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)

# pip install -U transformers accelerate
# 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 / provenance /validation-metrics.json

HCHs

Upload RivetCoder-9B-A4B v0.1.0

745106e verified 3 months ago

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343 Bytes

{
  "ce_improvement": 0.0043891161448869065,
  "finite": true,
  "fused_ce": 0.6014361901441425,
  "host_expert_off_ce": 0.6058253062890294,
  "mean_absolute_residual_scale": 0.07969798704336123,
  "mean_residual_scale": -0.002938792503376438,
  "mean_token_gate": 0.19380086263020832,
  "records": 4,
  "supervised_tokens": 1417
}

	{
	"ce_improvement": 0.0043891161448869065,
	"finite": true,
	"fused_ce": 0.6014361901441425,
	"host_expert_off_ce": 0.6058253062890294,
	"mean_absolute_residual_scale": 0.07969798704336123,
	"mean_residual_scale": -0.002938792503376438,
	"mean_token_gate": 0.19380086263020832,
	"records": 4,
	"supervised_tokens": 1417
	}