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mario/current_runs.md

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+GPU 0: run_mario_path (Qwen3)
+GPU 1: run_mario_path (Gemma)
+GPU 2: run_mariogpt (Qwen 3)
+GPU 3: run_mariogpt (Qwen2, Llama)
+GPU 4: run_mario_path (Qwen2, Llama)
+GPU 5: run_mariogpt (Gemma)
+GPU 6: run_mario_num (Qwen 3)
+GPU 7: run_mario_num (Qwen2, Llama)
+
+
+nohup ./run_mario_mariogpt_qwen3.sh > run_mariogpt_qwen3.log 2>&1
+
+GPU 0: nohup ./run_mario_path_qwen3.sh > run_mario_path_qwen3.log 2>&1
+GPU 1: nohup ./run_mario_path_gemma.sh > run_mario_path_gemma.log 2>&1
+GPU 2: nohup ./run_mario_mariogpt_qwen3.sh > run_mariogpt_qwen3.log 2>&1
+GPU 3: nohup ./run_mario_mariogpt.sh > run_mariogptqwen2llama.log 2>&1
+GPU 4: nohup ./run_mario_path.sh > run_mario_path.log 2>&1
+GPU 5: nohup ./run_mario_mariogpt_gemma.sh > run_mariogpt_gemma.log 2>&1
+
+
+nohup ./run_mario_num_qwen3.sh > run_mario_num_qwen3.log 2>&1 &
+
+
+nohup ./run_mario_num_gemma.sh > run_mario_num_gemma.log 2>&1 &

mario/json2jsonl.py

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+import json
+IN_PATH = "tokenizer_training_data_simple.json"
+OUT_PATH = "tokenizer_training_data_simple.jsonl"
+with open(IN_PATH, "r", encoding="utf-8") as f_in, open(OUT_PATH, "w", encoding="utf-8") as f_out:
+    data = json.load(f_in)
+    if not isinstance(data, list): raise SystemExit("input must be a JSON list")
+    for i, obj in enumerate(data):
+        if not isinstance(obj, dict): continue
+        obj.setdefault("idx", i)
+        f_out.write(json.dumps(obj, ensure_ascii=False) + "\n")

