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berkeley-nest
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berkeley-nest
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Important: Please use the exact chat template provided below for the model. Otherwise there will be a degrade in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.
Our model follows the exact chat template and usage as Openchat 3.5. Please refer to their model card for more details. In addition, our model is hosted on LMSYS Chatbot Arena for free test.
The conversation template is the same as Openchat 3.5:
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import transformerstokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")# Single-turntokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_idsassert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]# Multi-turntokens = 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_idsassert 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]# Coding Modetokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_idsassert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]
python
import transformerstokenizer = transformers.AutoTokenizer.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")model = transformers.AutoModelForCausalLM.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")def generate_response(prompt):input_ids = tokenizer(prompt, return_tensors="pt").input_idsoutputs = model.generate(input_ids,max_length=256,pad_token_id=tokenizer.pad_token_id,eos_token_id=tokenizer.eos_token_id,)response_ids = outputs[0]response_text = tokenizer.decode(response_ids, skip_special_tokens=True)return response_text# Single-turn conversationprompt = "Hello, how are you?"single_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:"response_text = generate_response(single_turn_prompt)print("Response:", response_text)## Multi-turn conversationprompt = "Hello"follow_up_question = "How are you today?"response = ""multi_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: {response}<|end_of_turn|>GPT4 Correct User: {follow_up_question}<|end_of_turn|>GPT4 Correct Assistant:"response_text = generate_response(multi_turn_prompt)print("Multi-turn conversation response:", response_text)### Coding conversationprompt = "Implement quicksort using C++"coding_prompt = f"Code User: {prompt}<|end_of_turn|>Code Assistant:"response = generate_response(coding_prompt)print("Coding conversation response:", response)
The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.
We would like to thank Wei-Lin Chiang from Berkeley for detailed feedback of the blog and the projects. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.
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@misc{starling2023,title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},url = {},author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Jiao, Jiantao},month = {November},year = {2023}}