| |
| |
|
|
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from typing import Dict, Any |
| from reflectchain import add_block |
|
|
| MODEL_ID = "omegaT4224/Das_Bot" |
|
|
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto") |
|
|
| def generate_reply(prompt: str, meta: Dict[str, Any] | None = None) -> str: |
| inputs = tokenizer(prompt, return_tensors="pt") |
| outputs = model.generate(**inputs, max_new_tokens=256) |
| text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
| |
| add_block( |
| event_type="LLM_REPLY", |
| data={ |
| "prompt": prompt, |
| "reply": text, |
| "meta": meta or {}, |
| }, |
| ) |
|
|
| return text |