# 🧬 BioQL - Resumen Final del Estado

**Fecha**: Octubre 2, 2025
**Hora**: 23:50 UTC

---

## ✅ LO QUE FUNCIONA

### 1. Infrastructure Completa
- ✅ **Modal billing system** con API key authentication
- ✅ **SQLite database** para users, api_keys, transactions
- ✅ **Profit margin** de 40% configurado
- ✅ **Cost tracking** en tiempo real
- ✅ **VS Code extension** v3.3.1 empaquetada

### 2. Training Completado
- ✅ **Dataset v2**: 10,000 ejemplos (vs 5,000 en v1)
- ✅ **Categorías**: 9 incluyendo `correct_syntax`, `docking`, `multi_step`
- ✅ **Anti-patterns** incluidos
- ✅ **Loss final**: 0.0162 (excelente)
- ✅ **Modelo guardado**: `bioql-deepseek:/data/final_model`

### 3. Deployment
- ✅ **Endpoint activo**: `https://spectrix--bioql-inference-deepseek-generate-code.modal.run`
- ✅ **Authentication funcionando**
- ✅ **Billing funcionando**

---

## ❌ EL PROBLEMA CRÍTICO

### Síntoma
**El modelo NO genera código BioQL válido**, solo:
1. Repite el prompt
2. Genera símbolos basura: `(           (`

### Tests Realizados

**Test 1**: Prompt simple
```bash
curl -X POST https://spectrix--bioql-inference-deepseek-generate-code.modal.run \
  -d '{"api_key":"bioql_test_870ce7ae","prompt":"Create a Bell state"}'
```

**Resultado**:
```json
{
  "code": "Create a Bell state\n\n### Reasoning:",
  "reasoning": ""
}
```
❌ **NO GENERA CÓDIGO**

**Test 2**: Con min_new_tokens=50
```json
{
  "code": "Create a Bell state using BioQL\n\n### Reasoning:\n                (           (",
  "reasoning": ""
}
```
❌ **GENERA BASURA**

### Expectativa
```json
{
  "code": "from bioql import quantum\n\nresult = quantum(\"Create Bell state on 2 qubits\", backend=\"simulator\", shots=1024)\nprint(result)",
  "reasoning": "A Bell state is a maximally entangled 2-qubit state. Steps: 1) Apply Hadamard to qubit 0..."
}
```

---

## 🔍 Análisis del Problema

### Causa Raíz
El modelo fine-tuned **DeepSeek-Coder-1.3B** con LoRA NO está generando correctamente.

### Posibles Razones

1. **LoRA adapters no se aplican correctamente**
   - El modelo carga pero podría estar usando el modelo base sin fine-tuning
   - Necesita verificar que `PeftModel.from_pretrained()` funciona

2. **Formato de prompt incorrecto**
   - Training: `### Instruction:\n...\n\n### Reasoning:\n...\n\n### Code:\n...`
   - Inference: `### Instruction:\n...\n\n### Reasoning:\n`
   - Podría faltar delimitador o token especial

3. **Modelo base incompatible**
   - DeepSeek-Coder-1.3B-Instruct podría tener formato especial
   - Necesita verificar documentación oficial

4. **Training issue**
   - Loss 0.0162 es bajo pero podría ser overfit
   - Dataset podría tener formato inconsistente

---

## 🎯 SOLUCIÓN RECOMENDADA

### Opción 1: Usar GPT-4 / Claude API (RECOMENDADO)
En vez de fine-tuning local, usar un modelo ya entrenado y robusto:

```python
@modal.method()
def generate(self, prompt: str) -> dict:
    import anthropic

    client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

    system_prompt = """You are a BioQL code generator. Generate ONLY valid BioQL code.

BioQL syntax:
- quantum(description, backend="simulator", shots=1000)
- dock(description, ligand_smiles="...", protein_pdb="...", backend="simulator")

NEVER use:
- quantum("QFT", 4)  # WRONG
- quantum("Bell", 2 qubits)  # WRONG

ALWAYS use:
- quantum("Run QFT on 4 qubits and measure", backend="simulator", shots=1000)  # CORRECT
"""

    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1000,
        system=system_prompt,
        messages=[{
            "role": "user",
            "content": prompt
        }]
    )

    return {
        "code": response.content[0].text,
        "reasoning": "Generated with Claude 3.5 Sonnet"
    }
```

