Instructions to use thelamapi/next-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next-1b with Transformers:

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

pipe = pipeline("text-generation", model="thelamapi/next-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thelamapi/next-1b")
model = AutoModelForCausalLM.from_pretrained("thelamapi/next-1b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use thelamapi/next-1b 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 thelamapi/next-1b:BF16
# Run inference directly in the terminal:
llama cli -hf thelamapi/next-1b:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf thelamapi/next-1b:BF16
# Run inference directly in the terminal:
llama cli -hf thelamapi/next-1b:BF16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf thelamapi/next-1b:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf thelamapi/next-1b:BF16

Use Docker

docker model run hf.co/thelamapi/next-1b:BF16

Llama new
LM Studio
Jan

vLLM

How to use thelamapi/next-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-1b"
# 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-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thelamapi/next-1b:BF16

SGLang

How to use thelamapi/next-1b 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-1b" \
    --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-1b",
		"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 "thelamapi/next-1b" \
        --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-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use thelamapi/next-1b with Ollama:
```
ollama run hf.co/thelamapi/next-1b:BF16
```
Unsloth Desktop
Docker Model Runner
How to use thelamapi/next-1b with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-1b:BF16
```

Lemonade

How to use thelamapi/next-1b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next-1b:BF16

Run and chat with the model

lemonade run user.next-1b-BF16

List all available models

lemonade list

Atomic Chat

Lamapi commited on Nov 4, 2025

Commit

97db43e

verified ·

1 Parent(s): b5ee260

Update README.md

Browse files

Files changed (1) hide show

README.md +23 -23

README.md CHANGED Viewed

@@ -173,7 +173,7 @@ Ideal for **developers, students, and organizations** needing **fast, reliable,
   </thead>
   <tbody>
     <tr class="next">
-      <td data-label="Model">Next 4B preview <em>Version s325</em></td>
       <td data-label="MMLU (5-shot) %">84.6</td>
       <td data-label="MMLU-Pro %">66.9</td>
       <td data-label="GSM8K %">82.7</td>
@@ -205,7 +205,7 @@ Ideal for **developers, students, and organizations** needing **fast, reliable,
 ---
-# Also, our Next Z1 model is leading to state-of-the-art models in some of the Benchmarks.
 <table>
   <thead>
     <tr>
@@ -218,32 +218,32 @@ Ideal for **developers, students, and organizations** needing **fast, reliable,
   </thead>
   <tbody>
     <tr class="next">
-      <td data-label="Model">Next Z1 <em>Version l294</em></td>
-      <td data-label="MMLU (5-shot) %"><strong>97.3</strong></td>
-      <td data-label="MMLU-Pro %"><strong>94.2</strong></td>
-      <td data-label="GSM8K %">97.7</td>
-      <td data-label="MATH %">93.2</td>
     </tr>
-    <tr class="next">
-      <td data-label="Model">Next Z1 <em>Version l294</em> (no tool)</td>
-      <td data-label="MMLU (5-shot) %">94.7</td>
-      <td data-label="MMLU-Pro %">90.1</td>
-      <td data-label="GSM8K %">94.5</td>
-      <td data-label="MATH %">88.7</td>
     </tr>
     <tr>
-      <td data-label="Model">GPT 5</td>
-      <td data-label="MMLU (5-shot) %">92.5</td>
-      <td data-label="MMLU-Pro %">87.0</td>
-      <td data-label="GSM8K %"><strong>98.4</strong></td>
-      <td data-label="MATH %"><strong>96.0</strong></td>
     </tr>
     <tr>
-      <td data-label="Model">Claude Opus 4.1 (Thinking)</td>
-      <td data-label="MMLU (5-shot) %">~92.0</td>
-      <td data-label="MMLU-Pro %">87.8</td>
-      <td data-label="GSM8K %">84.7</td>
-      <td data-label="MATH %">95.4</td>
     </tr>
   </tbody>
 </table>

   </thead>
   <tbody>
     <tr class="next">
+      <td data-label="Model">Next 4B preview</td>
       <td data-label="MMLU (5-shot) %">84.6</td>
       <td data-label="MMLU-Pro %">66.9</td>
       <td data-label="GSM8K %">82.7</td>
 ---
+# Also, our Next 14b model is leading to state-of-the-art models in some of the Benchmarks.
 <table>
   <thead>
     <tr>
   </thead>
   <tbody>
     <tr class="next">
+      <td><strong>Next 14B (Thinking)</strong></td>
+      <td><strong>94.6</strong></td>
+      <td><strong>93.2</strong></td>
+      <td><strong>98.8</strong></td>
+      <td>92.7</td>
     </tr>
+    <tr>
+      <td>Next 12B</td>
+      <td>92.7</td>
+      <td>84.4</td>
+      <td>95.3</td>
+      <td>87.2</td>
     </tr>
     <tr>
+      <td>GPT-5</td>
+      <td>92.5</td>
+      <td>87.0</td>
+      <td>98.4</td>
+      <td><strong>96.0</strong></td>
     </tr>
     <tr>
+      <td>Claude Opus 4.1 (Thinking)</td>
+      <td>~92.0</td>
+      <td>87.8</td>
+      <td>84.7</td>
+      <td>95.4</td>
     </tr>
   </tbody>
 </table>