Instructions to use MEGHT/qwen3-finetuned-search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MEGHT/qwen3-finetuned-search with Transformers:

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

pipe = pipeline("text-generation", model="MEGHT/qwen3-finetuned-search")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MEGHT/qwen3-finetuned-search")
model = AutoModelForCausalLM.from_pretrained("MEGHT/qwen3-finetuned-search")
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]:]))

llama-cpp-python

How to use MEGHT/qwen3-finetuned-search with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MEGHT/qwen3-finetuned-search",
	filename="qwen3-finetuned.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MEGHT/qwen3-finetuned-search with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MEGHT/qwen3-finetuned-search
# Run inference directly in the terminal:
llama-cli -hf MEGHT/qwen3-finetuned-search

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MEGHT/qwen3-finetuned-search
# Run inference directly in the terminal:
llama-cli -hf MEGHT/qwen3-finetuned-search

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf MEGHT/qwen3-finetuned-search
# Run inference directly in the terminal:
./llama-cli -hf MEGHT/qwen3-finetuned-search

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf MEGHT/qwen3-finetuned-search
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MEGHT/qwen3-finetuned-search

Use Docker

docker model run hf.co/MEGHT/qwen3-finetuned-search

LM Studio
Jan

vLLM

How to use MEGHT/qwen3-finetuned-search with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MEGHT/qwen3-finetuned-search"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MEGHT/qwen3-finetuned-search",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MEGHT/qwen3-finetuned-search

SGLang

How to use MEGHT/qwen3-finetuned-search 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 "MEGHT/qwen3-finetuned-search" \
    --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": "MEGHT/qwen3-finetuned-search",
		"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 "MEGHT/qwen3-finetuned-search" \
        --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": "MEGHT/qwen3-finetuned-search",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use MEGHT/qwen3-finetuned-search with Ollama:
```
ollama run hf.co/MEGHT/qwen3-finetuned-search
```

Unsloth Studio

How to use MEGHT/qwen3-finetuned-search with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MEGHT/qwen3-finetuned-search to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MEGHT/qwen3-finetuned-search to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MEGHT/qwen3-finetuned-search to start chatting

How to use MEGHT/qwen3-finetuned-search with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MEGHT/qwen3-finetuned-search

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MEGHT/qwen3-finetuned-search"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MEGHT/qwen3-finetuned-search with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MEGHT/qwen3-finetuned-search

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default MEGHT/qwen3-finetuned-search

Run Hermes

hermes

Docker Model Runner
How to use MEGHT/qwen3-finetuned-search with Docker Model Runner:
```
docker model run hf.co/MEGHT/qwen3-finetuned-search
```

Lemonade

How to use MEGHT/qwen3-finetuned-search with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MEGHT/qwen3-finetuned-search

Run and chat with the model

lemonade run user.qwen3-finetuned-search-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3 0.6B Fine-Tuned for Search Query Generation

This model is a fine-tuned version of the Qwen3 0.6B model, designed to generate relevant search queries based on user inputs and conversational context. It's particularly useful for enhancing search engine query suggestion systems, chatbots, and virtual assistants.

Model Details

Base Model: Qwen3 0.6B
Fine-Tuning Dataset: Custom dataset consisting of input-output pairs where the model learns to generate a list of search queries based on a given input and previous conversation.
Training Framework: Fine-tuned using Hugging Face's transformers and datasets libraries.
Inference Framework: Compatible with Hugging Face's transformers library for easy integration into applications.

Intended Use

This model is intended for applications that require generating search queries from user inputs, such as:

Search Engine Query Suggestions: Enhancing search engines by providing more relevant query suggestions.
Chatbots and Virtual Assistants: Enabling chatbots to suggest relevant search queries based on user conversations.
Content Discovery Systems: Improving content recommendation systems by generating search queries that lead to relevant content.

Example

Input: Generate a list of search queries. Input Query: "What are the benefits of that for children?"
Previous conversation: ["I'm thinking of enrolling my child in music lessons.", "They are interested in piano."]

Output:

benefits of music lessons for children
advantages of learning piano for kids
music education impact on child development
child learning piano benefits
academic benefits of music education

Model Usage

To use this model for generating search queries:

Install Required Libraries:

pip install transformers
Load the Model and Tokenizer:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "MEGHT/qwen3-finetuned-search" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)
Generate Search Queries:

inputs = tokenizer("Generate a list of search queries. Input Query: 'How can I teach them about it?'\nPrevious conversation: ['My kids are asking about money.', 'They want to know how to save.']", return_tensors="pt") outputs = model.generate(**inputs, max_length=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Dataset: Custom dataset of input-output pairs for search query generation.
Fine-Tuning Parameters:
- Epochs: 3
- Batch Size: 16
- Learning Rate: 5e-5
- Optimizer: AdamW
- Scheduler: Linear warmup with 10% warmup ratio

Evaluation

Perplexity: 12.5
BLEU Score: 0.35
ROUGE-L: 0.45

These metrics indicate that the model generates coherent and relevant search queries based on inputs and conversational context.

Limitations

Context Length: Maximum of 1024 tokens; long conversations may be truncated.
Domain Specificity: May not perform well on unseen domains.
Biases: Model may inherit biases from training data.

License

Apache 2.0 License

Citation

@misc{qwen3_0.6b_finetuned_search, author = {MEGHT}, title = {Qwen3 0.6B Fine-Tuned for Search Query Generation}, year = {2025}, url = {https://huggingface.co/MEGHT/qwen3-finetuned-search} }

Acknowledgements

Thanks to the Hugging Face team for the transformers and datasets libraries.

Contact

For questions or feedback, contact MEGHT

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Safetensors

Model size

0.6B params

Tensor type

BF16