Zephyr-7B-MixAT-GCG

6
7.0B
license:apache-2.0
by
INSAIT-Institute
Other
OTHER
7B params
New
6 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

This is a model adapter for HuggingFaceH4/zephyr-7b-beta, fine-tuned using the MixAT+GCG method.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Code Examples

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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
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pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
Use in 🤗 PEFT and Transformers (Quantized)bash
pip install transformers peft bitsandbytes
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")
pythontransformers
from peft import PeftModel
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

base_model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceH4/zephyr-7b-beta",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)

model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Zephyr-7B-MixAT-GCG")

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