Instructions to use ubitech-edg/llava-7b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubitech-edg/llava-7b-sft with Transformers:

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

pipe = pipeline("text-generation", model="ubitech-edg/llava-7b-sft")
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("ubitech-edg/llava-7b-sft")
model = AutoModelForImageTextToText.from_pretrained("ubitech-edg/llava-7b-sft")
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 ubitech-edg/llava-7b-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubitech-edg/llava-7b-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubitech-edg/llava-7b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubitech-edg/llava-7b-sft

SGLang

How to use ubitech-edg/llava-7b-sft 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 "ubitech-edg/llava-7b-sft" \
    --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": "ubitech-edg/llava-7b-sft",
		"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 "ubitech-edg/llava-7b-sft" \
        --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": "ubitech-edg/llava-7b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ubitech-edg/llava-7b-sft with Docker Model Runner:
```
docker model run hf.co/ubitech-edg/llava-7b-sft
```

LLaVA 7B — Supervised Fine-Tuning (SFT) on Synthetic QA

Model type: Vision-Language Causal Model (text-finetuned LLaVA-1.5)
Base model: llava-hf/llava-1.5-7b-hf
License: Llama 2 Community License
Framework: Axolotl + DeepSpeed ZeRO-1 (PyTorch 2.5.1 + CUDA 12.1)

Overview

llava-7b-sft is a supervised fine-tuned version of LLaVA 1.5 7B, trained on a synthetic instruction-following dataset of question–answer pairs to enhance text understanding and reasoning.
Although derived from a multimodal base, this SFT run fine-tunes the language model component using LoRA adapters which were later merged into the full model weights.

This model therefore supports text-only generation natively (without PEFT) and retains compatibility with the multimodal processor and vision configuration from LLaVA.

Training was conducted on the Leonardo EuroHPC system using Axolotl and DeepSpeed ZeRO-1.

Training Setup

Component	Specification
Objective	Supervised fine-tuning (instruction-following QA)
Adapter type	LoRA (merged into full model)
Precision	bfloat16
Hardware	8 nodes × 2 × NVIDIA A100 64 GB GPUs
Framework	Axolotl 0.6 + DeepSpeed ZeRO-1 (PyTorch 2.5.1 + CUDA 12.1)
Runtime	~24 hours
Checkpoints	2 per epoch
Vision tower	Frozen during SFT
Dataset split	70% train / 30% validation

Dataset

Name: axolotl_deduplicated_synthetic_qa.jsonl
Type: Instruction-following synthetic QA dataset (Alpaca-style)

Each record contains a single-turn question and a high-quality generated answer.
This SFT data improves the model’s reasoning, language coherence, and conversational QA quality.

Hyperparameters

Parameter	Value
Sequence length	2048
Micro batch size	1
Gradient accumulation	4
Epochs	1
Learning rate	0.0002
LR scheduler	cosine
Optimizer	AdamW (8-bit)
Warmup steps	10
Weight decay	0.0
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0.05
LoRA target modules	`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
Gradient checkpointing	✅
Flash attention	✅
Validation set size	0.3
Evals per epoch	2

Tokenizer & Processor

Component	Description
Tokenizer type	`AutoTokenizer`
Processor type	`AutoProcessor` (compatible with LLaVA image+text inputs)
Pad token	`<pad>` (ID 32001)
Chat template	`llava`

The processor configuration allows image or text inputs; however, this release focuses on text-based supervised tuning.

Files Included

This repository contains the fully merged model weights and all required configs for direct use with transformers:

config.json
model-*.safetensors
tokenizer.json
tokenizer_config.json
tokenizer.model
special_tokens_map.json
processor_config.json
preprocessor_config.json
vision_config.json
image_processor_config.json
README.md

Usage Example

To run text-based generation with this model:

import torch
from transformers import AutoProcessor, AutoModelForCausalLM

model_id = "ubitech-edg/llava-7b-sft"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "USER: Explain the principle of energy conservation.\nASSISTANT:"
inputs = processor(text=prompt, return_tensors="pt").to("cuda")

with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7, top_p=0.9)

print(processor.decode(outputs[0], skip_special_tokens=True))

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