)
+            if next_stop == current_idx: break