Instructions to use OsaurusAI/Laguna-XS.2-JANGTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/Laguna-XS.2-JANGTQ 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("OsaurusAI/Laguna-XS.2-JANGTQ")

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
LM Studio

Pi new

How to use OsaurusAI/Laguna-XS.2-JANGTQ with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Laguna-XS.2-JANGTQ"

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": "OsaurusAI/Laguna-XS.2-JANGTQ"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OsaurusAI/Laguna-XS.2-JANGTQ 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 "OsaurusAI/Laguna-XS.2-JANGTQ"

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 OsaurusAI/Laguna-XS.2-JANGTQ

Run Hermes

hermes

MLX LM

How to use OsaurusAI/Laguna-XS.2-JANGTQ with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "OsaurusAI/Laguna-XS.2-JANGTQ"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OsaurusAI/Laguna-XS.2-JANGTQ"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "OsaurusAI/Laguna-XS.2-JANGTQ",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Osaurus-AI commited on 11 days ago

Commit

dda9305

verified ·

1 Parent(s): 659302e

docs: prepend REQUIRED warning — jangtq_runtime.safetensors sidecar must be downloaded

Browse files

Files changed (1) hide show

README.md +22 -0

README.md CHANGED Viewed

@@ -14,6 +14,28 @@ tags:
   - agentic-coding
   - quantized
 ---
 ![Osaurus](osaurus-x-banner.png)

   - agentic-coding
   - quantized
 ---
+> ## ⚠️ REQUIRED — `jangtq_runtime.safetensors` sidecar must be downloaded
+>
+> Osaurus uses the native Swift JANGTQ runtime. **Every JANGTQ bundle on
+> OsaurusAI ships a small `jangtq_runtime.safetensors` sidecar (~10 KB–~165 KB)
+> alongside the weight shards.** The Swift loader will refuse to start with
+> the error
+> ```
+> Error: Model '<name>' declares JANGTQ (weight_format: "mxtq") but is
+>        missing required sidecar file 'jangtq_runtime.safetensors'.
+>        Re-download the full model or obtain the sidecar from the original
+>        publisher.
+> ```
+> if the file is absent.
+>
+> If your local copy doesn't have it (older download, partial sync, etc):
+> ```bash
+> hf download OsaurusAI/Laguna-XS.2-JANGTQ jangtq_runtime.safetensors --local-dir <your-dir>
+> ```
+> The file holds the deterministic codebooks + Hadamard rotation signs the
+> Swift loader uses to decode `*.tq_packed` weights. It must match the seed
+> the bundle was quantized with (`mxtq_seed=42`).
 ![Osaurus](osaurus-x-banner.png)