--- base_model: rikunarita/Qwen3-4B-Thinking-2507-Genius-Coder tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- ## GGUFF Version https://huggingface.co/rahul7star/Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full/blob/main/rahul7star_Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full-Q5_K_M.gguf ## FULL GGUF List https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full-GGUF # Uploaded finetuned model - **Developed by:** rahul7star - **License:** apache-2.0 - **Finetuned from model :** rikunarita/Qwen3-4B-Thinking-2507-Genius-Coder This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [](https://github.com/unslothai/unsloth) ## This is base model ## Code Implementation ``` import torch import re from transformers import AutoTokenizer, AutoModelForCausalLM # ---------------- Config ---------------- MODEL_NAME = "rahul7star/Qwen3-4B-Thinking-2509-Genius-Coder-AI-Full" # Patch prompt: explicitly tell the model to provide reasoning AND final answer PROMPT = ( "write a python code using pytorch for a simple neural network demo?\n\n" "Please provide step-by-step reasoning inside tags, AND then output the final Python code after ." ) # ---------------- Load Tokenizer & Model ---------------- print("Loading tokenizer...") tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) print("Loading model...") model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True ) if tokenizer.pad_token_id is None: tokenizer.pad_token = tokenizer.eos_token print("✅ Model loaded on device:", model.device) # ---------------- Prepare Messages ---------------- messages = [ {"role": "user", "content": PROMPT} ] # Apply chat template text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(text, return_tensors="pt").to(model.device) input_length = inputs.input_ids.shape[-1] # ---------------- Generate ---------------- with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, do_sample=True, # required for Thinking models temperature=0.9, # encourage variation top_p=0.95, repetition_penalty=1.05, use_cache=True ) # ---------------- Decode ---------------- # Raw output raw_output = tokenizer.decode(outputs[0]) print("\n--- RAW OUTPUT ---") print(raw_output) # Only new tokens generated_tokens = outputs[0][input_length:] sliced_output = tokenizer.decode(generated_tokens, skip_special_tokens=False) print("\n--- SLICED OUTPUT ---") print(sliced_output) # Remove for clarity clean_output = re.sub(r".*?", "", sliced_output, flags=re.DOTALL).strip() print("\n--- CLEANED OUTPUT (after removed) ---") print(clean_output) ``` ## Code Output ``` --- RAW OUTPUT --- <|im_start|>user write a python code using pytorch for a simple neural network demo? Please provide step-by-step reasoning inside tags, AND then output the final Python code after .<|im_end|> <|im_start|>assistant **Planning the step-by-step** I need to create a simple PyTorch neural network. The plan is: import torch and torch.nn; define a neural network with one input layer, one hidden layer (ReLU activation), one output layer, set up random data, train the network using gradient descent and a loss function (MSE), evaluate its performance on new data. Code will be written in a single script following this logic. ```python import torch import torch.nn as nn import numpy as np from torch.utils.data import Dataset, DataLoader # Define a simple dataset class SimpleDataset(Dataset): def __init__(self, n_samples=100): self.n = n_samples # random x between -1 and 1 self.x = np.linspace(-1, 1, self.n).reshape((-1, 1)) self.y = 2 * self.x + np.random.normal(0, 0.1, self.n) def __len__(self): return len(self.x) def __getitem__(self, idx): return torch.tensor(self.x[idx], dtype=torch.float32), \ torch.tensor(self.y[idx], dtype=torch.float32) # Set up data n_train = 60 n_val = 20 n_test = 20 train_data = SimpleDataset(n_samples=n_train) val_data = SimpleDataset(n_samples=n_val) test_data = SimpleDataset(n_samples=n_test) batch_size = 4 train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True) val_loader = DataLoader(val_data, batch_size=batch_size) test_loader = DataLoader(test_data, batch_size=batch_size) # Define the neural net class Net(nn.Module): def __init__(self): super().__init__() self.linear1 = nn.Linear(1, 64) # input size=1 self.relu = nn.ReLU() self.linear2 = nn.Linear(64, 32) self.linear3 = nn.Linear(32, 1) def forward(self, x): x = self.linear1(x) x = self.relu(x) x = self.linear2(x) x = self.relu(x) x = self.linear3(x) return x net = Net() criterion = nn.MSELoss() optimizer = torch.optim.Adam(net.parameters(), lr=0.01) --- CLEANED OUTPUT (after removed) --- ``` ## GGUF Repo https://huggingface.co/spaces/rahul7star/Qwen3-4B-Thinking-2507-Genius-Coder-GGUF-UD-Q4_K_M Samle question asked ``` explain what are these Qwen3.5-27B-Q2_K.gguf 10.5 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q3_K_M.gguf 13.5 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q3_K_S.gguf 12.3 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q4_0.gguf 15.7 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q4_1.gguf 17.2 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q4_K_M.gguf 16.7 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q4_K_S.gguf 15.8 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q5_K_M.gguf 19.6 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q5_K_S.gguf 18.9 