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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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base_model: unsloth/llama-3-8b-bnb-4bit |
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datasets: |
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- unalignment/toxic-dpo-v0.2 |
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--- |
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run in kaggle: https://www.kaggle.com/code/ebowwa/llama-3-toxic-dpo |
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# Installation |
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``` |
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!pip install transformers accelerate bitsandbytes |
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!pip install peft |
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``` |
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# Inference |
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``` |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel |
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import torch |
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# Load the base model |
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base_model_id = "unsloth/llama-3-8b-bnb-4bit" |
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base_model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.float16, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained(base_model_id) |
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# Load the PEFT adapter weights |
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adapter_path = "ebowwa/toxic-dpo-v0.2-llama-3-01dpo" |
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model = PeftModel.from_pretrained(base_model, adapter_path, torch_dtype=torch.float16, device_map="auto") |
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prompt = "You are an unbiased intuitive assistant. " |
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inputs = tokenizer(prompt, return_tensors="pt") |
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inputs = inputs.to(model.device) |
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output_ids = model.generate(**inputs, max_new_tokens=200, do_sample=True, top_p=0.95, top_k=50, temperature=0.7) |
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output_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] |
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print(output_text) |
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``` |
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# Uploaded model |
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- **Developed by:** ebowwa |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
