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Model: Ayansk11/qwen3-4b-financial-sentiment-grpo
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+tags:
+- finance
+- sentiment-analysis
+- chain-of-thought
+- grpo
+- unsloth
+- ollama
+datasets:
+- FinGPT/fingpt-sentiment-train
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# 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)
+
+```bash
+# 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)
+
+```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