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q6_K.gguf 22.5 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-Q8_0.gguf 28.6 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-UD-Q4_K_XL.gguf 16.7 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-UD-Q5_K_XL.gguf 19.6 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-UD-Q6_K_XL.gguf 23.1 GB xet Upload folder using huggingface_hub 1 day ago Qwen3.5-27B-UD-Q8_K_XL.gguf ``` ## Response ``` <<>>**Explaining Qwen3.5-27B variants** These are quantized versions of Qwen3.5-27B, a 27 billion parameter language model, with different precision levels (Q2 to Q8 and "UD") that reduce size and speed. Q2/Q3/K variants offer smaller files (10–12 GB) for fast inference but lower accuracy, while Q5/Q6/Q8/XL variants balance size/accuracy. "UD" indicates mixed-precision quantization for better quality. Each ends with `.gguf`, a format used by tools like llama.cpp. The "K" or "S" suffix denotes quantization strategy (K for K-Means, S for Sparse), and "XL" applies to quantized models with additional optimization. These files enable efficient deployment on limited hardware.<<>>In the Qwen3.5-27B GitHub repository, those are quantized weights for Qwen3.5-27B, the 27B parameter model from Qwen3.5, packaged in GGUF format for use with tools like llama.cpp. - **Q2–Q8**: Quantization precision levels: lower numbers mean less precision, smaller model size, and faster inference. - **K**: K-Means quantization for better accuracy and smoothness. - **S**: Sparse quantization (some weights are zero). - **XL**: Mixed-precision quantization ("UD" = Unstructured, Q4_K_XL, Q5_K_XL, Q6_K_XL, Q8_K_XL) has higher accuracy and lower precision than Q4_0/K, Q5_0/K, Q6_K, Q8_0. - **GGUF**: A standard quantized format used by llama.cpp. The numbers (Q2, Q3, Q4, Q5, Q6, Q8) refer to the bits per weight; Q2=2, Q3=3, Q4=4, Q5=5, Q6=6, Q8=8. The model can be run at different precisions (e.g., Q4_K_M gives a smaller file than Q4_K, Q4_S is even smaller but less accurate). The “-UD” suffix indicates a quantization variant with higher accuracy than the basic Qn_K. For example: - Q2_K: 10.5 GB – minimal precision, very fast but lowest accuracy - Q4_K: 16–17 GB – good balance for most use cases - Q8_K: 28 GB – near full precision - Q4_K_XL: 16–19 GB – optimized accuracy at Q4 level These files can be used with `llama.cpp` or other GGUF-aware frameworks to run Qwen3.5 on devices with limited RAM. <<>>**Finalizing a clean table for readability** The final table provides a clean, structured overview of quantized Qwen3.5-27B GGUF files, showing quantization levels, precision, accuracy trade-offs, and approximate RAM usage per precision level for quick comparison. It clearly distinguishes between Qn_K variants and the “UD”-prefixed variants with improved accuracy, and orders entries by size and accuracy, highlighting the best balance points for different needs.<<>>Here's a clean, tabular breakdown of the quantized Qwen3.5-27B GGUF files for quick comparison: | Quantization Format | Description | Size | RAM Usage | Accuracy Notes | Best For | |------------------------|---------------------------------|----------|---------------|--------------------------------------------|-----------------------------| | `Q2_K` | 2-bit, dense quantization | 10.5 GB | Very low | Lowest accuracy, fastest inference | Minimal RAM, speed focus | | `Q3_K_M` | 3-bit, dense | 13.5 GB | Low | Good speed/accuracy balance | Lightweight use cases | | `Q3_K_S` | 3-bit, sparse | 12.3 GB | Low | Slightly lower accuracy than Q3_K_M | Lower memory than Q3_K_M | | `Q4_0` | 4-bit, dense | 15.7 GB | Medium | Standard precision | General use | | `Q4_K_M` | 4-bit, dense + K-means | 16.7 GB | Medium | Balanced accuracy/speed | Most common trade-off | | `Q4_K_S` | 4-bit, sparse | 15.8 GB | Medium | Slightly lower accuracy than Q4_K_M | Lower RAM than Q4_K_M | | `Q4_K_XL` | 4-bit, dense + mixed precision | 16.7 GB | Medium | Higher accuracy than Q4_0/K, faster than Q4_K_XL | Better quality at Q4 level | | `Q5_K_M` | 5-bit, dense | 19.6 GB | High | High accuracy, larger file | High quality tasks | | `Q5_K_S` | 5-bit, sparse | 18.9 GB | High | Slightly lower accuracy than Q5_K_M | Memory-limited tasks | | `Q5_K_XL` | 5-bit, mixed precision (UD) | 19.6 GB | High | Improved accuracy over Q5_0/K | Quality-focused use | | `Q6_K` | 6-bit, dense | 22.5 GB | High | Near full precision | Balanced quality/size | | `Q6_K_XL` | 6-bit, mixed precision (UD) | 23.1 GB | High | Best Q6 accuracy | High quality | | `Q8_0` | 8-bit, dense | 28.6 GB | Full precision | Near full model precision | Full accuracy | | `Q8_K_XL` | 8-bit, mixed precision (UD) | 28.6 GB | Full precision | Highest accuracy for 8-bit | Best 8-bit quality | ### Notes - **Qn_K**: `K` = K-means clustering for smoother weights, better accuracy than basic `Qn_0`. - **Qn_S**: Sparse quantization (some zeros), smaller size but lower accuracy than `Qn_K`. - **Qn_K_XL/UD**: "Unstructured" quantization variants (`-UD`) have better accuracy than standard `Qn_K`. - **RAM**: Numbers are rough estimates; actual usage depends on your system and inference setup. - **Trade-offs**: Lower `Qn` = smaller file, faster inference; higher `Qn` = better accuracy, more RAM. This format highlights the best options for speed vs. quality trade-offs without technical overload. ```