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--- |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- upstage |
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- solar |
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- pytorch |
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extra_gated_prompt: >- |
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Terms and Conditions |
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1. You shall not redistribute the original pre-trained model. |
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2. You are granted permission to use this model for your own fine-tuning purposes. |
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3. You may open-source the resulting fine-tuned model with any license, including for commercial use. |
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extra_gated_fields: |
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First Name: text |
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Last Name: text |
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Country: country |
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Organization/Company: text |
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geo: ip_location |
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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Upstage Privacy Policy: checkbox |
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extra_gated_button_content: Submit |
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--- |
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# solar-pro-preview-pretrained |
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solar-pro-preview-pretrained is a pre-trained model made by Upstage. |
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## Usage |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("upstage/solar-pro-preview-pretrained") |
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model = AutoModelForCausalLM.from_pretrained( |
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"upstage/solar-pro-preview-pretrained", |
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device_map="cuda:0", |
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torch_dtype='auto', |
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trust_remote_code=True, |
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) |
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``` |
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## Fine-tuning |
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If you want to use it for chat purpose, please fine-tune it first. |
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Please refer to the following chat template when fine-tuning. |
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```python |
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# Generating Text for Multi-Turn Interaction |
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# For multi-turn conversations, use this approach: |
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context = [ |
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{"role": "system", "content": "You are Solar, an AI bot by Upstage, loved by many people."}, |
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{"role": "user", "content": "Hi, there!"}, |
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{"role": "assistant", "content": "Hello, how can I help you?"}, |
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{"role": "user", "content": "Send me a message of support."}, |
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] |
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prompt = tokenizer.apply_chat_template(context, tokenize=False, add_generation_prompt=True) |
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print("# Input") |
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print(prompt) |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, use_cache=True, max_new_tokens=4096) |
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print("# Output") |
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print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[-1]:])) |
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``` |
