muradil211/AetherSearch-SFT

🔭 AetherSearch SFT

A compact search agent that learns to reason, retrieve, and answer

Fine-tuned from Qwen2.5-3B-Instruct on 2,000 complete search trajectories.

🏠 Project · 🧪 Training code · 📚 Dataset

🔌 Bring your own retriever. AetherSearch SFT is a search-agent policy, not a self-contained QA service. The host runtime must execute each <search>...</search> request and return evidence inside <information>...</information>.

✨ Highlights

• 🔎 Search-native behavior — learns when and what to search before answering.
• 🔁 Single- and multi-search trajectories — trained on 1,025 single-search and 975 multi-search examples.
• 🧾 Evidence-in-the-loop reasoning — retrieved passages stay visible as context while being excluded from the training loss.
• ⚡ Compact 3B backbone — built on Qwen2.5-3B-Instruct for accessible experimentation and deployment.
• 🧪 Reproducible release — public trainer, launcher, data checksum, schema tests, and artifact manifest are included or linked.

🧠 How it works

Question
   │
   ▼
<think>reason about what is missing</think>
   │
   ▼
<search>focused retrieval query</search> ─────► Search / RAG backend
   ▲                                                  │
   └──── <information>retrieved evidence</information> ◄────┘
   │
   ├── repeat the search loop when more evidence is needed
   ▼
<answer>evidence-grounded final answer</answer>

The model produces the reasoning, search, and answer spans. Your runtime owns retrieval: parse a completed <search> span, run the query, append the result as <information>, and resume generation until the model emits <answer>.

📊 Model at a glance

Field	Value
🧱 Base model	Qwen/Qwen2.5-3B-Instruct
🧬 Base revision	aa8e72537993ba99e69dfaafa59ed015b17504d1
🏗️ Architecture	Qwen2 causal language model
🔢 Parameters	3,085,938,688
🎛️ Weight dtype	BF16
📏 Context	32,768 positions; training sequences capped at 4,096
📚 Training data	2,000 complete trajectories
🔍 Search mix	1,025 single-search + 975 multi-search trajectories
🎓 Training stage	One full-trajectory SFT stage

🚀 Quick start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "muradil211/AetherSearch-SFT"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.config.use_cache = True
model.eval()

💡 Loading the checkpoint is only the first step. For end-to-end use, wrap generation in the retrieval loop shown above and preserve the XML protocol exactly.

🧬 Checkpoint identity

This model was trained once on the 2,000 records in the canonical final_sft_2000.jsonl dataset, using the same configuration as the public AetherSearch SFT-2000 training code. The release contains the final model artifacts and reproducible code, not server-local logs or optimizer state.

Dataset SHA-256

fec609652d3832c7a6c0ee2861c6f946b6cf7c3d3d40fc5d9be9b75df6325dcb

🧪 Training recipe

Setting	Value	Setting	Value
Epochs	1	Learning rate	2e-6
Scheduler	Cosine	Global batch size	24
Precision	BF16 + TF32	Max sequence length	4,096
Padding	Dynamic	Distributed training	DeepSpeed ZeRO-3

The training configuration matches the public SFT-2000 recipe: one epoch, learning rate 2e-6, cosine scheduling, BF16, TF32, gradient checkpointing, dynamic padding, effective global batch size 24, and DeepSpeed ZeRO-3. On the three-worker training topology, per-device batch size 1 and gradient accumulation 8 resolve to that global batch. The completed checkpoint is exported as final_model/.

The public launcher is hardware-topology independent: it uses the devices made visible by the surrounding runtime and derives gradient accumulation to keep global batch 24 unchanged. It does not embed physical GPU IDs, node addresses, NCCL fabric settings, allocator tuning, or server-local paths.

🎯 Supervision contract

• ⬛ System, user, and question tokens are masked.
• ⬛ Complete <information>...</information> spans are masked.
• ✅ Assistant <think>, <search>, and <answer> spans are supervised.
• ✅ The final assistant <|im_end|> token is supervised.

The trainer, launcher, configuration, checksum, and schema tests are published in the AetherSearch SFT directory.

📦 Files and integrity

The release contains two BF16 SafeTensors shards, the shard index, model and generation configuration, tokenizer assets, this model card, the project logo, and MODEL_MANIFEST.sha256. It intentionally excludes optimizer states, intermediate checkpoints, training_args.bin, evaluation bundles, and all log files.

After download, verify the release from its repository directory:

sha256sum -c MODEL_MANIFEST.sha256

⚠️ Limitations

Generated searches and answers can be incorrect, unsupported, or unsafe; retrieval and answer verification remain the caller's responsibility. No evaluation result is claimed by this model card.

📜 Terms

No additional blanket license is asserted here. Review the Qwen2.5-3B-Instruct license and the AetherSearch SFT data attribution and rights status before redistribution or downstream use.

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Built for experiments in agentic search and retrieval-augmented reasoning. 🔎✨