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--- |
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base_model: apple/DCLM-7B |
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datasets: |
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- HuggingFaceH4/ultrachat_200k |
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- teknium/OpenHermes-2.5 |
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- princeton-nlp/gemma2-ultrafeedback-armorm |
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license: apple-ascl |
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tags: |
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- text |
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--- |
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# DCLM-7B-Chat |
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This is a fine-tuned version of the DCLM-7B baseline model trained for chat |
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completions. |
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## Quick start |
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To use the model, `open_lm` must first be installed: |
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```shell |
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pip install git+https://github.com/mlfoundations/open_lm.git |
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``` |
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Then simply load the model and generate responses: |
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```python |
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from open_lm.hf import * |
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from transformers import ( |
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AutoModelForCausalLM, |
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AutoTokenizer, |
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) |
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model = AutoModelForCausalLM.from_pretrained("mathewhe/DCLM-7B-Chat") |
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tokenizer = AutoTokenizer.from_pretrained("mathewhe/DCLM-7B-Chat") |
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messages = [ |
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{"role": "user", "content": "What is an LLM?"}, |
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] |
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inputs = tokenizer.apply_chat_template(messages) |
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print(tokenizer.decode(model.generate(**inputs)[0])) |
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``` |
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Alternatively, copy the included `chat_class.py` module into your local |
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directory and just import the `Chat` class: |
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``` |
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from chat_class import Chat |
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chat = Chat() # default args: Chat("mathewhe/DCLM-7B-Chat", device="cuda") |
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# for one-off instructions |
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instruction = "Write a list of ingredients for banana pudding." |
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print(chat.instruct(instruction)) |
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# for multi-turn chat |
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response1 = chat.message("Who was Stan Lee?") |
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response2 = chat.message("What was his wife's name?") |
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# to reset the chat |
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chat.reset() |
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``` |
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## Chat template |
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This model uses the following chat template and does not support a separate |
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system prompt: |
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``` |
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<|endoftext|>[INST] <user-message> [/INST][ASST] <llm-response> [/ASST]<|endoftext|> |
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``` |
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The included tokenizer will correctly format messages, so you should not have |
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to manually format the input text. |
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Instead, use the tokenizer's `apply_chat_template()` method on a list of |
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messages. |
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Each message should be a dict with two keys: |
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- "role": Either "user" or "assistant". |
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- "content": The message to include. |
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For example: |
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```python |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("mathewhe/DCLM-7B-Chat") |
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messages = [ |
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{"role": "user", "content": "Solve for x: 3x=4"}, |
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{"role": "assistant", "content": "3x=4\n(3x)/3=(4)/3\nx=4/3"}, |
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{"role": "user", "content": "Please explain your work."}, |
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] |
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print(tokenizer.apply_chat_template(messages, tokenize=False) |
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``` |
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outputs |
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``` |
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<|endoftext|>[INST] Solve for x: 3x=4 [/INST][ASST] 3x=4 |
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(3x)/3=(4)/3 |
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x=4/3 [/ASST]<|endoftext|><|endoftext|>[INST] Please explain your work [/INST] |
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``` |
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See the example code in the included `chat_class.py` module for more details. |
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