fumea-f-dense

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "uaytug/fumea-f-dense",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("uaytug/fumea-f-dense", trust_remote_code=True)

messages = [
    {"role": "system", "content": "You are a financial analysis assistant."},
    {"role": "user", "content": "Explain the PEG ratio and when it is most useful for stock valuation."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

output = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
    repetition_penalty=1.1,
)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

tools = [
    {
        "name": "analyze_ohlcv",
        "description": "Analyze OHLCV time-series data for pattern recognition and trend detection",
        "parameters": {
            "type": "object",
            "properties": {
                "symbol": {"type": "string", "description": "Trading symbol"},
                "ohlcv_data": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "timestamp": {"type": "string"},
                            "open": {"type": "number"},
                            "high": {"type": "number"},
                            "low": {"type": "number"},
                            "close": {"type": "number"},
                            "volume": {"type": "number"}
                        }
                    }
                },
                "indicators": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "Technical indicators: RSI, MACD, BB, EMA_20, SMA_50"
                },
                "prediction_horizon": {"type": "integer"}
            },
            "required": ["symbol", "ohlcv_data"]
        }
    }
]

messages = [
    {"role": "user", "content": "Run a technical analysis on TSLA with RSI and Bollinger Bands."}
]

text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)