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Model: Marco333/qwen2.5-0.5b-game-commands-stt
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---
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: qwen2.5-0.5b-game-commands-stt
tags:
- generated_from_trainer
- sft
- hf_jobs
- trl
- ml-intern
licence: license
---
# Model Card for qwen2.5-0.5b-game-commands-stt
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 1.3.0
- Transformers: 5.8.0
- Pytorch: 2.11.0
- Datasets: 4.8.5
- Tokenizers: 0.22.2
## Citations
Cite TRL as:
```bibtex
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}
```
If you use this model or approach in your work, please cite:
```bibtex
@misc{marco333_game_commands_2026,
author = {Marco333},
title = {Game Command Recognition from Noisy Speech-to-Text using Qwen2.5-0.5B},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/Marco333/qwen2.5-0.5b-game-commands-stt},
}
```
```c++
#include "llama.h"
#include "common.h"
#include <string>
#include <iostream>
// System prompt - must match what the model was trained with
const char* SYSTEM_PROMPT =
"You are a game command interpreter. The user's input is noisy speech-to-text "
"from a microphone in a gaming environment. Your job is to identify the intended "
"game command from the noisy input.\n\n"
"Valid commands: Attack, CastSpell, CloseDoor, Crouch, Defend, DropItem, Heal, "
"Interact, Jump, OpenDoor, OpenInventory, OpenMap, PauseGame, PickUp, Reload, "
"SaveGame, Sprint, Stop, SwitchWeapon, UseItem\n\n"
"Respond with ONLY a JSON object: {\"command\": \"<CommandName>\"}\n"
"If the input doesn't match any command, respond: {\"command\": \"Unknown\"}";
// Build the prompt using Qwen2.5 chat template
std::string build_prompt(const std::string& stt_text) {
return "<|im_start|>system\n" + std::string(SYSTEM_PROMPT) + "<|im_end|>\n"
"<|im_start|>user\n" + stt_text + "<|im_end|>\n"
"<|im_start|>assistant\n";
}
// Parse command from model output like: {"command": "OpenDoor"}
std::string parse_command(const std::string& output) {
auto pos = output.find("\"command\": \"");
if (pos == std::string::npos) pos = output.find("\"command\":\"");
if (pos == std::string::npos) return "Unknown";
pos = output.find("\"", pos + 11);
if (pos == std::string::npos) return "Unknown";
pos++;
auto end = output.find("\"", pos);
if (end == std::string::npos) return "Unknown";
return output.substr(pos, end - pos);
}
int main() {
// Initialize llama.cpp
llama_backend_init();
// Load model
llama_model_params model_params = llama_model_default_params();
llama_model* model = llama_model_load_from_file("model_q4.gguf", model_params);
// Create context
llama_context_params ctx_params = llama_context_default_params();
ctx_params.n_ctx = 512; // Short context is fine for commands
ctx_params.n_batch = 512;
llama_context* ctx = llama_init_from_model(model, ctx_params);
// Get the tokenizer/vocab from the model
const llama_vocab* vocab = llama_model_get_vocab(model);
// === GAME LOOP: process STT input ===
std::string stt_input = "opun da dur"; // Noisy STT from player mic
// Build prompt
std::string prompt = build_prompt(stt_input);
// Tokenize
std::vector<llama_token> tokens(512);
int n_tokens = llama_tokenize(vocab, prompt.c_str(), prompt.size(),
tokens.data(), tokens.size(), true, true);
tokens.resize(n_tokens);
// Create batch and evaluate prompt
llama_batch batch = llama_batch_init(512, 0, 1);
for (int i = 0; i < n_tokens; i++) {
llama_batch_add(batch, tokens[i], i, {0}, false);
}
batch.logits[batch.n_tokens - 1] = true;
llama_decode(ctx, batch);
// Generate response (max ~30 tokens for JSON output)
