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--- |
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library_name: transformers |
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license: mit |
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--- |
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# caliburn 12b-merged |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is a 12 billion parameter language model created by merging multiple existing models using the MergeKit library. It is designed for general text generation tasks. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is a large language model with 12 billion parameters, created by merging multiple pre-existing models using the MergeKit library. The model is based on the transformer architecture and is fine-tuned for general text generation tasks. |
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- **Developed by:** The user who created this merged model |
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- **Model type:** Transformer-based language model |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** Multiple source models merged using MergeKit |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** N/A |
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- **Demo [optional]:** N/A |
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## Uses |
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### Direct Use |
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This model can be used for various natural language processing tasks, including: |
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- Text generation |
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- Code completion |
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- Question answering |
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- Summarization |
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### Downstream Use [optional] |
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The model can be fine-tuned for specific tasks or domains to improve performance on targeted applications. |
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### Out-of-Scope Use |
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This model should not be used for generating harmful, biased, or unethical content. It should not be relied upon for critical decision-making without human oversight. |
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## Bias, Risks, and Limitations |
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- The model may inherit biases present in its training data or source models. |
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- It may generate incorrect or nonsensical information. |
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- The model's outputs should be carefully reviewed and fact-checked. |
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### Recommendations |
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Users should be aware of the model's limitations and potential biases. It's recommended to use the model with appropriate content filtering and human oversight, especially for public-facing applications. |
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## How to Get Started with the Model |
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Use the following code to get started with the model: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("./models/12b-merged") |
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model = AutoModelForCausalLM.from_pretrained("./models/12b-merged", torch_dtype=torch.float16).to("cuda") |
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prompt = "Your prompt here" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs.to("cuda"), max_new_tokens=100) |
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result = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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print(result) |
