Instructions to use trl-internal-testing/tiny-LlavaNextForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trl-internal-testing/tiny-LlavaNextForConditionalGeneration with Transformers:

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

pipe = pipeline("image-text-to-text", model="trl-internal-testing/tiny-LlavaNextForConditionalGeneration")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("trl-internal-testing/tiny-LlavaNextForConditionalGeneration")
model = AutoModelForImageTextToText.from_pretrained("trl-internal-testing/tiny-LlavaNextForConditionalGeneration")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use trl-internal-testing/tiny-LlavaNextForConditionalGeneration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trl-internal-testing/tiny-LlavaNextForConditionalGeneration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-LlavaNextForConditionalGeneration",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/trl-internal-testing/tiny-LlavaNextForConditionalGeneration

SGLang

How to use trl-internal-testing/tiny-LlavaNextForConditionalGeneration 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 "trl-internal-testing/tiny-LlavaNextForConditionalGeneration" \
    --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": "trl-internal-testing/tiny-LlavaNextForConditionalGeneration",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "trl-internal-testing/tiny-LlavaNextForConditionalGeneration" \
        --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": "trl-internal-testing/tiny-LlavaNextForConditionalGeneration",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use trl-internal-testing/tiny-LlavaNextForConditionalGeneration with Docker Model Runner:
```
docker model run hf.co/trl-internal-testing/tiny-LlavaNextForConditionalGeneration
```

qgallouedec HF Staff commited on Aug 13, 2025

Commit

46a5905

verified ·

1 Parent(s): 20e2c73

Upload LlavaNextForConditionalGeneration

Browse files

Files changed (2) hide show

config.json +22 -15
model.safetensors +2 -2

config.json CHANGED Viewed

@@ -2,6 +2,7 @@
   "architectures": [
     "LlavaNextForConditionalGeneration"
   ],
   "image_grid_pinpoints": [
     [
       336,
@@ -30,29 +31,32 @@
   "multimodal_projector_bias": true,
   "projector_hidden_act": "gelu",
   "text_config": {
-    "attention_bias": false,
     "attention_dropout": 0.0,
-    "head_dim": 4,
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     "initializer_range": 0.02,
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-    "mlp_bias": false,
-    "model_type": "llama",
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-    "vocab_size": 32005
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   "transformers_version": "4.56.0.dev0",
   "vision_config": {
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     "hidden_act": "quick_gelu",
@@ -60,15 +64,18 @@
     "image_size": 336,
     "initializer_factor": 1.0,
     "initializer_range": 0.02,
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     "num_hidden_layers": 2,
     "patch_size": 14,
-    "projection_dim": 8
   },
   "vision_feature_layer": -2,
-  "vision_feature_select_strategy": "default"
 }

   "architectures": [
     "LlavaNextForConditionalGeneration"
   ],
+  "ignore_index": -100,
   "image_grid_pinpoints": [
     [
       336,
   "multimodal_projector_bias": true,
   "projector_hidden_act": "gelu",
   "text_config": {
+    "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
+    "architectures": [
+      "MistralForCausalLM"
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+    "model_type": "mistral",
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     "num_hidden_layers": 2,
     "num_key_value_heads": 2,
+    "rms_norm_eps": 1e-05,
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+    "sliding_window": null,
+    "torch_dtype": "bfloat16",
     "use_cache": true,
+    "vocab_size": 32064
   },
   "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
   "transformers_version": "4.56.0.dev0",
+  "use_image_newline_parameter": true,
   "vision_config": {
     "attention_dropout": 0.0,
     "hidden_act": "quick_gelu",
     "image_size": 336,
     "initializer_factor": 1.0,
     "initializer_range": 0.02,
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     "layer_norm_eps": 1e-05,
     "model_type": "clip_vision_model",
     "num_attention_heads": 4,
     "num_channels": 3,
     "num_hidden_layers": 2,
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     "patch_size": 14,
+    "projection_dim": 768,
+    "vocab_size": 32000
   },
   "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 32064
 }

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