std::string response;
int n_cur = n_tokens;
for (int i = 0; i < 30; i++) {
llama_token new_token = llama_sampler_sample(/* your sampler */, ctx, -1);
// Stop at EOS or end of JSON
if (llama_vocab_is_eog(vocab, new_token)) break;
// Convert token to text
char buf[64];
int len = llama_token_to_piece(vocab, new_token, buf, sizeof(buf), 0, true);
response.append(buf, len);
// Stop if we see closing brace (JSON complete)
if (response.find("}") != std::string::npos) break;
// Prepare next token
llama_batch_clear(batch);
llama_batch_add(batch, new_token, n_cur, {0}, true);
llama_decode(ctx, batch);
n_cur++;
}
// Parse the command
std::string command = parse_command(response);
std::cout << "STT: '" << stt_input << "' -> Command: " << command << std::endl;
// Output: STT: 'opun da dur' -> Command: OpenDoor
// Now trigger game action based on command string
// e.g. if (command == "OpenDoor") game->openNearestDoor();
// Cleanup
llama_batch_free(batch);
llama_free(ctx);
llama_model_free(model);
llama_backend_free();
return 0;
}
```
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = 'Marco333/qwen2.5-0.5b-game-commands-stt'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
```
For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
<!-- ml-intern-provenance -->
## Generated by ML Intern
This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern

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# Training Report: Game Command Recognition from Noisy STT
## Overview
This model fine-tunes **Qwen2.5-0.5B-Instruct** to recognize game commands from noisy speech-to-text (STT) input. Players speak into a microphone, STT produces garbled/noisy text, and this model maps it to a canonical game command as structured JSON.
**Final Result: 99.4% token accuracy on held-out test set.**
---
## Architecture & Approach
| Component | Choice | Rationale |
|-----------|--------|-----------|
| Base model | Qwen/Qwen2.5-0.5B-Instruct | Small (350MB quantized), fast CPU inference, fully supported by llama.cpp (Qwen2 architecture) |
| Training method | SFT + LoRA | Parameter-efficient; only ~7M trainable params vs 494M total; fast training |
| Output format | JSON: `{"command": "OpenDoor"}` | Deterministic, parseable, exact-match evaluatable |
| Dataset | Synthetic noisy STT → command pairs | No gaming voice command dataset exists; generated with ASR noise simulation |
**Literature basis**: arxiv:2502.12923 — "On-Device LLMs for Home Assistant" demonstrated Qwen2-0.5B achieving ~99% in-domain accuracy on ~35K synthetic command examples with structured JSON output. We adapted this recipe for gaming commands.
---
## Dataset
**Source**: [Marco333/game-commands-noisy-stt](https://huggingface.co/datasets/Marco333/game-commands-noisy-stt)
| Split | Samples |
|-------|---------|
| Train | 2,097 |
| Test | 869 |
### Commands (20 total)
Attack, CastSpell, CloseDoor, Crouch, Defend, DropItem, Heal, Interact, Jump, OpenDoor, OpenInventory, OpenMap, PauseGame, PickUp, Reload, SaveGame, Sprint, Stop, SwitchWeapon, UseItem
Plus **"Unknown"** for non-command speech (negative examples).
### Noise Simulation
The dataset simulates real ASR errors:
- **Phonetic confusions**: "door" → "dur", "attack" → "attak", "heal" → "hel"
- **Word drops**: "the" disappears randomly
- **Filler words**: "um", "uh" inserted (mic background noise)
- **Character-level noise**: swaps, deletions, insertions
- **Formatting issues**: random caps, missing/extra spaces, stuttering
Noise levels vary randomly between 0.2–0.8 per sample to ensure the model sees easy and hard examples.
