Text Generation
Transformers
Safetensors
English
bard
feature-extraction
interactive-fiction
text-adventure
storytelling
custom_code
Instructions to use lowerkey/bard-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowerkey/bard-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lowerkey/bard-small", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lowerkey/bard-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lowerkey/bard-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lowerkey/bard-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lowerkey/bard-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lowerkey/bard-small
- SGLang
How to use lowerkey/bard-small with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "lowerkey/bard-small" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lowerkey/bard-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "lowerkey/bard-small" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lowerkey/bard-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lowerkey/bard-small with Docker Model Runner:
docker model run hf.co/lowerkey/bard-small
Download bard_rules.py from lowerkey/bard-small: direct link, hf CLI and curl.
- Browser
- Download file 4.29 kB
-
https://huggingface.co/lowerkey/bard-small/resolve/main/bard_rules.py
- Command line
-
hf download hf://lowerkey/bard-small/bard_rules.py
-
curl -L -o bard_rules.py https://huggingface.co/lowerkey/bard-small/resolve/main/bard_rules.py
4.29 kB
| """The rules of the game: reading what the model wrote, and deciding how much of it to believe.""" | |
| import re | |
| import unicodedata | |
| MAX_INPUT = 300 | |
| ANNOUNCE = {"money", "gold", "health"} | |
| AMOUNTS = {"money", "gold", "coins", "health"} | |
| CHANGE = re.compile(r"^\s*(.+?)\s*<\|(add|set)\|>\s*(-?\s*\d+)\s*$") | |
| SPELLED_MARKER = re.compile(r"<?\|\s*(vars|choices|sep|end|add|set)\s*\|\s*>?", re.I) | |
| STRAY_MARKER = re.compile(r"<\|[a-z_]+\|>") | |
| META_CHOICE = re.compile( | |
| r"leave (a )?comments?|rate (this|the) (story|game)|end (the )?game|give (me )?(a )?rating|" | |
| r"back to (the )?(menu|site|story list|main menu)|return to (the )?main menu|main menu|" | |
| r"play again|^\W*(restart|start over|start again|comments?)\W*$|(reset|refresh) (the )?game|" | |
| r"previous page|^\W*go back to the (beginning|start)\W*$|" | |
| r"^\W*(the )?end\W*(\[[^\]]*\]|\([^)]*\))?\W*$|^\W*the end of (the|this|your) " | |
| r"(tale|story|game|adventure|line)\W*$|this is the end of (your|the) (adventure|story|game)", | |
| re.I) | |
| MONEY_WORDS = ( | |
| r"\b(money|coins?|gold|silver|copper|cash|dollars?|bucks|credits?|price|pay|paid|" | |
| r"buy|bought|sell|sold|cost|steal|stole|stolen|rob|robbed|reward|bribe|purse|wallet|" | |
| r"payment|fee|tip|earn|earned|spend|spent|wage|loot|treasure)\b|\$" | |
| ) | |
| HEALTH_WORDS = ( | |
| r"\b(health|hurt|wound|wounded|injur\w*|bleed\w*|blood|pain|heal\w*|damage\w*|hit|" | |
| r"stab\w*|cut|bite|bitten|burn\w*|poison\w*|potion|bandage\w*|recover\w*|weak\w*)\b" | |
| ) | |
| EVIDENCE = { | |
| "money": re.compile(MONEY_WORDS, re.I), | |
| "gold": re.compile(MONEY_WORDS, re.I), | |
| "health": re.compile(HEALTH_WORDS, re.I), | |
| } | |
| def is_story_choice(choice): | |
| return not META_CHOICE.search(choice) | |
| def is_backed_by(name, narration): | |
| rule = EVIDENCE.get(name) | |
| return rule is None or bool(rule.search(narration)) | |
| def is_possible(name, old, new): | |
| if name not in AMOUNTS or old is None: | |
| return True | |
| return 0 <= new <= max(1000, 10 * old) | |
| def clean_input(text): | |
| text = "".join(c for c in text if c == " " or unicodedata.category(c)[0] != "C") | |
| text = text.replace("<|", "<").replace("|>", ">") | |
| return re.sub(r"\s+", " ", text).strip()[:MAX_INPUT] | |
| def state_text(state): | |
| return "<|sep|>".join(f"{name}\n{value}" for name, value in state.items()) | |
| def narration_so_far(raw): | |
| visible = re.split(r"<\||\|", raw, maxsplit=1)[0] | |
| return visible[:-1] if visible.endswith("<") else visible | |
| def without_repeats(choices): | |
| seen, kept = set(), [] | |
| for choice in choices: | |
| key = re.sub(r"\W+", " ", choice.lower()).strip() | |
| if key not in seen: | |
| seen.add(key) | |
| kept.append(choice) | |
| return kept | |
| def parse_changes(text): | |
| changes, unreadable = [], [] | |
| for item in filter(None, text.split("<|sep|>")): | |
| match = CHANGE.match(item) | |
| if match: | |
| name = re.sub(r"\s+", " ", match[1]).strip().lower() | |
| changes.append((name, match[2], int(match[3].replace(" ", "")))) | |
| else: | |
| unreadable.append(item) | |
| return changes, unreadable | |
| def parse_turn(raw): | |
| problems = [] | |
| fixed = SPELLED_MARKER.sub(lambda m: f"<|{m[1].lower()}|>", raw) | |
| if fixed != raw: | |
| problems.append("a marker written as text") | |
| raw = fixed | |
| narration, _, choice_text = raw.partition("<|choices|>") | |
| narration, _, change_text = narration.partition("<|vars|>") | |
| changes, unreadable = parse_changes(change_text) | |
| problems += [f"unreadable change {item!r}" for item in unreadable] | |
| choices = [c.strip() for c in choice_text.split("<|sep|>") if c.strip()] | |
| choices = without_repeats([c for c in choices if is_story_choice(c)]) | |
| stray = STRAY_MARKER.findall(narration) | |
| if stray: | |
| problems.append(f"stray markers in the narration: {stray}") | |
| narration = STRAY_MARKER.sub(" ", narration) | |
| return narration.strip(), changes, choices, problems | |
| def apply_changes(state, changes, narration): | |
| for name, op, value in changes: | |
| old = state.get(name) | |
| new = value if op == "set" else (old or 0) + value | |
| if not is_backed_by(name, narration): | |
| continue | |
| if name in ANNOUNCE and op == "set" and value != 0: | |
| continue | |
| if is_possible(name, old, new): | |
| state[name] = new | |