Instructions to use kiel2/KielLens-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kiel2/KielLens-chat with Transformers:

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

pipe = pipeline("image-text-to-text", model="kiel2/KielLens-chat")
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("kiel2/KielLens-chat")
model = AutoModelForMultimodalLM.from_pretrained("kiel2/KielLens-chat", 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]:]))

PEFT
How to use kiel2/KielLens-chat with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use kiel2/KielLens-chat with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kiel2/KielLens-chat",
	filename="KielLens-chat-Q4_K_M.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kiel2/KielLens-chat with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kiel2/KielLens-chat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kiel2/KielLens-chat:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kiel2/KielLens-chat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kiel2/KielLens-chat:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kiel2/KielLens-chat:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kiel2/KielLens-chat:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kiel2/KielLens-chat:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kiel2/KielLens-chat:Q4_K_M

Use Docker

docker model run hf.co/kiel2/KielLens-chat:Q4_K_M

LM Studio
Jan

vLLM

How to use kiel2/KielLens-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kiel2/KielLens-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kiel2/KielLens-chat",
		"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/kiel2/KielLens-chat:Q4_K_M

SGLang

How to use kiel2/KielLens-chat 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 "kiel2/KielLens-chat" \
    --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": "kiel2/KielLens-chat",
		"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 "kiel2/KielLens-chat" \
        --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": "kiel2/KielLens-chat",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use kiel2/KielLens-chat with Ollama:
```
ollama run hf.co/kiel2/KielLens-chat:Q4_K_M
```

Unsloth Studio

How to use kiel2/KielLens-chat with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kiel2/KielLens-chat to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kiel2/KielLens-chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kiel2/KielLens-chat to start chatting

Atomic Chat new
Docker Model Runner
How to use kiel2/KielLens-chat with Docker Model Runner:
```
docker model run hf.co/kiel2/KielLens-chat:Q4_K_M
```

Lemonade

How to use kiel2/KielLens-chat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kiel2/KielLens-chat:Q4_K_M

Run and chat with the model

lemonade run user.KielLens-chat-Q4_K_M

List all available models

lemonade list

Developed by: KielTech

Model Type: Vision-Language Model (Multimodal Chat & Analysis)

Base Model: llava-hf/llava-1.5-7b-hf

License: Apache 2.0

Fine-tuning Method: Parameter-Efficient Fine-Tuning (PEFT) / LoRA

Frameworks: Transformers, TRL, PEFT

Model Description KielLens-chat is a high-performance, fine-tuned vision-language model designed for conversational image-grounded reasoning. By building upon the LLaVA architecture, KielLens-chat is optimized for fluid, human-like interaction with visual data, enabling users to upload images and ask complex, context-aware questions.

Intended Uses Multimodal Chat: Natural, image-based conversational assistants.

Visual Question Answering (VQA): Extracting insights and descriptive details from photographs.

Contextual Reasoning: Understanding relationships between objects, people, and environments within an image.

How to Get Started You can load the adapters directly on top of the base model using the transformers library:

Python from peft import PeftModel from transformers import AutoModelForCausalLM

Load the base model

base_model = AutoModelForCausalLM.from_pretrained("llava-hf/llava-1.5-7b-hf")

Load the KielLens-chat adapters

model = PeftModel.from_pretrained(base_model, "kiel2/KielLens-chat") Training Procedure The model was fine-tuned using the TRL (Transformer Reinforcement Learning) library with the following configurations:

Quantization: 4-bit (via NF4) to maintain efficiency without compromising inference speed.

Target Modules: Vision-attention layers (q_proj, v_proj).

Optimization: Paged AdamW 32-bit.

Precision: Mixed precision (bf16/fp16).

Limitations and Ethical Considerations Input Sensitivity: Performance may vary based on image resolution and lighting quality.

Bias: As an extension of the LLaVA architecture, the model may reflect biases present in its pre-training data.

Generalization: While optimized for chat, the model should be used with human oversight for mission-critical visual analysis tasks.

💡 Pro-Tip for your HF Page: Make sure to add the YAML metadata block at the very top of your README.md file (the system uses this to categorize your model):

Downloads last month: 527

Safetensors

Model size

7B params

Tensor type

F16

Model tree for kiel2/KielLens-chat

Base model

llava-hf/llava-1.5-7b-hf

Adapter

(153)

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