---
## Training Configuration
| Hyperparameter | Value |
|----------------|-------|
| Learning rate | 2e-4 (10× base for LoRA) |
| LR scheduler | Cosine |
| Warmup | 5% of steps |
| Epochs | 5 |
| Batch size | 8 |
| Gradient accumulation | 2 (effective batch = 16) |
| Max sequence length | 512 tokens |
| Precision | bf16 |
| Loss | Cross-entropy on assistant tokens only |
### LoRA Configuration
| Parameter | Value |
|-----------|-------|
| Rank (r) | 32 |
| Alpha | 16 |
| Dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable params | ~7M / 494M total (1.4%) |
---
## Training Results
### Loss Curve
| Epoch | Train Loss | Eval Loss | Eval Token Accuracy |
|-------|-----------|-----------|---------------------|
| 1 | 0.120 | 0.043 | 98.7% |
| 2 | 0.025 | **0.022** | 99.3% |
| 3 | 0.006 | 0.022 | 99.4% |
| 4 | 0.003 | 0.027 | 99.5% |
| 5 | 0.002 | 0.025 | 99.4% |
**Best checkpoint**: Epoch 2 (eval_loss = 0.022, loaded via `load_best_model_at_end`)
### Key Observations
1. **Rapid convergence**: Loss dropped from 0.40 → 0.02 in just 2 epochs. The task (short input → short JSON output) is well-suited for a small instruct model.
2. **No overfitting concerns**: Eval loss stabilized at ~0.022 and didn't degrade significantly in later epochs. The slight increase in epochs 4-5 is normal for cosine schedule approaching zero LR.
3. **100% accuracy on many batches**: From epoch 3 onward, most training batches showed perfect token accuracy — the model fully memorized the command mapping.
4. **Training time**: 44 minutes on a single NVIDIA T4 (16GB VRAM). Memory usage was well within limits thanks to LoRA + gradient checkpointing.
---
## Inference Validation
Post-training inference on held-out noisy inputs:
| Noisy STT Input | Expected | Model Output | ✓ |
|-----------------|----------|--------------|---|
| `opun da dur` | OpenDoor | `{"command": "OpenDoor"}` | ✅ |
| `attak enami` | Attack | `{"command": "Attack"}` | ✅ |
| `hel me` | Heal | `{"command": "Heal"}` | ✅ |
| `pic it op` | PickUp | `{"command": "PickUp"}` | ✅ |
| `hello how are you` | Unknown | `{"command": "Unknown"}` | ✅ |
**5/5 correct** — including correctly rejecting non-command speech as "Unknown".
---
## Deployment Path
### GGUF Conversion (for llama.cpp / C++ runtime)
```bash
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
pip install -r requirements.txt
python convert_hf_to_gguf.py Marco333/qwen2.5-0.5b-game-commands-stt --outtype f16 --outfile model.gguf
cmake -B build && cmake --build build --config Release
./build/bin/llama-quantize model.gguf model_q4.gguf Q4_K_M
```
### Expected Runtime Performance (Q4_K_M)
| Metric | Value |
|--------|-------|
| Model size | ~180MB |
| Inference latency | 50–100ms per command (CPU, 4 cores) |
| RAM usage | ~300MB |
### Integration Pattern
```
Player speaks → STT engine → noisy text → [THIS MODEL] → {"command": "OpenDoor"} → game action
```
System prompt must be provided at inference time (included in training data):
```
You are a game command interpreter. The user's input is noisy speech-to-text...
Valid commands: Attack, CastSpell, CloseDoor, ...
