hint-corpus Skill | Agent Skills

hint-corpus

Full pipeline to produce chitta-hint-tuned (Qwen3-0.6B Q4_K_M) from scratch.

Quick Start

# 1. Generate corpus (requires Ollama + gemma4:26b or any capable model)
python3 $PLUGIN_DIR/scripts/generate_hint_corpus.py \
    --out /maps/projects/caeg/scratch/kbd606/tmp/hint_corpus_raw.jsonl \
    --model gemma4:26b \
    --target 3000

# 2. Convert to Qwen3 ChatML for unsloth
python3 $PLUGIN_DIR/scripts/convert_to_chatml.py \
    --in  /maps/projects/caeg/scratch/kbd606/tmp/hint_corpus_raw.jsonl \
    --out /maps/projects/caeg/scratch/kbd606/tmp/hint_corpus_chatml.jsonl \
    --split 0.1

# 3. Fine-tune Qwen3-0.6B + export GGUF
bash $PLUGIN_DIR/scripts/finetune_hint_qwen.sh \
    --data /maps/projects/caeg/scratch/kbd606/tmp/hint_corpus_chatml.jsonl \
    --steps 300

# 4. Register with Ollama
bash $PLUGIN_DIR/chitta-mcp/enrichers/setup_hint_model.sh

Where $PLUGIN_DIR = /maps/projects/fernandezguerra/apps/repos/cc-soul (or installed plugin path).

Stage 1 — Corpus Generation

generate_hint_corpus.py builds diverse synthetic conversation excerpts and labels them via a teacher LLM. It covers:

| Axis | Examples | |------|---------| | Profession | bioinformatician, nurse, teacher, architect, chef... | | Location | city, country, living situation | | Language background | native/non-native/bilingual | | Relationships | partner, children, pets | | Health | dietary restrictions, exercise habits | | Hobbies | sports, arts, gaming, gardening... | | Preferences | dark mode, editors, morning/evening person | | Education | PhD, self-taught, vocational |

35% hard negatives (questions, debugging requests, factual queries — output: -).

Key flags:

--target N        # examples to generate (default: 1500; recommend 3000)
--model MODEL     # teacher model (default: llama3.3:70b; gemma4:26b works well)
--neg-ratio 0.35  # fraction of negatives
--dry-run         # preview templates, no LLM calls

Expected runtime: ~2h for 3000 examples with gemma4:26b on a single GPU node.

Stage 2 — ChatML Conversion

convert_to_chatml.py wraps each {"input", "output"} row in a ShareGPT conversation with the system prompt baked in.

System prompt (fixed, version-controlled):

You extract personal facts from conversation excerpts. Given a message or conversation, output a single concise third-person sentence about the user (e.g. "User lives in Copenhagen.", "User has two cats."). If no stable personal fact is present, output exactly: -

--split 0.1 writes a 10% eval holdout to <out>_eval.jsonl.

Stage 3 — Fine-tuning

finetune_hint_qwen.sh runs QLoRA via unsloth:

| Hyperparameter | Default | |----------------|---------| | Base model | Qwen/Qwen3-0.6B | | LoRA rank | 16 | | LoRA alpha | 32 | | Max steps | 200 | | Batch size | 4 × grad_accum 4 = 16 effective | | Learning rate | 2e-4 | | Quantisation | 4-bit QLoRA (bitsandbytes) |

Requirements:

pip install "unsloth[colab-new]" xformers trl peft accelerate bitsandbytes

GPU note: Qwen3-0.6B fits in ~4 GB VRAM at 4-bit. CPU training is possible but slow (~30 min/100 steps).

After training, the script:

Merges LoRA → fp16 safetensors ($OUT_DIR)
Converts to F16 GGUF via convert_hf_to_gguf.py (needs llama.cpp)
Quantises to Q4_K_M via llama-quantize (~480 MB)

Override paths via environment:

CHITTA_HINT_DATA=/path/to/corpus.jsonl
CHITTA_HINT_MODEL_DIR=/path/to/merged_output
CHITTA_HINT_GGUF_DIR=/path/to/gguf_output
LLAMA_CONVERT=/path/to/llama.cpp/convert_hf_to_gguf.py
LLAMA_QUANTIZE=/path/to/llama-quantize

Stage 4 — Ollama Registration

setup_hint_model.sh registers the Q4_K_M GGUF with Ollama as chitta-hint-tuned.

It checks $CHITTA_HINT_GGUF_DIR for the GGUF, falls back to F16, then safetensors.

After registration, test with:

chitta hint_enrich --dry-run
# or via MCP:
chitta run_hint_enricher --dry_run true --limit 10

Embedding Quality Check

After registration, run the embedding benchmark:

python3 /maps/projects/caeg/scratch/kbd606/tmp/test_embeddings.py

Target metrics vs Qwen2.5-0.5B baseline: | Metric | Baseline | Target | |--------|----------|--------| | Personal↔Personal cosine | 0.76 | >0.85 | | Separation ratio (pp−pn) | 0.28 | >0.40 | | NN accuracy | 5/8 | 7/8+ |

Qwen3-0.6B shares its architecture with Qwen3-Embedding-0.6B (MTEB STS 86.57) — use --pooling last and L2-normalize embeddings.

Notes

Single GGUF, dual use: same checkpoint serves generation (personal fact extraction) and embedding (last-token pooling + L2 norm). Append <|endoftext|> as final token for embedding mode.
Corpus is general-purpose — not specific to any user. Covers 10+ diversity axes so the model generalises across professions, cultures, and relationship types.
Iterative improvement: run /hint-corpus again after accumulating new session data. Use --target 5000 if separation metrics plateau at 3k.

Agent Skills: hint-corpus

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