Instructions to use srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4 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("srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4")

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 srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4"

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": "srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4 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 "srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4"

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 srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4

Run Hermes

hermes

MLX LM

How to use srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "srv-sngh/gemma-4-12B-agentic-fable5-composer2.5-v2-nvfp4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

srv-sngh commited on 6 days ago

Commit

00ca9ca

verified ·

1 Parent(s): 8d1b2e7

Card: final complete benchmark table (base vs coder vs agentic, HE/HE+/MBPP/MBPP+/GSM8K, off+on)

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README.md +4 -5

README.md CHANGED Viewed

@@ -63,11 +63,10 @@ GSM8K = 150‑problem subset, 8‑shot. **Not** EvalPlus‑leaderboard‑compara
 |---|---|---|---|---|---|---|
 | Google gemma-4-12B-it (base) | off | 57.3 | 56.7 | 42.1 | 37.6 | 95.3 |
 | Google gemma-4-12B-it (base) | on | 48.8 | 48.8 | 49.5 | 43.9 | 90.7 |
-| coder v1 | off | 81.7 | — | 79.4 | — | 90.7 |
-| **agentic v2** ⟵ this model | off | 83.5 | 81.7 | — | — | — |
-| **agentic v2** ⟵ this model | on | 86.0 | 82.9 | — | — | — |
-> ⚠️ **Partial — benchmark run still in progress.** Empty cells (—) are filling in; this card updates as the full sweep completes.
 **Takeaways:** the code/agentic fine‑tunes massively out‑code the Google base on HumanEval/MBPP, while
 the base is stronger at math (GSM8K). Reasoning‑on helps the fine‑tunes but tends to *hurt* the base's

 |---|---|---|---|---|---|---|
 | Google gemma-4-12B-it (base) | off | 57.3 | 56.7 | 42.1 | 37.6 | 95.3 |
 | Google gemma-4-12B-it (base) | on | 48.8 | 48.8 | 49.5 | 43.9 | 90.7 |
+| coder v1 | off | 81.7 | 78.0 | 79.4 | 68.3 | 90.7 |
+| coder v1 | on | 80.5 | 76.2 | 80.4 | 68.8 | 90.0 |
+| **agentic v2** ⟵ this model | off | 83.5 | 81.7 | 84.1 | 74.1 | 90.7 |
+| **agentic v2** ⟵ this model | on | 86.0 | 82.9 | 83.6 | 73.0 | 91.3 |
 **Takeaways:** the code/agentic fine‑tunes massively out‑code the Google base on HumanEval/MBPP, while
 the base is stronger at math (GSM8K). Reasoning‑on helps the fine‑tunes but tends to *hurt* the base's