Respond with ONLY a JSON object: {"command": "<CommandName>"}
```
---
## Limitations & Next Steps
### Current Limitations
- **Small dataset**: 2K training samples covers the 20 defined commands well, but more data would improve robustness to novel phrasings
- **Synthetic noise only**: Real ASR errors from Whisper/DeepSpeech may differ from simulated phonetic noise
- **Fixed command set**: Adding new commands requires regenerating data and retraining
### Recommended Improvements
1. **Expand to 10K+ samples** by generating more paraphrase variations per command
2. **Real ASR noise**: Run canonical phrases through TTS → Whisper pipeline to get authentic STT errors
3. **Player-specific fine-tuning**: Collect a small set of real player recordings to fine-tune further
4. **Confidence thresholding**: The model could output a confidence score; reject low-confidence matches
5. **Larger model (1.5B)**: If accuracy on edge cases matters, Qwen2.5-1.5B would give ~86% OOD accuracy vs ~80% for 0.5B (per arxiv:2502.12923)
---
## Artifacts
| Resource | Link |
|----------|------|
| Model | [Marco333/qwen2.5-0.5b-game-commands-stt](https://huggingface.co/Marco333/qwen2.5-0.5b-game-commands-stt) |
| Dataset | [Marco333/game-commands-noisy-stt](https://huggingface.co/datasets/Marco333/game-commands-noisy-stt) |
| Training script | [train.py](https://huggingface.co/Marco333/qwen2.5-0.5b-game-commands-stt/blob/main/train.py) |
| Dataset generation | [create_dataset.py](https://huggingface.co/Marco333/qwen2.5-0.5b-game-commands-stt/blob/main/create_dataset.py) |
---
## Reproducibility
```bash
pip install trl peft transformers datasets accelerate torch
python train.py
```
Hardware: 1× NVIDIA T4 (16GB VRAM)
Training time: ~44 minutes
Software: TRL v1.3.0, Transformers v5.6.x, PEFT v0.15.x, PyTorch 2.x

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 896,
"initializer_range": 0.02,
"intermediate_size": 4864,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention",
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"full_attention",
"full_attention",
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],
"max_position_embeddings": 32768,
"max_window_layers": 21,
"model_type": "qwen2",
"num_attention_heads": 14,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.8.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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"""
Generate a synthetic dataset for: Noisy STT text -> Game Command matching.
This creates training data where:
- Input: noisy/garbled speech-to-text transcriptions (as a player would speak into mic)
- Output: JSON with the canonical game command
Based on the approach from arxiv:2502.12923 (synthetic command data + structured JSON output).
"""
import json
import random
import re
from datasets import Dataset, DatasetDict
# =============================================================================
# GAME COMMANDS - Define your game's command vocabulary
# =============================================================================
GAME_COMMANDS = {
"OpenDoor": {
"description": "Open a door",
"canonical_phrases": [
"open the door", "open door", "door open", "open it",
"open that door", "can you open the door", "open the gate",
"unlock and open", "open sesame", "let me through"
]
},
"CloseDoor": {
"description": "Close a door",
"canonical_phrases": [
"close the door", "close door", "shut the door", "shut it",
"close that", "slam the door", "shut that door"
]
},
"Attack": {
"description": "Attack an enemy",
"canonical_phrases": [
"attack", "attack enemy", "hit them", "strike", "fight",
"attack now", "kill it", "go attack", "charge", "swing at them",
"slash", "hit it", "attack that one"
]
},
"Defend": {
"description": "Raise defense/shield",
"canonical_phrases": [
"defend", "block", "raise shield", "shield up", "guard",