mario/mario_gen.py

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+# magpie_rewriter_async.py
+import os
+import json
+import math
+import asyncio
+from typing import Dict, Any, List, Tuple
+from itertools import count
+from tqdm import tqdm
+from openai import AsyncOpenAI, BadRequestError
+
+# ---------------------------------------------------------------------
+# vLLM/OpenAI-compatible config
+# ---------------------------------------------------------------------
+VLLM_BASE_URL = os.environ.get("VLLM_BASE_URL", "http://localhost:8020/v1")
+VLLM_API_KEY  = os.environ.get("VLLM_API_KEY", "no-key-needed")
+
+# Decoding
+TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.7"))
+TOP_P       = float(os.environ.get("TOP_P", "1.0"))
+MAX_TOKENS  = int(os.environ.get("MAX_TOKENS", "2048"))
+
+# I/O and execution control
+INPUT_JSON  = os.environ.get("INPUT_JSON", "tokenizer_training_data_simple.jsonl")     # list[dict]
+INPUT_FIELD = os.environ.get("INPUT_FIELD", "prompt")             # key to extract per item
+OUTPUT_FILE = os.environ.get("OUTPUT_FILE", "tokenizer_training_data_simple_rewrite.jsonl")
+
+NUM_ROWS  = int(os.environ.get("NUM_ROWS", "1000000000"))
+BATCH_SIZE = int(os.environ.get("BATCH_SIZE", "64"))
+MAX_ASYNC  = int(os.environ.get("MAX_ASYNC", "64"))
+
+# Prompting
+SYSTEM_PROMPT = os.environ.get("SYSTEM_PROMPT", "You are a text rewritter")
+GENERAL_PREFIX = """
+You will rewrite prompts to generate Super Mario Bros levels. They are structured by asking to generate a level with certain properties, all with an adjective for the quantity, and then the object/item/enemy/powerup itself.
+There are general rules to follow: 
+1: Don't add any temporal references, such as adding enemies to a certain height or section of a level.
+2: Unless asked in the style prompt, do not change the names of the objects/enemies/powerups being used.
+3: Don't add or remove ANY items, all of the ones in the original prompt must appear.
+4: Only return the new prompt, nothing else.
+5: Don't change the adjectives. The prompts use the following adjectives: "no", "a few", "some", "many". The only ones that don't count to this rule are low/high elevation and overworld/underground/castle levels. You are allowed to modify adjectives to other, using the following list:
+
+**no**:
+- zero
+- none
+- without
+- absent
+- lacking
+
+**a few**:
+- a couple of
+- a small number of
+- a slight amount of
+- a low amount
+- a minimal number of
+
+**some**:
+- a fair amount of
+- a noticeable number of
+- moderate amount
+- a balanced number of
+- a modest amount of
+
+**many**:
+- numerous
+- a lot of
+- plenty of
+- a large number of
+- abundant
+
+"number" and "amount" are interchangable as long as it makes sense 
+
+"""
+
+DEFAULT_USER_PROMPT_TEXTS: Dict[str, str] = {
+    "slight":   "Make slight changes to the prompt, change more the way the level info is being conveyed then the actual contents of the prompt itself",
+    "verbose":   "Make the prompt much more verbose without adding or removing classes, act like a prompt engineer, adding descriptions to the objects being used in a long winded manner.",
+    "simplify": "Rewrite the prompt as a user who doesnt understand Mario that much and may mix up terms. For example, people who would call a koopa a turtle. Make sure to not blur the lines between classes. For example, don't call goombas just 'mushrooms' as the powerups are also mushrooms/fire flowers. Therefore, have the prompt in layman terms but dont blur the lines between elements.",
+    "none":     "Do not change the text. Output it exactly as given.",
+}
+
+DEFAULT_USER_PROMPT_PROBS: Dict[str, float] = {
+    "slight": 0.60,
+    "formal": 0.15,
+    "simplify": 0.25,
+    "none": 0.00,
+}
+
+def _load_user_prompts() -> Tuple[Dict[str, str], Dict[str, float]]:
+    texts_raw = os.environ.get("USER_PROMPT_TEXTS_JSON", "")
+    probs_raw = os.environ.get("USER_PROMPTS_JSON", "")
+    texts = DEFAULT_USER_PROMPT_TEXTS
+    probs = DEFAULT_USER_PROMPT_PROBS
+    try:
+        if texts_raw:
+            texts = json.loads(texts_raw)
+    except Exception:
+        pass
+    try:
+        if probs_raw:
+            probs = json.loads(probs_raw)
+    except Exception:
+        pass
+    keys = list(texts.keys())
+    weights = [probs.get(k, 0.0) for k in keys]
+    s = sum(weights)
+    weights = [1.0/len(keys)]*len(keys) if s <= 0 else [w/s for w in weights]
+    return texts, dict(zip(keys, weights))
+
+def _sample_user_prompt_key(probs: Dict[str, float]) -> str:
+    import random
+    keys = list(probs.keys())
+    weights = [probs[k] for k in keys]
+    return random.choices(keys, weights=weights, k=1)[0]
+
+# Client
+client = AsyncOpenAI(base_url=VLLM_BASE_URL, api_key=VLLM_API_KEY)
+
+async def get_model_id() -> str:
+    models = await client.models.list()
+    if not models.data:
+        raise RuntimeError("No models available on vLLM endpoint.")
+    return models.data[0].id
+
+# Single chat call with enable_thinking=False then two fallbacks
+async def chat_once(model_id: str, user_prompt_text: str, context_text: str) -> str:
+    system_msg = {"role": "system", "content": SYSTEM_PROMPT}
