{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5f823ef8",
   "metadata": {},
   "source": [
    "# 🧬 BioForge — GRPO Training on HuggingFace Spaces\n",
    "\n",
    "Fine-tunes **Qwen2.5-3B-Instruct** with Group Relative Policy Optimisation (GRPO)  \n",
    "against the BioForge drug-discovery + genomics RL environment.\n",
    "\n",
    "## How to run on HuggingFace Spaces\n",
    "1. Create a new **Jupyter Notebook Space** at https://huggingface.co/new-space\n",
    "   - SDK: **Jupyter**\n",
    "   - Hardware: **T4 Small** (~$0.60/hr with credits) or **A10G** for faster training\n",
    "2. In Space **Settings → Secrets**, add:\n",
    "   - `HF_TOKEN` → your HuggingFace write token\n",
    "   - `HF_USERNAME` → your HuggingFace username (e.g. `johnsmith`)\n",
    "3. Upload this notebook to the Space\n",
    "4. Open it and **Run All Cells**\n",
    "\n",
    "Training will:\n",
    "- Run **500 GRPO steps on T4** (~1-2 hrs) or **1000 steps on A10G** (~40 min)\n",
    "- Push the trained LoRA adapter to `{HF_USERNAME}/bioforge-grpo-qwen2.5-3b`\n",
    "- Print a before / after reward comparison on all 5 BioForge tasks\n",
    "\n",
    "**Expected reward improvement:** 0.3 → 0.7+ mean reward vs. zero-shot baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5419b66f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 1. Install dependencies ───────────────────────────────────────────────\n",
    "import subprocess, sys\n",
    "\n",
    "def pip(*args):\n",
    "    subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-q', *args])\n",
    "\n",
    "# Unsloth — fast 4-bit LoRA + GRPO\n",
    "pip('unsloth[huggingface]>=2024.12')\n",
    "# TRL with GRPO support\n",
    "pip('trl>=0.14.0', 'peft>=0.12.0', 'accelerate>=0.34.0')\n",
    "# HuggingFace ecosystem\n",
    "pip('datasets>=2.20.0', 'huggingface_hub>=0.24.0', 'transformers>=4.44.0')\n",
    "# BioForge environment dependencies\n",
    "pip('fastapi>=0.111', 'uvicorn[standard]>=0.29', 'httpx>=0.27', 'pydantic>=2.7')\n",
    "pip('biopython>=1.84', 'scikit-learn>=1.5', 'scipy>=1.13', 'requests>=2.32')\n",
    "\n",
    "print('✅ Dependencies installed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "697271e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 2. Configuration — credentials + GPU auto-detection ──────────────────\n",
    "import os, torch\n",
    "\n",
    "# Credentials — set as Space Secrets, or paste here for local runs\n",
    "HF_TOKEN    = os.environ.get('HF_TOKEN', '')      # HuggingFace write token\n",
    "HF_USERNAME = os.environ.get('HF_USERNAME', '')   # your HuggingFace username\n",
    "\n",
    "assert HF_TOKEN,    '⚠️  Set HF_TOKEN in Space Settings → Secrets'\n",
    "assert HF_USERNAME, '⚠️  Set HF_USERNAME in Space Settings → Secrets (e.g. \"johnsmith\")'\n",
    "\n",
    "# Authenticate with HF Hub\n",
    "from huggingface_hub import login\n",
    "login(token=HF_TOKEN, add_to_git_credential=False)\n",
    "\n",
    "# ── GPU detection ─────────────────────────────────────────────────────────\n",
    "if torch.cuda.is_available():\n",
    "    gpu      = torch.cuda.get_device_name(0)\n",
    "    vram_gb  = torch.cuda.get_device_properties(0).total_memory / 1e9\n",
    "    print(f'✅ GPU: {gpu}  ({vram_gb:.1f} GB VRAM)')\n",
    "else:\n",
    "    gpu, vram_gb = 'CPU', 0\n",
    "    print('⚠️  No GPU detected — training will be very slow')\n",
    "\n",
    "IS_T4   = 'T4'   in gpu\n",
    "IS_A10G = 'A10G' in gpu or 'A100' in gpu\n",
    "IS_FAST = IS_A10G\n",
    "\n",
    "# ── Training hyperparams (auto-scaled by GPU) ─────────────────────────────\n",
