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README.md
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## Model Description
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`
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## Usage
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### Apply Delta Weights
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```sh
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python3 apply_delta.py --base-model-path /path/to/model_weights/llama-65b --target-model-path
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```
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Start chatting with `
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("
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model = AutoModelForCausalLM.from_pretrained("
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system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
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system_prompt += "### Instruction:\nYou are
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message = "Write me a poem please"
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prompt = f"{system_prompt}### Input: {message}\n\n### Response:\n"
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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```
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Input:
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Your prompt here
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### Response
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The output of
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```
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## Model Details
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* **Developed by**: [Stability AI](https://stability.ai/)
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* **Model type**:
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* **Language(s)**: English
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* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
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* **License**: Fine-tuned checkpoints (`
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* **Contact**: For questions and comments about the model, please email `lm@stability.ai`
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### Training Dataset
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## Model Description
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`Stable Beluga 1` is a Llama65B model fine-tuned on an Orca style Dataset
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## Usage
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### Apply Delta Weights
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Stable Beluga 1 cannot be used from the `stabilityai/StableBeluga1-Delta` weights alone. To obtain the correct model, one must add back the difference between LLaMA 65B and `stabilityai/FreeWilly1-Delta-SafeTensor` weights. We provide the [`apply_delta.py`](https://huggingface.co/stabilityai/FreeWilly1-Delta-SafeTensor/raw/main/apply_delta.py) script to automate the conversion, which you can run as:
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```sh
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python3 apply_delta.py --base-model-path /path/to/model_weights/llama-65b --target-model-path StableBeluga1 --delta-path stabilityai/StableBeluga1-Delta
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```
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Start chatting with `Stable Beluga 1` using the following code snippet:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("your_path_to_StableBeluga1", use_fast=False)
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model = AutoModelForCausalLM.from_pretrained("your_path_to_StableBeluga1", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
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system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
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system_prompt += "### Instruction:\nYou are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal.\n\n"
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message = "Write me a poem please"
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prompt = f"{system_prompt}### Input: {message}\n\n### Response:\n"
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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Stable Beluga 1 should be used with prompts formatted similarly to Alpaca as below:
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```
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Input:
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Your prompt here
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### Response:
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The output of Stable Beluga 1
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```
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## Model Details
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* **Developed by**: [Stability AI](https://stability.ai/)
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* **Model type**: Stable Beluga 1 is an auto-regressive language model fine-tuned on LLaMA65B.
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* **Language(s)**: English
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* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
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* **License**: Fine-tuned checkpoints (`StableBeluga1`) is licensed under the Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
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* **Contact**: For questions and comments about the model, please email `lm@stability.ai`
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### Training Dataset
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