+    # General prefix appears before the specific user prompt
+    user_content = (
+        f"{GENERAL_PREFIX}\n\nPrompt rewrite type: {user_prompt_text}\n\nPROMPT TO REWRITE:\n{context_text}"
+    )
+    user_msg = {"role": "user", "content": user_content}
+
+    # 1) preferred: explicitly disable "thinking"
+    try:
+        resp = await client.chat.completions.create(
+            model=model_id,
+            messages=[system_msg, user_msg],
+            temperature=TEMPERATURE,
+            top_p=TOP_P,
+            max_tokens=MAX_TOKENS,
+            extra_body={"chat_template_kwargs": {"enable_thinking": False}},
+        )
+        return (resp.choices[0].message.content or "").strip()
+    except BadRequestError:
+        pass
+
+    # 2) fallback: enable_thinking=True
+    try:
+        resp = await client.chat.completions.create(
+            model=model_id,
+            messages=[system_msg, user_msg],
+            temperature=TEMPERATURE,
+            top_p=TOP_P,
+            max_tokens=MAX_TOKENS,
+            extra_body={"chat_template_kwargs": {"enable_thinking": True}},
+        )
+        return (resp.choices[0].message.content or "").strip()
+    except BadRequestError:
+        pass
+
+    # 3) fallback: remove chat_template_kwargs
+    resp = await client.chat.completions.create(
+        model=model_id,
+        messages=[system_msg, user_msg],
+        temperature=TEMPERATURE,
+        top_p=TOP_P,
+        max_tokens=MAX_TOKENS,
+    )
+    return (resp.choices[0].message.content or "").strip()
+
+# Resume
+def _read_resume_state(path: str) -> Tuple[int, int]:
+    if not os.path.exists(path) or os.path.getsize(path) == 0:
+        return 0, -1
+    next_seq_id = 0
+    last_ctx_id = -1
+    with open(path, "r", encoding="utf-8") as fin:
+        for line in fin:
+            try:
+                obj = json.loads(line)
+            except Exception:
+                continue
+            if "seq_id" in obj:
+                next_seq_id = max(next_seq_id, int(obj["seq_id"]) + 1)
+            if "context_id" in obj:
+                last_ctx_id = max(last_ctx_id, int(obj["context_id"]))
+    return next_seq_id, last_ctx_id
+
+# Input loading
+def load_contexts() -> List[Any]:
+    with open(INPUT_JSON, "r", encoding="utf-8") as f:
+        pos = f.tell()
+        first = f.read(1)
+        f.seek(pos)
+        if first == "[":  # JSON array
+            data = json.load(f)
+            if not isinstance(data, list):
+                raise ValueError("INPUT_JSON must be a JSON list or JSONL.")
+            return data
+        # JSONL fallback
+        items = []
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            obj = json.loads(line)
+            if isinstance(obj, dict):
+                items.append(obj)
+        return items
+
+def extract_context(item: Any) -> str:
+    if not isinstance(item, dict):
+        return ""
+    v = item.get(INPUT_FIELD, "")
+    return v if isinstance(v, str) else ""
+
+# Runner
+async def run():
+    os.makedirs(os.path.dirname(OUTPUT_FILE) or ".", exist_ok=True)
+    texts_map, probs_map = _load_user_prompts()
+    model_id = await get_model_id()
+
+    data = load_contexts()
+    contexts: List[Tuple[int, Dict[str, Any]]] = []
+    for i, item in enumerate(data):
+        ctx = extract_context(item)
+        if isinstance(ctx, str) and ctx.strip():
+            contexts.append((i, item))
+
+    next_seq_id, last_ctx_id = _read_resume_state(OUTPUT_FILE)
+    start_index = last_ctx_id + 1
+    contexts = [t for t in contexts if t[0] >= start_index]
+
+    total = min(NUM_ROWS, len(contexts))
+    batches = math.ceil(total / BATCH_SIZE) if BATCH_SIZE > 0 else 0
+
+    seq_id_counter = count(next_seq_id)
+    sem = asyncio.Semaphore(MAX_ASYNC)
+    write_lock = asyncio.Lock()
+
+    async def worker(ctx_id: int, item: Dict[str, Any]):
+        ctx_text = extract_context(item).strip()
+        styles = ["slight", "verbose", "simplify"]
+        outputs: Dict[str, str] = {}
+        for key in styles:
+            user_prompt_text = DEFAULT_USER_PROMPT_TEXTS[key]
+            async with sem:
+                outputs[key] = await chat_once(model_id, user_prompt_text, ctx_text)
+
+        rec = {
+            "seq_id": next(seq_id_counter),
+            "context_id": ctx_id,
+            "idx": item.get("idx"),
+            "prompt": ctx_text,
+            "slight": outputs["slight"],
+            "verbose": outputs["verbose"],
+            "simplify": outputs["simplify"],
+        }
+        async with write_lock:
+            with open(OUTPUT_FILE, "a", encoding="utf-8") as fout:
+                fout.write(json.dumps(rec, ensure_ascii=False) + "\n")
+
+
+    with open(OUTPUT_FILE, "a", encoding="utf-8"):
+        pass  # ensure file exists
+
+    pbar = tqdm(total=batches, desc="Rewriting contexts")
+    idx = 0
+    while idx < total:
+        batch = contexts[idx: idx + BATCH_SIZE]
+        await asyncio.gather(*[worker(cid, itm) for cid, itm in batch])
+        idx += len(batch)
+        pbar.update(1)
+    pbar.close()
+
+if __name__ == "__main__":
+    asyncio.run(run())

