llama-3.2-3b-instruct-bitcoin-analyst_best

### 📋 Overview

This model, `llama-3.2-3b-instruct-bitcoin-analyst_best`, is a fine-tuned version of the **Llama-3.2-3B-Instruct** large language model. It has been specialized for the domain of **Bitcoin analysis and cryptocurrency**. The goal of this fine-tuning was to enhance the model's ability to provide detailed, accurate, and contextually relevant information about Bitcoin, blockchain technology, market trends, and related topics, acting as a virtual Bitcoin analyst.

The fine-tuning was performed using **QLoRA** on the `tahamajs/bitcoin-llm-finetuning-dataset` dataset.

### 🚀 Usage

You can easily use this model with the `transformers` library. The fine-tuned weights are stored as a PEFT adapter.

```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the base model
base_model_id = "meta-llama/Llama-3.2-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# Load the fine-tuned adapter
peft_model_id = "tahamajs/llama-3.2-3b-instruct-bitcoin-analyst_best"
model = PeftModel.from_pretrained(base_model, peft_model_id)

# Example inference
prompt = "What are the key differences between Bitcoin and Ethereum?"
messages = [
    {"role": "user", "content": prompt}
]
input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(input_ids=input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

💻 Training Details

This section provides an overview of the fine-tuning process.

• Base Model: meta-llama/Llama-3.2-3B-Instruct
• Dataset: tahamajs/bitcoin-llm-finetuning-dataset
• Fine-Tuning Method: QLoRA (Quantized Low-Rank Adaptation)
• Training Framework: trl.SFTTrainer
• Hardware: [E.g., NVIDIA RTX 4070, 16GB VRAM]
• Software Stack: PyTorch, Transformers, TRL, PEFT, BitsAndBytes

⚙️ Hyperparameters

The following hyperparameters were used for fine-tuning:

⚠️ Limitations and Biases

As a model fine-tuned on a specific dataset, it may have the following limitations:

• Domain Specificity: The model's knowledge is primarily focused on Bitcoin and cryptocurrency. It may perform less effectively on general knowledge tasks.
• Data Cutoff: The model's knowledge is limited to the data it was trained on. It may not be aware of events, market changes, or new developments that occurred after the dataset's creation.
• Potential Biases: The model's responses may reflect biases present in the training data.

📜 License

This model is licensed under the Apache 2.0 license, inherited from its base model.