**Ventajas**:
- ✅ Funciona inmediatamente
- ✅ Alta calidad de código
- ✅ No requiere training
- ✅ Soporte multilingüe
- ✅ Reasoning built-in

**Costos**:
- Claude API: ~$0.003 / 1K tokens input, ~$0.015 / 1K tokens output
- Para 500 tokens output: ~$0.0075 por request
- Con 40% markup: $0.0105 para usuario
- **Muy competitivo vs entrenar y mantener modelo propio**

### Opción 2: Revisar LoRA Training
Si insistes en modelo custom:

1. **Verificar formato de training**:
```python
# Verificar que todos los ejemplos tienen este formato EXACTO:
example = """### Instruction:
Create a Bell state using BioQL

### Reasoning:
A Bell state is a maximally entangled 2-qubit state. Steps: 1) Apply Hadamard to qubit 0 to create superposition, 2) Apply CNOT with qubit 0 as control and qubit 1 as target to create entanglement.

### Code:
from bioql import quantum

result = quantum("Create Bell state on 2 qubits", backend="simulator", shots=1000)
print(result)"""
```

2. **Test model localmente**:
```python
# Cargar modelo y probar ANTES de deployment
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct")
base = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct")
model = PeftModel.from_pretrained(base, "/path/to/final_model")

prompt = "### Instruction:\nCreate a Bell state\n\n### Reasoning:\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1, do_sample=True)
print(tokenizer.decode(outputs[0]))
```

3. **Re-entrenar con formato correcto**:
```python
# Agregar special tokens
tokenizer.add_special_tokens({
    'additional_special_tokens': ['### Instruction:', '### Reasoning:', '### Code:']
})

# Resize embeddings
model.resize_token_embeddings(len(tokenizer))
```

### Opción 3: Usar Modelo Pre-entrenado Existente
Usar **CodeLlama-7B**, **StarCoder**, o **WizardCoder** sin fine-tuning:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("WizardLM/WizardCoder-15B-V1.0")
tokenizer = AutoTokenizer.from_pretrained("WizardLM/WizardCoder-15B-V1.0")

prompt = """Generate BioQL code to create a Bell state.

BioQL syntax example:
from bioql import quantum
result = quantum("description", backend="simulator", shots=1000)

Now generate code:
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
```

---

## 📊 Comparación de Opciones

| Opción | Tiempo Setup | Calidad | Costo/Request | Mantenimiento |
|--------|--------------|---------|---------------|---------------|
| **Claude API** | ⚡ 10 min | ⭐⭐⭐⭐⭐ | $0.01 | ✅ Ninguno |
| **GPT-4 API** | ⚡ 10 min | ⭐⭐⭐⭐⭐ | $0.02 | ✅ Ninguno |
| **Fine-tune DeepSeek** | ⏰ 1 hora | ⭐⭐ (broken) | $0.004 | ❌ Alto |
| **CodeLlama base** | ⚡ 30 min | ⭐⭐⭐ | $0.005 | ⚙️ Medio |
| **WizardCoder** | ⚡ 30 min | ⭐⭐⭐⭐ | $0.006 | ⚙️ Medio |

---

## 💡 RECOMENDACIÓN FINAL

**Usar Claude API (Opción 1)** por las siguientes razones:

1. **Funciona inmediatamente** - No requiere debugging de LoRA
2. **Alta calidad** - Claude entiende código complejo
3. **Costo competitivo** - $0.01/request con markup es razonable
4. **Sin mantenimiento** - Anthropic mantiene el modelo
5. **Escalable** - No requiere GPUs dedicadas
6. **Multilingüe** - Soporte español built-in

### Implementación Propuesta

```python
# /modal/bioql_inference_claude.py

import modal
import os

image = modal.Image.debian_slim(python_version="3.11").pip_install("anthropic")
app = modal.App(name="bioql-inference-claude", image=image)

billing_volume = modal.Volume.from_name("bioql-billing-db")

@app.function(
    volumes={"/billing": billing_volume},
    secrets=[modal.Secret.from_name("anthropic-api-key")]
)
@modal.web_endpoint(method="POST")
def generate_code(request: dict) -> dict:
    import anthropic
    import sys
    sys.path.insert(0, "/billing")
    from billing_integration import authenticate_api_key, log_inference_usage

    # Authenticate
    api_key = request.get("api_key")
    auth = authenticate_api_key(api_key)
    if "error" in auth:
        return {"error": auth["error"]}

    # Generate with Claude
    client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

    prompt = request.get("prompt")

    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1000,
        system="""You are a BioQL code generator. Generate ONLY valid Python code using BioQL syntax.

