rcrc-chat-v5-gemma-1b-cpt-sft/README.md

license, base_model, datasets, language, tags, library_name

license	base_model	datasets	language	tags	library_name
gemma	perfectPresentation/rcrc-gemma-1b-cpt	perfectPresentation/rcrc-qa-v5	ar en	chat rcrc arabic gemma gemma3 closed-book	transformers

rcrc-chat-v5-gemma-1b-cpt-sft — Closed-Book RCRC Chatbot

A 1B closed-book chatbot for the Royal Commission for Riyadh City (RCRC). Trained as Path B of the v5 comparison: continued pre-training on raw KB text, then chat SFT on Qwen3-235B-synthesized QA pairs.

Closed-book here means the model answers from baked-in knowledge — there is no retrieval at inference.

Pipeline

google/gemma-3-1b-pt
   ↓  CPT: 3 epochs on cleaned RCRC + Hanifa raw text
perfectPresentation/rcrc-gemma-1b-cpt
   ↓  SFT: 3 epochs on rcrc-qa-v5 (16,761 Qwen-synthesized QA pairs)
perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft   ← this repo

Training data

perfectPresentation/rcrc-qa-v5: 16,426 train + 335 validation single-turn (system, user, assistant) pairs synthesized by Qwen/Qwen3-235B-A22B-Instruct-2507 from the cleaned RCRC website + Hanifa Urban Code chunks.

Training recipe

Base	perfectPresentation/rcrc-gemma-1b-cpt
Epochs	3
LR	2e-5, cosine, 5% warmup
Effective batch	16 (per_device 4 × grad_accum 4)
Max seq length	1024, packing enabled
Final eval loss	0.71
Final eval token-accuracy	83.5%
Hardware	HF Jobs · 1× L4 (~1.6 h)

Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tok = AutoTokenizer.from_pretrained("perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft")
model = AutoModelForCausalLM.from_pretrained(
    "perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft",
    dtype=torch.bfloat16,
)

messages = [
    {"role": "system", "content":
        "أنت مساعد للهيئة الملكية لمدينة الرياض. تجيب على استفسارات المستخدمين "
        "عن خدمات وبرامج ومشاريع وأنظمة الهيئة بدقة وأدب."},
    {"role": "user", "content": "ما هو الكود العمراني لوادي حنيفة؟"},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(
    **inputs, max_new_tokens=400, do_sample=True, temperature=0.5, top_p=0.9,
    repetition_penalty=1.15, no_repeat_ngram_size=6,
)
print(tok.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Honest evaluation: closed-book vs RAG

On a 50-question internal eval set (RCRC + Hanifa + edge cases), this closed-book model was compared to a sibling RAG pipeline (the v3-rag checkpoint reading retrieved chunks from rcrc-rag-index-v2):

	accuracy	relevance	clarity	wins
RAG (v3-rag + index v2)	3.92	4.08	4.42	34/50
This model (closed-book)	2.64	3.36	3.56	13/50
Ties				3/50

Where closed-book is competitive: dialect responses (Najdi/Hijazi), free-form opinion-style queries.

Where RAG dominates: factual specifics from the Hanifa Urban Code, project details, organizational facts, numerical specs.

For accuracy-sensitive deployments on the RCRC + Hanifa corpus, a RAG pipeline at the same parameter count outperforms this closed-book model by ~1.3 points on average. This model is provided for completeness of the v5 study and for offline / no-retrieval scenarios.

Limitations

• 1B-scale closed-book recall is brittle on specifics (numbers, exact procedure steps). Verify against rcrc.gov.sa.
• Training data was Qwen-synthesized; some questions may carry the synthesizer's biases.
• No multi-turn conversational SFT — single-turn QA only.

GGUF builds (llama.cpp / Ollama)

Quantized GGUF files live at the repo root.

File	Quant	Approx size
rcrc-v5-gemma-1b-cpt-sft-F16.gguf	F16	~2.0 GB
rcrc-v5-gemma-1b-cpt-sft-Q8_0.gguf	Q8_0	~1.0 GB
rcrc-v5-gemma-1b-cpt-sft-Q5_K_M.gguf	Q5_K_M	~720 MB
rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf	Q4_K_M	~620 MB

Ollama (one-liner)

ollama run huggingface.co/perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft:Q4_K_M
# or :Q8_0, :Q5_K_M, :F16

If you hit a host-redirect error (hf.co → huggingface.co), upgrade Ollama to a recent version, or use the huggingface.co/... URL above.

Manual Modelfile route

hf download perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf Modelfile --local-dir ./model
cd model
ollama create rcrc-v5-gemma-1b-cpt-sft -f Modelfile
ollama run rcrc-v5-gemma-1b-cpt-sft

llama.cpp

hf download perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf --local-dir .
./llama-cli -m rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf -cnv

GGUF builds (llama.cpp / Ollama)

Quantized GGUF files live under gguf/. They are built directly from the safetensors above with llama.cpp convert_hf_to_gguf.py followed by llama-quantize.

File	Quant	Approx size
gguf/rcrc-v5-gemma-1b-cpt-sft-F16.gguf	F16	~2.0 GB
gguf/rcrc-v5-gemma-1b-cpt-sft-Q8_0.gguf	Q8_0	~1.0 GB
gguf/rcrc-v5-gemma-1b-cpt-sft-Q5_K_M.gguf	Q5_K_M	~720 MB
gguf/rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf	Q4_K_M	~620 MB

llama.cpp

hf download perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft gguf/rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf --local-dir .
./llama-cli -m gguf/rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf -cnv

Ollama

hf download perfectPresentation/rcrc-chat-v5-gemma-1b-cpt-sft gguf/rcrc-v5-gemma-1b-cpt-sft-Q4_K_M.gguf gguf/Modelfile --local-dir ./model
cd model/gguf
ollama create rcrc-v5-gemma-1b-cpt-sft -f Modelfile
ollama run rcrc-v5-gemma-1b-cpt-sft