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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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- mistral |
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- Zenith-7B |
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pipeline_tag: text-generation |
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--- |
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# Model Card for Zenith-7B |
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Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020). |
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## Instruction format |
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In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. |
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E.g. |
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``` |
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text = "<s>[INST] What is your favourite condiment? [/INST]" |
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"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " |
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"[INST] Do you have mayonnaise recipes? [/INST]" |
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``` |
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This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B") |
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tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B") |
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messages = [ |
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{"role": "user", "content": "What is your favourite condiment?"}, |
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{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, |
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{"role": "user", "content": "Do you have mayonnaise recipes?"} |
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] |
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") |
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model_inputs = encodeds.to(device) |
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model.to(device) |
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) |
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decoded = tokenizer.batch_decode(generated_ids) |
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print(decoded[0]) |
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``` |
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## Model Architecture |
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This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: |
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- Grouped-Query Attention |
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- Sliding-Window Attention |
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- Byte-fallback BPE tokenizer |