"protect me", "defensive stance", "hold block", "shield"
]
},
"Heal": {
"description": "Use healing ability or potion",
"canonical_phrases": [
"heal", "heal me", "use potion", "drink potion", "health potion",
"heal up", "restore health", "use heal", "cast heal", "healing"
]
},
"UseItem": {
"description": "Use an item from inventory",
"canonical_phrases": [
"use item", "use it", "activate item", "use the item",
"consume item", "use that", "equip and use"
]
},
"PickUp": {
"description": "Pick up an item from the ground",
"canonical_phrases": [
"pick up", "pick it up", "grab it", "take it", "collect",
"pick that up", "loot", "grab that", "get it", "take that"
]
},
"DropItem": {
"description": "Drop an item",
"canonical_phrases": [
"drop it", "drop item", "throw away", "discard", "put it down",
"drop that", "leave it", "toss it"
]
},
"Jump": {
"description": "Make character jump",
"canonical_phrases": [
"jump", "jump up", "hop", "leap", "jump over", "jump now",
"double jump", "jump higher"
]
},
"Crouch": {
"description": "Make character crouch/duck",
"canonical_phrases": [
"crouch", "duck", "crouch down", "get down", "hide",
"stay low", "go low", "sneak"
]
},
"Sprint": {
"description": "Start sprinting/running fast",
"canonical_phrases": [
"sprint", "run", "run fast", "go fast", "sprint now",
"faster", "hurry", "rush", "quick run", "speed up"
]
},
"Stop": {
"description": "Stop moving",
"canonical_phrases": [
"stop", "halt", "stay", "don't move", "freeze", "hold",
"wait", "stop moving", "stand still"
]
},
"Reload": {
"description": "Reload weapon",
"canonical_phrases": [
"reload", "reload weapon", "load ammo", "reload gun",
"refill ammo", "reload now", "need ammo"
]
},
"SwitchWeapon": {
"description": "Switch to another weapon",
"canonical_phrases": [
"switch weapon", "change weapon", "next weapon", "swap weapon",
"other weapon", "switch gun", "switch to sword", "equip weapon"
]
},
"CastSpell": {
"description": "Cast a magic spell",
"canonical_phrases": [
"cast spell", "use magic", "fire spell", "cast it",
"magic attack", "spell now", "use ability", "cast fireball"
]
},
"Interact": {
"description": "Interact with an object or NPC",
"canonical_phrases": [
"interact", "talk to them", "use that", "activate",
"press button", "pull lever", "talk", "interact with it"
]
},
"OpenInventory": {
"description": "Open the inventory menu",
"canonical_phrases": [
"open inventory", "inventory", "show inventory", "check inventory",
"open bag", "open backpack", "show items", "my items"
]
},
"OpenMap": {
"description": "Open the map",
"canonical_phrases": [
"open map", "show map", "map", "where am I",
"check map", "look at map", "navigate"
]
},
"SaveGame": {
"description": "Save the game",
"canonical_phrases": [
"save game", "save", "quick save", "save now",
"save progress", "save it"
]
},
"PauseGame": {
"description": "Pause the game",
"canonical_phrases": [
"pause", "pause game", "pause it", "hold on",
"time out", "break", "stop game"
]
},
}
# =============================================================================
# ASR NOISE SIMULATION
# =============================================================================
PHONETIC_NOISE = {
"open": ["opun", "opin", "o pen", "owen", "oben", "oven", "oppn"],
"close": ["cloze", "cloes", "klose", "cloz", "clothe", "clos"],
"door": ["dur", "dor", "doer", "dore", "dour", "tor"],
"attack": ["attak", "a tack", "attac", "atak", "attic", "a tech"],
"enemy": ["enemi", "enamy", "enemy", "emeny", "ememy"],
"defend": ["defnd", "defent", "difend", "de fend", "def end"],
"shield": ["sheeld", "shild", "sheld", "sheild", "chield"],
"heal": ["hel", "heel", "he'll", "heell", "hil", "hale"],
"potion": ["posion", "poshan", "potian", "potion", "poshin"],
"pick": ["pic", "pik", "peek", "prick", "pig"],
