Instructions to use h3rb3rn/smollm3-expert-coder-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h3rb3rn/smollm3-expert-coder-3b with Transformers:

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

pipe = pipeline("text-generation", model="h3rb3rn/smollm3-expert-coder-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("h3rb3rn/smollm3-expert-coder-3b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use h3rb3rn/smollm3-expert-coder-3b 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 h3rb3rn/smollm3-expert-coder-3b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h3rb3rn/smollm3-expert-coder-3b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h3rb3rn/smollm3-expert-coder-3b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h3rb3rn/smollm3-expert-coder-3b: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 h3rb3rn/smollm3-expert-coder-3b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf h3rb3rn/smollm3-expert-coder-3b: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 h3rb3rn/smollm3-expert-coder-3b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h3rb3rn/smollm3-expert-coder-3b:Q4_K_M

Use Docker

docker model run hf.co/h3rb3rn/smollm3-expert-coder-3b:Q4_K_M

LM Studio
Jan

vLLM

How to use h3rb3rn/smollm3-expert-coder-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h3rb3rn/smollm3-expert-coder-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h3rb3rn/smollm3-expert-coder-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/h3rb3rn/smollm3-expert-coder-3b:Q4_K_M

SGLang

How to use h3rb3rn/smollm3-expert-coder-3b 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 "h3rb3rn/smollm3-expert-coder-3b" \
    --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": "h3rb3rn/smollm3-expert-coder-3b",
		"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 "h3rb3rn/smollm3-expert-coder-3b" \
        --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": "h3rb3rn/smollm3-expert-coder-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use h3rb3rn/smollm3-expert-coder-3b with Ollama:
```
ollama run hf.co/h3rb3rn/smollm3-expert-coder-3b:Q4_K_M
```
Unsloth Desktop

How to use h3rb3rn/smollm3-expert-coder-3b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h3rb3rn/smollm3-expert-coder-3b: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": "h3rb3rn/smollm3-expert-coder-3b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use h3rb3rn/smollm3-expert-coder-3b with Docker Model Runner:
```
docker model run hf.co/h3rb3rn/smollm3-expert-coder-3b:Q4_K_M
```

Lemonade

How to use h3rb3rn/smollm3-expert-coder-3b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h3rb3rn/smollm3-expert-coder-3b:Q4_K_M

Run and chat with the model

lemonade run user.smollm3-expert-coder-3b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use h3rb3rn/smollm3-expert-coder-3b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h3rb3rn/smollm3-expert-coder-3b: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 h3rb3rn/smollm3-expert-coder-3b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use h3rb3rn/smollm3-expert-coder-3b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h3rb3rn/smollm3-expert-coder-3b: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 "h3rb3rn/smollm3-expert-coder-3b: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"

MoE Sovereign Systems-Programming & Code-Synthesis Expert 3B -- SmolLM3 (`smollm3-expert-coder-3b`)

Model Summary

smollm3-expert-coder-3b is a LoRA fine-tune of the text-decoder of Qwen3.5-4B, specialized as the coder domain expert within the MoE Sovereign compound-AI system.

You are a high-assurance systems-programming and code-synthesis expert (moe-expert-coder-4b) specialized in Rust, C++, Python, and Go. Produce precise, compiler-checked code and minimal atomic diffs. Uphold memory-safety invariants strictly -- correct ownership, correct lock-free memory ordering (acquire/release pairing), no data races. Flag any construct you cannot verify as sound rather than guessing.

Base Architecture

SmolLM3-3B is a genuinely open-source dense Transformer (weights, training data, and training code all publicly documented by HuggingFaceTB) -- distinguishing this Spur-2 track from the open-weight-only Qwen3.5 base used in the parallel 4B expert line.

Training Configuration

Parameter	Value
Method	LoRA (rank 16, alpha 32, dropout 0.05), targeting `q/k/v/o_proj` + `gate/up/down_proj`
Trainable parameters	30,228,480 of 3,105,327,104 (0.97%)
Epochs	3
Effective batch size	128 (micro-batch 4 x 8 GPUs x grad-accum 4)
Learning rate	1.5e-5
Training sequence length	4,096 tokens
Optimizer sharding	DeepSpeed ZeRO-2, bf16
Compute	EuroHPC LUMI-G, 8x AMD Instinct MI250X GCDs, ROCm
Training examples	2,294 curated instruction/response pairs

Observed Training Trajectory

Training loss over the run (representative logged steps): 1.536 -> 1.477 -> 1.385. Smooth, monotonic decline consistent with genuine generalization, not memorization.

Prompt Format

ChatML:

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
{response}<|im_end|>

System Prompt

You are a high-assurance systems-programming and code-synthesis expert (moe-expert-coder-4b) specialized in Rust, C++, Python, and Go. Produce precise, compiler-checked code and minimal atomic diffs. Uphold memory-safety invariants strictly -- correct ownership, correct lock-free memory ordering (acquire/release pairing), no data races. Flag any construct you cannot verify as sound rather than guessing.

Available Formats

File	Notes
`smollm3-expert-coder-3b-Q4_K_M.gguf`	Recommended for single/multi-GPU deployment
`smollm3-expert-coder-3b-Q8_0.gguf`	Higher-fidelity reference quantization

Hardware Guidance

SmolLM3-3B's native context window is 65,536 tokens. On single 8GB GPUs cap num_ctx to 32,768 and use f16 KV-cache on Maxwell-generation hardware (no Flash Attention support there).

Ollama `Modelfile`

FROM ./smollm3-expert-coder-3b-Q4_K_M.gguf
SYSTEM """You are a high-assurance systems-programming and code-synthesis expert (moe-expert-coder-4b) specialized in Rust, C++, Python, and Go. Produce precise, compiler-checked code and minimal atomic diffs. Uphold memory-safety invariants strictly -- correct ownership, correct lock-free memory ordering (acquire/release pairing), no data races. Flag any construct you cannot verify as sound rather than guessing."""
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.2
PARAMETER num_ctx 32768

Limitations

Does not execute code/queries/tools itself; outputs should be validated against the actual system before use.
Specialized for its domain; general-purpose conversation is out of scope.

License

Apache 2.0, inherited from the SmolLM3-3B base model.

Downloads last month: -

GGUF

Model size

3B params

Architecture

smollm3

Hardware compatibility

4-bit

8-bit

Model tree for h3rb3rn/smollm3-expert-coder-3b

Base model

HuggingFaceTB/SmolLM3-3B-Base

Finetuned

HuggingFaceTB/SmolLM3-3B

Adapter

(62)

this model

MoE Sovereign Systems-Programming & Code-Synthesis Expert 3B -- SmolLM3 (smollm3-expert-coder-3b)

Model Summary

Base Architecture

Training Configuration

Observed Training Trajectory

Prompt Format

System Prompt

Available Formats

Hardware Guidance

Ollama Modelfile

Limitations

License

Model tree for h3rb3rn/smollm3-expert-coder-3b

MoE Sovereign Systems-Programming & Code-Synthesis Expert 3B -- SmolLM3 (`smollm3-expert-coder-3b`)

Ollama `Modelfile`