GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
chauben
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
chauben
Model Tree
Qwen/Qwen2.5-14B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Base model | Qwen/Qwen2.5-14B-Instruct |
| Parameters | ~15B (BF16) |
| Fine-tuning | QDoRA (4-bit NF4 + DoRA r=64 + rsLoRA) + NEFTune (α=5) |
| Training | 2× NVIDIA RTX 3090, DDP, TRL SFTTrainer |
| Domain | Stevens academic advising (courses, faculty, programs, admissions, etc.) |
| Format | Safetensors, Qwen ChatML template |
Answers student-style questions about Stevens in a helpful, markdown-formatted advising tone:
| Split | Examples |
|---|---|
| Train | 71,883 |
| Eval | 7,988 |
| Total | 79,871 |
Built from Stevens-related sources and LLM-assisted Q&A generation (Gemini + Qwen scoring), formatted as multi-turn chat JSONL. Approximate mix: ~95% single-turn, ~5% multi-turn; categories dominated by course and general (~65% combined).
| Parameter | Value |
|---|---|
| Epochs | 2 |
| Effective batch size | 32 |
| Learning rate | 8e-5 |
| Max seq length | 2048 |
| LoRA rank | 64 |
| LoRA alpha | 128 |
| DoRA / rsLoRA | enabled |
| NEFTune α | 5 |
| Optimizer | paged_adamw_8bit |
Run name: advisorai-qwen25-14b-qdora-neftune-v1
Post-training: DoRA adapter merged into base → uploaded as this Hub checkpoint.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch MODEL_ID = "chauben/advisorai-qwen2.5-14b-stevens" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,) messages = [ { "role": "system", "content": ( "You are AdvisorAI, a knowledgeable academic advisor for " "Stevens Institute of Technology. Be specific — cite course codes " "and requirements when available. Use markdown." ), }, {"role": "user", "content": "What are the requirements for the CS MS program at Stevens?"},] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.3, top_p=0.9, do_sample=True, repetition_penalty=1.05, pad_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) Citation:@misc{advisorai-qwen25-14b-stevens-2026, title = {AdvisorAI: Fine-Tuned Qwen2.5-14B for Stevens Institute Academic Advising}, author = {Nitin Chaube}, year = {2026}, howpublished = {\url{https://huggingface.co/chauben/advisorai-qwen2.5-14b-stevens}}, note = {Fine-tuned from Qwen/Qwen2.5-14B-Instruct; QDoRA + NEFTune}}from transformers import AutoModelForCausalLM, AutoTokenizerimport torch MODEL_ID = "chauben/advisorai-qwen2.5-14b-stevens" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,) messages = [ { "role": "system", "content": ( "You are AdvisorAI, a knowledgeable academic advisor for " "Stevens Institute of Technology. Be specific — cite course codes " "and requirements when available. Use markdown." ), }, {"role": "user", "content": "What are the requirements for the CS MS program at Stevens?"},] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.3, top_p=0.9, do_sample=True, repetition_penalty=1.05, pad_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) Citation:@misc{advisorai-qwen25-14b-stevens-2026, title = {AdvisorAI: Fine-Tuned Qwen2.5-14B for Stevens Institute Academic Advising}, author = {Nitin Chaube}, year = {2026}, howpublished = {\url{https://huggingface.co/chauben/advisorai-qwen2.5-14b-stevens}}, note = {Fine-tuned from Qwen/Qwen2.5-14B-Instruct; QDoRA + NEFTune}}