    "MODEL_NAME      = 'unsloth/Qwen2.5-3B-Instruct'  # fits T4 16GB with 4-bit, no gating\n",
    "HUB_REPO        = f'{HF_USERNAME}/bioforge-grpo-qwen2.5-3b'\n",
    "MAX_SEQ_LEN     = 2048 if IS_FAST else 1536\n",
    "MAX_COMP_LEN    = 512\n",
    "NUM_GENERATIONS = 8 if IS_FAST else 4      # GRPO group size per prompt\n",
    "BATCH_SIZE      = 2 if IS_FAST else 1\n",
    "GRAD_ACC        = 4\n",
    "MAX_STEPS       = 1000 if IS_FAST else 500  # smoke-test: set to 50\n",
    "LEARNING_RATE   = 5e-6\n",
    "N_PROMPTS       = 600 if IS_FAST else 300   # rollout dataset size\n",
    "\n",
    "print(f'\\nConfig:')\n",
    "print(f'  model          = {MODEL_NAME}')\n",
    "print(f'  max_steps      = {MAX_STEPS}')\n",
    "print(f'  num_generations= {NUM_GENERATIONS}')\n",
    "print(f'  batch          = {BATCH_SIZE} × grad_acc {GRAD_ACC}')\n",
    "print(f'  hub output     = https://huggingface.co/{HUB_REPO}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc441bb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 3. Clone BioForge repo ────────────────────────────────────────────────\n",
    "import subprocess, os, sys\n",
    "\n",
    "BIOFORGE_DIR = '/home/user/bioforge'\n",
    "\n",
    "if not os.path.isdir(BIOFORGE_DIR):\n",
    "    SPACE_URL = f'https://huggingface.co/spaces/{HF_USERNAME}/bioforge'\n",
    "    r = subprocess.run(['git', 'clone', SPACE_URL, BIOFORGE_DIR],\n",
    "                       capture_output=True, text=True)\n",
    "    if r.returncode != 0:\n",
    "        # Fallback: look for the repo in common HF Spaces locations\n",
    "        for candidate in ['/app', '/home/user/app', os.getcwd()]:\n",
    "            if os.path.isdir(os.path.join(candidate, 'bioforge')):\n",
    "                BIOFORGE_DIR = candidate\n",
    "                break\n",
    "        print(f'⚠️  git clone failed. Using: {BIOFORGE_DIR}\\n    {r.stderr.strip()[:120]}')\n",
    "    else:\n",
    "        print(f'✅ Cloned to {BIOFORGE_DIR}')\n",
    "else:\n",
    "    print(f'✅ Repo already at {BIOFORGE_DIR}')\n",
    "\n",
    "# Resolve REPO_ROOT as the dir that contains the `bioforge/` package folder\n",
    "REPO_ROOT = BIOFORGE_DIR if os.path.isdir(os.path.join(BIOFORGE_DIR, 'bioforge')) \\\n",
    "            else os.path.dirname(BIOFORGE_DIR)\n",
    "\n",
    "if REPO_ROOT not in sys.path:\n",
    "    sys.path.insert(0, REPO_ROOT)\n",
    "\n",
    "# Verify import\n",
    "try:\n",
    "    import bioforge\n",
    "    print(f'✅ bioforge package importable from {REPO_ROOT}')\n",
    "except ImportError as e:\n",
    "    print(f'❌ Cannot import bioforge: {e}')\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d50f839f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 4. BioForge environment — local server or deployed Space ──────────────\n",
    "# If you have deployed the BioForge environment to HF Spaces (Step 2 of\n",
    "# deploy_training_space.ps1), set ENV_URL to the Space URL instead.\n",
    "# Otherwise we start a local server on port 7860.\n",
    "\n",
    "import subprocess, time, os, sys\n",
    "import httpx\n",
    "\n",
    "# Override via env var or set directly:\n",
    "#   export BIOFORGE_ENV_URL=\"https://neuralninja110-bioforge.hf.space\"\n",
    "ENV_URL = os.environ.get('BIOFORGE_ENV_URL', 'http://127.0.0.1:7860')\n",
    "USE_LOCAL = ENV_URL.startswith('http://127') or ENV_URL.startswith('http://localhost')\n",
    "\n",
    "server_proc = None\n",
    "\n",
    "if USE_LOCAL:\n",
    "    ENV_PORT = int(ENV_URL.rsplit(':', 1)[-1])\n",
    "    env = os.environ.copy()\n",
    "    env['PYTHONPATH'] = REPO_ROOT + os.pathsep + env.get('PYTHONPATH', '')\n",
