Instructions to use HCHs/RivetCoder-9B-A4B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HCHs/RivetCoder-9B-A4B-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="HCHs/RivetCoder-9B-A4B-FP8", 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-FP8", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HCHs/RivetCoder-9B-A4B-FP8 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-FP8"
# 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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HCHs/RivetCoder-9B-A4B-FP8

SGLang

How to use HCHs/RivetCoder-9B-A4B-FP8 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-FP8" \
    --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-FP8",
		"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-FP8" \
        --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-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HCHs/RivetCoder-9B-A4B-FP8 with Docker Model Runner:
```
docker model run hf.co/HCHs/RivetCoder-9B-A4B-FP8
```

RivetCoder-9B-A4B-FP8 / config.json

HCHs

Upload RivetCoder-9B-A4B-FP8 TorchAO checkpoint

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	{
	"architectures": [
	"FuseGlmForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_fuse_glm.FuseGlmConfig",
	"AutoModelForCausalLM": "modeling_fuse_glm.FuseGlmForCausalLM"
	},
	"block_auto_adjust_ff_dim": false,
	"block_dim": 2048,
	"block_ffn_dim_multiplier": 1.0,
	"block_mlp_init_scale": 1.0,
	"block_multiple_of": 256,
	"block_norm_eps": 1e-05,
	"block_out_init_scale": 1.0,
	"block_use_swiglu": true,
	"block_use_xavier_init": true,
	"bos_token_id": 124894,
	"conv_L_cache": 3,
	"conv_bias": false,
	"conv_dim": 2048,
	"conv_use_xavier_init": true,
	"dtype": "bfloat16",
	"eos_token_id": 124900,
	"full_attn_idxs": null,
	"fuse_glm_coding_enabled": true,
	"fuse_glm_expert_intermediate_size": 2048,
	"fuse_glm_gate_clamp_max": 10.0,
	"fuse_glm_hard_token_gate_at_eval": false,
	"fuse_glm_layer_indices": [
	0,
	1,
	2,
	3,
	4,
	5,
	6,
	7,
	8,
	9,
	10,
	11,
	12,
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	19,
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	],
	"fuse_glm_num_experts": 16,
	"fuse_glm_output_router_diagnostics": false,
	"fuse_glm_residual_scale_max": 0.1,
	"fuse_glm_router_aux_loss_coef": 0.0,
	"fuse_glm_token_gate_bias": -4.0,
	"fuse_glm_token_gate_threshold": 0.5,
	"fuse_glm_top_k": 4,
	"fuse_glm_up_clamp_max": 10.0,
	"fuse_glm_up_clamp_min": -10.0,
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 10752,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention"
	],
	"max_position_embeddings": 131072,
	"model_type": "fuse_glm",
	"norm_eps": 1e-05,
	"num_attention_heads": 32,
	"num_heads": 32,
	"num_hidden_layers": 30,
	"num_key_value_heads": 8,
	"pad_token_id": 124893,
	"quantization_config": {
	"include_input_output_embeddings": false,
	"modules_to_not_convert": null,
	"quant_method": "torchao",
	"quant_type": {
	"default": {
	"_data": {
	"activation_dtype": {
	"_data": "float8_e4m3fn",
	"_type": "torch.dtype"
	},
	"activation_value_lb": null,
	"activation_value_ub": null,
	"granularity": [
	{
	"_data": {},
	"_type": "PerTensor",
	"_version": 1
	},
	{
	"_data": {},
	"_type": "PerTensor",
	"_version": 1
	}
	],
	"kernel_preference": {
	"_data": "AUTO",
	"_type": "KernelPreference"
	},
	"mm_config": {
	"_data": {
	"emulate": false,
	"pad_inner_dim": false,
	"use_fast_accum": true
	},
	"_type": "Float8MMConfig",
	"_version": 1
	},
	"set_inductor_config": true,
	"weight_dtype": {
	"_data": "float8_e4m3fn",
	"_type": "torch.dtype"
	}
	},
	"_type": "Float8DynamicActivationFloat8WeightConfig",
	"_version": 2
	}
	},
	"untie_embedding_weights": false
	},
	"rivet_coder": {
	"active_parameters_approx": 4210000000,
	"base_repo_id": "HCHs/RivetCoder-9B-A4B",
	"donor": "zai-org/GLM-5.3-Flash",
	"experts_per_layer": 16,
	"experts_per_token": 4,
	"host": "LiquidAI/LFM2.5-2.6B",
	"name": "RivetCoder-9B-A4B-FP8",
	"quantization": "TorchAO Float8DynamicActivationFloat8WeightConfig",
	"repo_id": "HCHs/RivetCoder-9B-A4B-FP8",
	"router_teacher": "qwen/qwen3.8-27b",
	"total_parameters_approx": 8740000000,
	"training_steps": 60
	},
	"rope_parameters": {
	"rope_theta": 10000000.0,
	"rope_type": "default"
	},
	"tie_word_embeddings": true,
	"transformers_version": "5.16.1",
	"use_cache": true,
	"use_pos_enc": true,
	"vocab_size": 128000
	}