Jamba 8xMoe (Slerp Merge)

This model has been merged from Jamba a 52B parameter model with 16 experts. It used an accumulative SLERP to merge experts from 16 to 8.

4 Bit Inference Code

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_id = "isemmanuelolowe/Jamba-8xMoE_slerp"

tokenizer = AutoTokenizer.from_pretrained(model_id)
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    # load_in_8bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    llm_int8_skip_modules=["mamba"],
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    quantization_config=quantization_config
)

input_ids = tokenizer("Here is how to do bubble sort\n```python\n", return_tensors="pt")["input_ids"].to("cuda")

out = model.generate(input_ids, max_new_tokens=256, temperature=0, repetition_penalty=1)
print(tokenizer.batch_decode(out, skip_special_tokens=True))

OUTPUT: Here is how to do bubble sort

['Here is how to do bubble sort\n```python\ndef bubble_sort(array):\n    for i in 0, len(array):\n    for j in 0, len(array):\n    if a[i] < a[j]\n    a[i], a[j]\n\n```\n\n\n\n\n\n\n']