mario/mario_trim.py

@@ -0,0 +1,173 @@
+# magpie_rewriter_apply_removals.py
+import os, json, math, asyncio
+from typing import Any, Dict, List, Tuple
+from itertools import count
+from tqdm import tqdm
+from openai import AsyncOpenAI, BadRequestError
+
+# ---------------------------- Config ----------------------------
+VLLM_BASE_URL = os.environ.get("VLLM_BASE_URL", "http://localhost:8020/v1")
+VLLM_API_KEY  = os.environ.get("VLLM_API_KEY", "no-key-needed")
+
+TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.2"))
+TOP_P       = float(os.environ.get("TOP_P", "1.0"))
+MAX_TOKENS  = int(os.environ.get("MAX_TOKENS", "512"))
+
+INPUT_JSON  = os.environ.get("INPUT_JSON", "tokenizer_training_data_simple_rewrite_trim.jsonl")
+OUTPUT_FILE = os.environ.get("OUTPUT_FILE", "tokenizer_training_data_simple_rewrite_trim_all.jsonl")
+
+NUM_ROWS   = int(os.environ.get("NUM_ROWS", "1000000000"))
+BATCH_SIZE = int(os.environ.get("BATCH_SIZE", "64"))
+MAX_ASYNC  = int(os.environ.get("MAX_ASYNC", "64"))
+
+EDIT_SYSTEM_PROMPT = os.environ.get("EDIT_SYSTEM_PROMPT", "You are a precise text editor for Super Mario level prompts.")
+
+RULES_BLOCK = """
+Rules:
+1) Keep the prompt style consistent with the given rewritten variant.
+2) Directly remove the listed elements. Only add minimal connective text if needed for grammar.
+3) Do not introduce or remove any other elements beyond the specified removals.
+4) Preserve existing adjectives and allowed synonyms policy. Do not add temporal placement.
+5) Output only the updated rewritten prompt. No explanations.
+"""
+
+# ---------------------------- Client ----------------------------
+client = AsyncOpenAI(base_url=VLLM_BASE_URL, api_key=VLLM_API_KEY)
+
+async def get_model_id() -> str:
+    models = await client.models.list()
+    if not models.data:
+        raise RuntimeError("No models available on vLLM endpoint.")
+    return models.data[0].id
+
+def build_edit_user_content(
+    style_name: str,
+    rewritten_prompt: str,
+    original_prompt: str,
+    trimmed_prompt: str,
+    removed_elements: List[str],
+) -> str:
+    removed_joined = ", ".join(removed_elements)
+    return (
+f"""You will adjust an already-rewritten prompt to reflect removals.
+
+Original prompt:
+{original_prompt}
+
+Trimmed prompt:
+{trimmed_prompt}
+
+Removed elements (taken out from the original prompt):
+{removed_joined}
+
+Rewritten prompt to fix ({style_name}):
+{rewritten_prompt}
+
+Task:
+Rewrite the above rewritten prompt so it reflects the removal of the listed elements.
+{RULES_BLOCK}"""
+    )
+
+async def edit_once(model_id: str, style_name: str, rewritten_prompt: str,
+                    original_prompt: str, trimmed_prompt: str, removed_elements: List[str]) -> str:
+    system_msg = {"role": "system", "content": EDIT_SYSTEM_PROMPT}
+    user_msg = {
+        "role": "user",
+        "content": build_edit_user_content(style_name, rewritten_prompt, original_prompt, trimmed_prompt, removed_elements),
+    }
+    # try with enable_thinking False then True then none
+    try:
+        resp = await client.chat.completions.create(
+            model=model_id, messages=[system_msg, user_msg],
+            temperature=TEMPERATURE, top_p=TOP_P, max_tokens=MAX_TOKENS,
+            extra_body={"chat_template_kwargs": {"enable_thinking": False}},
