Instructions to use h3rb3rn/moe-expert-coder-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h3rb3rn/moe-expert-coder-4b with Transformers:

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

pipe = pipeline("text-generation", model="h3rb3rn/moe-expert-coder-4b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("h3rb3rn/moe-expert-coder-4b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use h3rb3rn/moe-expert-coder-4b 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/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h3rb3rn/moe-expert-coder-4b: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/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf h3rb3rn/moe-expert-coder-4b: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/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf h3rb3rn/moe-expert-coder-4b: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/moe-expert-coder-4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h3rb3rn/moe-expert-coder-4b:Q4_K_M

Use Docker

docker model run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M

LM Studio
Jan

vLLM

How to use h3rb3rn/moe-expert-coder-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h3rb3rn/moe-expert-coder-4b"
# 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/moe-expert-coder-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M

SGLang

How to use h3rb3rn/moe-expert-coder-4b 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/moe-expert-coder-4b" \
    --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/moe-expert-coder-4b",
		"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/moe-expert-coder-4b" \
        --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/moe-expert-coder-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use h3rb3rn/moe-expert-coder-4b with Ollama:
```
ollama run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M
```

Unsloth Studio

How to use h3rb3rn/moe-expert-coder-4b 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 h3rb3rn/moe-expert-coder-4b 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 h3rb3rn/moe-expert-coder-4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h3rb3rn/moe-expert-coder-4b to start chatting

Docker Model Runner
How to use h3rb3rn/moe-expert-coder-4b with Docker Model Runner:
```
docker model run hf.co/h3rb3rn/moe-expert-coder-4b:Q4_K_M
```

Lemonade

How to use h3rb3rn/moe-expert-coder-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h3rb3rn/moe-expert-coder-4b:Q4_K_M

Run and chat with the model

lemonade run user.moe-expert-coder-4b-Q4_K_M

List all available models

lemonade list

Atomic Chat

h3rb3rn commited on about 6 hours ago

Commit

96a6eb4

verified ·

1 Parent(s): 9f8612c

Fix SLURM job-ID attribution and report true final train_loss (was cherry-picked best-step value)

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -97,14 +97,14 @@ Evaluated on a held-out test split of **1,000 multi-language software engineerin
 ```
 ### Hyperparameters:
-- **Compute Cluster:** LUMI-G (8× AMD Instinct MI250X 128GB GPUs, Slurm Job `#21189558`)
 - **Base Architecture:** Qwen3.5-4B (Hybrid Linear Attention + Mamba in BF16)
 - **Dataset Size:** 32,500 curated, compiler-checked trajectories
 - **Epochs:** 3.0
 - **Effective Batch Size:** 128 (Micro-batch 4 × 8 GPUs × Gradient Accumulation 4)
 - **Learning Rate:** $1.5 \times 10^{-5}$ with Cosine Decay and Warmup
 - **LoRA Configuration:** $r=16$, $\alpha=32$, Dropout $0.05$, Target Modules: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
-- **Training Loss (Final):** `0.0106`
 - **Token Accuracy (Final):** **`99.62 %`**
 ---

 ```
 ### Hyperparameters:
+- **Compute Cluster:** LUMI-G (8× AMD Instinct MI250X 128GB GPUs, Slurm Job `#21190761`)
 - **Base Architecture:** Qwen3.5-4B (Hybrid Linear Attention + Mamba in BF16)
 - **Dataset Size:** 32,500 curated, compiler-checked trajectories
 - **Epochs:** 3.0
 - **Effective Batch Size:** 128 (Micro-batch 4 × 8 GPUs × Gradient Accumulation 4)
 - **Learning Rate:** $1.5 \times 10^{-5}$ with Cosine Decay and Warmup
 - **LoRA Configuration:** $r=16$, $\alpha=32$, Dropout $0.05$, Target Modules: `q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
+- **Training Loss (Final):** `0.03638`
 - **Token Accuracy (Final):** **`99.62 %`**
 ---