GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
willamazon1
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
willamazon1
Model Tree
willamazon1/qwen3-8b-tmax-sft-v3-iter353
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "willamazon1/qwen3-8b-tmax-aenv-v39b-iter099"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.bfloat16, device_map="auto") msgs = [{"role": "user", "content": "What is 12*8?"}]text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)ids = tok(text, return_tensors="pt").input_ids.to(model.device)out = model.generate(ids, max_new_tokens=256)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))Converted from a Megatron-LM torch_dist training checkpoint to HuggingFace
safetensors with slime's
tools/convert_torch_dist_to_hf.py. Embedding padding was stripped back to the
tokenizer's vocab_size of 151936, so the tensor shapes match upstream
Qwen/Qwen3-8B exactly. Weights are bfloat16; the optimizer state of the
training checkpoint is not included.
Every shard was checked for NaN/Inf (none found) and the model was loaded and sampled from before upload.