Instructions to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal 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 jcbtc/Qwen3.8-27B-IU4-Kairic-Signal
# Run inference directly in the terminal:
llama cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal
# Run inference directly in the terminal:
llama cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

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 jcbtc/Qwen3.8-27B-IU4-Kairic-Signal
# Run inference directly in the terminal:
./llama-cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

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 jcbtc/Qwen3.8-27B-IU4-Kairic-Signal
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

Use Docker

docker model run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

LM Studio
Jan

vLLM

How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jcbtc/Qwen3.8-27B-IU4-Kairic-Signal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jcbtc/Qwen3.8-27B-IU4-Kairic-Signal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

Ollama
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with Ollama:
```
ollama run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Signal
```
Unsloth Desktop

How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

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": "jcbtc/Qwen3.8-27B-IU4-Kairic-Signal"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with Docker Model Runner:
```
docker model run hf.co/jcbtc/Qwen3.8-27B-IU4-Kairic-Signal
```

Lemonade

How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

Run and chat with the model

lemonade run user.Qwen3.8-27B-IU4-Kairic-Signal-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

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 jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jcbtc/Qwen3.8-27B-IU4-Kairic-Signal with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Qwen3.8-27B-IU4-Kairic-Signal

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 "jcbtc/Qwen3.8-27B-IU4-Kairic-Signal" \
  --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"

Qwen3.8-27B-IU4-Kairic-Signal / build-report.json

jcbtc

Release KAIRIC SIGNAL v1.0: card, artwork, launcher and verification

c3ab424 verified 12 days ago

Raw

History Blame Contribute Delete

2.89 kB

	{
	"model": "Qwen3.8-27B-IU4-Kairic-Signal",
	"version": "1.0",
	"artifact_sha256": "a80dc095e54a6aaa852a592ae4d9167b49e92aec5e48ded82e11f5e7fa71983c",
	"artifact_bytes": 16617792704,
	"donor_sha256": "360caf7381907c3eca7ac0afd1228efc016af747f3f38637fb1c7f94daabac2a",
	"tensor_count": 866,
	"changed_tensors": [
	"output.weight"
	],
	"unchanged_tensor_count": 865,
	"all_other_payload_bytes_identical": true,
	"head_quantization": {
	"elements_checked": 1271398400,
	"independent_q8_byte_equivalence": true,
	"q8_sha256": "98f54a3659ef77b21b0d630f5b02853f3648bd88e7c098bc739d2155761c6ea7",
	"q8_bytes": 1350860800,
	"rmse": 7.562298675432215e-05,
	"relative_l2": 0.005503545738293579,
	"max_absolute_error": 0.0013256072998046875,
	"method": "Pinned quantize_row_q8_0_ref, independently checked for every block with NumPy",
	"reference_source_sha256": "3ae6aea8879172b12383485c4342647ee9289594e867c6c2f98a06bd43825211"
	},
	"source_lineage": {
	"changed_tensors": [
	"lm_head.weight"
	],
	"unchanged_last_shard_tensors": [
	"mtp.fc.weight",
	"mtp.layers.0.input_layernorm.weight",
	"mtp.layers.0.mlp.down_proj.weight",
	"mtp.layers.0.mlp.gate_proj.weight",
	"mtp.layers.0.mlp.up_proj.weight",
	"mtp.layers.0.post_attention_layernorm.weight",
	"mtp.layers.0.self_attn.k_norm.weight",
	"mtp.layers.0.self_attn.k_proj.weight",
	"mtp.layers.0.self_attn.o_proj.weight",
	"mtp.layers.0.self_attn.q_norm.weight",
	"mtp.layers.0.self_attn.q_proj.weight",
	"mtp.layers.0.self_attn.v_proj.weight",
	"mtp.norm.weight",
	"mtp.pre_fc_norm_embedding.weight",
	"mtp.pre_fc_norm_hidden.weight"
	],
	"other_shards_with_identical_hub_sha256": [
	"model-00001-of-00018.safetensors",
	"model-00002-of-00018.safetensors",
	"model-00003-of-00018.safetensors",
	"model-00004-of-00018.safetensors",
	"model-00005-of-00018.safetensors",
	"model-00006-of-00018.safetensors",
	"model-00007-of-00018.safetensors",
	"model-00008-of-00018.safetensors",
	"model-00009-of-00018.safetensors",
	"model-00010-of-00018.safetensors",
	"model-00011-of-00018.safetensors",
	"model-00012-of-00018.safetensors",
	"model-00013-of-00018.safetensors",
	"model-00014-of-00018.safetensors",
	"model-00015-of-00018.safetensors",
	"model-00016-of-00018.safetensors",
	"model-00017-of-00018.safetensors"
	],
	"signal_revision": "0bbbf805612fd8bb8583c783f0d65f6d155eae3c",
	"base_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
	"signal_head_bf16_sha256": "1688c4e75b60533232cd12adb46423e5e887d6cb39bc5fc122e0f8c7efa1022e"
	},
	"validation": "Structural and head numerical checks passed. AMD inference, task quality, throughput and MTP acceptance have not yet been measured for this build."
	}