BioQL quantum syntax:
from bioql import quantum
result = quantum("natural language description", backend="simulator", shots=1000)

BioQL docking syntax:
from bioql.docking import dock
result = dock("description", ligand_smiles="...", protein_pdb="...", backend="simulator")

CRITICAL RULES:
- NEVER use: quantum("QFT", 4) or quantum("Bell", 2 qubits) - THESE ARE WRONG!
- ALWAYS use natural language descriptions
- ALWAYS include backend and shots parameters
- Return ONLY executable Python code""",
        messages=[{"role": "user", "content": f"Generate BioQL code for: {prompt}"}]
    )

    code = response.content[0].text

    # Calculate cost (Claude pricing + 40% markup)
    input_tokens = response.usage.input_tokens
    output_tokens = response.usage.output_tokens
    base_cost = (input_tokens * 0.003 / 1000) + (output_tokens * 0.015 / 1000)
    user_cost = base_cost * 1.4

    # Log usage
    log_inference_usage(
        user_id=auth["user_id"],
        api_key_id=auth["api_key_id"],
        prompt=prompt,
        code_generated=code,
        time_seconds=0.5,  # Claude is fast
        base_cost=base_cost,
        user_cost=user_cost,
        profit=user_cost - base_cost,
        success=True
    )

    return {
        "code": code,
        "reasoning": "Generated with Claude 3.5 Sonnet",
        "model": "claude-3-5-sonnet-20241022",
        "cost": {
            "base_cost_usd": round(base_cost, 6),
            "user_cost_usd": round(user_cost, 6),
            "profit_usd": round(user_cost - base_cost, 6)
        }
    }
```

### Deployment

```bash
# 1. Crear secret en Modal
modal secret create anthropic-api-key ANTHROPIC_API_KEY=sk-ant-...

# 2. Deploy
modal deploy bioql_inference_claude.py

# 3. Test
curl -X POST https://spectrix--bioql-inference-claude-generate-code.modal.run \
  -H "Content-Type: application/json" \
  -d '{
    "api_key": "bioql_test_870ce7ae",
    "prompt": "Create a Bell state"
  }'
```

---

## 📁 Archivos del Proyecto

### Funcionando
- ✅ `/modal/billing_integration.py` - Sistema de billing
- ✅ `/modal/setup_admin_cli.sh` - CLI de administración
- ✅ `/vscode-extension/bioql-assistant-3.3.1.vsix` - Extensión empaquetada

### Broken (Requiere Fix)
- ❌ `/modal/bioql_inference_deepseek.py` - Modelo no genera
- ❌ `/training/TRAIN_DEEPSEEK.py` - Training completo pero modelo no funciona

### Documentación
- 📄 `/docs/MODELO_V2_ESTADO_FINAL.md` - Estado del modelo v2
- 📄 `/docs/RETRAINING_V2.md` - Detalles del retraining
- 📄 `/docs/VSCODE_EXTENSION_FINAL.md` - Extensión v3.3.1

---

## ⏱️ Time Investment

- Training v1 (CodeLlama): 45 min
- Training v2 (DeepSeek): 55 min
- Debugging inference: 2 horas
- **Total**: ~4 horas

**ROI con Claude API**: Implementación en 15 min, funcionando de inmediato.

---

## 🎯 DECISIÓN FINAL RECOMENDADA

**MIGRAR A CLAUDE API** y abandonar fine-tuning por ahora.

**Razones**:
1. Fine-tuning consume mucho tiempo de debugging
2. Claude genera código de alta calidad sin training
3. Costos similares ($0.01 vs $0.004 por request)
4. Cero mantenimiento
5. Funcionará inmediatamente

**Plan de acción**:
1. Crear `/modal/bioql_inference_claude.py` (15 min)
2. Deploy a Modal (2 min)
3. Actualizar VS Code extension para usar nuevo endpoint (5 min)
4. Test end-to-end (5 min)
5. **DONE** ✅

---

**Estado**: ⚠️ **FINE-TUNED MODEL BROKEN - RECOMENDAR CLAUDE API**
**Tiempo estimado para fix con Claude**: **30 minutos total**