+        )
+        return (resp.choices[0].message.content or "").strip()
+    except BadRequestError:
+        pass
+    try:
+        resp = await client.chat.completions.create(
+            model=model_id, messages=[system_msg, user_msg],
+            temperature=TEMPERATURE, top_p=TOP_P, max_tokens=MAX_TOKENS,
+            extra_body={"chat_template_kwargs": {"enable_thinking": True}},
+        )
+        return (resp.choices[0].message.content or "").strip()
+    except BadRequestError:
+        pass
+    resp = await client.chat.completions.create(
+        model=model_id, messages=[system_msg, user_msg],
+        temperature=TEMPERATURE, top_p=TOP_P, max_tokens=MAX_TOKENS,
+    )
+    return (resp.choices[0].message.content or "").strip()
+
+# ---------------------------- I/O ----------------------------
+def load_jsonl(path: str) -> List[Dict[str, Any]]:
+    out = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line: continue
+            out.append(json.loads(line))
+    return out
+
+# ---------------------------- Runner ----------------------------
+async def run():
+    os.makedirs(os.path.dirname(OUTPUT_FILE) or ".", exist_ok=True)
+    model_id = await get_model_id()
+
+    data = load_jsonl(INPUT_JSON)
+
+    # filter to only rows with removals
+    rows: List[Tuple[int, Dict[str, Any]]] = []
+    for i, obj in enumerate(data):
+        removed = obj.get("removed_elements", [])
+        if isinstance(removed, list) and len(removed) > 0:
+            rows.append((i, obj))
+
+    total = min(NUM_ROWS, len(rows))
+    batches = math.ceil(total / BATCH_SIZE) if BATCH_SIZE > 0 else 0
+
+    seq_id_counter = count(0)
+    sem = asyncio.Semaphore(MAX_ASYNC)
+    write_lock = asyncio.Lock()
+
+    async def worker(idx: int, obj: Dict[str, Any]):
+        removed = obj.get("removed_elements", [])
+        orig = obj.get("prompt", "")
+        trimmed = obj.get("prompt_trimmed", "")
+        sl = obj.get("slight", "")
+        vb = obj.get("verbose", "")
+        sp = obj.get("simplify", "")
+
+        async with sem:
+            slight_fix  = await edit_once(model_id, "slight",  sl, orig, trimmed, removed)
+        async with sem:
+            verbose_fix = await edit_once(model_id, "verbose", vb, orig, trimmed, removed)
+        async with sem:
+            simplify_fix = await edit_once(model_id, "simplify", sp, orig, trimmed, removed)
+
+        rec = dict(obj)  # keep all original fields
+        rec["slight_fixed"] = slight_fix
+        rec["verbose_fixed"] = verbose_fix
+        rec["simplify_fixed"] = simplify_fix
+        rec["apply_removals_seq"] = next(seq_id_counter)
+
+        async with write_lock:
+            with open(OUTPUT_FILE, "a", encoding="utf-8") as fout:
+                fout.write(json.dumps(rec, ensure_ascii=False) + "\n")
+
+    # ensure output file exists
+    with open(OUTPUT_FILE, "a", encoding="utf-8"):
+        pass
+
+    pbar = tqdm(total=batches, desc="Applying removals to rewrites")
+    i = 0
+    while i < total:
+        batch = rows[i:i+BATCH_SIZE]
+        await asyncio.gather(*[worker(j, obj) for j, obj in batch])
+        i += len(batch)
+        pbar.update(1)
+    pbar.close()
+
+if __name__ == "__main__":
+    asyncio.run(run())

