TradingAgents/PULL_OLLAMA_MODELS.md

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# Quick Guide: Pull Ollama Models for TradingAgents
## ⚠️ IMPORTANT: Pull Models Before Running
When you see a **404 error** like:
```
ResponseError: 404 page not found (status code: 404)
```
It means **the model isn't downloaded yet**. You must pull it first!
## 📥 How to Pull Models
Open a terminal and run:
```bash
# RECOMMENDED - Start with this
ollama pull llama3.2
# OR choose from these tool-compatible models:
ollama pull llama3.2:1b # Fastest (1B)
ollama pull llama3.1 # Better quality (8B)
ollama pull mistral-nemo # Mistral (12B)
ollama pull qwen2.5:7b # Qwen (7B)
ollama pull qwen2.5-coder:7b # Coding-focused (7B)
```
## ✅ Verify Models Are Installed
```bash
ollama list
```
You should see your models listed:
```
NAME ID SIZE MODIFIED
llama3.2:latest abc123... 2.0 GB 2 minutes ago
mistral-nemo def456... 7.1 GB 1 hour ago
```
## 🎯 Recommended Setup for TradingAgents
### For Quick Testing (Fastest)
```bash
ollama pull llama3.2:1b
```
- **Size**: ~1GB
- **Speed**: Very fast
- **Quality**: Good enough for testing
### For Production Use (Balanced)
```bash
ollama pull llama3.2
```
- **Size**: ~2GB
- **Speed**: Fast
- **Quality**: Good
### For Best Quality (Slower)
```bash
ollama pull llama3.1
```
- **Size**: ~5GB
- **Speed**: Medium
- **Quality**: Excellent
### For Mistral Fans
```bash
ollama pull mistral-nemo
```
- **Size**: ~7GB
- **Speed**: Medium
- **Quality**: Excellent
### For Qwen Models
```bash
# Standard Qwen
ollama pull qwen2.5:7b
# OR Coding-focused variant
ollama pull qwen2.5-coder:7b
```
- **Size**: ~4-5GB each
- **Speed**: Fast
- **Quality**: Very good
## 🚀 Complete Workflow
### 1. Pull a Model
```bash
ollama pull llama3.2
```
### 2. Verify It's Downloaded
```bash
ollama list
```
### 3. Run TradingAgents
```bash
python -m cli.main
```
### 4. Select Settings
- **Provider**: Ollama
- **Quick-Thinking**: llama3.2 (or your choice)
- **Deep-Thinking**: llama3.2 (or your choice)
## 📊 Model Comparison
| Model | Size | Download Time* | RAM Usage | Speed | Quality | Tools Support |
|-------|------|---------------|-----------|-------|---------|---------------|
| **llama3.2:1b** | 1GB | ~1 min | 2GB | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ✅ |
| **llama3.2** | 2GB | ~2 min | 4GB | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ✅ |
| **llama3.1** | 5GB | ~5 min | 8GB | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ✅ |
| **mistral-nemo** | 7GB | ~7 min | 12GB | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ✅ |
| **qwen2.5:7b** | 4.7GB | ~5 min | 7GB | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ✅ |
| **qwen2.5-coder** | 4.7GB | ~5 min | 7GB | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ✅ |
*Approximate download time on typical broadband connection
## ⚡ Pro Tips
### 1. Pull Multiple Models
You can have multiple models installed and switch between them:
```bash
ollama pull llama3.2 # Fast for testing
ollama pull llama3.1 # High quality for production
```
### 2. Check Model Info
```bash
ollama show llama3.2
```
### 3. Remove Unwanted Models
```bash
ollama rm llama3 # Remove old llama3 (doesn't support tools)
```
### 4. Keep Models Updated
```bash
ollama pull llama3.2 # Updates to latest version
```
## 🐛 Troubleshooting
### Error: "404 page not found"
**Solution**: Model not downloaded. Pull it first:
```bash
ollama pull llama3.2
```
### Error: "model 'qwen2.5' not found"
**Solution**: Use full tag:
```bash
ollama pull qwen2.5:7b # Not just "qwen2.5"
```
### Slow Performance
**Solution**: Use smaller model:
```bash
ollama pull llama3.2:1b
```
### Out of Memory
**Solution**: Use smaller model or close other applications:
```bash
ollama pull llama3.2:1b # Only needs ~2GB RAM
```
### Model Takes Forever to Download
**Solution**: Start with smallest model:
```bash
ollama pull llama3.2:1b # Only 1GB download
```
## 🎓 Learning Path
### Beginner
1. Start with: `ollama pull llama3.2:1b`
2. Test with simple analysis
3. Upgrade if needed
### Intermediate
1. Use: `ollama pull llama3.2`
2. Good balance of speed and quality
3. Most popular choice
### Advanced
1. Try: `ollama pull llama3.1` or `mistral-nemo`
2. Best quality for complex analysis
3. Requires more resources
## 📝 Summary
**TL;DR - Quick Start:**
```bash
# 1. Pull the recommended model
ollama pull llama3.2
# 2. Verify it's there
ollama list
# 3. Run the app
python -m cli.main
```
**That's it!** 🚀
---
## Need Help?
Check if Ollama is running:
```bash
ollama list
```
If you see an error, start Ollama:
```bash
ollama serve
```
Then pull your model and try again!