GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
ba144220
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
ba144220
Model Tree
ba144220/cs224r-default-project-sft
Input Modalities
Output Modalities
Supported Functionality
This model is preference-tuned using IPO on pairwise chosen/rejected completions for Countdown problems. Given a target number and a set of allowed numbers, the model produces chain-of-thought reasoning inside <think> tags and a final answer inside <answer> tags.
| Hyperparameter | Value |
|---|---|
| Base model | ba144220/cs224r-default-project-sft (SFT-tuned Qwen2.5-0.5B) |
| Dataset | asingh15/countdown_tasks_3to4-dpo |
| Loss type | IPO |
| Beta | 0.1 |
| Epochs | 1 |
| Learning rate | 5e-6 |
| LR schedule | Cosine with 5% warmup |
| Batch size | 64 (gradient accumulation = 16) |
| Weight decay | 0.01 |
| Precision | bfloat16 |
| Gradient checkpointing | Enabled |
| Hardware | 1x NVIDIA H100 (Modal) |
| Max prompt length | 512 |
| Max response length | 1024 |
Evaluated on asingh15/countdown_tasks_3to4 test split (40 prompts) using vLLM with temperature 0.6, top-k 20, top-p 0.95, sampling K=16 responses per prompt.
| Metric | SFT Baseline | IPO (this model) |
|---|---|---|
| Average Score | 0.3660 | 0.4080 |
| Pass@1 | 0.30 | 0.375 |
| Pass@16 | 0.75 (30/40) | 0.75 (30/40) |
| Correct (score=1.0) | 244/800 | 287/800 |
python
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("ba144220/cs224r-default-project-ipo")tokenizer = AutoTokenizer.from_pretrained("ba144220/cs224r-default-project-ipo") messages = [{"role": "user", "content": "Using the numbers [3, 4, 6, 8], create an equation that equals 24."}]input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.6, top_k=20, top_p=0.95, do_sample=True)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))Yuchi Hsu (yuchihsu@stanford.edu) and Ryan He (ryanhe@stanford.edu), Stanford CS224R Spring 2026.
from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("ba144220/cs224r-default-project-ipo")tokenizer = AutoTokenizer.from_pretrained("ba144220/cs224r-default-project-ipo") messages = [{"role": "user", "content": "Using the numbers [3, 4, 6, 8], create an equation that equals 24."}]input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.6, top_k=20, top_p=0.95, do_sample=True)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))