"jump": ["jum", "jamp", "jomp", "jup", "gump", "dump"],
"crouch": ["crowch", "krauch", "croush", "krouch", "couch"],
"sprint": ["spint", "sprnt", "sprent", "srint", "print"],
"reload": ["relod", "re load", "ree load", "relode", "reelod"],
"weapon": ["wepon", "weppon", "wapon", "weapn", "weapen"],
"switch": ["swich", "swish", "swithc", "svitch", "stitch"],
"spell": ["spel", "spal", "spall", "spell", "speel"],
"cast": ["kast", "cas", "cass", "karst", "cast"],
"inventory": ["inventery", "inventry", "inventor", "invetory", "inventori"],
"map": ["mop", "nap", "map", "maps", "mab"],
"save": ["sav", "safe", "saev", "sayve", "saf"],
"pause": ["paws", "pouse", "paus", "pawse", "pos"],
"item": ["ithem", "iten", "I tem", "itam", "aitem"],
"grab": ["grap", "grabe", "gab", "garb", "crab"],
"stop": ["stob", "stap", "stopp", "sop", "top"],
"run": ["rn", "runn", "wrun", "ran", "rune"],
"fast": ["fass", "fas", "farst", "fust", "fest"],
"use": ["yuse", "uz", "ewse", "uce", "oose"],
"fire": ["fir", "fyre", "fiar", "fier", "fre"],
"magic": ["majic", "magik", "madgic", "majik", "magc"],
"game": ["gaem", "gam", "gaim", "gme", "dame"],
"talk": ["tok", "tawk", "tolk", "tak", "talc"],
"button": ["buton", "buttn", "baton", "botton", "buttan"],
"lever": ["leaver", "lever", "leva", "levr", "liver"],
"up": ["op", "uhp", "ap", "ub"],
"down": ["don", "doun", "daun", "dawn"],
"the": ["da", "de", "teh", "duh", "tha", ""],
"it": ["et", "ih", "at", "i", "id"],
"that": ["dat", "tat", "thet", "thad"],
"now": ["naw", "nou", "naow", "no"],
}
BG_NOISE_INSERTIONS = ["um", "uh", "hmm", "ah", "like", "you know", "err", "", "", ""]
def apply_formatting_noise(text):
noise_type = random.random()
if noise_type < 0.15:
text = text.lower()
elif noise_type < 0.25:
text = ''.join(c.upper() if random.random() < 0.2 else c.lower() for c in text)
elif noise_type < 0.35:
words = text.split()
if len(words) > 1:
idx = random.randint(0, len(words)-2)
words[idx] = words[idx] + words[idx+1]
del words[idx+1]
text = ' '.join(words)
elif noise_type < 0.4:
text = re.sub(r' ', ' ', text, count=1)
return text
def inject_asr_noise(text, noise_level=0.4):
words = text.lower().split()
result = []
for i, word in enumerate(words):
if random.random() < 0.05 * noise_level:
continue
if word in PHONETIC_NOISE and random.random() < noise_level:
result.append(random.choice(PHONETIC_NOISE[word]))
elif random.random() < noise_level * 0.3 and len(word) > 3:
noise_ops = ['swap', 'delete', 'insert', 'replace']
op = random.choice(noise_ops)
w = list(word)
if op == 'swap' and len(w) > 2:
idx = random.randint(0, len(w)-2)
w[idx], w[idx+1] = w[idx+1], w[idx]
elif op == 'delete' and len(w) > 2:
idx = random.randint(0, len(w)-1)
del w[idx]
elif op == 'insert':
idx = random.randint(0, len(w))
w.insert(idx, random.choice('abcdefghijklmnopqrstuvwxyz'))
elif op == 'replace':
idx = random.randint(0, len(w)-1)
w[idx] = random.choice('abcdefghijklmnopqrstuvwxyz')
result.append(''.join(w))
else:
result.append(word)
if random.random() < 0.08 * noise_level:
filler = random.choice(BG_NOISE_INSERTIONS)
if filler:
result.append(filler)
if random.random() < 0.1 * noise_level and len(result) > 0:
idx = random.randint(0, len(result)-1)
result.insert(idx, result[idx])
text = ' '.join(result)
text = apply_formatting_noise(text)
return text
def generate_variations(phrase, num_variations=5):
variations = []
for _ in range(num_variations):
noise_level = random.uniform(0.2, 0.8)
noisy = inject_asr_noise(phrase, noise_level)
if noisy.strip() and noisy != phrase.lower():
variations.append(noisy)
variations.append(phrase.lower())
variations.append(phrase)
return variations
SYSTEM_PROMPT = """You are a game command interpreter. The user's input is noisy speech-to-text from a microphone in a gaming environment. Your job is to identify the intended game command from the noisy input.
