Instructions to use harshatheg/GPC-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harshatheg/GPC-1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="harshatheg/GPC-1")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("harshatheg/GPC-1")
model = AutoModelForMultimodalLM.from_pretrained("harshatheg/GPC-1", device_map="auto")

PEFT
How to use harshatheg/GPC-1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use harshatheg/GPC-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harshatheg/GPC-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harshatheg/GPC-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/harshatheg/GPC-1

SGLang

How to use harshatheg/GPC-1 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 "harshatheg/GPC-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harshatheg/GPC-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "harshatheg/GPC-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harshatheg/GPC-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use harshatheg/GPC-1 with Docker Model Runner:
```
docker model run hf.co/harshatheg/GPC-1
```

GPC-1 / assets /runtime_manifest.json

harshatheg

Release v1

c87a0aa about 2 months ago

Raw History Blame Contribute Delete

1.48 kB

{
  "adapter": {
    "adapter_config_sha256": "a17d24feae695bb21f1635d865f41ecc0e65f3b9e05f8a5e8e8ad5deb97535be",
    "adapter_model_sha256": "2b2521cff2a98afdd42e3051c8c6de60ad928b1268244f2c13b4193059c6e1f4",
    "adapter_tensor_digest": "f0e421ca7439a84b50f840d34dea59454c4cc389215101a79ae35c38d0309a00",
    "default_path": "adapter",
    "distribution": "bundled_sanitized_adapter"
  },
  "admission": {
    "accepted_modes": [
      "atomic",
      "numeric101_text",
      "numeric101_image",
      "finite_joint"
    ],
    "default_max_input_tokens": 262144,
    "note": "Operational default, not a model context-length claim. Requests over the configured limit fail without truncation."
  },
  "assets": {
    "base_files": "assets/base_files.sha256.json",
    "base_files_sha256": "c163d38c5dcd10d307ec81f5b4709b8ec154ef3640d2197bdd1bba3ce27e5993",
    "codebook255": "assets/codebook255.json",
    "codebook255_sha256": "4e2e52b4baf578c66bc1d9cf713cb9208ebbd44ce4bd09ed55d79f48fc0f67b7"
  },
  "base": {
    "attention": "sdpa",
    "dtype": "torch.bfloat16",
    "model_id": "harshatheg/GPC-1",
    "revision": "base-278124e95f96d21b",
    "tokenizer_json_sha256": "5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42",
    "upstream_model_id": "Qwen/Qwen3.5-35B-A3B",
    "upstream_revision": "59d61f3ce65a6d9863b86d2e96597125219dc754"
  },
  "protocol": "gpc1-inference-release-runtime-v1",
  "served_model_id": "gpc-1",
  "weight_format": "adapter"
}

	{
	"adapter": {
	"adapter_config_sha256": "a17d24feae695bb21f1635d865f41ecc0e65f3b9e05f8a5e8e8ad5deb97535be",
	"adapter_model_sha256": "2b2521cff2a98afdd42e3051c8c6de60ad928b1268244f2c13b4193059c6e1f4",
	"adapter_tensor_digest": "f0e421ca7439a84b50f840d34dea59454c4cc389215101a79ae35c38d0309a00",
	"default_path": "adapter",
	"distribution": "bundled_sanitized_adapter"
	},
	"admission": {
	"accepted_modes": [
	"atomic",
	"numeric101_text",
	"numeric101_image",
	"finite_joint"
	],
	"default_max_input_tokens": 262144,
	"note": "Operational default, not a model context-length claim. Requests over the configured limit fail without truncation."
	},
	"assets": {
	"base_files": "assets/base_files.sha256.json",
	"base_files_sha256": "c163d38c5dcd10d307ec81f5b4709b8ec154ef3640d2197bdd1bba3ce27e5993",
	"codebook255": "assets/codebook255.json",
	"codebook255_sha256": "4e2e52b4baf578c66bc1d9cf713cb9208ebbd44ce4bd09ed55d79f48fc0f67b7"
	},
	"base": {
	"attention": "sdpa",
	"dtype": "torch.bfloat16",
	"model_id": "harshatheg/GPC-1",
	"revision": "base-278124e95f96d21b",
	"tokenizer_json_sha256": "5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42",
	"upstream_model_id": "Qwen/Qwen3.5-35B-A3B",
	"upstream_revision": "59d61f3ce65a6d9863b86d2e96597125219dc754"
	},
	"protocol": "gpc1-inference-release-runtime-v1",
	"served_model_id": "gpc-1",
	"weight_format": "adapter"
	}