Instructions to use NIM-AI/NIM-2-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NIM-AI/NIM-2-Coder-7B with Transformers:

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

pipe = pipeline("text-generation", model="NIM-AI/NIM-2-Coder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NIM-AI/NIM-2-Coder-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NIM-AI/NIM-2-Coder-7B 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 NIM-AI/NIM-2-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NIM-AI/NIM-2-Coder-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NIM-AI/NIM-2-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NIM-AI/NIM-2-Coder-7B: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 NIM-AI/NIM-2-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NIM-AI/NIM-2-Coder-7B: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 NIM-AI/NIM-2-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NIM-AI/NIM-2-Coder-7B:Q4_K_M

Use Docker

docker model run hf.co/NIM-AI/NIM-2-Coder-7B:Q4_K_M

LM Studio
Jan

vLLM

How to use NIM-AI/NIM-2-Coder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NIM-AI/NIM-2-Coder-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NIM-AI/NIM-2-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NIM-AI/NIM-2-Coder-7B:Q4_K_M

SGLang

How to use NIM-AI/NIM-2-Coder-7B 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 "NIM-AI/NIM-2-Coder-7B" \
    --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": "NIM-AI/NIM-2-Coder-7B",
		"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 "NIM-AI/NIM-2-Coder-7B" \
        --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": "NIM-AI/NIM-2-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use NIM-AI/NIM-2-Coder-7B with Ollama:
```
ollama run hf.co/NIM-AI/NIM-2-Coder-7B:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use NIM-AI/NIM-2-Coder-7B with Docker Model Runner:
```
docker model run hf.co/NIM-AI/NIM-2-Coder-7B:Q4_K_M
```

Lemonade

How to use NIM-AI/NIM-2-Coder-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NIM-AI/NIM-2-Coder-7B:Q4_K_M

Run and chat with the model

lemonade run user.NIM-2-Coder-7B-Q4_K_M

List all available models

lemonade list

Atomic Chat

sujayanimneth commited on about 19 hours ago

Commit

f9fa412

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1 Parent(s): a35a6cc

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	@@ -0,0 +1,57 @@

+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- code
+- coding
+- software-engineering
+- autonomous-agent
+- gguf
+- ollama
+library_name: transformers
+---
+# NIM-2 Coder (7B)
+**NIM-2 Coder** is a specialized, high-density 7-billion parameter language model engineered by **NIM AI** for advanced software engineering, algorithmic design, and full-stack development.
+Engineered specifically to punch above its weight class on consumer hardware, NIM-2 Coder delivers complete, type-safe, production-ready code with deep architectural reasoning while running fully locally within 8 GB VRAM.
+[![GitHub](https://img.shields.io/badge/GitHub-N--I--M--AI-black?logo=github)](https://github.com/N-I-M-AI)
+[![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+---
+## Key Highlights
+* **Autonomous Code Synthesis:** Writes idiomatic, complete code across Python, TypeScript/JavaScript, Rust, Go, C++, and Bash with zero placeholders.
+* **Deterministic Logic & Edge Cases:** Trained on multi-stage algorithmic problem decomposition, cyclic graph traversals, and strict type constraints.
+* **Hardware Optimized:** Packaged in high-throughput `Q4_K_M` GGUF quantization (~4.6 GB), allowing full offloading to consumer GPUs like the NVIDIA RTX 4060 (8 GB) and Apple Silicon.
+* **Agentic Precision:** Minimal conversational fluff—outputs immediate technical rationale followed by runnable implementations.
+---
+## Technical Specifications
+| Parameter | Specification |
+| :--- | :--- |
+| **Model Name** | NIM-2 Coder |
+| **Organization** | NIM AI (`N-I-M-AI`) |
+| **Architecture** | Dense Auto-regressive Transformer |
+| **Parameters** | 7.6 Billion |
+| **Context Length** | 4,096 tokens (dynamically extendable) |
+| **Format** | `Q4_K_M` GGUF (~4.6 GB) / LoRA FP16 |
+| **Prompt Template** | ChatML (`<|im_start|>`, `<|im_end|>`) |
+---
+## Quickstart Guide
+### Run Directly via Ollama (Recommended)
+Pull and execute directly from Hugging Face:
+```bash
+ollama run hf.co/N-I-M-AI/NIM-2-Coder-7B:NIM-2-Coder-7B-Q4_K_M.gguf