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

Libraries

How to use thelamapi/next-4b with Transformers:

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

pipe = pipeline("image-text-to-text", model="thelamapi/next-4b")
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("thelamapi/next-4b")
model = AutoModelForImageTextToText.from_pretrained("thelamapi/next-4b")
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]:]))

llama-cpp-python

How to use thelamapi/next-4b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next-4b",
	filename="next-4b-f16.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 thelamapi/next-4b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-4b:F16
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-4b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-4b:F16
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-4b:F16

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 thelamapi/next-4b:F16
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next-4b:F16

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 thelamapi/next-4b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next-4b:F16

Use Docker

docker model run hf.co/thelamapi/next-4b:F16

LM Studio
Jan

vLLM

How to use thelamapi/next-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thelamapi/next-4b",
		"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/thelamapi/next-4b:F16

SGLang

How to use thelamapi/next-4b 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 "thelamapi/next-4b" \
    --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": "thelamapi/next-4b",
		"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 "thelamapi/next-4b" \
        --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": "thelamapi/next-4b",
		"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 thelamapi/next-4b with Ollama:
```
ollama run hf.co/thelamapi/next-4b:F16
```

Unsloth Studio new

How to use thelamapi/next-4b 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 thelamapi/next-4b 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 thelamapi/next-4b to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use thelamapi/next-4b with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-4b:F16
```

Lemonade

How to use thelamapi/next-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next-4b:F16

Run and chat with the model

lemonade run user.next-4b-F16

List all available models

lemonade list

Lamapi commited on Oct 23, 2025

Commit

131d5b4

1 Parent(s): 1f22a61

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -20

README.md CHANGED Viewed

@@ -134,27 +134,24 @@ This model is ideal for **researchers, developers, and organizations** who need
 ---
 <style>
-  table { width:fit-content; border-collapse:separate; border-spacing:0 3px;font-family:system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;background:rgba(15,22,32,0.4);border-radius:16px;padding: 10px; border:none;transition:.2s all ease;}
-  thead th { text-align:center; padding:4px 10px; font-size:13px; text-transform:uppercase; color:rgb(200,200,200);border:none; }
-  tbody tr { transition: transform 0.18s ease, box-shadow 0.18s ease; border:none !important;transition:.2s all ease;border-radius:16px;background:rgba(0, 0, 0, 0.38);}
-  tbody .turkish:hover {box-shadow:0 6px 15px rgba(0, 0, 0, 0.27);scale:1.01;background:rgba(80, 38, 38, 0.6);}
-  tbody .next:hover {box-shadow:0 6px 15px rgba(0, 0, 0, 0.27);scale:1.02;background: rgba(0, 59, 225, 1)}
-  tbody tr:hover { box-shadow:0 0px 15px rgba(102, 102, 102, 0.13); background:rgba(139, 139, 139, 0.16)}
   td { padding:8px 10px;border:0px transparent !important;outline:transparent !important; text-align:center; }
   td:first-child { font-weight:600;text-align:left }
   /* tbody .turkish td { background: rgba(255, 0, 0, 0.2) !important; color:rgb(200,200,200); font-weight:400;border:0px !important; scale:1.0; } */
   /* tbody .next td { background: rgba(0, 89, 255, 0.49)!important; color:rgb(200,200,200); font-weight:600;border:0px !important; scale:1.00;outline:none;border:none !important;} */
   .next{
-    background: rgba(0, 89, 255, 0.49);
-  }
-  .turkish{
-    background:rgba(51, 34, 34, 0.64);
   }
   tbody tr td:first-child { border-top-left-radius:12px; border-bottom-left-radius:12px; }
-  tbody tr td:last-child { border-top-right-radius:12px; border-bottom-right-radius:12px; } strong{
-    font-size:16px;font-weight:700;
   }
-  em{opacity:0.7;font-size:11px !important;}
 </style>
 # Our Next 1B and Next 4B models are leading to all of the tiny models in benchmarks.
@@ -198,13 +195,6 @@ This model is ideal for **researchers, developers, and organizations** who need
       <td data-label="GSM8K %">11.9</td>
       <td data-label="MATH %">30.6</td>
     </tr>
-    <tr class="turkish">
-      <td data-label="Model">Kumru 7B <em>not verified</em></td>
-      <td data-label="MMLU (5-shot) %">30.7</td>
-      <td data-label="MMLU-Pro %">28.6</td>
-      <td data-label="GSM8K %">15.38</td>
-      <td data-label="MATH %">6.4</td>
-    </tr>
   </tbody>
 </table>

 ---
 <style>
+  table { width:fit-content; border-collapse:separate; border-spacing:0 3px;font-family:system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif;color:rgb(255, 255, 255)!important;background:rgb(28, 41, 59);border-radius:16px;padding: 10px; border:none;transition:.2s all ease;}
+  thead th { text-align:center; padding:4px 10px; font-size:13px; text-transform:uppercase; color:rgb(255, 255, 255)!important;border:none; }
+  tbody tr { transition: transform 0.18s ease, box-shadow 0.18s ease; border:none !important;transition:.2s all ease;border-radius:16px;background:rgba(11, 23, 27, 1);}
+  tbody .next:hover {box-shadow:0 6px 15px rgba(0, 76, 148, 0.1);background: rgb(0, 59, 225)}
+  tbody tr:hover { box-shadow:0 0px 15px rgba(12, 12, 12, 0.4); background:rgba(17, 34, 53, 1)}
   td { padding:8px 10px;border:0px transparent !important;outline:transparent !important; text-align:center; }
   td:first-child { font-weight:600;text-align:left }
   /* tbody .turkish td { background: rgba(255, 0, 0, 0.2) !important; color:rgb(200,200,200); font-weight:400;border:0px !important; scale:1.0; } */
   /* tbody .next td { background: rgba(0, 89, 255, 0.49)!important; color:rgb(200,200,200); font-weight:600;border:0px !important; scale:1.00;outline:none;border:none !important;} */
   .next{
+    background: rgb(0, 89, 255);
   }
   tbody tr td:first-child { border-top-left-radius:12px; border-bottom-left-radius:12px; }
+  tbody tr td:last-child { border-top-right-radius:12px; border-bottom-right-radius:12px; }
+  strong{
+    font-size:16px;font-weight:700;color:rgba(255, 255, 255, 1)!important;
   }
+  em{opacity:1;font-size:11px !important;}
 </style>
 # Our Next 1B and Next 4B models are leading to all of the tiny models in benchmarks.
       <td data-label="GSM8K %">11.9</td>
       <td data-label="MATH %">30.6</td>
     </tr>
   </tbody>
 </table>