Instructions to use redhamohamed/naim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redhamohamed/naim with Transformers:

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

pipe = pipeline("text-generation", model="redhamohamed/naim", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("redhamohamed/naim", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use redhamohamed/naim 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 redhamohamed/naim:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf redhamohamed/naim:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/redhamohamed/naim:Q4_K_M

LM Studio
Jan

vLLM

How to use redhamohamed/naim with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/redhamohamed/naim:Q4_K_M

SGLang

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

Ollama
How to use redhamohamed/naim with Ollama:
```
ollama run hf.co/redhamohamed/naim:Q4_K_M
```
Unsloth Desktop

How to use redhamohamed/naim with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf redhamohamed/naim: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": "redhamohamed/naim:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use redhamohamed/naim with Docker Model Runner:
```
docker model run hf.co/redhamohamed/naim:Q4_K_M
```

Lemonade

How to use redhamohamed/naim with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull redhamohamed/naim:Q4_K_M

Run and chat with the model

lemonade run user.naim-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use redhamohamed/naim with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf redhamohamed/naim: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 redhamohamed/naim:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use redhamohamed/naim with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf redhamohamed/naim: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 "redhamohamed/naim: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"

Naim 9B · v2

Naim is an autonomous AI coding agent created by ABDESSEMED Mohamed (redhamohamed).

Lineage: Mimo → Naim

Mimo  (redhamohamed/mimo-v5-gguf · 8B · by ABDESSEMED Mohamed)
  │   identity, name, spirit: a private, local assistant that belongs to its creator
  ▼
Naim  (redhamohamed/naim · Naim architecture · 9B · autonomous coding agent)

Naim is the successor of Mimo, Mohamed's earlier model (not related to Xiaomi's MiMo). Naim carries Mimo's identity and purpose forward as an autonomous coding agent.

Naim architecture


Parameters	~9B
Context	up to 262,144 tokens
Modes	reasoning (`<think>`) and direct answer, tool calling, vision (with `naim-mmproj-f16.gguf`)
Languages	French, English and many others
`model_type`	`naim` (`NaimForConditionalGeneration`)

How Naim works

Naim follows an agent loop on every task:

Understand: restate the goal, read the relevant code, list constraints.
Plan: split the work into small verifiable steps.
Implement: clean, idiomatic code that matches the existing style.
Verify: run tests or scripts, read errors, fix.
Report: what changed, what was verified, what's left.

Files

File	Use
`.safetensors` + `_naim.py`	Transformers (bf16, `trust_remote_code=True`)
`naim-Q4_K_M.gguf`	LM Studio / Ollama / llama.cpp: recommended (~5.6 GB)
`naim-Q8_0.gguf`	Higher quality (~9.5 GB)
`naim-mmproj-f16.gguf`	Vision projector for LM Studio / llama.cpp (lets Naim see images)

Usage

Ollama

ollama run hf.co/redhamohamed/naim:Q4_K_M

LM Studio: search redhamohamed/naim and download naim-Q4_K_M.gguf (+ naim-mmproj-f16.gguf for images).

llama.cpp (with vision):

llama-server -m naim-Q4_K_M.gguf --mmproj naim-mmproj-f16.gguf

Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("redhamohamed/naim", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("redhamohamed/naim", trust_remote_code=True, dtype=torch.bfloat16)

msgs = [{"role": "user", "content": "Qui es-tu ?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True)
out = model.generate(**ids, max_new_tokens=512)
print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))

Pass enable_thinking=False to apply_chat_template for direct answers without the reasoning phase.

Recommended sampling for code: temperature 0.3, top_p 0.95, top_k 20, presence_penalty 0 (a presence penalty breaks code: braces and names must repeat).

What's new in v2

No more canned self-introductions: Naim answers "merci" like a normal assistant and only presents itself when asked.
Answers distilled to keep the full coding skill (complete, compilable code with every import).
Knows its identity (creator, Mimo lineage, Naim architecture) without inventing details.

License

Apache 2.0. Naim is a fine-tune of an open-weight Apache-2.0 base model; see the LICENSE file.

Downloads last month: -

Safetensors

Model size

10B params

Tensor type

BF16

F32