Instructions to use shashikanth-a/llava-1.5-7b-hf-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shashikanth-a/llava-1.5-7b-hf-4bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="shashikanth-a/llava-1.5-7b-hf-4bit")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("shashikanth-a/llava-1.5-7b-hf-4bit")
model = AutoModelForMultimodalLM.from_pretrained("shashikanth-a/llava-1.5-7b-hf-4bit", device_map="auto")
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]:]))

MLX

How to use shashikanth-a/llava-1.5-7b-hf-4bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("shashikanth-a/llava-1.5-7b-hf-4bit")
config = load_config("shashikanth-a/llava-1.5-7b-hf-4bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use shashikanth-a/llava-1.5-7b-hf-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shashikanth-a/llava-1.5-7b-hf-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shashikanth-a/llava-1.5-7b-hf-4bit",
		"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/shashikanth-a/llava-1.5-7b-hf-4bit

SGLang

How to use shashikanth-a/llava-1.5-7b-hf-4bit 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 "shashikanth-a/llava-1.5-7b-hf-4bit" \
    --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": "shashikanth-a/llava-1.5-7b-hf-4bit",
		"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 "shashikanth-a/llava-1.5-7b-hf-4bit" \
        --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": "shashikanth-a/llava-1.5-7b-hf-4bit",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use shashikanth-a/llava-1.5-7b-hf-4bit with Docker Model Runner:
```
docker model run hf.co/shashikanth-a/llava-1.5-7b-hf-4bit
```
Atomic Chat

llava-1.5-7b-hf-4bit / config.json

shashikanth-a

Upload folder using huggingface_hub

bdd4e1e verified almost 2 years ago

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	{
	"architectures": [
	"LlavaForConditionalGeneration"
	],
	"ignore_index": -100,
	"image_seq_length": 576,
	"image_token_index": 32000,
	"model_type": "llava",
	"pad_token_id": 32001,
	"projector_hidden_act": "gelu",
	"quantization": {
	"group_size": 64,
	"bits": 4
	},
	"text_config": {
	"_attn_implementation_autoset": false,
	"_name_or_path": "lmsys/vicuna-7b-v1.5",
	"add_cross_attention": false,
	"architectures": [
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	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"initializer_range": 0.02,
	"intermediate_size": 11008,
	"is_decoder": false,
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	"label2id": {
	"LABEL_0": 0,
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	},
	"length_penalty": 1.0,
	"max_length": 20,
	"max_position_embeddings": 4096,
	"min_length": 0,
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	"model_type": "llama",
	"no_repeat_ngram_size": 0,
	"num_attention_heads": 32,
	"num_beam_groups": 1,
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	"num_key_value_heads": 32,
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	"output_hidden_states": false,
	"output_scores": false,
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	"prefix": null,
	"pretraining_tp": 1,
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	"pruned_heads": {},
	"remove_invalid_values": false,
	"repetition_penalty": 1.0,
	"return_dict": true,
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	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"sep_token_id": null,
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	"tf_legacy_loss": false,
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	"tokenizer_class": null,
	"top_k": 50,
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	"torch_dtype": "float16",
	"torchscript": false,
	"typical_p": 1.0,
	"use_bfloat16": false,
	"use_cache": true,
	"vocab_size": 32064
	},
	"tie_word_embeddings": false,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.46.3",
	"unsloth_fixed": true,
	"vision_config": {
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	"_name_or_path": "",
	"add_cross_attention": false,
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	"decoder_start_token_id": null,
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	"encoder_no_repeat_ngram_size": 0,
	"eos_token_id": null,
	"exponential_decay_length_penalty": null,
	"finetuning_task": null,
	"forced_bos_token_id": null,
	"forced_eos_token_id": null,
	"hidden_act": "quick_gelu",
	"hidden_size": 1024,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"image_size": 336,
	"initializer_factor": 1.0,
	"initializer_range": 0.02,
	"intermediate_size": 4096,
	"is_decoder": false,
	"is_encoder_decoder": false,
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1
	},
	"layer_norm_eps": 1e-05,
	"length_penalty": 1.0,
	"max_length": 20,
	"min_length": 0,
	"model_type": "clip_vision_model",
	"no_repeat_ngram_size": 0,
	"num_attention_heads": 16,
	"num_beam_groups": 1,
	"num_beams": 1,
	"num_channels": 3,
	"num_hidden_layers": 24,
	"num_return_sequences": 1,
	"output_attentions": false,
	"output_hidden_states": false,
	"output_scores": false,
	"pad_token_id": 32001,
	"patch_size": 14,
	"prefix": null,
	"problem_type": null,
	"projection_dim": 768,
	"pruned_heads": {},
	"remove_invalid_values": false,
	"repetition_penalty": 1.0,
	"return_dict": true,
	"return_dict_in_generate": false,
	"sep_token_id": null,
	"suppress_tokens": null,
	"task_specific_params": null,
	"temperature": 1.0,
	"tf_legacy_loss": false,
	"tie_encoder_decoder": false,
	"tie_word_embeddings": true,
	"tokenizer_class": null,
	"top_k": 50,
	"top_p": 1.0,
	"torch_dtype": null,
	"torchscript": false,
	"typical_p": 1.0,
	"use_bfloat16": false,
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	"vision_feature_layer": -2,
	"vision_feature_select_strategy": "default",
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	}