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tayyib-sayyid
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Model Details
Model Provider
tayyib-sayyid
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Stage | Metric | Score |
|---|---|---|
| Base model (no fine-tune) | LLM-judge avg (1-5), 10 prompts | 2.0 |
| This adapter (sft_trial_1) | LLM-judge avg (1-5), 10 prompts | 3.4 |
| Best GRPO trial on top of this | regex exact-match (0-10) | 2.0 |
Judge model: groq/llama-3.3-70b-versatile. Evaluation prompts are 10 held-out GSM8K
test problems formatted with the ChatML template and the #### N
terminator.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_id = "Qwen/Qwen2.5-0.5B-Instruct"adapter_id = "tayyib-sayyid/qwen2.5-0.5b-gsm8k-lora" tok = AutoTokenizer.from_pretrained(adapter_id)base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")model = PeftModel.from_pretrained(base, adapter_id)model.eval() prompt = ( "Natalia sold clips to 48 of her friends in April, and then she sold " "half as many clips in May. How many clips did Natalia sell altogether " "in April and May?")messages = [{"role": "user", "content": prompt}]inputs = tok.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)out = model.generate(inputs, max_new_tokens=256, do_sample=False)print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))The model is trained to end every answer with #### <number> so a simple
regex (r"####\s*(-?[\d,\.]+)") is enough to extract the final
numeric answer.
Qwen/Qwen2.5-0.5B-Instructopenai/gsm8k (main config), 5,000-row training subset
(seed 42), ChatML rendered with the #### N final-answer terminator.SFTTrainer, transformers 4.4x.| Field | Value |
|---|---|
r | 8 |
alpha | 16 |
dropout | 0.05 |
target_modules | q_proj, v_proj |
task_type | CAUSAL_LM |
| Field | Value |
|---|---|
| learning rate | 0.0002 |
| optimizer | paged_adamw_8bit |
| per-device batch size | 4 |
| gradient accumulation | 4 |
| effective batch size | 16 |
| epochs | 1 |
| warmup ratio | 0.03 |
| weight decay | 0.0 |
| max sequence length |
This adapter was produced as part of NLP Assignment 4 at IBA. The full pipeline, hyperparameter sweep tables, and LaTeX report live in the source repository.
| 1024 |