Instructions to use HCHs/RivetCoder-9B-A4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HCHs/RivetCoder-9B-A4B with Transformers:

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

pipe = pipeline("text-generation", model="HCHs/RivetCoder-9B-A4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("HCHs/RivetCoder-9B-A4B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HCHs/RivetCoder-9B-A4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HCHs/RivetCoder-9B-A4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HCHs/RivetCoder-9B-A4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HCHs/RivetCoder-9B-A4B

SGLang

How to use HCHs/RivetCoder-9B-A4B 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 "HCHs/RivetCoder-9B-A4B" \
    --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": "HCHs/RivetCoder-9B-A4B",
		"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 "HCHs/RivetCoder-9B-A4B" \
        --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": "HCHs/RivetCoder-9B-A4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HCHs/RivetCoder-9B-A4B with Docker Model Runner:
```
docker model run hf.co/HCHs/RivetCoder-9B-A4B
```

RivetCoder-9B-A4B

File size: 960 Bytes

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# Attribution notice

RivetCoder-9B-A4B combines the following sources:

- Host model: `LiquidAI/LFM2.5-2.6B`, revision
  `654f9463ce32b05d0429d76fe1f580b27d4c1ac0`, under the LFM Open License v1.0.
- Expert donor: `zai-org/GLM-5.3-Flash`, revision
  `3f1971b7b5f7a528c9c4ef6212c8785298a8c24a`, under the MIT License.
- Router sequence teacher: `qwen/qwen3.8-27b`, accessed through a local LM Studio
  endpoint. No Qwen weights are included.
- Architectural reference: `Akahsizrr/fuse-1-Lite`, revision
  `430c959e47556ae53fed18a9d97f7cf30876e6ff`.

Modifications and new work include GLM expert selection, a fixed tied
identity-Hadamard bridge, expert/router weight folding, per-layer Top-4 routing,
token gating, bounded residual scaling, Qwen-supervised routing-control training,
and the custom `fuse_glm` Transformers implementation shipped in this repository.

See `provenance/` for the complete local build record with personal filesystem
paths removed.