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| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import gradio as gr | |
| model_id = "RWKV/rwkv-raven-1b5" | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| def chat(question): | |
| prompt = f"### Instruction: {question}\n### Response:" | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| output = model.generate(inputs["input_ids"], max_new_tokens=500) | |
| response = tokenizer.decode(output[0].tolist(), skip_special_tokens=True) | |
| print(response) | |
| return response | |
| iface = gr.Interface(fn=chat, | |
| inputs=gr.inputs.Textbox(label="Enter your text"), | |
| outputs="text", | |
| title="Chat with Raven") | |
| # index = construct_index("docs") | |
| iface.launch() | |
| ### Instruction: How do I train the RWKV on specific data? | |
| ### Response: To train the RWKV on specific data, you can use the `train_rwkv` | |
| # function from the `sklearn.model_selection` module. | |
| # This function takes a list of data points as input and returns a list of predictions for each data point. You can then use this list of predictions to train the RWKV on your specific data. | |