Model description | Example output | Banchmark results | How to use | Training and finetuning
---- # Model description >Jamba is a state-of-the-art, hybrid SSM-Transformer LLM. It delivers throughput gains over traditional Transformer-based models, while outperforming or matching the leading models of its size class on most common benchmarks.Jamba is the first production-scale Mamba implementation, which opens up interesting research and application opportunities. While this initial experimentation shows encouraging gains, we expect these to be further enhanced with future optimizations and explorations.This model card is for the base version of Jamba. It’s a pretrained, mixture-of-experts (MoE) generative text model, with 12B active parameters and a total of 52B parameters across all experts. It supports a 256K context length, and can fit up to 140K tokens on a single 80GB GPU. ---- # Example output: **Example 1:** - Input: "سلام، خوبی؟" - Output: "سلام، خوشحالم که با شما صحبت می کنم. چطور می توانم به شما کمک کنم؟" **Example 2:** - Input: "سلام، خوبی؟" - Output: "سلام، خوشحالم که با شما صحبت می کنم. چطور می توانم به شما کمک کنم؟" ---- # Banchmark results | model | dataset | score | |---------------|-------------------|--------| | base-model-7b | ARC-easy |41.92% | | base-model-7b | ARC-easy |39.12% | | fa-model-7b | ARC-easy |37.89% | | base-model-7b | ARC-challenge |37.12% | | fa-model-7b | ARC-challenge |39.29% | ---- # How to use ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aidal/Persian-Mistral-7B") model = AutoModelForCausalLM.from_pretrained("aidal/Persian-Mistral-7B") input_text = "پایتخت ایران کجاست؟" input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` ---- # Training and finetuning