Instructions to use JANGQ-AI/Laguna-S-2.1-JANG_6M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/Laguna-S-2.1-JANG_6M with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("JANGQ-AI/Laguna-S-2.1-JANG_6M")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use JANGQ-AI/Laguna-S-2.1-JANG_6M with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/Laguna-S-2.1-JANG_6M"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "JANGQ-AI/Laguna-S-2.1-JANG_6M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JANGQ-AI/Laguna-S-2.1-JANG_6M with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/Laguna-S-2.1-JANG_6M"

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 JANGQ-AI/Laguna-S-2.1-JANG_6M

Run Hermes

hermes

OpenClaw new

How to use JANGQ-AI/Laguna-S-2.1-JANG_6M with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "JANGQ-AI/Laguna-S-2.1-JANG_6M"

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 "JANGQ-AI/Laguna-S-2.1-JANG_6M" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use JANGQ-AI/Laguna-S-2.1-JANG_6M with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "JANGQ-AI/Laguna-S-2.1-JANG_6M"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "JANGQ-AI/Laguna-S-2.1-JANG_6M"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "JANGQ-AI/Laguna-S-2.1-JANG_6M",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

JANGQ-AI/Laguna-S-2.1-JANG_6M

JANG_6M JANG affine bundle of poolside/Laguna-S-2.1 — 118B-parameter / ~8B-active MoE for agentic coding and long-horizon work, quantized for Apple Silicon.

Attention (incl. the softplus output gate), shared expert, router, and norms stay high precision; the 256-routed-expert bulk carries 6-bit gate/up/down, protected by activation-aware (AWQ) scales chosen by measured quantization error against a no-AWQ baseline (3.1% lower reconstruction error at this bit width).

Bundle

Field	Value
Source	`poolside/Laguna-S-2.1` @ `e80da38`
Architecture	`laguna` — 48 layers (12 global + 36 SWA w512), 118B-A8B, 256 experts top-10 + shared, 1M ctx
On-disk size	96.5 GB (21 shards)
Routed experts	6-bit gate/up/down affine, group 64, AWQ folded
Attention q/k/v/o + g_proj	8-bit affine
Shared expert / dense FFN	8-bit affine
Embeddings / lm_head	6-bit / 8-bit affine
Router, e_score bias, norms	fp16 passthrough
Modality	text-only (verified from tensor index)

Measured (M5 Max, 128 GB, greedy, wired)

Metric	Value
Decode	30.7 tok/s
Long-context cache parity	teacher-forced top-1 agreement 1.000 / 1.000 (pre/post the 512 sliding window, 2,913-token pass)

Chat / reasoning

GLM-style think tags; enable_thinking toggles reasoning (thinking is ON by default in this revision's template; pass enable_thinking=False to disable)
The FULL chat template is inlined in tokenizer_config.json (upstream ships only an {% include %} stub that most runtimes cannot resolve — inlining is what makes the reasoning toggle actually work)
Stop tokens eos_token_id = [2, 24] — id 24 is end-of-turn and must be in the stop set
Template emits its own leading 〈|EOS|〉 (bos 2): do not prepend another
Tool calls: <tool_call>name<arg_key>k</arg_key><arg_value>v</arg_value></tool_call>
Vendor sampling: temperature 1.0, top_p 1.0, top_k 20

Loaders must honor the per-module {bits, group_size, mode} overrides in config.json[quantization].

Quantized and verified by Jinho Jang (eric@jangq.ai). License: OpenMDW-1.1 (inherited).

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Safetensors

Model size

26B params

Tensor type

U32

F16

MLX

Hardware compatibility

Quantized

Model tree for JANGQ-AI/Laguna-S-2.1-JANG_6M

Base model

poolside/Laguna-S-2.1

Finetuned

(14)

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