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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use thelamapi/next-70b-GGUF 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-70b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thelamapi/next-70b-GGUF:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

SGLang

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

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

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

Start the llama.cpp server

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

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use thelamapi/next-70b-GGUF with Hermes Agent:

Start the llama.cpp server

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

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "thelamapi/next-70b-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Lamapi commited on Mar 12

Commit

875b269

verified ·

1 Parent(s): ea3edc7

Update README.md

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README.md +179 -3

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@@ -1,3 +1,179 @@
----
-license: apache-2.0
----

+---
+language:
+- tr
+- en
+- de
+- es
+- fr
+- ru
+- zh
+- ja
+- ko
+license: mit
+tags:
+- turkish
+- türkiye
+- ai
+- lamapi
+- next
+- next-x1
+- text-generation
+- open-source
+- 70b
+- large-language-model
+- llm
+- transformer
+- artificial-intelligence
+- machine-learning
+- nlp
+- multilingual
+- instruction-tuned
+- chat
+- generative-ai
+- optimized
+- trl
+- sft
+- enterprise
+- industrial
+pipeline_tag: text-generation
+datasets:
+- mlabonne/FineTome-100k
+- Gryphe/ChatGPT-4o-Writing-Prompts
+- uclanlp/Brief-Pro
+- neulab/agent-data-collection
+- openai/gsm8k
+- HuggingFaceH4/MATH-500
+- princeton-nlp/SWE-bench_Verified
+library_name: transformers
+base_model:
+- thelamapi/next-70b
+---
+![70b](https://cdn-uploads.huggingface.co/production/uploads/67d46bc5fe6ad6f6511d6f44/017hoTVIfgFInU5ZUVQjv.png)
+# 🚀 Next 70B (ultra1295)
+### *Türkiye’s Most Powerful AI — Industrial Scale, High Precision, and Enterprise-Ready*
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![Language: Multilingual](https://img.shields.io/badge/Language-Multilingual-red.svg)]()
+[![HuggingFace](https://img.shields.io/badge/🤗-Lamapi/Next--70B-orange.svg)](https://huggingface.co/Lamapi/next-70b)
+[![Discord](https://cdn.modrinth.com/data/cached_images/e84c69448cbf878a167f996d63e1a253437fcea2.png)](https://discord.gg/XgH4EpyPD2)
+---
+## 📖 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.
+![WhatsApp Image 2025-11-29 at 15.37.04_764ee845](https://cdn-uploads.huggingface.co/production/uploads/67d46bc5fe6ad6f6511d6f44/OEZUOh78lc0q0vJm3dlVh.jpeg)
+---
+## 🚀 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
+```
+```python
+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](mailto:lamapicontact@gmail.com)
+* 🤗 **HuggingFace:** [Lamapi](https://huggingface.co/Lamapi)
+---
+> **Next 70B** — Türkiye’s flagship AI model. Built for those who demand **accuracy**, **speed**, and **scale**.
+[![Follow on HuggingFace](https://img.shields.io/badge/Follow-HuggingFace-yellow?logo=huggingface)](https://huggingface.co/Lamapi)