Instructions to use Lamapi/next-4b-Q2_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lamapi/next-4b-Q2_K-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="Lamapi/next-4b-Q2_K-GGUF")
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 AutoModel
model = AutoModel.from_pretrained("Lamapi/next-4b-Q2_K-GGUF", dtype="auto")

llama-cpp-python

How to use Lamapi/next-4b-Q2_K-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lamapi/next-4b-Q2_K-GGUF",
	filename="next-4b-q2_k.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

llama.cpp

How to use Lamapi/next-4b-Q2_K-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lamapi/next-4b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf Lamapi/next-4b-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lamapi/next-4b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf Lamapi/next-4b-Q2_K-GGUF:Q2_K

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 Lamapi/next-4b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf Lamapi/next-4b-Q2_K-GGUF:Q2_K

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 Lamapi/next-4b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lamapi/next-4b-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/Lamapi/next-4b-Q2_K-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use Lamapi/next-4b-Q2_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lamapi/next-4b-Q2_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lamapi/next-4b-Q2_K-GGUF",
		"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/Lamapi/next-4b-Q2_K-GGUF:Q2_K

SGLang

How to use Lamapi/next-4b-Q2_K-GGUF 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 "Lamapi/next-4b-Q2_K-GGUF" \
    --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": "Lamapi/next-4b-Q2_K-GGUF",
		"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 "Lamapi/next-4b-Q2_K-GGUF" \
        --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": "Lamapi/next-4b-Q2_K-GGUF",
		"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 Lamapi/next-4b-Q2_K-GGUF with Ollama:
```
ollama run hf.co/Lamapi/next-4b-Q2_K-GGUF:Q2_K
```

Unsloth Studio new

How to use Lamapi/next-4b-Q2_K-GGUF 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 Lamapi/next-4b-Q2_K-GGUF 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 Lamapi/next-4b-Q2_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lamapi/next-4b-Q2_K-GGUF to start chatting

Docker Model Runner
How to use Lamapi/next-4b-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/Lamapi/next-4b-Q2_K-GGUF:Q2_K
```

Lemonade

How to use Lamapi/next-4b-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lamapi/next-4b-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.next-4b-Q2_K-GGUF-Q2_K

List all available models

lemonade list

Lamapi commited on Oct 28, 2025

Commit

0ae7759

verified ·

1 Parent(s): 0ae3216

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+---
+language:
+- tr
+- en
+- de
+- ka
+- el
+- ku
+- es
+- sl
+- sk
+- af
+- da
+- nl
+- fa
+- fi
+- fr
+- ga
+- hi
+- hu
+- hy
+- ja
+- kg
+- kk
+- ko
+- ky
+- la
+- lb
+- id
+- it
+- is
+- za
+- zh
+- zu
+- cs
+- vi
+- be
+- bg
+- bs
+- ne
+- mn
+- rm
+- ro
+- ru
+- te
+- th
+- tk
+- tt
+- uk
+- uz
+- ug
+- pl
+- pt
+- 'no'
+license: mit
+tags:
+- turkish
+- türkiye
+- english
+- ai
+- lamapi
+- gemma3
+- next
+- next-x1
+- efficient
+- text-generation
+- open-source
+- 4b
+- huggingface
+- large-language-model
+- llm
+- causal
+- transformer
+- artificial-intelligence
+- machine-learning
+- ai-research
+- natural-language-processing
+- language
+- multilingual
+- multimodal
+- nlp
+- finetuned
+- lightweight
+- creative
+- summarization
+- question-answering
+- chat
+- generative-ai
+- optimized
+- unsloth
+- trl
+- sft
+- chemistry
+- code
+- biology
+- finance
+- legal
+- music
+- art
+- state-of-the-art
+- climate
+- medical
+- agent
+- text-generation-inference
+- merge
+- dense
+- llama-cpp
+- gguf-my-repo
+pipeline_tag: image-text-to-text
+datasets:
+- mlabonne/FineTome-100k
+- ITCL/FineTomeOs
+- Gryphe/ChatGPT-4o-Writing-Prompts
+- dongguanting/ARPO-SFT-54K
+- GreenerPastures/All-Your-Base-Full
+- Gryphe/Opus-WritingPrompts
+- HuggingFaceH4/MATH-500
+- mlabonne/smoltalk-flat
+- mlabonne/natural_reasoning-formatted
+- OpenSPG/KAG-Thinker-training-dataset
+- uclanlp/Brief-Pro
+- CognitiveKernel/CognitiveKernel-Pro-SFT
+- SuperbEmphasis/Claude-4.0-DeepSeek-R1-RP-SFWish
+- QuixiAI/dolphin-r1
+- mlabonne/lmsys-arena-human-sft-55k
+library_name: transformers
+base_model: Lamapi/next-4b
+---
+# Lamapi/next-4b-Q2_K-GGUF
+This model was converted to GGUF format from [`Lamapi/next-4b`](https://huggingface.co/Lamapi/next-4b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
+Refer to the [original model card](https://huggingface.co/Lamapi/next-4b) for more details on the model.
+## Use with llama.cpp
+Install llama.cpp through brew (works on Mac and Linux)
+```bash
+brew install llama.cpp
+```
+Invoke the llama.cpp server or the CLI.
+### CLI:
+```bash
+llama-cli --hf-repo Lamapi/next-4b-Q2_K-GGUF --hf-file next-4b-q2_k.gguf -p "The meaning to life and the universe is"
+```
+### Server:
+```bash
+llama-server --hf-repo Lamapi/next-4b-Q2_K-GGUF --hf-file next-4b-q2_k.gguf -c 2048
+```
+Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
+Step 1: Clone llama.cpp from GitHub.
+```
+git clone https://github.com/ggerganov/llama.cpp
+```
+Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
+```
+cd llama.cpp && LLAMA_CURL=1 make
+```
+Step 3: Run inference through the main binary.
+```
+./llama-cli --hf-repo Lamapi/next-4b-Q2_K-GGUF --hf-file next-4b-q2_k.gguf -p "The meaning to life and the universe is"
+```
+or
+```
+./llama-server --hf-repo Lamapi/next-4b-Q2_K-GGUF --hf-file next-4b-q2_k.gguf -c 2048
+```