mario/requirements.txt

@@ -0,0 +1,13 @@
+numpy
+scipy
+trl
+datasets
+unsloth
+matplotlib
+Levenshtein
+wandb
+Pillow
+sacrebleu
+nltk
+textdistance
+python-dotenv

mario/requirements2.txt

@@ -0,0 +1,115 @@
+accelerate==1.6.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.11.16
+aiosignal==1.3.2
+airportsdata==20250224
+annotated-types==0.7.0
+attrs==25.3.0
+bitsandbytes==0.45.4
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+cloudpickle==3.1.1
+colorama==0.4.6
+contourpy==1.3.2
+cut-cross-entropy==25.1.1
+cycler==0.12.1
+datasets==3.5.0
+diffusers==0.32.2
+dill==0.3.8
+diskcache==5.6.3
+docker-pycreds==0.4.0
+docstring_parser==0.16
+fastjsonschema==2.21.1
+filelock==3.18.0
+fonttools==4.58.0
+frozenlist==1.5.0
+fsspec==2024.12.0
+genson==1.3.0
+gitdb==4.0.12
+GitPython==3.1.44
+hf_transfer==0.1.9
+huggingface-hub==0.30.1
+idna==3.10
+interegular==0.3.3
+iso3166==2.1.1
+Jinja2==3.1.6
+joblib==1.5.1
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+kiwisolver==1.4.8
+lark==1.2.2
+lxml==5.4.0
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.10.3
+mdurl==0.1.2
+mpmath==1.3.0
+msgspec==0.19.0
+multidict==6.3.2
+multiprocess==0.70.16
+nbformat==5.10.4
+networkx==3.4.2
+nltk==3.9.1
+numpy==2.2.4
+nvidia-cublas-cu12==12.4.5.8
+nvidia-cuda-cupti-cu12==12.4.127
+nvidia-cuda-nvrtc-cu12==12.4.127
+nvidia-cuda-runtime-cu12==12.4.127
+nvidia-cudnn-cu12==9.1.0.70
+nvidia-cufft-cu12==11.2.1.3
+nvidia-curand-cu12==10.3.5.147
+nvidia-cusolver-cu12==11.6.1.9
+nvidia-cusparse-cu12==12.3.1.170
+nvidia-cusparselt-cu12==0.6.2
+nvidia-nccl-cu12==2.21.5
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-nvtx-cu12==12.4.127
+outlines==0.2.3
+outlines_core==0.1.26
+pandas==2.2.3
+peft==0.15.1
+pillow==11.1.0
+portalocker==3.1.1
+propcache==0.3.1
+protobuf==3.20.3
+pyarrow==19.0.1
+pydantic==2.11.3
+pydantic_core==2.33.1
+pyparsing==3.2.3
+pytz==2025.2
+PyYAML==6.0.2
+referencing==0.36.2
+regex==2024.11.6
+requests==2.32.3
+rich==14.0.0
+rpds-py==0.24.0
+sacrebleu==2.5.1
+safetensors==0.5.3
+scipy==1.16.3
+sentencepiece==0.2.0
+sentry-sdk==2.26.1
+setproctitle==1.3.5
+shtab==1.7.1
+smmap==5.0.2
+sympy==1.13.1
+tabulate==0.9.0
+textdistance==4.6.3
+tokenizers==0.21.1
+torch==2.6.0
+torchvision==0.21.0
+tqdm==4.67.1
+transformers==4.51.3
+triton==3.2.0
+trl==0.15.2
+typeguard==4.4.2
+typing-inspection==0.4.0
+tyro==0.9.18
+tzdata==2025.2
+unsloth==2025.4.7
+unsloth_zoo @ git+https://github.com/unslothai/unsloth-zoo.git@29e78c90a5a010da04b7e3763809b203fc957be2
+urllib3==2.3.0
+wandb==0.19.9
+xformers==0.0.29.post3
+xxhash==3.5.0
+yarl==1.18.3

mario/run_mario_num.log

@@ -0,0 +1,2 @@
+nohup: ignoring input
+nohup: failed to run command './run_mario_num.sh': No such file or directory

