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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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- adeocybersecurity/DockerCommand |
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pipeline_tag: text-generation |
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--- |
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# Uploaded model |
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- **Developed by:** junelegend |
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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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## Model Details |
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This model is finetuned on [adeocybersecurity/DockerCommand](https://huggingface.co/datasets/adeocybersecurity/DockerCommand) dataset using the base [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) model. These are only the lora adapaters of the model, the base model is automatically downloaded. |
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## How to use |
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``` |
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from unsloth import FastLanguageModel |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name = "llama-3-docker-command-lora", |
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max_seq_length = max_seq_length, |
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dtype = dtype, |
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load_in_4bit = load_in_4bit, |
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) |
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference |
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inputs = tokenizer( |
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[ |
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alpaca_prompt.format( |
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"translate this sentence in docker command.", # instruction |
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"Give me a list of all containers, indicating their status as well.", # input |
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"", # output - leave this blank for generation! |
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) |
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], return_tensors = "pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True) |
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tokenizer.batch_decode(outputs) |
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``` |
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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) |
