TinnyLlamaGambling

TinnyLlamaGambling

A compact instruction-following model focused on responsible, factual gambling topics: RTP, house edge, expected value (EV), bankroll management, dice/coin probabilities, and Responsible Gambling guidance. Data is fully synthetic, generated with local LLMs via the provided scripts, cleaned and shuffled, then fine-tuned with LoRA and merged into a standalone model.

Model

• Architecture: TinyLlama 1.1B and/or Mistral 7B with LoRA
• Language: English
• Task: text generation / instruction tuning (short, structured answers)

Data

• Source: synthetic JSONL with fields instruction, input, output
• Generation: sampling-controlled
• Cleaning: trim, drop empty/too-long, shuffle

Training

• Method: TRL SFT + PEFT LoRA; cosine LR, short epochs, seq len ≈ 1k
• LoRA targets: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
• Merge: merge_lora to produce a standard Transformers checkpoint

Intended Use

• Educational explanations of gambling math and concepts (EV, RTP, house edge)
• High-level bankroll/risk topics and Responsible Gambling reminders
• Not financial/betting advice

Limitations

• Synthetic data may contain errors or simplifications
• Do not use outputs to make wagering decisions
• Avoid encouraging gambling or irresponsible behavior

Inference (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "galanviorel/TinnyLlamaGambling"
tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
mdl = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "### Instruction:\nExplain house edge for a game with RTP=96%.\n\n### Response:\n"
x = tok(prompt, return_tensors="pt")
y = mdl.generate(**x, max_new_tokens=128, temperature=0.7, top_p=0.9)
print(tok.decode(y[0], skip_special_tokens=True))

Recommended Generation Params

• temperature: 0.6–0.9
• top_p: 0.9–0.95
• repetition_penalty: 1.05–1.15
• max_new_tokens: 128–256

License

• Model card/code: Apache-2.0
• Base models: per their original licenses
• Data: synthetic; follow local laws and Responsible Gambling guidelines