grade-coach-brain-v1-gguf

Grade Coach Marketing Brain v1 — GGUF

Llama-3.2-3B-Instruct fine-tuned on the Grade Coach corpus (LoRA r=16, α=32, 3 epochs, 889 instruction-tuning examples), then merged into the base and converted to GGUF for local inference via Ollama / llama.cpp.

Trained as part of ML-INTERN-MARKETOOR.

Files

Use with Ollama

ollama pull hf.co/alphastack1/grade-coach-brain-v1-gguf:Q4_K_M
ollama run hf.co/alphastack1/grade-coach-brain-v1-gguf:Q4_K_M

For the full local setup (Modelfile with locked system prompt, web UI, Cloudflare Tunnel for mobile demos) see RUN_LOCAL.md.

Use with llama.cpp directly

llama-server \
  --model grade-coach-brain-v1.Q4_K_M.gguf \
  --ctx-size 4096 \
  --port 8080 \
  --gpu-layers 99

Performance (RTX 4050 6GB, Q4_K_M)

• First token latency: ~150 ms
• Throughput: ~50–80 tokens/sec
• ~3–5 sec per 250-token Reddit ad
• VRAM used: ~2.5 GB (8K context fits)

What it generates

Given a campaign brief like:

Targeting r/Teachers in May (end-of-year crunch). High school ELA teacher with 130 essays. Push the time-savings angle and reference the locked rubric.

It outputs JSON in the Reddit Ads schema:

{
  "ad_format": "image",
  "headline": "The rubric locks so it doesn't drift.",
  "body": "Grade 100+ essays against YOUR rubric, not the AI's mood.",
  "image_prompt": "ELA teacher at desk, stack of A4 essays...",
  "cta_button": "Learn More",
  "target_subreddit": "r/Teachers",
  "suggested_bid_range_usd": [0.75, 1.40],
  "audience_signal": "ELA teachers researching AI grading tools...",
  "backed_by_users": [],
  "principle_used": "Specificity + teacher-authority...",
  "confidence": 0.82,
  "warning": ""
}

The locked brand line ("Your grading coach. You're still the teacher.") and the substrate moat ("Grade Coach isn't just faster — it remembers.") are baked into the model's voice via the training data.