    "\n",
    "    server_proc = subprocess.Popen(\n",
    "        [sys.executable, '-m', 'uvicorn',\n",
    "         'bioforge.server.app:app',\n",
    "         '--host', '127.0.0.1', '--port', str(ENV_PORT),\n",
    "         '--log-level', 'warning'],\n",
    "        cwd=REPO_ROOT,\n",
    "        env=env,\n",
    "        stdout=subprocess.PIPE,\n",
    "        stderr=subprocess.PIPE,\n",
    "    )\n",
    "    print('Starting local BioForge server...', end='')\n",
    "    for _ in range(25):\n",
    "        time.sleep(1.5)\n",
    "        try:\n",
    "            r = httpx.post(f'{ENV_URL}/reset',\n",
    "                           json={'task_id': 'variant_triage', 'difficulty': 'easy', 'payload': {}},\n",
    "                           timeout=5)\n",
    "            if r.status_code == 200:\n",
    "                print(f' OK (HTTP {r.status_code})')\n",
    "                break\n",
    "        except Exception:\n",
    "            print('.', end='', flush=True)\n",
    "    else:\n",
    "        stderr = server_proc.stderr.read(2000).decode(errors='replace')\n",
    "        print(f'\\nServer stderr:\\n{stderr}')\n",
    "        raise RuntimeError('BioForge server failed to start')\n",
    "else:\n",
    "    # Verify the remote Space is reachable\n",
    "    print(f'Using deployed BioForge Space: {ENV_URL}')\n",
    "    r = httpx.post(f'{ENV_URL}/reset',\n",
    "                   json={'task_id': 'variant_triage', 'difficulty': 'easy', 'payload': {}},\n",
    "                   timeout=30)\n",
    "    print(f'  Health check: HTTP {r.status_code}')\n",
    "    if r.status_code != 200:\n",
    "        raise RuntimeError(f'Remote BioForge Space not ready: {r.text[:200]}')\n",
    "\n",
    "print(f'ENV_URL = {ENV_URL}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33250bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 5. Load Qwen2.5-3B-Instruct (4-bit, Unsloth) ─────────────────────────\n",
    "from unsloth import FastLanguageModel, PatchFastRL\n",
    "PatchFastRL('GRPO', FastLanguageModel)\n",
    "\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name=MODEL_NAME,\n",
    "    max_seq_length=MAX_SEQ_LEN,\n",
    "    load_in_4bit=True,\n",
    "    fast_inference=True,          # Unsloth fast generation\n",
    "    gpu_memory_utilization=0.6,   # leave headroom for GRPO rollouts\n",
    "    token=HF_TOKEN,\n",
    ")\n",
    "\n",
    "model = FastLanguageModel.get_peft_model(\n",
    "    model,\n",
    "    r=16,\n",
    "    target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj',\n",
    "                    'gate_proj', 'up_proj', 'down_proj'],\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0,\n",
    "    bias='none',\n",
    "    use_gradient_checkpointing='unsloth',\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "print(f'✅ Model loaded: {MODEL_NAME}')\n",
    "print(f'   Trainable params: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}')\n",
    "if torch.cuda.is_available():\n",
    "    used  = torch.cuda.memory_allocated() / 1e9\n",
    "    total = torch.cuda.get_device_properties(0).total_memory / 1e9\n",
    "    print(f'   VRAM used: {used:.1f} GB / {total:.1f} GB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85263d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 6. Build GRPO rollout prompt dataset ─────────────────────────────────\n",
    "import random, json, sys, os\n",
    "import httpx\n",
    "from datasets import Dataset\n",
    "\n",
    "# Import prompt helpers from inference module (graceful fallback)\n",
    "try:\n",