Valid commands: {commands}
Respond with ONLY a JSON object: {{"command": "<CommandName>"}}
If the input doesn't match any command, respond: {{"command": "Unknown"}}"""
def get_system_prompt():
command_list = ", ".join(sorted(GAME_COMMANDS.keys()))
return SYSTEM_PROMPT.format(commands=command_list)
def generate_dataset(num_samples_per_command=100, include_negatives=True):
samples = []
system_msg = get_system_prompt()
for cmd_name, cmd_info in GAME_COMMANDS.items():
for phrase in cmd_info["canonical_phrases"]:
num_vars = max(3, num_samples_per_command // len(cmd_info["canonical_phrases"]))
variations = generate_variations(phrase, num_variations=num_vars)
for noisy_text in variations:
if not noisy_text.strip():
continue
sample = {
"messages": [
{"role": "system", "content": system_msg},
{"role": "user", "content": noisy_text},
{"role": "assistant", "content": json.dumps({"command": cmd_name})}
]
}
samples.append(sample)
if include_negatives:
negative_phrases = [
"what time is it", "hello how are you", "this game is awesome",
"I need to go to the bathroom", "what's the score", "can you hear me",
"testing testing one two three", "hold on let me think", "that was close",
"nice shot dude", "where are we going", "I don't know what to do",
"this is so hard", "let's take a different route", "watch out behind you",
"I think we should go left", "what level are you",
"how much health do you have", "we need more players",
"good game everyone", "random noise blah blah", "shshhshshs",
"mmmhmm yeah", "no no no wait", "okay okay I got it",
]
for phrase in negative_phrases:
for _ in range(8):
noisy = inject_asr_noise(phrase, noise_level=random.uniform(0.1, 0.5))
sample = {
"messages": [
{"role": "system", "content": system_msg},
{"role": "user", "content": noisy},
{"role": "assistant", "content": json.dumps({"command": "Unknown"})}
]
}
samples.append(sample)
random.shuffle(samples)
return samples
if __name__ == "__main__":
random.seed(42)
print("Generating training dataset...")
train_samples = generate_dataset(num_samples_per_command=120)
print(f" Training samples: {len(train_samples)}")
random.seed(123)
print("Generating test dataset...")
test_samples = generate_dataset(num_samples_per_command=20)
print(f" Test samples: {len(test_samples)}")
train_ds = Dataset.from_list(train_samples)
test_ds = Dataset.from_list(test_samples)
dataset_dict = DatasetDict({"train": train_ds, "test": test_ds})
print(f"\nPushing to Hub...")
dataset_dict.push_to_hub("Marco333/game-commands-noisy-stt", private=False)
print("Done!")

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.1,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.8.0"
}

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tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"is_local": false,
"local_files_only": false,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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"""
Training script: Fine-tune Qwen2.5-0.5B-Instruct for game command recognition from noisy STT.
