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--- |
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library_name: peft |
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license: mit |
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datasets: |
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- timdettmers/openassistant-guanaco |
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- tatsu-lab/alpaca |
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- BI55/MedText |
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language: |
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- en |
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pipeline_tag: question-answering |
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--- |
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Here is a README.md explaining how to run the Archimedes model locally: |
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# Archimedes Model |
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This README provides instructions for running the Archimedes conversational AI assistant locally. |
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## Requirements |
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- Python 3.6+ |
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- [Transformers](https://huggingface.co/docs/transformers/installation) |
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- [Peft](https://github.com/hazyresearch/peft) |
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- PyTorch |
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- Access to the LLAMA 2 model files or a cloned public model |
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Install requirements: |
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``` |
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!pip install transformers |
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!pip install peft |
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!pip install torch |
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!pip install datasets |
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!pip install bitsandbytes |
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``` |
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## Usage |
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```python |
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import transformers |
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from peft import LoraConfig, get_peft_model |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
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login() # Need access to the gated model. |
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# Load LLAMA 2 model |
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model_name = "meta-llama/Llama-2-13b-chat-hf" |
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# Quantization configuration |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.float16, |
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) |
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# Load model |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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quantization_config=bnb_config, |
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trust_remote_code=True |
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) |
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# Load LoRA configuration |
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lora_config = LoraConfig.from_pretrained('harpyerr/archimedes-300s-7b-chat') |
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model = get_peft_model(model, lora_config) |
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# Load tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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# Define prompt |
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text = "Can you tell me who made Space-X?" |
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prompt = "You are a helpful assistant. Please provide an informative response. \n\n" + text |
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# Generate response |
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device = "cuda:0" |
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inputs = tokenizer(prompt, return_tensors="pt").to(device) |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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This loads the LLAMA 2 model, applies 4-bit quantization and LoRA optimizations, constructs a prompt, and generates a response. |
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See the [docs](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) for more details. |
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