Reinforcement Learning
Transformers
Safetensors
qwen3
text-generation
tau-bench
tool-use
agent
slime
text-generation-inference
Instructions to use willhx/Qwen3-8B-Base-TauSFT-Tau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use willhx/Qwen3-8B-Base-TauSFT-Tau with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("willhx/Qwen3-8B-Base-TauSFT-Tau") model = AutoModelForCausalLM.from_pretrained("willhx/Qwen3-8B-Base-TauSFT-Tau") - Notebooks
- Google Colab
- Kaggle
Qwen3-8B-Base-TauSFT-Tau
RL checkpoint of Qwen3-8B-Base for tau-bench agentic tool-use, trained with
slime (Megatron-LM training + SGLang rollout).
Pipeline: Qwen3-8B-Base → SFT on tau-bench trajectories (TauSFT) → RL on tau-bench (Tau).
This is the checkpoint at iteration 180, converted from Megatron torch_dist format to
Hugging Face safetensors (bf16).
Training setup
| 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.
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Base model
Qwen/Qwen3-8B-Base