Instructions to use auddin/llama3-8b-neris-validator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use auddin/llama3-8b-neris-validator with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "auddin/llama3-8b-neris-validator")

Transformers

How to use auddin/llama3-8b-neris-validator with Transformers:

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

pipe = pipeline("text-generation", model="auddin/llama3-8b-neris-validator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("auddin/llama3-8b-neris-validator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use auddin/llama3-8b-neris-validator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "auddin/llama3-8b-neris-validator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "auddin/llama3-8b-neris-validator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/auddin/llama3-8b-neris-validator

SGLang

How to use auddin/llama3-8b-neris-validator 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 "auddin/llama3-8b-neris-validator" \
    --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": "auddin/llama3-8b-neris-validator",
		"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 "auddin/llama3-8b-neris-validator" \
        --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": "auddin/llama3-8b-neris-validator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use auddin/llama3-8b-neris-validator with Docker Model Runner:
```
docker model run hf.co/auddin/llama3-8b-neris-validator
```

from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel

base_model = "meta-llama/Llama-3.1-8B-Instruct" adapter_model = "your-username/llama3-neris-auditor-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_model)

base = AutoModelForCausalLM.from_pretrained( base_model, torch_dtype="auto", device_map="auto" )

model = PeftModel.from_pretrained(base, adapter_model)

Downloads last month: 10

Model tree for auddin/llama3-8b-neris-validator

Base model

meta-llama/Llama-3.1-8B

Finetuned

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

Adapter

(2967)

this model