Model Details
This model is a mixed gguf:q2ks of Qwen/Qwen3-235B-A22B generated by intel/auto-round algorithm. Embedding layer and lm-head layer are fallback to 8 bits and non expert layers are fallback to 4 bits. Please refer to Section Generate the model for more details.
How To Use
LLamacpp Inference
./llama-cli -hf Intel/Qwen3-235B-A22B-q2ks-mixed-AutoRound-inc-v1:q2_k_s --conversation
"""
> Hi
<think>
Okay, the user said "Hi". That's a greeting. I should respond in a friendly and welcoming manner. Let me make sure to acknowledge their greeting and offer assistance. Maybe something like, "Hello! How can I assist you today?" That's simple and to the point, and opens the door for them to ask questions or request help. I should keep it approachable and positive. Alright, that should do it.
</think>
Hello! How can I assist you today?
> Create a Flappy Bird game in Python
<think>
Okay, the user wants me to create a Flappy Bird game in Python. Let me think about how to approach this.
First, I remember that Flappy Bird is a simple game where the bird jumps through pipes. So, I need to handle movement, collision detection, and scoring. Python has libraries like Pygame which are good for game development.
Wait, the user might not be familiar with Pygame. I should include installation instructions. Oh right, they might need to install pygame first. So I should mention using pip install pygame.
Now, breaking down the game components. There's the bird, the pipes, and the game loop. Let me outline each part.
For the bird, it needs to move up when the user presses a key, and gravity pulls it down. The position and velocity variables will track that.
Pipes: They should move from right to left, and when they go off-screen, they reset to the right with a random height. Need to generate pipes periodically.
Collision detection: Check if the bird's position overlaps with any pipe. Also, check if the bird hits the ground or goes above the screen.
Game loop: Handle events, update positions, check collisions, and redraw everything. Also, keep track of the score when the bird passes a pipe.
Wait, maybe I should structure the code step by step. Start by initializing Pygame, setting up the screen, colors, and then create classes or functions for Bird, Pipe, and the game mechanics.
But since the user might want a quick example without too much complexity, I can write a simplified version. Let me make sure variables are properly initialized, like bird's velocity, pipe speed, and score.
Also, need to handle the game over screen and restart option. Oh right, when there's a collision, display "Game Over" and wait for the user to quit or restart.
Testing for possible issues: The pipes might not generate correctly if the height is not random enough. Should generate pipes at intervals and check their positions.
Wait, in Pygame, the coordinates start at (0,0) top-left. So bird's y position should be adjusted with gravity. Maybe use rectangles for collision detection.
I should write code that creates a list of pipes, updates their positions each frame, and removes them when off-screen. Then add new pipes as needed.
Also, the score increases when the bird passes a pipe. Need to track which pipes have been passed already to increment the score.
Let me put all together: Initialize Pygame, set up display, define classes or variables, game loop, events, updates, rendering, and collision checks.
Hmm, I should make sure the code is well-commented so the user can follow along. Also, mention that they can adjust parameters like gravity, jump strength, pipe speed, etc.
Wait, possible bugs: If the bird's rectangle isn't correctly sized, collision detection might not work. Need to ensure that the bird's rect and pipe rects are properly defined.
Also, the initial position of the pipes should be off-screen to the right, then move left each frame. When a pipe moves past the left edge, reset its position to the right with new height.
Testing the code might be necessary, but since I can't run it here, I'll make sure to structure it logically. Maybe include a main loop that runs until the user quits, handles keyboard inputs, updates bird and pipes, checks collisions.
So putting it all together, the code should be functional and simple enough for someone learning Pygame to understand. Alright, I'll draft the code step by step now.
