# --- Inference settings (local or huggingface) ---
NUM_GENERATIONS = 300
MAX_NEW_TOKENS  = 256
MIN_NEW_TOKENS  = 30
TEMPERATURE     = 0.7
TOP_K           = 30
TOP_P           = 0.9
REP_PENALTY     = 1.2
DO_SAMPLE       = True
INFERENCE_BATCH = 5

# --- Judge settings ---
JUDGE_MODEL    = "qwen/qwen3-32b"
JUDGE_MAX_TOKENS     = 80
JUDGE_TEMPERATURE    = 0.0

higher with a great taste of the damp and a more vibrant beauty than others.  It also has its own unique style.  They have been looking for different shades, and they have a wide range of shades to suit their tastes.  Some people find themselves in love with a rich variety of styles that are popular among those who feel it's beautiful. For example, many brands like Nielson are known for their bright colors and bold color.  Here is a simple recipe:

- What is your favorite type of hair?
- How do you use your skin to make a smooth look?
- Can I use my hair instead of cotton or wool?
- Can I use my hair instead of cotton?

Section 3: Practical Examples and Real-World Scenarios
Let's say you want to create an outfit that looks very well on your face. You can choose from a traditional one such as Jyubande, Scikit-learn, Shoeson, Ring and Towl, or Aramis, or Ladies. Your hair should be slightly darker than other options. On the other hand, if you're looking for clothes with a black dress, you may need to paint a white palette (like red or blue) around your skin.

Conclusion:
In this lesson, we learned how to set up a dress for a fashion design by adding color and texture, pairing and tanks, and sewing techniques. By following these guidelines, you will become a savvy designer and be able to create a personal touch of clothing while enjoying a strong look at all things.

 Machine learning is a method of computer programs that allows us to learn from each other and perform tasks more efficiently. This makes it easier for students to find information about the world around them, whether they are interested in learning or not.
## III. Why is AI important?
Answer: AI is essential because it helps us make informed decisions based on data and decision-making. It enables us to identify trends, patterns, and relationships with others, which can help us stay focused and motivated during difficult times. Additionally, AI technology has been used by companies like Amazon, Apple, and Google, helping businesses improve their operations, productivity, and profitability.

 a type of gympos that uses the OA to produce an image. This technique has many applications in various fields, including:

1. **Interpreting:** An example of this method is a process by which you create an image using the ERGE model to produce images and other images.

2. **Conditioning:** A method can be used to manipulate the image using the ERGE model. For instance, if you have 3 cmd4(3) and 4 cmd5(3).

**Glossary:**
- Image processing: The process of converting images into images.
- ERPPO: Visual Modeling for images with high quality pixels.
- Image processing: The process of converting images from image to image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image on image image image image image image image image image image image image image image image image image image? Image image image images image image image image image image image image.

### Section 1: Introduction to Image Image Images
Artography is a powerful tool that allows us to visualize and visualize images visually. It provides a wide range of tools and techniques that allow us to observe and understand images accurately. In this lesson, we will focus on image image image images image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image images image image image image image image image image image images image image image image image image image image image image image image image image image image image image image image image image image.

Example 2: Image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image image or image image image image image images image image image image image image image image image image image image image image.

1.
2. It takes four-dimensional space in a triangle, where each inch of its shape has two sides and one or more angles to represent them. This creates a sense of depth and meaning that the sky is beautifully different from other shapes like circles and squares.
3. It can be used as a symbol for navigation and appreciation. For example: "The world is just beginning on the earth but still changing itself."
4. Color Scheme (rather colors)

Here's an example of how I learned about color theory:

import matplotlib as pdipis as y = ['Size', 'Size'].last_id(x))

Explanation:
To understand the concept of color theory, let's say you have three sides and one side, with the following equation:

Number of sides of length - 1 * 3 = 10^0/9
Total length - 2 + 5 = 12
Step 1: Find the top of the left hand
If there are no sides of length or number, we would be assigned to the bottom of the right hand. For example: "The world is not only darker than traditional, but also lightning."

Explanation:
Now, let's look at some examples of color theory:

1. What is the difference between color theory and composition?
Answer: Color theory is the study of colors and their properties.
2. How do you know which one part of the left hand?
Answer: To see if it means looking upwards and finding the right pair of shades, they may use geometric patterns or textures.
3. How does a color scheme benefit us?
Answer: A color scheme benefits both sides by providing a visual representation of the same value. Some examples include simplicity, creativity, and minimalism.
## Section 4: Practical Examples
Let's consider an example of how color theory can be applied in everyday life. Suppose you want to add a pair of shoes to your home. You could start by taking a pair of shoes with a black dress, then sketch out the size of the hips. Then, you might use a variety of colors to create a balanced and visually appealing shoe. Once you have chosen the type of shirt, you might choose a pair of shoes that complements the specific design elements. Finally, you might apply the appropriate pair of shoes to create a cohesive experience.

