--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B tags: - text-generation-inference - transformers - unsloth - llama - trl - sft - huggingface inference: true license: apache-2.0 language: - en datasets: - Josephgflowers/Finance-Instruct-500k --- # Uploaded model - **Developed by:** abhi9ab - **License:** apache-2.0 - **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Llama-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) --- # Model Card The goal of this model is to enhance the base model's performance on financial tasks by fine-tuning it on a specialized financial dataset. Using LoRA, this model has been optimized for low-rank adaptation, allowing efficient fine-tuning with fewer resources. --- ## Model Details - Base Model: [unsloth/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B) - Model Type: Language Model (Distilled) - Fine-Tuning Technique: LoRA (Low-Rank Adaptation) - Fine-Tuned Model: DeepSeek-R1-Distill-Llama-8B-finance-v1 - Dataset: [Josephgflowers/Finance-Instruct-500k](https://huggingface.co/datasets/Josephgflowers/Finance-Instruct-500k) (reduced to 5k JSONL entries) - Platform: Free-tier Kaggle Notebook - Library: Hugging Face Transformers, Unsloth and Pytorch This model is a fine-tuned version of the [unsloth/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B), utilizing LoRA for efficient parameter adaptation. It has been specifically tuned on a reduced version (5k) of the [Josephgflowers/Finance-Instruct-500k](https://huggingface.co/datasets/Josephgflowers/Finance-Instruct-500k) dataset to enhance performance in finance-related tasks. --- ## Intended Use The model is intended for tasks related to financial question answering, generation, and instructions that require domain-specific knowledge in finance. It can also be used in other natural language understanding and generation tasks that benefit from fine-tuning on a finance-specific dataset. --- ## Dataset The model was fine-tuned on a subset of the Finance-Instruct-500k dataset from Hugging Face, specifically reduced to 5,000 JSONL entries for the fine-tuning process. This dataset contains financial questions and answers, providing a rich set of examples for training the model. --- ## Training Data - Dataset Name: [Josephgflowers/Finance-Instruct-500k](https://huggingface.co/datasets/Josephgflowers/Finance-Instruct-500k) - Data Size: 5k samples (subset from original dataset) - Domain: Finance - Task: Instruction-based fine-tuning for financial information retrieval and generation. --- ## Notes - This fine-tuning was performed on the free-tier of Kaggle Notebook, so training time and available resources are limited. - Ensure that your runtime in Colab/Kaggle is set to a GPU environment to speed up the training process. - The reduced 5k dataset is a smaller sample for experimentation. You can scale this up depending on your needs and available resources. --- ## Performance The model performs well in financial instruction tasks, delivering accurate responses based on the reduced dataset. Performance can be further evaluated through specific finance-related benchmarks. --- ## Usage ```bash from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abhi9ab/DeepSeek-R1-Distill-Llama-8B-finance-v1") model = AutoModelForCausalLM.from_pretrained("abhi9ab/DeepSeek-R1-Distill-Llama-8B-finance-v1") inputs = tokenizer("Example finance-related query", return_tensors="pt") outputs = model.generate(inputs['input_ids']) ``` --- ## Acknowledgement - Josephgflowers for the dataset. - Hugging Face Transformers library for model implementation and Unsloth for LoRA-based fine-tuning. ---