Instructions to use asgaardlab/VideoGameBunny-v1_0-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use asgaardlab/VideoGameBunny-v1_0-8B with Transformers:

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

pipe = pipeline("text-generation", model="asgaardlab/VideoGameBunny-v1_0-8B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("asgaardlab/VideoGameBunny-v1_0-8B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use asgaardlab/VideoGameBunny-v1_0-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "asgaardlab/VideoGameBunny-v1_0-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "asgaardlab/VideoGameBunny-v1_0-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/asgaardlab/VideoGameBunny-v1_0-8B

SGLang

How to use asgaardlab/VideoGameBunny-v1_0-8B 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 "asgaardlab/VideoGameBunny-v1_0-8B" \
    --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": "asgaardlab/VideoGameBunny-v1_0-8B",
		"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 "asgaardlab/VideoGameBunny-v1_0-8B" \
        --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": "asgaardlab/VideoGameBunny-v1_0-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use asgaardlab/VideoGameBunny-v1_0-8B with Docker Model Runner:
```
docker model run hf.co/asgaardlab/VideoGameBunny-v1_0-8B
```

taesiri commited on Aug 26, 2024

Commit

b5381d2

verified ·

1 Parent(s): a2d87c4

Update config.json

Browse files

Files changed (1) hide show

config.json +2 -2

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "BAAI/Bunny-v1_1-Llama-3-8B-V",
   "architectures": [
     "BunnyLlamaForCausalLM"
   ],
@@ -22,7 +22,7 @@
   "mm_hidden_size": 3456,
   "mm_projector_lr": null,
   "mm_projector_type": "mlp2x_gelu",
-  "mm_vision_tower": "./weights/siglip-so400m-patch14-384",
   "model_type": "bunny-llama",
   "num_attention_heads": 32,
   "num_hidden_layers": 32,

 {
+  "_name_or_path": "BAAI/VideoGameBunny-v1_1-Llama-3-8B-V",
   "architectures": [
     "BunnyLlamaForCausalLM"
   ],
   "mm_hidden_size": 3456,
   "mm_projector_lr": null,
   "mm_projector_type": "mlp2x_gelu",
+  "mm_vision_tower": "google/siglip-so400m-patch14-384",
   "model_type": "bunny-llama",
   "num_attention_heads": 32,
   "num_hidden_layers": 32,