Instructions to use sagedsama/lora_practice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sagedsama/lora_practice with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "sagedsama/lora_practice")

Transformers

How to use sagedsama/lora_practice with Transformers:

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

pipe = pipeline("text-generation", model="sagedsama/lora_practice")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sagedsama/lora_practice", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sagedsama/lora_practice with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sagedsama/lora_practice

SGLang

How to use sagedsama/lora_practice 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 "sagedsama/lora_practice" \
    --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": "sagedsama/lora_practice",
		"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 "sagedsama/lora_practice" \
        --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": "sagedsama/lora_practice",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sagedsama/lora_practice with Docker Model Runner:
```
docker model run hf.co/sagedsama/lora_practice
```

lora_practice / tokenizer_config.json

sagedsama

This is just my first lora practice and test

54b98b7 verified 3 days ago

raw

history blame contribute delete

354 Bytes

	{
	"backend": "tokenizers",
	"bos_token": "<\|begin_of_text\|>",
	"clean_up_tokenization_spaces": true,
	"eos_token": "<\|eot_id\|>",
	"is_local": false,
	"local_files_only": false,
	"model_input_names": [
	"input_ids",
	"attention_mask"
	],
	"model_max_length": 131072,
	"pad_token": "<\|eot_id\|>",
	"tokenizer_class": "TokenizersBackend"
	}