</think>
Here's a simplified Flappy Bird implementation in Python using Pygame:
```python
import pygame
import random
# Initialize Pygame
pygame.init()
# Screen dimensions
WIDTH, HEIGHT = 400, 600
screen = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Flappy Bird")
# Colors
WHITE = (255, 255, 255)
BLUE = (0, 0, 255)
GREEN = (0, 255, 0)
BLACK = (0, 0, 0)
# Game variables
gravity = 0.5
bird_velocity = 0
bird_x = 100
bird_y = HEIGHT // 2
pipe_speed = 3
pipe_width = 60
pipe_gap = 150
score = 0
# Pipes list
pipes = []
def create_pipe():
height = random.randint(100, HEIGHT - 150)
return [
pygame.Rect(WIDTH, 0, pipe_width, height),
pygame.Rect(WIDTH, height + pipe_gap, pipe_width, HEIGHT - height - pipe_gap)
]
pipes.append(create_pipe())
# Fonts
font = pygame.font.Font(None, 36)
game_over_font = pygame.font.Font(None, 72)
def draw():
screen.fill(BLUE)
pygame.draw.rect(screen, WHITE, (bird_x, bird_y, 30, 30))
for pipe in pipes:
pygame.draw.rect(screen, GREEN, pipe[0])
pygame.draw.rect(screen, GREEN, pipe[1])
text = font.render(f"Score: {score}", True, WHITE)
screen.blit(text, (10, 10))
def check_collision():
if bird_y < 0 or bird_y + 30 > HEIGHT:
return True
for pipe in pipes:
if pipe[0].colliderect(pygame.Rect(bird_x, bird_y, 30, 30)) or \
pipe[1].colliderect(pygame.Rect(bird_x, bird_y, 30, 30)):
return True
return False
# Game loop
running = True
game_over = False
clock = pygame.time.Clock()
while running:
clock.tick(60) # 60 FPS
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
game_over = False
if event.type == pygame.KEYDOWN and not game_over:
if event.key == pygame.K_SPACE:
bird_velocity = -10
if not game_over:
# Update bird position
bird_velocity += gravity
bird_y += bird_velocity
# Update pipes
for pipe in pipes:
pipe[0].x -= pipe_speed
pipe[1].x -= pipe_speed
# Add new pipes
if pipes[-1][0].x < WIDTH - 200:
pipes.append(create_pipe())
# Remove off-screen pipes
pipes = [pipe for pipe in pipes if pipe[0].x > -pipe_width]
# Check collisions
if check_collision():
game_over = True
# Update score
for pipe in pipes:
if pipe[0].x + pipe_width < bird_x and pipe[0].x + pipe_width + pipe_speed >= bird_x:
score += 1
draw()
if game_over:
game_over_text = game_over_font.render("Game Over", True, WHITE)
screen.blit(game_over_text, (WIDTH//2 - 150, HEIGHT//2 - 36))
pygame.display.flip()
pygame.quit()
```
To use this code:
1. Install Pygame:
```bash
pip install pygame
```
2. Save the code to a file (e.g., `flappy_bird.py`)
3. Run the file using Python
Controls:
- Spacebar to flap the bird
Features:
- Simple physics (gravity)
- Randomly generated pipes
- Score tracking
- Collision detection
- Basic game over screen
You can enhance this basic implementation by:
1. Adding images/sprites for the bird and pipes
2. Implementing sound effects
3. Adding start screen and restart functionality
4. Improving the scoring system
5. Adding difficulty progression
Let me know if you'd like help expanding any specific feature!
"""
Generate the model
auto-round>0.5.1
https://github.com/intel/auto-round/pull/667 this pr should be merge
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round import AutoRound
model_name = "Qwen/Qwen3-235B-A22B"
model = AutoModelForCausalLM.from_pretrained(model_name,
device_map="cpu", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
layer_config = {}
for n, m in model.named_modules():
if n == "lm_head" or isinstance(m,torch.nn.Embedding):
layer_config[n] = {"bits": 8}
elif isinstance(m, torch.nn.Linear) and (not "expert" in n or "shared_experts" in n) and n != "lm_head":
layer_config[n] = {"bits": 4}
autoround = AutoRound(model, tokenizer, iters=0, layer_config=layer_config, nsamples=512)
autoround.quantize_and_save("/dataset/Qwen3-235B-A22B-q2ks", format="gguf:q2_k_s")
Ethical Considerations and Limitations
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of the model, developers should perform safety testing.
Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
- Intel Neural Compressor link
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
Cite
@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
- Downloads last month
- 93
Hardware compatibility
Log In
to view the estimation
2-bit
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support
Model tree for Intel/Qwen3-235B-A22B-q2ks-mixed-AutoRound-inc-v1
Base model
Qwen/Qwen3-235B-A22B