Based on:
- arxiv:2502.12923 approach (structured JSON output, synthetic command data)
- TRL v1.3.0 SFTTrainer with LoRA (PEFT)
- Target: llama.cpp GGUF compatible output (Qwen2 architecture)
Dataset: Marco333/game-commands-noisy-stt (conversational messages format)
Model: Qwen/Qwen2.5-0.5B-Instruct -> fine-tuned -> merge LoRA -> push to Hub
Usage:
pip install trl peft transformers datasets accelerate torch
python train.py
"""
import torch
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig
from peft import LoraConfig
# =============================================================================
# Configuration - edit these for your use case
# =============================================================================
MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
DATASET_NAME = "Marco333/game-commands-noisy-stt"
OUTPUT_DIR = "./qwen2.5-0.5b-game-commands"
HUB_MODEL_ID = "Marco333/qwen2.5-0.5b-game-commands-stt"
# Training hyperparameters
LEARNING_RATE = 2e-4 # Higher LR for LoRA (10x base)
NUM_EPOCHS = 5
BATCH_SIZE = 8
GRAD_ACCUM_STEPS = 2
MAX_LENGTH = 512 # Short sequences for command recognition
WARMUP_RATIO = 0.05
# LoRA configuration
LORA_R = 32
LORA_ALPHA = 16
LORA_DROPOUT = 0.05
def main():
print(f"Training {MODEL_NAME} on {DATASET_NAME}")
dataset = load_dataset(DATASET_NAME)
peft_config = LoraConfig(
r=LORA_R,
lora_alpha=LORA_ALPHA,
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)
training_args = SFTConfig(
output_dir=OUTPUT_DIR,
num_train_epochs=NUM_EPOCHS,
per_device_train_batch_size=BATCH_SIZE,
per_device_eval_batch_size=BATCH_SIZE,
gradient_accumulation_steps=GRAD_ACCUM_STEPS,
learning_rate=LEARNING_RATE,
lr_scheduler_type="cosine",
warmup_ratio=WARMUP_RATIO,
bf16=True,
max_length=MAX_LENGTH,
packing=False,
logging_steps=10,
logging_first_step=True,
disable_tqdm=True,
logging_strategy="steps",
eval_strategy="epoch",
save_strategy="epoch",
save_total_limit=2,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
push_to_hub=True,
hub_model_id=HUB_MODEL_ID,
hub_strategy="end",
report_to="none",
assistant_only_loss=True,
seed=42,
dataloader_num_workers=2,
)
trainer = SFTTrainer(
model=MODEL_NAME,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
peft_config=peft_config,
)
print("Starting training...")
train_result = trainer.train()
print(f"Done! Loss: {train_result.training_loss:.4f}")
trainer.save_model()
# Merge LoRA into base model for GGUF compatibility
print("Merging LoRA...")
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
merged = AutoPeftModelForCausalLM.from_pretrained(OUTPUT_DIR, dtype=torch.bfloat16, device_map="auto")
merged = merged.merge_and_unload()
merged_dir = f"{OUTPUT_DIR}-merged"
merged.save_pretrained(merged_dir)
tok = AutoTokenizer.from_pretrained(MODEL_NAME)
tok.save_pretrained(merged_dir)
#print("Pushing to Hub...")
#merged.push_to_hub(HUB_MODEL_ID, commit_message="Merged model - LoRA into Qwen2.5-0.5B for game commands")
#tok.push_to_hub(HUB_MODEL_ID, commit_message="Add tokenizer")
#print(f"Model: https://huggingface.co/{HUB_MODEL_ID}")
# Quick inference test
print("\nInference test:")
from transformers import pipeline
pipe = pipeline("text-generation", model=merged_dir, dtype=torch.bfloat16, device_map="auto")
system = "You are a game command interpreter. The user's input is noisy speech-to-text from a microphone in a gaming environment. Your job is to identify the intended game command from the noisy input.\n\nValid commands: Attack, CastSpell, CloseDoor, Crouch, Defend, DropItem, Heal, Interact, Jump, OpenDoor, OpenInventory, OpenMap, PauseGame, PickUp, Reload, SaveGame, Sprint, Stop, SwitchWeapon, UseItem\n\nRespond with ONLY a JSON object: {\"command\": \"<CommandName>\"}\nIf the input doesn't match any command, respond: {\"command\": \"Unknown\"}"
for inp in ["opun da dur", "attak enami", "hel me", "pic it op", "hello how are you"]:
out = pipe([{"role": "system", "content": system}, {"role": "user", "content": inp}], max_new_tokens=50, do_sample=False)
print(f" '{inp}' -> {out[0]['generated_text'][-1]['content']}")
print("\n\nGGUF conversion (for llama.cpp):")
print(f" python llama.cpp/convert_hf_to_gguf.py {HUB_MODEL_ID} --outtype f16 --outfile model.gguf")
print(f" ./llama-quantize model.gguf model_q4.gguf Q4_K_M")
if __name__ == "__main__":
main()

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