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tags:
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#
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- **Cloud Provider:** [More Information Needed]
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license: apache-2.0
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tags:
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- openchat
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- mistral
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- C-RLFT
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datasets:
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- openchat/openchat_sharegpt4_dataset
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- imone/OpenOrca_FLAN
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- LDJnr/LessWrong-Amplify-Instruct
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- LDJnr/Pure-Dove
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- LDJnr/Verified-Camel
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- tiedong/goat
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- glaiveai/glaive-code-assistant
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- meta-math/MetaMathQA
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- OpenAssistant/oasst_top1_2023-08-25
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- TIGER-Lab/MathInstruct
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library_name: transformers
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pipeline_tag: text-generation
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---
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# OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
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<div align="center">
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<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
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</div>
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<p align="center">
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<a href="https://github.com/imoneoi/openchat">GitHub Repo</a> β’
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<a href="https://openchat.team">Online Demo</a> β’
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<a href="https://discord.gg/pQjnXvNKHY">Discord</a> β’
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<a href="https://twitter.com/imonenext">Twitter</a> β’
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<a href="https://huggingface.co/openchat">Huggingface</a> β’
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<a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a>
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</p>
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**π₯ The first 7B model Achieves Comparable Results with ChatGPT (March)! π₯**
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**π€ #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models π€**
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<div align="center" style="justify-content: center; align-items: center; "'>
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<img src="https://github.com/alpayariyak/openchat/blob/master/assets/3.5-benchmarks.png?raw=true" style="width: 100%; border-radius: 0.5em">
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</div>
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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.
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[![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533)
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## Usage
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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.
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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.
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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.
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<details>
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<summary>Example request (click to expand)</summary>
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```bash
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curl http://localhost:18888/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "openchat_3.5",
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"messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
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}'
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```
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Coding Mode
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```bash
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curl http://localhost:18888/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "openchat_3.5",
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"condition": "Code",
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"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."}]
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}'
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```
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</details>
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| Model | Size | Context | Weights | Serving |
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|--------------|------|---------|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
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| 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` |
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For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below.
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<details>
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<summary>Conversation templates (click to expand)</summary>
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```python
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import transformers
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tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")
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# Single-turn
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tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
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assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]
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# Multi-turn
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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
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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]
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# Coding Mode
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tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
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assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]
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```
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</details>
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The GPT4 template is also available as the integrated `tokenizer.chat_template`,
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which can be used instead of manually specifying the template:
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```python
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messages = [
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{"role": "user", "content": "Hello"},
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{"role": "assistant", "content": "Hi"},
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{"role": "user", "content": "How are you today?"}
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]
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tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
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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]
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```
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## Comparison with [X.AI Grok models](https://x.ai/)
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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?
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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! ππ‘
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(Written by OpenChat 3.5, with a touch of humor and wit.)
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| | License | # Param | Average | MMLU | HumanEval | MATH | GSM8k |
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|--------------|-------------|---------|----------|------|-----------|----------|----------|
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| OpenChat 3.5 | Apache-2.0 | 7B | **56.4** | 64.3 | 55.5 | **28.6** | **77.3** |
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| Grok-0 | Proprietary | 33B | 44.5 | 65.7 | 39.7 | 15.7 | 56.8 |
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| Grok-1 | Proprietary | ? | 55.8 | 73 | 63.2 | 23.9 | 62.9 |
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## <a id="benchmarks"></a> Benchmarks
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| Model | # Params | Average | MT-Bench | AGIEval | BBH MC | TruthfulQA | MMLU | HumanEval | BBH CoT | GSM8K |
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|--------------------|----------|----------|--------------|----------|----------|---------------|--------------|-----------------|-------------|--------------|
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| OpenChat-3.5 | **7B** | **61.6** | 7.81 | **47.4** | **47.6** | **59.1** | 64.3 | **55.5** | 63.5 | **77.3** |
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| ChatGPT (March)* | ? | 61.5 | **7.94** | 47.1 | **47.6** | 57.7 | **67.3** | 48.1 | **70.1** | 74.9 |
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| | | | | | | | | | | |
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| OpenHermes 2.5 | 7B | 59.3 | 7.54 | 46.5 | 49.4 | 57.5 | 63.8 | 48.2 | 59.9 | 73.5 |
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| OpenOrca Mistral | 7B | 52.7 | 6.86 | 42.9 | 49.4 | 45.9 | 59.3 | 38.4 | 58.1 | 59.1 |
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| Zephyr-Ξ²^ | 7B | 34.6 | 7.34 | 39.0 | 40.6 | 40.8 | 39.8 | 22.0 | 16.0 | 5.1 |
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| Mistral | 7B | - | 6.84 | 38.0 | 39.0 | - | 60.1 | 30.5 | - | 52.2 |
|
149 |
+
| Open-source SOTA** | 13B-70B | 61.4 | 7.71 | 41.7 | 49.7 | 62.3 | 63.7 | 73.2 | 41.4 | 82.3 |
|
150 |
+
| | | | WizardLM 70B | Orca 13B | Orca 13B | Platypus2 70B | WizardLM 70B | WizardCoder 34B | Flan-T5 11B | MetaMath 70B |
|
151 |
|
152 |
+
*: 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.
|
153 |
|
154 |
+
^: Zephyr-Ξ² often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data.
|
155 |
|
156 |
+
**: Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories.
|
157 |
|
158 |
+
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).
|
159 |
|
160 |
+
## Limitations
|
161 |
|
162 |
+
**Foundation Model Limitations**
|
163 |
+
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:
|
164 |
|
165 |
+
- Complex reasoning
|
166 |
+
- Mathematical and arithmetic tasks
|
167 |
+
- Programming and coding challenges
|
168 |
|
169 |
+
**Hallucination of Non-existent Information**
|
170 |
+
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.
|
171 |
|
172 |
+
**Safety**
|
173 |
+
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.
|
174 |
|
175 |
+
## License
|
176 |
|
177 |
+
Our OpenChat 3.5 code and models are distributed under the Apache License 2.0.
|
178 |
|
179 |
+
## Dataset Details
|
180 |
|
181 |
+
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:
|
182 |
|
183 |
+
- [OpenChat ShareGPT](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset)
|
184 |
+
- [Open-Orca with FLAN answers](https://huggingface.co/datasets/imone/OpenOrca_FLAN)
|
185 |
+
- 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)
|
186 |
+
- [GOAT](https://huggingface.co/datasets/tiedong/goat)
|
187 |
+
- [Glaive](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)
|
188 |
+
- [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
|
189 |
+
- [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
|
190 |
+
- [OpenAssistant](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25)
|
191 |
|
192 |
+
## Citation
|
193 |
|
194 |
+
```
|
195 |
+
@article{wang2023openchat,
|
196 |
+
title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
|
197 |
+
author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
|
198 |
+
journal={arXiv preprint arXiv:2309.11235},
|
199 |
+
year={2023}
|
200 |
+
}
|
201 |
+
```
|
202 |
|
203 |
+
## π Main Contributor
|
204 |
|
205 |
+
* Wang Guan [imonenext@gmail.com], Cheng Sijie [csj23@mails.tsinghua.edu.cn], LDJ
|
206 |
+
* We look forward to hearing you and collaborating on this exciting project!
|