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Rishi-19/
deepseek_finetuned_model_rishi

Name: deepseek_finetuned_model_rishi
Availability: InStock

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DeepSeek-R1-Distill-Llama-8B Fine-tuned on Finance Dataset

This model is a fine-tuned version of DeepSeek-R1-Distill-Llama-8B using LoRA adapters, trained on financial instruction data.

Model Details

Base Model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
Fine-tuning Method: LoRA
LoRA Parameters: r=4, alpha=16
Target Modules: q_proj, k_proj, v_proj, o_proj
Training Dataset: Rishi-19/finance-instruct-dataset

Use Cases

This model is optimized for financial analysis, valuation calculations, and financial advisory tasks.

Example Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import torch

# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the model and tokenizer
model_name = "Rishi-19/deepseek_finetuned_model_rishi"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the base model first
peft_config = PeftConfig.from_pretrained(model_name)
base_model = AutoModelForCausalLM.from_pretrained(
    peft_config.base_model_name_or_path, 
    torch_dtype=torch.float16,  # Use half precision to save memory
    device_map="auto",
    trust_remote_code=True
)

# Then load the PEFT adapter
model = PeftModel.from_pretrained(base_model, model_name)
model.eval()  # Set to evaluation mode

# Generate text
inputs = tokenizer("Calculate the Net Present Value of a project with initial investment of $1M", return_tensors="pt").to(device)
with torch.no_grad():
    outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0]))