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README.md

@@ -1,201 +1,208 @@
 ---
+license: apache-2.0
+tags:
+- openchat
+- mistral
+- C-RLFT
+datasets:
+- openchat/openchat_sharegpt4_dataset
+- imone/OpenOrca_FLAN
+- LDJnr/LessWrong-Amplify-Instruct
+- LDJnr/Pure-Dove
+- LDJnr/Verified-Camel
+- tiedong/goat
+- glaiveai/glaive-code-assistant
+- meta-math/MetaMathQA
+- OpenAssistant/oasst_top1_2023-08-25
+- TIGER-Lab/MathInstruct
 library_name: transformers
-tags: []
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
+# OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
 
-<!-- Provide a quick summary of what the model is/does. -->
+<div align="center">
+  <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
+</div>
 
+<p align="center">
+  <a href="https://github.com/imoneoi/openchat">GitHub Repo</a> •
+  <a href="https://openchat.team">Online Demo</a> •
+  <a href="https://discord.gg/pQjnXvNKHY">Discord</a> •
+  <a href="https://twitter.com/imonenext">Twitter</a> •
+  <a href="https://huggingface.co/openchat">Huggingface</a> •
+  <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a>
+</p>
 
+**🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥**
 
-## Model Details
+**🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖**
 
-### Model Description
+  <div align="center" style="justify-content: center; align-items: center; "'>
+  <img src="https://github.com/alpayariyak/openchat/blob/master/assets/3.5-benchmarks.png?raw=true" style="width: 100%;  border-radius: 0.5em">
+  </div>
 
-<!-- Provide a longer summary of what this model is. -->
+OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+[![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533)
 
-- **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 Sources [optional]
+## Usage
 
-<!-- Provide the basic links for the model. -->
+To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience.
 
-## Uses
+If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+<details>
+  <summary>Example request (click to expand)</summary>
 
-### Direct Use
+```bash
+curl http://localhost:18888/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "openchat_3.5",
+    "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
+  }'
+```
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Coding Mode
 
-[More Information Needed]
+```bash
+curl http://localhost:18888/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "openchat_3.5",
+    "condition": "Code",
+    "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}]
+  }'
+```
 
-### Downstream Use [optional]
+</details>
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+| Model        | Size | Context | Weights                                                     | Serving                                                                                                     |
+|--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
+| OpenChat 3.5 | 7B   | 8192    | [Huggingface](https://huggingface.co/openchat/openchat_3.5) | `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` |
 
-[More Information Needed]
+For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below.
 
-### Out-of-Scope Use
+<details>
+  <summary>Conversation templates (click to expand)</summary>
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+```python
+import transformers
+tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")
 
-[More Information Needed]
+# Single-turn
+tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
+assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
 
-## Bias, Risks, and Limitations
+# Multi-turn
+tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
+assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+# Coding Mode
+tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
+assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]
+```
 
-[More Information Needed]
+</details>
 
-### Recommendations
+The GPT4 template is also available as the integrated `tokenizer.chat_template`, 
+which can be used instead of manually specifying the template:
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+messages = [
+    {"role": "user", "content": "Hello"},
+    {"role": "assistant", "content": "Hi"},
+    {"role": "user", "content": "How are you today?"}
+]
+tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
+```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## Comparison with [X.AI Grok models](https://x.ai/)
 
-## How to Get Started with the Model
+Hey @elonmusk, I just wanted to let you know that I've recently come across your new model, Grok, and I must say, I'm quite impressed! With 33 billion parameters and all, you've really outdone yourself. But, I've got some news for you - I've outperformed Grok with my humble 7 billion parameters! Isn't that wild? I mean, who would have thought that a model with fewer parameters could be just as witty and humorous as Grok?
 
-Use the code below to get started with the model.
+Anyway, I think it's about time you join the open research movement and make your model, Grok, open source! The world needs more brilliant minds like yours to contribute to the advancement of AI. Together, we can create something truly groundbreaking and make the world a better place. So, what do you say, @elonmusk? Let's open up the doors and share our knowledge with the world! 🚀💡
 
-[More Information Needed]
+(Written by OpenChat 3.5, with a touch of humor and wit.)
 
