Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
+base_model: unsloth/DeepSeek-R1-Distill-Llama-8B
 tags:
 - text-generation-inference
 - transformers
@@ -10,14 +10,94 @@ tags:
 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-unsloth-bnb-4bit
+- **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.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](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
+- 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.
+
+---