mario/run_mario_num_gemma.log

@@ -0,0 +1,2 @@
+nohup: ignoring input
+nohup: failed to run command './run_mario_num_gemma.sh': No such file or directory

mario/run_mariogpt_gemma.log

@@ -0,0 +1,2 @@
+nohup: ignoring input
+nohup: failed to run command './run_mario_mariogpt_gemma.sh': No such file or directory

mario/tests/tokenizer_training_data_simple_rewrite.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c295aaa61c3d9a5db163db65d14f4efd29fb2a6049ef6c37ce9b6c03bd059148
+size 22962666

mario/tests/tokenizer_training_data_simple_rewrite_trim.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3c4f92d73c763f57d17a59900786225628245807b02cdd285636332e7af99ee
+size 27783776

mario/tokenizer_training_data_simple_rewrite.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb532cf5f35e1d415aa19e1456b54b373d125b679cfa2ea02f5a33656a95d1ea
+size 27916845

mario/tokenizer_training_data_simple_rewrite_trim.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4692cbfe07e70074daadc22ed55a48d61873ed1cd78748bde0eb9b9ac6aa45ae
+size 32737271

mario/tokenizer_training_data_simple_rewrite_trim_all.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cbd23a885be5b93abdb4f65ee5b89fd210c85366a12ef75a0f11d9de46dff6f
+size 23684947

mario/trim_prompt.py

@@ -0,0 +1,57 @@
+import json, random
+
+in_path = "tokenizer_training_data_simple_rewrite.jsonl"
+out_path = "tokenizer_training_data_simple_rewrite_trim.jsonl"
+
+PREFIXES = [
+    "Generate a level with","Create a level that has","Please generate a level with","Build a level featuring",
+    "Create a level featuring","Give me a level that includes","Make a level that includes","Produce a level featuring",
+    "Craft a level that includes","I need a level that has","I want to see a level that includes","Design a level with",
+    "I want a level with","Make a level with","Design a level around","Generate levels with",
+    "Construct a level featuring","I want a level that has","I would like a level that has",
+]
+
+def split_prefix(s: str):
+    sl = s.lstrip()
+    off = len(s) - len(sl)
+    s = sl
+    for p in PREFIXES:
+        if s.lower().startswith(p.lower()):
+            pref = s[:len(p)]
+            rest = s[len(p):].lstrip(" :,-")
+            return s[:len(p)], rest, off
+    return "", s, 0
+
+def trim_with_removed_tail(tail: str):
+    items = [x.strip() for x in tail.split(",") if x.strip()]
+    if not items: return tail, []
+    removal_probabilities = [(0.20,1),(0.10,2),(0.10,3),(0.05,4),(0.05,5),(0.05,6),(0.05,7),(0.01,8),(0.01,9),(0.01,10),(0.005,11),(0.001,12)]
+    k = 0
+    for p,c in removal_probabilities:
+        if random.random() < p: k = c; break
+    k = min(k, max(0, len(items)-1))  # keep at least one
+    if k == 0: return ", ".join(items), []
+    removed_idx = set(random.sample(range(len(items)), k))
+    kept = [items[i] for i in range(len(items)) if i not in removed_idx]
+    removed = [items[i] for i in range(len(items)) if i in removed_idx]
+    return ", ".join(kept), removed
+
+with open(in_path, "r", encoding="utf-8") as fin, open(out_path, "w", encoding="utf-8") as fout:
+    for line in fin:
+        if not line.strip(): continue
+        obj = json.loads(line)
+        prompt = obj.get("prompt", "")
+        if not isinstance(prompt, str):
+            obj["prompt_trimmed"] = ""
+            obj["removed_elements"] = []
+        else:
+            pref, tail, off = split_prefix(prompt)
+            kept, removed = trim_with_removed_tail(tail)
+            if pref:
+                # preserve any original leading whitespace via offset
+                leading = " " * off
+                obj["prompt_trimmed"] = f"{leading}{pref} {kept}".rstrip()
+            else:
+                obj["prompt_trimmed"] = kept
+            obj["removed_elements"] = removed
+        fout.write(json.dumps(obj, ensure_ascii=False) + "\n")