modrill
qwen3-4b-think-baseline-full-sft
Dedicated Endpoints
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Container
Run this model inference with full control and performance in your environment.
Get help setting up a custom Dedicated Endpoints.
Talk with our engineer to get a quote for reserved GPU instances with discounts.
README
License: apache-2.0Model Details
- Base model: Qwen/Qwen3-4B-Base
- Fine-tuning: Full SFT (DeepSpeed ZeRO-3), finetuning_type: full
- Dataset: think_all
- Mode: Think (
enable_thinking=true) - Training cutoff length: 24576 tokens
- Epochs: 2
- Learning rate: 2e-5
- Train loss: ~0.67
- Finished: 2026-06-08
Usage
HuggingFace Transformers
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "modrill/qwen3-4b-think-baseline-full-sft"tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype="auto", device_map="auto")
vLLM
bash
python -m vllm.entrypoints.openai.api_server \--model modrill/qwen3-4b-think-baseline-full-sft \--served-model-name think-baseline-full \--port 8801
Inference Tips
- Set
enable_thinking=truein the chat template - Recommended
max_tokens: 24576
License
Apache 2.0, consistent with the Qwen3 base model license.
Model provider
modrill
Model tree
Base
Qwen/Qwen3-4B-Base
Fine-tuned
this model
Modalities
Input
Text
Output
Text
Pricing
Dedicated Endpoints
View detailsSupported Functionality
Model APIs
Dedicated Endpoints
Container
More information