qwen2.5-0.5b-game-commands-stt/README.md

base_model, library_name, model_name, tags, licence

base_model	library_name	model_name	tags	licence
Qwen/Qwen2.5-0.5B-Instruct	transformers	qwen2.5-0.5b-game-commands-stt	generated_from_trainer sft hf_jobs trl ml-intern	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. It has been trained using 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:

@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:

@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},
}

#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

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.

Generated by ML Intern

This model repository was generated by 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