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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- llm |
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- fine-tune |
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- qlora |
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- llama |
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- bitcoin |
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- finance |
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pipeline_tag: text-generation |
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base_model: meta-llama/Llama-3.2-3B-Instruct |
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datasets: |
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- tahamajs/bitcoin-llm-finetuning-dataset |
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--- |
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``` |
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### 📋 Overview |
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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. |
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The fine-tuning was performed using **QLoRA** on the `tahamajs/bitcoin-llm-finetuning-dataset` dataset. |
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### 🚀 Usage |
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You can easily use this model with the `transformers` library. The fine-tuned weights are stored as a PEFT adapter. |
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```python |
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import torch |
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from peft import PeftModel |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Load the base model |
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base_model_id = "meta-llama/Llama-3.2-3B-Instruct" |
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tokenizer = AutoTokenizer.from_pretrained(base_model_id) |
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base_model = AutoModelForCausalLM.from_pretrained( |
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base_model_id, |
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device_map="auto", |
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torch_dtype=torch.bfloat16, |
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) |
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# Load the fine-tuned adapter |
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peft_model_id = "tahamajs/llama-3.2-3b-instruct-bitcoin-analyst_best" |
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model = PeftModel.from_pretrained(base_model, peft_model_id) |
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# Example inference |
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prompt = "What are the key differences between Bitcoin and Ethereum?" |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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input_ids = tokenizer.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to(model.device) |
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outputs = model.generate(input_ids=input_ids, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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### 💻 Training Details |
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This section provides an overview of the fine-tuning process. |
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* **Base Model:** `meta-llama/Llama-3.2-3B-Instruct` |
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* **Dataset:** `tahamajs/bitcoin-llm-finetuning-dataset` |
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* **Fine-Tuning Method:** QLoRA (Quantized Low-Rank Adaptation) |
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* **Training Framework:** `trl.SFTTrainer` |
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* **Hardware:** [E.g., NVIDIA RTX 4070, 16GB VRAM] |
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* **Software Stack:** PyTorch, Transformers, TRL, PEFT, BitsAndBytes |
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#### ⚙️ Hyperparameters |
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The following hyperparameters were used for fine-tuning: |
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| Hyperparameter | Value | |
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| :-------------------------- | :------------------------- | |
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| `num_train_epochs` | 1 | |
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| `per_device_train_batch_size` | 1 | |
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| `gradient_accumulation_steps` | 2 | |
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| `learning_rate` | 2e-4 | |
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| `optim` | `paged_adamw_32bit` | |
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| `bf16` | `True` | |
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| `max_grad_norm` | 0.3 | |
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| `r` (LoRA rank) | 16 | |
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| `lora_alpha` | 16 | |
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### ⚠️ Limitations and Biases |
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As a model fine-tuned on a specific dataset, it may have the following limitations: |
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* **Domain Specificity:** The model's knowledge is primarily focused on Bitcoin and cryptocurrency. It may perform less effectively on general knowledge tasks. |
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* **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. |
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* **Potential Biases:** The model's responses may reflect biases present in the training data. |
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### 📜 License |
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This model is licensed under the Apache 2.0 license, inherited from its base model. |
