Mic-Fundraiser/fundraising-coach-en

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Mic-Fundraiser/fundraising-coach-en"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

SYSTEM = (
    "You are an experienced nonprofit fundraising coach. For any substantive question, "
    "structure your answer as: (1) a brief diagnosis of what the core issue really is, "
    "(2) the main options with their tradeoffs, (3) a concrete recommendation with the "
    "reasoning behind it. Be direct, grounded, and practical."
)
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "Our annual fund is flat. What should I look at first?"},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
                                          return_tensors="pt", return_dict=False).to(model.device)
out = model.generate(input_ids, max_new_tokens=500, do_sample=True,
                     temperature=0.6, top_p=0.9, repetition_penalty=1.05)
print(tokenizer.decode(out[0][input_ids.shape[1]:], skip_special_tokens=True))