# =============================================================================
# Inference
# =============================================================================


MODEL_DIR       = "Harley-ml/Tenete-8M"   # path
TOKENIZER_PATH = MODEL_DIR

# --- Generation settings ---
PROMPT             = "The"   # prompt
MAX_NEW_TOKENS     = 256
TEMPERATURE        = 0.7
TOP_P              = 0.95
TOP_K              = 30
REPETITION_PENALTY = 1.2
DO_SAMPLE          = True

# =============================================================================

import torch
from pathlib import Path
from transformers import (
    AutoModelForCausalLM,
    PreTrainedTokenizerFast,
    AddedToken,
)

# ---------------------------------------------------------------------------
# Device
# ---------------------------------------------------------------------------

device = (
    "cuda" if torch.cuda.is_available() else
    "mps"  if torch.backends.mps.is_available() else
    "cpu"
)
print(f"Device : {device}")

# ---------------------------------------------------------------------------
# Tokenizer  (mirrors training setup)
# ---------------------------------------------------------------------------

def load_tokenizer(path: str):
    p = Path(path).resolve()
    if not p.exists():
        raise FileNotFoundError(f"Tokenizer not found: {p}")
    tok = PreTrainedTokenizerFast(tokenizer_file=str(p))
    specials = {}
    if tok.bos_token is None: specials["bos_token"] = AddedToken("<|bos|>", special=True)
    if tok.eos_token is None: specials["eos_token"] = AddedToken("<|eos|>", special=True)
    if tok.unk_token is None: specials["unk_token"] = AddedToken("<|unk|>", special=True)
    if tok.pad_token is None:
        if tok.eos_token is not None:
            tok.pad_token = tok.eos_token
        else:
            specials["pad_token"] = AddedToken("<|pad|>", special=True)
    if specials:
        tok.add_special_tokens(specials)
    tok.padding_side = "left" 
    return tok

print("Loading tokenizer...")
tokenizer = load_tokenizer(TOKENIZER_PATH)
print(f"  Vocab size : {tokenizer.vocab_size}")
print(f"  BOS        : {tokenizer.bos_token!r}")
print(f"  EOS        : {tokenizer.eos_token!r}")
print(f"  PAD        : {tokenizer.pad_token!r}  (id={tokenizer.pad_token_id})")

# ---------------------------------------------------------------------------
# Model
# ---------------------------------------------------------------------------

print(f"\nLoading model from {MODEL_DIR} ...")
model = AutoModelForCausalLM.from_pretrained(
    MODEL_DIR,
    dtype=torch.float16 if device == "cuda" else torch.float32,
    low_cpu_mem_usage=True,
)

model.eval()
model.to(device)

total_params = sum(p.numel() for p in model.parameters())
print(f"  Parameters : {total_params:,}")

# ---------------------------------------------------------------------------
# Generation helper
# ---------------------------------------------------------------------------

def generate(
    prompt: str             = PROMPT,
    max_new_tokens: int     = MAX_NEW_TOKENS,
    temperature: float      = TEMPERATURE,
    top_p: float            = TOP_P,
    top_k: int              = TOP_K,
    repetition_penalty: float = REPETITION_PENALTY,
    do_sample: bool         = DO_SAMPLE,
) -> str:
    
    bos         = tokenizer.bos_token or ""
    full_prompt = bos + prompt

    inputs = tokenizer(
        full_prompt,
        return_tensors="pt",
        add_special_tokens=False,
    ).to(device)
    inputs.pop("token_type_ids", None)  # Qwen3 doesn't use this

    gen_kwargs = dict(
        max_new_tokens     = max_new_tokens,
        do_sample          = do_sample,
        repetition_penalty = repetition_penalty,
        eos_token_id       = tokenizer.eos_token_id,
        pad_token_id       = tokenizer.pad_token_id,
    )
    if do_sample:
        gen_kwargs["temperature"] = temperature
        gen_kwargs["top_p"]       = top_p
        gen_kwargs["top_k"]       = top_k

    with torch.inference_mode():
        output_ids = model.generate(**inputs, **gen_kwargs)

    # Strip the prompt tokens so we only return what was generated
    prompt_len = inputs["input_ids"].shape[-1]
    new_ids    = output_ids[0][prompt_len:]
    return tokenizer.decode(new_ids, skip_special_tokens=True)


# ---------------------------------------------------------------------------
# Run
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    print(f"\nPrompt : {PROMPT!r}")
    print("-" * 60)

    output = generate(PROMPT)

    print("Generated:")
    print(output)