Training setup
Table | |
|---|
| Base model | Qwen/Qwen3-8B-Base |
| Framework | slime (Megatron-LM + SGLang) |
| Task | tau-bench (tool-use dialogues) |
| User simulator | zai-org/GLM-4.7-Flash via an OpenAI-compatible endpoint |
| Precision | bfloat16 |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "willhx/Qwen3-8B-Base-TauSFT-Tau"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bfloat16", device_map="auto")
The model is trained to emit tool calls in the tau-bench format; evaluate it inside a
tau-bench environment rather than as a general-purpose chat model.
Notes
This is an intermediate RL checkpoint released for research purposes. It has not been
safety-tuned, and it inherits the license and limitations of the underlying Qwen3-8B-Base model.