Instructions to use barha/granite-text-to-json-350m-alora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use barha/granite-text-to-json-350m-alora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.0-350m")
model = PeftModel.from_pretrained(base_model, "barha/granite-text-to-json-350m-alora")

Transformers

How to use barha/granite-text-to-json-350m-alora with Transformers:

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

pipe = pipeline("text-generation", model="barha/granite-text-to-json-350m-alora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("barha/granite-text-to-json-350m-alora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use barha/granite-text-to-json-350m-alora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "barha/granite-text-to-json-350m-alora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "barha/granite-text-to-json-350m-alora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/barha/granite-text-to-json-350m-alora

SGLang

How to use barha/granite-text-to-json-350m-alora 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 "barha/granite-text-to-json-350m-alora" \
    --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": "barha/granite-text-to-json-350m-alora",
		"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 "barha/granite-text-to-json-350m-alora" \
        --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": "barha/granite-text-to-json-350m-alora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use barha/granite-text-to-json-350m-alora with Docker Model Runner:
```
docker model run hf.co/barha/granite-text-to-json-350m-alora
```

granite-text-to-json-350m-alora / tokenizer_config.json

barha

aLoRA retrain of text-to-json on granite-4.0-350m (gate-verified: invocation [100264,78191,100265], r32/a64)

534df0c verified 2 months ago

Raw

History Blame Contribute Delete

394 Bytes

	{
	"add_prefix_space": false,
	"backend": "tokenizers",
	"bos_token": "<\|end_of_text\|>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|end_of_text\|>",
	"is_local": false,
	"local_files_only": false,
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "<\|pad\|>",
	"padding_side": "right",
	"tokenizer_class": "TokenizersBackend",
	"unk_token": "<\|unk\|>"
	}