GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
agk4444
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
agk4444
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
160 held-out questions (100 math, 60 reading & writing). Identical prompt and decoding
for both: greedy, 400 new tokens, thinking off, same **Answer: X** format instruction.
Strict scoring = unparseable counted wrong. Reports: eval_base.json, eval_tuned.json.
| Base (strict) | Tuned (strict) | |
|---|---|---|
| Math | 57% (57/100) | 71% (71/100) |
| Reading & Writing | 76.7% (46/60) | 66.7% (40/60) |
| Overall | 64.4% (103/160) | 69.4% (111/160) |
The tuned adapter also answers far more math questions (97/100 vs 71/100) — base refused 29 outright. Known weakness: reading & writing regressed 10 points, the adapter overfit toward math. More RW training data is recommended before the teacher is frozen.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-8B", torch_dtype=torch.float16, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, "agk4444/sat-tutor-qwen3-8b")model.eval() msgs = [{"role": "user", "content": "If x + 5 = 12, what is x?\n\n" "End your response with your final answer on its own line in exactly " "this format: **Answer: X** where X is A, B, C, or D."}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)inp = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inp, max_new_tokens=400, do_sample=False)print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))For local/phone inference (llama.cpp, Ollama, LM Studio), use the GGUF build:
agk4444/sat-tutor-qwen3-8b-gguf
(sat-tutor-qwen3-8b.Q4_K_M.gguf, ~4.9 GB)
© 2026 AGK FIRE INC. Released under Apache 2.0.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-8B", torch_dtype=torch.float16, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(model, "agk4444/sat-tutor-qwen3-8b")model.eval() msgs = [{"role": "user", "content": "If x + 5 = 12, what is x?\n\n" "End your response with your final answer on its own line in exactly " "this format: **Answer: X** where X is A, B, C, or D."}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)inp = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inp, max_new_tokens=400, do_sample=False)print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))