Instructions to use Parveshiiii/mistral-small-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Parveshiiii/mistral-small-int8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Parveshiiii/mistral-small-int8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Parveshiiii/mistral-small-int8")
model = AutoModelForCausalLM.from_pretrained("Parveshiiii/mistral-small-int8")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Parveshiiii/mistral-small-int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Parveshiiii/mistral-small-int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Parveshiiii/mistral-small-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Parveshiiii/mistral-small-int8

SGLang

How to use Parveshiiii/mistral-small-int8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Parveshiiii/mistral-small-int8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Parveshiiii/mistral-small-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Parveshiiii/mistral-small-int8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Parveshiiii/mistral-small-int8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Parveshiiii/mistral-small-int8 with Docker Model Runner:
```
docker model run hf.co/Parveshiiii/mistral-small-int8
```

🧠 Model Overview

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

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))

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for Parveshiiii/mistral-small-int8

Base model

mistralai/Mistral-7B-v0.1

Finetuned

mistralai/Mistral-7B-Instruct-v0.1

Quantized

(29)

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