Instructions to use tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8 with Transformers:

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

pipe = pipeline("text-generation", model="tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8")
model = AutoModelForCausalLM.from_pretrained("tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8

SGLang

How to use tokenlabsdotrun/Llama-3.1-8B-Quanto-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 "tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8 with Docker Model Runner:
```
docker model run hf.co/tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8
```

Llama-3.1-8B-Instruct Quantized (Quanto INT8)

This is a quantized version of meta-llama/Llama-3.1-8B-Instruct using quanto with INT8 weight quantization.

Model Details

Base Model: meta-llama/Llama-3.1-8B-Instruct
Quantization Method: quanto
Weight Precision: INT8 (qint8)
Original Size: ~16 GB (bfloat16)
Quantized Size: ~8.5 GB

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from quanto import quantize, freeze, qint8, safe_load

# Load base model structure
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True
)

# Quantize structure and load weights
quantize(model, weights=qint8)
state_dict = safe_load("model.safetensors")  # Use quanto's safe_load
model.load_state_dict(state_dict)
freeze(model)

# Load tokenizer and generate
tokenizer = AutoTokenizer.from_pretrained("tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8")
inputs = tokenizer("Hello, my name is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=10)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

License

This model inherits the Llama 3.1 Community License.

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for tokenlabsdotrun/Llama-3.1-8B-Quanto-Int8

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

(2733)

this model