Instructions to use thelamapi/next-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next-14b with Transformers:

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

pipe = pipeline("text-generation", model="thelamapi/next-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thelamapi/next-14b")
model = AutoModelForCausalLM.from_pretrained("thelamapi/next-14b")
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

vLLM

How to use thelamapi/next-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-14b"
# 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-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thelamapi/next-14b

SGLang

How to use thelamapi/next-14b 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-14b" \
    --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-14b",
		"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-14b" \
        --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-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thelamapi/next-14b with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-14b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🧠 Next 14B (l310)

Türkiye’s First Reasoning-Capable AI Model — Logical, Analytical, and Enterprise-Ready

📖 Overview

Next 14B is a 14-billion parameter large language model (LLM) built upon Qwen 3 architecture, trained to achieve superior reasoning and analytical capabilities.
It is Türkiye’s first reasoning-capable AI model, designed to think, infer, and make decisions — not just respond.

Unlike vision-based models, Next 14B focuses on pure cognitive performance, mastering complex problem solving, abstract logic, and human-level understanding in both Turkish and English.

⚡ Highlights

🇹🇷 Türkiye’s first reasoning-capable AI model
🧠 Advanced logical, analytical, and inferential reasoning
🌍 High multilingual understanding (Turkish, English, and beyond)
🏢 Enterprise-grade stability and consistency
💬 Instruction-tuned for dialogue, problem solving, and analysis

📊 Benchmark Performance

Model	MMLU (5-shot) %	MMLU-Pro %	GSM8K %	MATH %
Next 14B (Thinking)	94.6	93.2	98.8	92.7
Next 12B	92.7	84.4	95.3	87.2
Next 8B (Thinking)	91.0	88.5	96.2	88.0
GPT-5	92.5	87.0	98.4	96.0
Claude Opus 4.1 (Thinking)	~92.0	87.8	84.7	95.4

🚀 Installation & Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Lamapi/next-14b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

messages = [
    {"role": "system", "content": "You are Next-X1, a reasoning-capable AI assistant created by Lamapi. You think deeply, reason logically, and always answer concisely. Proudly made in Turkey."},
    {"role": "user", "content": "Explain why the sky appears blue using logical reasoning."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧩 Key Features

Feature	Description
🧠 Advanced Reasoning	Excels in abstract logic, critical thinking, and long-form analysis.
🇹🇷 Cultural & Multilingual Intelligence	Deep Turkish understanding, alongside fluent English and 30+ languages.
⚙️ Optimized for Efficiency	Available in quantized formats (Q8_0, Q4_K_M, FP16).
🧮 Mathematical & Analytical Skill	Performs exceptionally in structured problem solving and scientific reasoning.
🧩 Non-Vision Architecture	Focused purely on cognitive and linguistic understanding.
🏢 Enterprise Reliability	Consistent, interpretable outputs for professional use cases.

📐 Model Specifications

Specification	Details
Base Model	Qwen 3
Parameters	14 Billion
Architecture	Transformer (Causal LLM)
Modalities	Text-only
Fine-Tuning	Instruction-tuned and reinforced with cognitive reasoning datasets
Optimizations	Quantization-ready, FP16 support
Primary Focus	Reasoning, logic, decision-making, and language understanding

🎯 Ideal Use Cases

Analytical Chatbots for business and enterprise logic
Research Assistance — scientific, legal, or data-heavy reasoning
Education & Tutoring — explain concepts step-by-step
Creative Writing — coherent story logic and worldbuilding
Code & Algorithm Design — reasoning-based code generation
Decision Support Systems — scenario evaluation and inference

💡 Performance Highlights

Superior Reasoning: Outperforms previous-generation 12B models in logic-based benchmarks.
Robust Mathematical Understanding: Handles symbolic reasoning and complex equations.
Consistent Long-Context Memory: Capable of tracking context across multi-turn conversations.
Professional Reliability: Built for critical enterprise and research applications.

📄 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 14B — Türkiye’s first reasoning-capable large language model, combining logical depth, analytical intelligence, and enterprise reliability.

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Safetensors

Model size

15B params

Tensor type

BF16

Model tree for thelamapi/next-14b

Quantizations

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next-14b