This is a quantized variant of the Mistral 7B (small) model using LLM.int8() quantization via bitsandbytes. It reduces memory footprint while maintaining high-generation quality—ideal for single-GPU inference, research benchmarks, and lightweight downstream applications.

🔧 Model Specs

Total Parameters: ~7 Billion
Precision: INT8 with FP32 CPU offload
Quantization Threshold: 6.0
Device Map: Auto (compatible with CUDA / CPU offloading)
Tokenizer: Byte-level BPE

🚀 Usage Example

python

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model_id = "ParveshRawal/mistral-small-int8"
tokenizer = AutoTokenizer.from_pretrained(model_id)
quant_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,
    llm_int8_enable_fp32_cpu_offload=True
)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=quant_config
)

inputs = tokenizer("Tell me something about IndiaAI.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Parveshiiii/mistral-small-int8

🧠 Model Overview

🔧 Model Specs

🚀 Usage Example

Parveshiiii/
mistral-small-int8