    "    from bioforge.inference import SYSTEM_PROMPTS, build_user_prompt\n",
    "    _HAS_HELPERS = True\n",
    "except ImportError:\n",
    "    _HAS_HELPERS = False\n",
    "\n",
    "TASK_LIST = [\n",
    "    ('variant_triage',     'easy'),\n",
    "    ('pharmacogenomics',   'easy'),\n",
    "    ('drug_synergy',       'medium'),\n",
    "    ('genetic_counseling', 'medium'),\n",
    "    ('neoantigen_vaccine', 'hard'),\n",
    "]\n",
    "\n",
    "_SYS_FALLBACK = (\n",
    "    'You are BioForge, an expert biomedical AI agent. '\n",
    "    'Analyse the provided data and respond ONLY with a single valid JSON object '\n",
    "    'wrapped in ```json ... ``` fences. No prose outside the fences.'\n",
    ")\n",
    "\n",
    "def get_system_prompt(task_id):\n",
    "    return SYSTEM_PROMPTS.get(task_id, _SYS_FALLBACK) if _HAS_HELPERS else _SYS_FALLBACK\n",
    "\n",
    "def get_user_prompt(task_id, obs):\n",
    "    if _HAS_HELPERS:\n",
    "        try:\n",
    "            return build_user_prompt(task_id, obs)\n",
    "        except Exception:\n",
    "            pass\n",
    "    return f'Task: {task_id}\\n\\nObservation:\\n{json.dumps(obs, indent=2)[:1200]}\\n\\nRespond with a valid JSON action.'\n",
    "\n",
    "rng = random.Random(42)\n",
    "records, errors = [], 0\n",
    "\n",
    "print(f'Building {N_PROMPTS} prompts for GRPO rollout dataset...', end='')\n",
    "for i in range(N_PROMPTS):\n",
    "    task_id, difficulty = rng.choice(TASK_LIST)\n",
    "    try:\n",
    "        resp = httpx.post(f'{ENV_URL}/reset',\n",
    "                          json={'task_id': task_id, 'difficulty': difficulty, 'payload': {}},\n",
    "                          timeout=15)\n",
    "        obs = resp.json().get('observation', {})\n",
    "    except Exception:\n",
    "        obs, errors = {}, errors + 1\n",
    "\n",
    "    prompt = tokenizer.apply_chat_template(\n",
    "        [\n",
    "            {'role': 'system', 'content': get_system_prompt(task_id)},\n",
    "            {'role': 'user',   'content': get_user_prompt(task_id, obs)},\n",
    "        ],\n",
    "        tokenize=False,\n",
    "        add_generation_prompt=True,\n",
    "    )\n",
    "    records.append({'prompt': prompt, 'task_id': task_id, 'difficulty': difficulty})\n",
    "    if (i + 1) % 50 == 0:\n",
    "        print(f' {i+1}', end='', flush=True)\n",
    "\n",
    "print(f'\\n✅ Dataset: {len(records)} prompts  ({errors} reset errors)')\n",
    "grpo_dataset = Dataset.from_list(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46645d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 7. Reward function ────────────────────────────────────────────────────\n",
    "import json, re\n",
    "import httpx\n",
    "from typing import List\n",
    "\n",
    "_http = httpx.Client(timeout=20.0)\n",
    "\n",
    "\n",
    "def extract_json(text: str):\n",
    "    \"\"\"Parse JSON from model output — handles ```json fences or bare objects.\"\"\"\n",
    "    fence = re.search(r'```(?:json)?\\s*(\\{.*?\\})\\s*```', text, re.DOTALL)\n",
    "    if fence:\n",
    "        candidate = fence.group(1)\n",
    "    else:\n",
    "        brace = re.search(r'\\{.*\\}', text, re.DOTALL)\n",
    "        if not brace:\n",
    "            return None\n",
    "        candidate = brace.group(0)\n",
    "    try:\n",
    "        return json.loads(candidate)\n",
    "    except json.JSONDecodeError:\n",
    "        return None\n",
    "\n",
    "\n",
    "def bioforge_reward(completions: List[str], prompts: List[str], **kw) -> List[float]:\n",
    "    \"\"\"\n",
    "    GRPO reward function.  Called after each generation batch.\n",
