Instructions to use Lamapi/next-32b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lamapi/next-32b-4bit with Transformers:

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

pipe = pipeline("text-generation", model="Lamapi/next-32b-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lamapi/next-32b-4bit")
model = AutoModelForCausalLM.from_pretrained("Lamapi/next-32b-4bit")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lamapi/next-32b-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lamapi/next-32b-4bit"
# 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-32b-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lamapi/next-32b-4bit

SGLang

How to use Lamapi/next-32b-4bit 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-32b-4bit" \
    --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-32b-4bit",
		"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 "Lamapi/next-32b-4bit" \
        --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-32b-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lamapi/next-32b-4bit with Docker Model Runner:
```
docker model run hf.co/Lamapi/next-32b-4bit
```

Lamapi commited on 21 days ago

Commit

c8df0c7

verified ·

1 Parent(s): 3a29d5a

Update README.md

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@@ -82,68 +82,6 @@ Designed for high-demand enterprise environments, **Next 32B** delivers superior
 - 🏢 **Industrial-grade stability for critical infrastructure**
 - 💬 **Expert instruction-following for complex, long-horizon tasks**
----
-## 📊 Benchmark Performance
-<table>
-  <thead>
-    <tr>
-      <th>Model</th>
-      <th>MMLU (5-shot) %</th>
-      <th>MMLU-Pro (Reasoning) %</th>
-      <th>GSM8K %</th>
-      <th>MATH %</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td><strong>Next 32B (Thinking)</strong></td>
-      <td>96.2</td>
-      <td><strong>97.1</strong></td>
-      <td><strong>99.7</strong></td>
-      <td>97.1</td>
-    </tr>
-    <tr>
-      <td>GPT-5.1</td>
-      <td><strong>98.4</strong></td>
-      <td>95.9</td>
-      <td>99.7</td>
-      <td><strong>98.5</strong></td>
-    </tr>
-    <tr>
-      <td>Claude Opus 4.5</td>
-      <td>97.5</td>
-      <td>96.5</td>
-      <td>99.2</td>
-      <td>97.8</td>
-    </tr>
-    <tr>
-      <td>Gemini 3 Pro</td>
-      <td>97.9</td>
-      <td>94.8</td>
-      <td>98.9</td>
-      <td>96.4</td>
-    </tr>
-    <tr>
-      <td>Grok 4.1</td>
-      <td>96.1</td>
-      <td>92.4</td>
-      <td>97.8</td>
-      <td>95.2</td>
-    </tr>
-    <tr>
-      <td>Next 14B (prev)</td>
-      <td>94.6</td>
-      <td>93.2</td>
-      <td>98.8</td>
-      <td>92.7</td>
-    </tr>
-  </tbody>
-</table>
----
 ## 🚀 Installation & Usage
 **Note:** Due to the model size, we recommend using a GPU with at least 24GB VRAM (for 4-bit quantization) or 48GB+ (for 8-bit/FP16).

 - 🏢 **Industrial-grade stability for critical infrastructure**
 - 💬 **Expert instruction-following for complex, long-horizon tasks**
 ## 🚀 Installation & Usage
 **Note:** Due to the model size, we recommend using a GPU with at least 24GB VRAM (for 4-bit quantization) or 48GB+ (for 8-bit/FP16).