Instructions to use muradil211/AetherSearch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muradil211/AetherSearch with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="muradil211/AetherSearch")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muradil211/AetherSearch")
model = AutoModelForCausalLM.from_pretrained("muradil211/AetherSearch", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use muradil211/AetherSearch with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muradil211/AetherSearch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muradil211/AetherSearch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muradil211/AetherSearch

SGLang

How to use muradil211/AetherSearch with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "muradil211/AetherSearch" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muradil211/AetherSearch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "muradil211/AetherSearch" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muradil211/AetherSearch",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use muradil211/AetherSearch with Docker Model Runner:
```
docker model run hf.co/muradil211/AetherSearch
```

AetherSearch / README.md

Muradil Mamat

Upload AetherSearch model release

c15eb50 6 days ago

preview code

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	---
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- aethersearch
	- agentic-rl
	- search-augmented-generation
	- qwen2
	- sft
	- dpo
	- reinforcement-learning
	language:
	- en
	---

	# AetherSearch

	AetherSearch is a search-augmented language model release trained through a
	multi-stage post-training pipeline with SFT, DPO, and reinforcement learning.

	The released weights are provided in Hugging Face Transformers format and can be
	loaded with `AutoModelForCausalLM` and `AutoTokenizer`.

	## Files

	- `model.safetensors`: model weights.
	- `config.json` and `generation_config.json`: Transformers configuration.
	- `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt`,
	`added_tokens.json`, and `special_tokens_map.json`: tokenizer assets.
	- `MODEL_MANIFEST.sha256`: SHA256 checksums for the uploaded files.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo_id = "muradil211/AetherSearch"

	tokenizer = AutoTokenizer.from_pretrained(repo_id)
	model = AutoModelForCausalLM.from_pretrained(
	repo_id,
	torch_dtype="auto",
	device_map="auto",
	)
	```

	## Notes

	The companion training code is released at:
	https://github.com/Muradil-mamat-211/AetherSearch

	License and upstream base-model attribution should be set according to the
	actual base model and data release terms before wider redistribution.