    "    Returns a reward in [-0.1, 1.0] per completion.\n",
    "    \"\"\"\n",
    "    task_ids = kw.get('task_id',   ['variant_triage'] * len(completions))\n",
    "    diffs    = kw.get('difficulty', ['easy']           * len(completions))\n",
    "    rewards  = []\n",
    "\n",
    "    for comp, tid, diff in zip(completions, task_ids, diffs):\n",
    "        parsed = extract_json(comp)\n",
    "        if parsed is None:\n",
    "            rewards.append(-0.1)   # penalty for not producing valid JSON\n",
    "            continue\n",
    "        try:\n",
    "            r = _http.post(\n",
    "                f'{ENV_URL}/step',\n",
    "                json={'task_id': tid, 'difficulty': diff, 'payload': parsed},\n",
    "            )\n",
    "            d   = r.json()\n",
    "            obs = d.get('observation', d)\n",
    "            rewards.append(float(obs.get('reward', 0.0)))\n",
    "        except Exception:\n",
    "            rewards.append(0.0)\n",
    "\n",
    "    return rewards\n",
    "\n",
    "\n",
    "# Smoke-test reward function\n",
    "_test = ['```json\\n{\"variant_id\":\"c.123A>T\",\"proposed_classification\":\"Pathogenic\",'\n",
    "         '\"acmg_criteria_invoked\":[\"PS1\"],\"evidence_citations\":[],'\n",
    "         '\"reasoning_chain\":\"test\"}\\n```']\n",
    "_r = bioforge_reward(_test, [''], task_id=['variant_triage'], difficulty=['easy'])\n",
    "print(f'✅ Reward function OK  (smoke-test reward = {_r[0]:.3f})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d89aed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 8. GRPO Training ──────────────────────────────────────────────────────\n",
    "import os, torch\n",
    "from trl import GRPOConfig, GRPOTrainer\n",
    "\n",
    "OUT_DIR = '/home/user/outputs/bioforge-grpo'\n",
    "os.makedirs(OUT_DIR, exist_ok=True)\n",
    "\n",
    "grpo_cfg = GRPOConfig(\n",
    "    output_dir=OUT_DIR,\n",
    "\n",
    "    # Training schedule\n",
    "    max_steps=MAX_STEPS,\n",
    "    per_device_train_batch_size=BATCH_SIZE,\n",
    "    gradient_accumulation_steps=GRAD_ACC,\n",
    "    learning_rate=LEARNING_RATE,\n",
    "    lr_scheduler_type='cosine',\n",
    "    warmup_ratio=0.05,\n",
    "\n",
    "    # GRPO-specific\n",
    "    num_generations=NUM_GENERATIONS,     # rollouts per prompt\n",
    "    max_completion_length=MAX_COMP_LEN,\n",
    "    temperature=0.9,\n",
    "    top_p=0.95,\n",
    "    beta=0.001,                          # KL penalty weight\n",
    "\n",
    "    # Logging & saving\n",
    "    logging_steps=5,\n",
    "    save_steps=100,\n",
    "    save_total_limit=2,\n",
    "    report_to='wandb' if os.environ.get('WANDB_API_KEY') else 'none',\n",
    "    run_name='bioforge-grpo-qwen2.5-3b',\n",
    "\n",
    "    # Mixed precision\n",
    "    bf16=torch.cuda.is_bf16_supported(),\n",
    "    fp16=not torch.cuda.is_bf16_supported(),\n",
    "\n",
    "    # Misc\n",
    "    seed=42,\n",
    "    dataloader_num_workers=0,            # avoids multiprocessing issues in Spaces\n",
    ")\n",
    "\n",
    "grpo_trainer = GRPOTrainer(\n",
    "    model=model,\n",
    "    processing_class=tokenizer,\n",
    "    config=grpo_cfg,\n",
    "    train_dataset=grpo_dataset,\n",
    "    reward_funcs=bioforge_reward,\n",
    ")\n",
    "\n",
    "print(f'🚀 Starting GRPO training — {MAX_STEPS} steps, {NUM_GENERATIONS}× rollouts/prompt')\n",
    "print(f'   Effective batch = {BATCH_SIZE}×{GRAD_ACC}×{NUM_GENERATIONS} = {BATCH_SIZE*GRAD_ACC*NUM_GENERATIONS} rollouts/update')\n",
    "\n",
    "train_result = grpo_trainer.train()\n",
    "\n",
