Instructions to use thelamapi/next-70b-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thelamapi/next-70b-i1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="thelamapi/next-70b-i1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thelamapi/next-70b-i1-GGUF", dtype="auto")

llama-cpp-python

How to use thelamapi/next-70b-i1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thelamapi/next-70b-i1-GGUF",
	filename="next-70b.i1-IQ1_M.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 thelamapi/next-70b-i1-GGUF 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-70b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-70b-i1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thelamapi/next-70b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf thelamapi/next-70b-i1-GGUF:Q4_K_M

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-70b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thelamapi/next-70b-i1-GGUF:Q4_K_M

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-70b-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thelamapi/next-70b-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/thelamapi/next-70b-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use thelamapi/next-70b-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thelamapi/next-70b-i1-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": "thelamapi/next-70b-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thelamapi/next-70b-i1-GGUF:Q4_K_M

SGLang

How to use thelamapi/next-70b-i1-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 "thelamapi/next-70b-i1-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": "thelamapi/next-70b-i1-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 "thelamapi/next-70b-i1-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": "thelamapi/next-70b-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use thelamapi/next-70b-i1-GGUF with Ollama:
```
ollama run hf.co/thelamapi/next-70b-i1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use thelamapi/next-70b-i1-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 thelamapi/next-70b-i1-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 thelamapi/next-70b-i1-GGUF 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-70b-i1-GGUF to start chatting

Pi new

How to use thelamapi/next-70b-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf thelamapi/next-70b-i1-GGUF:Q4_K_M

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": "thelamapi/next-70b-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use thelamapi/next-70b-i1-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 thelamapi/next-70b-i1-GGUF:Q4_K_M

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 thelamapi/next-70b-i1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use thelamapi/next-70b-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/thelamapi/next-70b-i1-GGUF:Q4_K_M
```

Lemonade

How to use thelamapi/next-70b-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thelamapi/next-70b-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.next-70b-i1-GGUF-Q4_K_M

List all available models

lemonade list

🚀 Next 70B (ultra1295)

Türkiye’s Most Powerful AI — Industrial Scale, High Precision, and Enterprise-Ready

📖 Overview

Next 70B is a state-of-the-art 70-billion parameter large language model (LLM) engineered for maximum accuracy, versatility, and instruction following. Built upon an optimized transformer architecture, it delivers SOTA performance across coding, mathematics, and creative writing tasks.

As the flagship model of the series, Next 70B is designed to handle the most demanding enterprise workloads. It excels at nuanced language understanding in Turkish and English, complex data processing, and generating production-grade code, making it a superior alternative to proprietary models.

⚡ Highlights

🇹🇷 Türkiye’s most powerful open-weights AI model
🏆 Top-tier Performance: Beats GPT-5.1 in MATH (99.0%) and achieves near-perfect GSM8K scores.
🌍 Master-level multilingual understanding (Turkish, English, and 30+ languages)
💻 Coding Specialist: Exceptional Python and JavaScript generation capabilities (HumanEval 97.8%).
🏢 Industrial-grade stability for critical infrastructure
📝 Precise Instruction Following: High IFEval score (95.0) ensures strict adherence to formatting and constraints.

📊 Benchmark Performance

Next 70B demonstrates world-class performance, surpassing major competitors in key academic and industrial benchmarks.

🚀 Installation & Usage

Note: We recommend using a multi-GPU setup (e.g., 2x A100 80GB) for full precision or 48GB+ VRAM for 4-bit quantization.

!pip install unsloth

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained("Lamapi/next-70b")

messages = [
    {"role": "system", "content": "You are Next-X1, a helpful, smart, and precise AI assistant created by Lamapi."},
    {"role" : "user", "content" : "Write a Python script to optimize a neural network using PyTorch."}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 2048,
    temperature = 0.7, top_p = 0.95, top_k = 400,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

🧩 Key Features

Feature	Description
📚 Massive Knowledge Base	Trained on a diverse, high-quality dataset covering science, history, and law.
🇹🇷 Cultural Mastery	Native-level nuance in Turkish idioms and professional terminology.
⚙️ High-Performance Scaling	Optimized for high-throughput inference and low latency.
🧮 Scientific & Coding Excellence	99.0% MATH score. Solves complex engineering and algorithmic problems.
🎯 Precision Focused	Designed for tasks requiring strict output formats and high factual accuracy.
🏢 Enterprise Reliability	Consistent and safe outputs suitable for commercial applications.

📐 Model Specifications

Specification	Details
Base Model	Llama
Parameters	70 Billion
Architecture	Transformer (Causal LLM)
Modalities	Text-only
Fine-Tuning	SFT & DPO on high-quality instruct datasets
Optimizations	GQA, Flash Attention 3, Quantization-ready
Primary Focus	General Purpose Assistant, Math, Multilingual Chat

🎯 Ideal Use Cases

Enterprise Assistants — Customer support and internal knowledge management
Advanced Code Generation — Full-stack development and debugging
Content Creation — High-quality marketing copy, emails, and reports
Translation & Localization — Highly accurate translation between Turkish/English
Data Extraction — Structuring unstructured data into JSON/SQL
Academic Assistance — Solving math problems and summarizing research papers

📄 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 70B — Türkiye’s flagship AI model. Built for those who demand accuracy, speed, and scale.

Downloads last month: 132

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

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