Mistral-7B-v0.1 model fine-tuned on the Ultrafeedback dataset using techinques shown in the paper Self-Rewarding Language Models.
Instruction format
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.
E.g.
text = "<s>[INST] What is your favourite condiment? [/INST]"
"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> "
"[INST] Do you have mayonnaise recipes? [/INST]"
This format is available as a chat template via the apply_chat_template() method:
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23device ="cuda"# the device to load the model onto45model = AutoModelForCausalLM.from_pretrained("Xenon1/Zenith-7B")6tokenizer = AutoTokenizer.from_pretrained("Xenon1/Zenith-7B")78messages =[9{"role":"user","content":"What is your favourite condiment?"},10{"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!"},11{"role":"user","content":"Do you have mayonnaise recipes?"}12]1314encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")1516model_inputs = encodeds.to(device)17model.to(device)1819generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)20decoded = tokenizer.batch_decode(generated_ids)21print(decoded[0])
Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