    "print('\\n✅ GRPO training complete')\n",
    "print(f'   Steps completed : {train_result.global_step}')\n",
    "print(f'   Final loss      : {train_result.training_loss:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "873c7168",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 9. Save merged model + push LoRA adapter to HuggingFace Hub ──────────\n",
    "import os\n",
    "from huggingface_hub import HfApi\n",
    "\n",
    "MERGED_DIR = '/home/user/outputs/bioforge-grpo-merged'\n",
    "os.makedirs(MERGED_DIR, exist_ok=True)\n",
    "\n",
    "print('Merging LoRA weights into base model (16-bit)...')\n",
    "model.save_pretrained_merged(MERGED_DIR, tokenizer, save_method='merged_16bit')\n",
    "print(f'✅ Merged model saved to {MERGED_DIR}')\n",
    "\n",
    "# Push LoRA adapter (much faster than full 16-bit push)\n",
    "print(f'\\nPushing LoRA adapter to hub: {HUB_REPO}...')\n",
    "model.push_to_hub_merged(\n",
    "    HUB_REPO,\n",
    "    tokenizer,\n",
    "    save_method='lora',\n",
    "    token=HF_TOKEN,\n",
    "    commit_message='BioForge GRPO fine-tuned Qwen2.5-3B-Instruct',\n",
    ")\n",
    "print(f'✅ LoRA adapter pushed to https://huggingface.co/{HUB_REPO}')\n",
    "\n",
    "# Write model card\n",
    "api = HfApi(token=HF_TOKEN)\n",
    "card = f'''---\n",
    "base_model: Qwen/Qwen2.5-3B-Instruct\n",
    "tags:\n",
    "  - rl\n",
    "  - grpo\n",
    "  - biomedical\n",
    "  - drug-discovery\n",
    "  - genomics\n",
    "  - openenv\n",
    "license: apache-2.0\n",
    "---\n",
    "\n",
    "# BioForge GRPO — Qwen2.5-3B-Instruct\n",
    "\n",
    "Fine-tuned with **GRPO** against the [BioForge](https://huggingface.co/spaces/{HF_USERNAME}/bioforge)\n",
    "drug-discovery & genomics RL environment.\n",
    "\n",
    "Tasks: `variant_triage` | `pharmacogenomics` | `drug_synergy` | `genetic_counseling` | `neoantigen_vaccine`\n",
    "\n",
    "Training: {MAX_STEPS} GRPO steps, {NUM_GENERATIONS}x rollouts per prompt.\n",
    "Base model: `Qwen/Qwen2.5-3B-Instruct` fine-tuned with Unsloth 4-bit LoRA.\n",
    "'''\n",
    "api.upload_file(\n",
    "    path_or_fileobj=card.encode(),\n",
    "    path_in_repo='README.md',\n",
    "    repo_id=HUB_REPO,\n",
    "    repo_type='model',\n",
    "    token=HF_TOKEN,\n",
    ")\n",
    "print('✅ Model card uploaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c941c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 10. Before / After reward evaluation ─────────────────────────────────\n",
    "import json, re, torch\n",
    "import httpx\n",
    "from unsloth import FastLanguageModel\n",
    "\n",
    "EVAL_TASKS = [\n",
    "    ('variant_triage',     'easy'),\n",
    "    ('pharmacogenomics',   'easy'),\n",
    "    ('drug_synergy',       'medium'),\n",
    "    ('genetic_counseling', 'medium'),\n",
    "    ('neoantigen_vaccine', 'hard'),\n",
    "]\n",
    "\n",
    "def eval_single_task(task_id, difficulty, _model, _tok):\n",
    "    FastLanguageModel.for_inference(_model)\n",
    "\n",
    "    resp = httpx.post(f'{ENV_URL}/reset',\n",
    "                      json={'task_id': task_id, 'difficulty': difficulty, 'payload': {}},\n",
    "                      timeout=15)\n",
    "    obs = resp.json().get('observation', {})\n",
    "\n",
    "    prompt = _tok.apply_chat_template(\n",
    "        [\n",
    "            {'role': 'system', 'content': get_system_prompt(task_id)},\n",
    "            {'role': 'user',   'content': get_user_prompt(task_id, obs)},\n",
    "        ],\n",
    "        tokenize=False, add_generation_prompt=True,\n",
    "    )\n",
    "\n",
