isemmanuelolowe
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README.md
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license: mit
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---
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---
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license: mit
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---
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# Jamba 8xMoe (Slerp Merge)
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This model has been merged from [Jamba](https://huggingface.co/ai21labs/Jamba-v0.1) a 52B parameter model with 16 experts. It used an accumulative SLERP to merge experts from 16 to 8.
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4 Bit Inference Code
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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model_id = "isemmanuelolowe/Jamba-8xMoE_slerp"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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# load_in_8bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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llm_int8_skip_modules=["mamba"],
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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quantization_config=quantization_config
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)
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input_ids = tokenizer("Here is how to do bubble sort\n```python\n", return_tensors="pt")["input_ids"].to("cuda")
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out = model.generate(input_ids, max_new_tokens=256, temperature=0, repetition_penalty=1)
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print(tokenizer.batch_decode(out, skip_special_tokens=True))
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```
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OUTPUT:
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Here is how to do bubble sort
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```bash
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['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']
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```
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