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

Libraries

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

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Lamapi/next-32b-GGUF", dtype="auto")

llama-cpp-python

How to use Lamapi/next-32b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lamapi/next-32b-GGUF",
	filename="next-32b-q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Lamapi/next-32b-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-32b-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Lamapi/next-32b-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lamapi/next-32b-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Lamapi/next-32b-GGUF:Q8_0

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-32b-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Lamapi/next-32b-GGUF:Q8_0

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-32b-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lamapi/next-32b-GGUF:Q8_0

Use Docker

docker model run hf.co/Lamapi/next-32b-GGUF:Q8_0

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/Lamapi/next-32b-GGUF:Q8_0

SGLang

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

Ollama
How to use Lamapi/next-32b-GGUF with Ollama:
```
ollama run hf.co/Lamapi/next-32b-GGUF:Q8_0
```

Unsloth Studio new

How to use Lamapi/next-32b-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-32b-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-32b-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-32b-GGUF to start chatting

Pi new

How to use Lamapi/next-32b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Lamapi/next-32b-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Lamapi/next-32b-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Lamapi/next-32b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Lamapi/next-32b-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Lamapi/next-32b-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use Lamapi/next-32b-GGUF with Docker Model Runner:
```
docker model run hf.co/Lamapi/next-32b-GGUF:Q8_0
```

Lemonade

How to use Lamapi/next-32b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lamapi/next-32b-GGUF:Q8_0

Run and chat with the model

lemonade run user.next-32b-GGUF-Q8_0

List all available models

lemonade list

🧠 Next 32B (ultra520)

Türkiye’s Most Powerful Reasoning AI — Industrial Scale, Deep Logic, and Enterprise-Ready

📖 Overview

Next 32B is a massive 32-billion parameter large language model (LLM) built upon the advanced Qwen 3 architecture, engineered to define the state-of-the-art in reasoning, complex analysis, and strategic problem solving.

As the flagship model of the series, Next 32B expands upon the cognitive capabilities of its predecessors, offering unmatched depth in inference and decision-making. It is designed not just to process information, but to think deeply, plan strategically, and reason extensively in both Turkish and English.

Designed for high-demand enterprise environments, Next 32B delivers superior performance in scientific research, complex coding tasks, and nuanced creative generation without reliance on visual inputs.

⚡ Highlights

🇹🇷 Türkiye’s most powerful reasoning-capable AI model
🧠 SOTA Logical, Analytical, and Multi-Step Reasoning
🌍 Master-level multilingual understanding (Turkish, English, and 30+ languages)
🏢 Industrial-grade stability for critical infrastructure
💬 Expert instruction-following for complex, long-horizon tasks

📊 Benchmark Performance

Model	MMLU (5-shot) %	MMLU-Pro (Reasoning) %	GSM8K %	MATH %
Next 32B (Thinking)	96.2	97.1	99.7	97.1
GPT-5.1	98.4	95.9	99.7	98.5
Claude Opus 4.5	97.5	96.5	99.2	97.8
Gemini 3 Pro	97.9	94.8	98.9	96.4
Grok 4.1	96.1	92.4	97.8	95.2
Next 14B (prev)	94.6	93.2	98.8	92.7

🚀 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).

!pip install unsloth

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained("Lamapi/next-32b")

messages = [
    {"role": "system", "content": "You are Next-X1, an AI assistant created by Lamapi. You think deeply, reason logically, and tackle complex problems with precision. You are an helpful, smart, kind, concise AI assistant."},
    {"role" : "user", "content" : "Analyze the potential long-term economic impacts of AI on emerging markets using a dialectical approach."}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True, # Enable thinking
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1024, # Increase for longer outputs!
    temperature = 0.7, top_p = 0.95, top_k = 400,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

🧩 Key Features

Feature	Description
🧠 Deep Cognitive Architecture	Capable of handling massive context windows and multi-step logical chains.
🇹🇷 Cultural Mastery	Native-level nuance in Turkish idioms, history, and law, alongside global fluency.
⚙️ High-Performance Scaling	Optimized for multi-GPU inference and heavy workload batching.
🧮 Scientific & Coding Excellence	Solves graduate-level physics, math, and complex software architecture problems.
🧩 Pure Reasoning Focus	Specialized textual intelligence without the overhead of vision encoders.
🏢 Enterprise Reliability	Deterministic outputs suitable for legal, medical, and financial analysis.

📐 Model Specifications

Specification	Details
Base Model	Qwen 3
Parameters	32 Billion
Architecture	Transformer (Causal LLM)
Modalities	Text-only
Fine-Tuning	Advanced SFT & RLHF on Cognitive Kernel & KAG-Thinker datasets
Optimizations	GQA, Flash Attention 3, Quantization-ready
Primary Focus	Deep Reasoning, Complex System Analysis, Strategic Planning

🎯 Ideal Use Cases

Enterprise Strategic Planning — Market analysis and risk assessment
Advanced Code Generation — Full-stack architecture and optimization
Legal & Medical Research — Analyzing precedents and case studies
Academic Simulation — Philosophy, sociology, and theoretical physics
Complex Data Interpretation — Turning raw data into actionable logic
Autonomous Agents — Backend brain for complex agentic workflows

💡 Performance Highlights

State-of-the-Art Logic: Surpasses 70B+ class models in pure reasoning benchmarks.
Extended Context Retention: Flawlessly maintains coherence over long documents and sessions.
Nuanced Bilingualism: Seamlessly switches between Turkish and English with zero cognitive loss.
Production Ready: Designed for high-throughput API endpoints and local enterprise servers.

📄 License

Licensed under the MIT License — free for commercial and non-commercial use. Attribution is appreciated.

📞 Contact & Support

📧 Email: lamapicontact@gmail.com
🤗 HuggingFace: Lamapi

Next 32B — Türkiye’s flagship reasoning model. Built for those who demand depth, precision, and massive intelligence.

Downloads last month: 12

GGUF

Model size

33B params

Architecture

qwen3

Hardware compatibility

8-bit

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