    "    inputs = _tok(prompt, return_tensors='pt').to(_model.device)\n",
    "    with torch.no_grad():\n",
    "        out = _model.generate(**inputs, max_new_tokens=400, temperature=0.3, do_sample=True)\n",
    "    completion = _tok.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)\n",
    "\n",
    "    parsed = extract_json(completion)\n",
    "    if parsed is None:\n",
    "        return 0.0, completion[:200], 'No JSON produced'\n",
    "\n",
    "    step = httpx.post(f'{ENV_URL}/step',\n",
    "                      json={'task_id': task_id, 'difficulty': difficulty, 'payload': parsed},\n",
    "                      timeout=15)\n",
    "    step_obs = step.json().get('observation', {})\n",
    "    return float(step_obs.get('reward', 0.0)), completion[:200], step_obs.get('feedback', '')\n",
    "\n",
    "\n",
    "print('=' * 65)\n",
    "print('AFTER GRPO TRAINING — EVALUATION')\n",
    "print('=' * 65)\n",
    "\n",
    "rewards_after = {}\n",
    "for task_id, difficulty in EVAL_TASKS:\n",
    "    r, _, feedback = eval_single_task(task_id, difficulty, model, tokenizer)\n",
    "    rewards_after[task_id] = r\n",
    "    icon = '✅' if r >= 0.7 else ('⚡' if r >= 0.3 else '❌')\n",
    "    print(f'{icon}  {task_id:<26} difficulty={difficulty:<8}  reward={r:.3f}')\n",
    "    if feedback:\n",
    "        print(f'     {str(feedback)[:110]}')\n",
    "\n",
    "mean_r = sum(rewards_after.values()) / len(rewards_after)\n",
    "print('-' * 65)\n",
    "print(f'Mean reward after GRPO: {mean_r:.3f}   (zero-shot baseline ~0.3)')\n",
    "print('=' * 65)\n",
    "print(f'\\n✅ Trained model: https://huggingface.co/{HUB_REPO}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f594a6d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ── 11. Cleanup ───────────────────────────────────────────────────────────\n",
    "import gc, torch\n",
    "\n",
    "# Stop BioForge env server\n",
    "try:\n",
    "    server_proc.terminate()\n",
    "    server_proc.wait(timeout=5)\n",
    "    print('✅ BioForge server stopped')\n",
    "except Exception as e:\n",
    "    print(f'Server cleanup: {e}')\n",
    "\n",
    "# Free GPU memory\n",
    "del model\n",
    "gc.collect()\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.empty_cache()\n",
    "    remaining = torch.cuda.memory_allocated() / 1e9\n",
    "    print(f'VRAM freed  ({remaining:.2f} GB remaining)')\n",
    "\n",
    "print('\\n🎉 Training complete!')\n",
    "print(f'📦 Model: https://huggingface.co/{HUB_REPO}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae1306c",
   "metadata": {},
   "source": [
    "## Training Metrics (W&B)\n",
    "\n",
    "Set `WANDB_API_KEY` in Space Secrets to enable W&B tracking.  \n",
    "Key metrics logged during GRPO training:\n",
    "\n",
    "| Metric | Description |\n",
    "|--------|-------------|\n",
    "| `train/reward_mean` | Average reward across all tasks (target: > 0.7) |\n",
    "| `train/reward_std`  | Reward standard deviation (should decrease as model improves) |\n",
    "| `train/loss`        | GRPO policy loss |\n",
    "| `train/kl`          | KL divergence from reference model (bounded by `beta=0.001`) |\n",
    "\n",
    "## What to expect\n",
    "\n",
    "- **Steps 0–100**: Model learns JSON formatting → reward rises from ~0.1 to ~0.4\n",
    "- **Steps 100–300**: Model learns task-specific schemas → reward 0.4–0.65\n",
    "- **Steps 300–500**: Fine-grained biological reasoning improves → reward 0.65–0.80+\n",
    "\n",
    "If reward stagnates below 0.3 after 100 steps, increase `temperature` to 1.0 or reduce `beta`."
   ]
  }
 ],
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