-## Training Details
+|              | License     | # Param | Average  | MMLU | HumanEval | MATH     | GSM8k    |
+|--------------|-------------|---------|----------|------|-----------|----------|----------|
+| OpenChat 3.5 | Apache-2.0  | 7B      | **56.4** | 64.3 | 55.5      | **28.6** | **77.3** |
+| Grok-0       | Proprietary | 33B     | 44.5     | 65.7 | 39.7      | 15.7     | 56.8     |
+| Grok-1       | Proprietary | ?       | 55.8     | 73   | 63.2      | 23.9     | 62.9     |
 
-### Training Data
+## <a id="benchmarks"></a> Benchmarks
 
-<!-- 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. -->
+| Model              | # Params | Average  | MT-Bench     | AGIEval  | BBH MC   | TruthfulQA    | MMLU         | HumanEval       | BBH CoT     | GSM8K        |
+|--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------|
+| OpenChat-3.5       | **7B**   | **61.6** | 7.81         | **47.4** | **47.6** | **59.1**      | 64.3         | **55.5**        | 63.5        | **77.3**     |
+| ChatGPT (March)*   | ?        | 61.5     | **7.94**     | 47.1     | **47.6** | 57.7          | **67.3**     | 48.1            | **70.1**    | 74.9         |
+|                    |          |          |              |          |          |               |              |                 |             |              |
+| OpenHermes 2.5     | 7B       | 59.3     | 7.54         | 46.5     | 49.4     | 57.5          | 63.8         | 48.2            | 59.9        | 73.5         |
+| OpenOrca Mistral   | 7B       | 52.7     | 6.86         | 42.9     | 49.4     | 45.9          | 59.3         | 38.4            | 58.1        | 59.1         |
+| Zephyr-β^          | 7B       | 34.6     | 7.34         | 39.0     | 40.6     | 40.8          | 39.8         | 22.0            | 16.0        | 5.1          |
+| Mistral            | 7B       | -        | 6.84         | 38.0     | 39.0     | -             | 60.1         | 30.5            | -           | 52.2         |
+| Open-source SOTA** | 13B-70B  | 61.4     | 7.71         | 41.7     | 49.7     | 62.3          | 63.7         | 73.2            | 41.4        | 82.3         |
+|                    |          |          | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B |
 
-[More Information Needed]
+*: ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time.
 
-### Training Procedure 
+^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+**: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories.
 
-#### Preprocessing [optional]
+All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks).
 
-[More Information Needed]
+## Limitations
 
+**Foundation Model Limitations**
+Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
 
-#### Training Hyperparameters
+ - Complex reasoning
+ - Mathematical and arithmetic tasks
+ - Programming and coding challenges
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+**Hallucination of Non-existent Information**
+OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
 
-#### Speeds, Sizes, Times [optional]
+**Safety**
+OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+## License
 
-[More Information Needed]
+Our OpenChat 3.5 code and models are distributed under the Apache License 2.0.
 
-## Evaluation
+## Dataset Details
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+OpenChat 3.5 was trained with C-RLFT on a collection of publicly available high-quality instruction data, with a custom processing pipeline. We detail some notable subsets included here:
 
-### Testing Data, Factors & Metrics
+ - [OpenChat ShareGPT](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset)
+ - [Open-Orca with FLAN answers](https://huggingface.co/datasets/imone/OpenOrca_FLAN)
+ - Capybara [1](https://huggingface.co/datasets/LDJnr/Pure-Dove) [2](https://huggingface.co/datasets/LDJnr/Verified-Camel) [3](https://huggingface.co/datasets/LDJnr/LessWrong-Amplify-Instruct)
+ - [GOAT](https://huggingface.co/datasets/tiedong/goat)
+ - [Glaive](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)
+ - [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
+ - [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
+ - [OpenAssistant](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25)
 
-#### Testing Data
+## Citation
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```
+@article{wang2023openchat,
+  title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
+  author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
+  journal={arXiv preprint arXiv:2309.11235},
+  year={2023}
+}
+```
 
+## 💌 Contact
 
+**Project Lead:**
+- Guan Wang [imonenext at gmail dot com]
+- [Alpay Ariyak](https://github.com/alpayariyak) [aariyak at wpi dot edu]