Instructions to use Ayansk11/qwen3-4b-financial-sentiment-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ayansk11/qwen3-4b-financial-sentiment-grpo with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ayansk11/qwen3-4b-financial-sentiment-grpo",
	filename="qwen3-4b.Q5_K_M.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 Ayansk11/qwen3-4b-financial-sentiment-grpo with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

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 Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

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 Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

Use Docker

docker model run hf.co/Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

LM Studio
Jan

vLLM

How to use Ayansk11/qwen3-4b-financial-sentiment-grpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ayansk11/qwen3-4b-financial-sentiment-grpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ayansk11/qwen3-4b-financial-sentiment-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

Ollama
How to use Ayansk11/qwen3-4b-financial-sentiment-grpo with Ollama:
```
ollama run hf.co/Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M
```

Unsloth Studio

How to use Ayansk11/qwen3-4b-financial-sentiment-grpo 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 Ayansk11/qwen3-4b-financial-sentiment-grpo 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 Ayansk11/qwen3-4b-financial-sentiment-grpo to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ayansk11/qwen3-4b-financial-sentiment-grpo to start chatting

How to use Ayansk11/qwen3-4b-financial-sentiment-grpo with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

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": "Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Ayansk11/qwen3-4b-financial-sentiment-grpo with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

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 Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Ayansk11/qwen3-4b-financial-sentiment-grpo with Docker Model Runner:
```
docker model run hf.co/Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M
```

Lemonade

How to use Ayansk11/qwen3-4b-financial-sentiment-grpo with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ayansk11/qwen3-4b-financial-sentiment-grpo:Q5_K_M

Run and chat with the model

lemonade run user.qwen3-4b-financial-sentiment-grpo-Q5_K_M

List all available models

lemonade list

Qwen3-4B Financial Sentiment Analyzer with Chain-of-Thought

Fine-tuned Qwen3-4B model for financial sentiment analysis with explicit reasoning.

Training Details

Base Model: Qwen3-4B
Method: SFT Warm-up + GRPO (Group Relative Policy Optimization)
Dataset: 8,541 financial news samples with CoT explanations
Training Time: ~4 hours on A100

Usage

With Ollama (Recommended for Mac M4)

# Download GGUF and Modelfile
huggingface-cli download Ayansk11/qwen3-4b-financial-sentiment-grpo --include "*.gguf" "Modelfile" --local-dir .

# Create Ollama model
ollama create financial-sentiment -f Modelfile

# Run inference
ollama run financial-sentiment "Analyze: Apple reported record Q4 earnings."

With Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Ayansk11/qwen3-4b-financial-sentiment-grpo")
tokenizer = AutoTokenizer.from_pretrained("Ayansk11/qwen3-4b-financial-sentiment-grpo")

messages = [
    {"role": "system", "content": "You are a financial sentiment analyst..."},
    {"role": "user", "content": "Analyze: Tesla stock dropped 10%"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Output Format

<reasoning>
1. Key financial indicators: [analysis]
2. Tone and language: [analysis]
3. Market implications: [analysis]
</reasoning>
<answer>positive/negative/neutral</answer>

Performance

Mac M4 Inference: 40-60 tokens/sec (Q5_K_M)
Memory Usage: ~4 GB (quantized)
File Size: ~2.89 GB (Q5_K_M GGUF)

Files

*.gguf - Quantized model for Ollama/llama.cpp
Modelfile - Ollama configuration with proper stop tokens
*.safetensors - Full PyTorch weights

License

Apache 2.0

Downloads last month: 49

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Ayansk11/qwen3-4b-financial-sentiment-grpo

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Quantized

(227)

this model

Ayansk11
/

qwen3-4b-financial-sentiment-grpo