A newer version of the Gradio SDK is available: 6.22.0
base_model: Qwen/Qwen3-4B-Instruct-2507
library_name: peft
license: apache-2.0
pipeline_tag: text-generation
tags:
- dota-2
- qlora
- peft
- trl
- gradio
- build-small-hackathon
datasets:
- build-small-hackathon/dota2tuned-data
DOTA2Tuned Qwen3 4B LoRA
This is the Tiny fine-tuned adapter used by DOTA2Tuned, a Hugging Face Build Small Hackathon Gradio app for Dota 2 draft coaching, hero/item/skill meta lookup, counter/synergy reasoning, build timing summaries, and match prediction explanations.
The adapter is based on Qwen/Qwen3-4B-Instruct-2507, keeping the total model size below the hackathon <=32B parameter limit. It was trained with TRL SFT and QLoRA on Modal GPU infrastructure, then served through Modal-backed inference from the Hugging Face Space.
The app also defines Balanced and Quality profiles for openbmb/MiniCPM4.1-8B and Qwen/Qwen3-30B-A3B-Instruct-2507. Those adapters are trained through the same Modal pipeline and exposed through the app's model selector when available.
Current profile status:
- Tiny Qwen3 4B: trained and published at
build-small-hackathon/dota2tuned-qwen3-4b-2507-lora. - Balanced MiniCPM4.1 8B: trained and published at
build-small-hackathon/dota2tuned-minicpm4-1-8b-lora. - Quality Qwen3 30B-A3B: trained on Modal H200 and published at
build-small-hackathon/dota2tuned-qwen3-30b-a3b-2507-lora.
Intended Behavior
DOTA2Tuned does not use the language model as the source of truth for picks, counters, builds, or match probabilities. Deterministic statistics, a draft predictor, and retrieved patch/stat cards provide the evidence. The adapter's job is to turn that evidence into concise Dota 2 explanations.
The model should:
- answer draft, counter, synergy, build, and match-prediction questions from supplied evidence;
- preserve hero names, item names, patch IDs, scores, deltas, sample sizes, and confidence labels;
- caveat weak data and low sample sizes;
- refuse or qualify answers when evidence is missing, generic, stale, or mentions fake heroes/items.
The model should not invent unsupported hero names, item names, scores, timings, patch changes, or match data.
Training Data
Training examples are generated from the DOTA2Tuned pipeline and published in the companion dataset repo:
https://huggingface.co/datasets/build-small-hackathon/dota2tuned-data
The current examples combine normalized Dota 2 stat cards, STRATZ match evidence, draft recommendation outputs, item timing summaries, skill-build summaries, patch-change retrieval snippets, and refusal/caveat behaviors. The refreshed SFT set has 942 examples generated from 15,508 retrieval documents.
Usage
This repo contains a PEFT LoRA adapter. Load it with the base model through PEFT:
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model_id = "build-small-hackathon/dota2tuned-qwen3-4b-2507-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoPeftModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{
"role": "system",
"content": (
"You are DOTA2Tuned. Use only supplied evidence for concrete Dota 2 "
"claims and caveat weak data."
),
},
{
"role": "user",
"content": (
"Question: Suggest one mid hero against Phantom Assassin and Witch Doctor.\n\n"
"Evidence:\n"
"Phantom Assassin current pro stat card: 3 pro picks, 1 win.\n"
"Witch Doctor current pro stat card: 8 pro picks, 5 wins."
),
},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=160, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.shape[-1] :], skip_special_tokens=True))
For the shipped app, use the Space:
https://build-small-hackathon-dota2tuned.hf.space
Evaluation Notes
The deployed adapter was smoke-tested through Modal and the public Space with prompts covering:
- draft recommendations against Phantom Assassin and Witch Doctor;
- counter/synergy explanations;
- Shadow Fiend build timing without concrete evidence;
- fake hero rejection;
- patch/meta caveats;
- low sample-size caveats.
One grounding weakness was found during testing: generic build prompts could produce unsupported item advice. The deployed Modal inference prompt now explicitly forbids invented hero names, item names, scores, timings, patches, and match data, and asks for uncertainty when evidence is missing or generic.
Limitations
- The adapter is only as reliable as the supplied Dota 2 evidence.
- The current hackathon dataset is compact, so many hero, item, skill, and matchup samples still need caveats.
- Match prediction is draft-only and does not model player skill, execution, lane assignments, itemization, or in-game state.
- Dota 2 patches change quickly; refresh the data pipeline before making current-meta claims.
- The adapter is not affiliated with Valve, OpenDota, STRATZ, Steam, Hugging Face, Modal, or Qwen.