Instructions to use eVici-AS/llm-ner-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eVici-AS/llm-ner-3b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "eVici-AS/llm-ner-3b")

Transformers

How to use eVici-AS/llm-ner-3b with Transformers:

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

pipe = pipeline("text-generation", model="eVici-AS/llm-ner-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("eVici-AS/llm-ner-3b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eVici-AS/llm-ner-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eVici-AS/llm-ner-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eVici-AS/llm-ner-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eVici-AS/llm-ner-3b

SGLang

How to use eVici-AS/llm-ner-3b 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 "eVici-AS/llm-ner-3b" \
    --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": "eVici-AS/llm-ner-3b",
		"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 "eVici-AS/llm-ner-3b" \
        --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": "eVici-AS/llm-ner-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eVici-AS/llm-ner-3b with Docker Model Runner:
```
docker model run hf.co/eVici-AS/llm-ner-3b
```

llm-ner-3b / training_meta.json

sigurdriseth

Update new model trained on more multi-product orders.

79a53d0 verified 6 months ago

Raw History Blame Contribute Delete

377 Bytes

	{
	"base_model": "unsloth/Llama-3.2-3B-Instruct",
	"best_epoch": 28,
	"best_val_loss": 0.020325099364171952,
	"lr": 0.0003,
	"effective_batch": 512,
	"lora_r": 16,
	"lora_alpha": 16,
	"lora_target_modules": [
	"q_proj",
	"k_proj",
	"v_proj",
	"o_proj",
	"gate_proj",
	"up_proj",
	"down_proj",
	"lm_head"
	],
	"max_seq_len": 512,
	"seed": 42
	}