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rahul7star
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Model Details
Model Provider
rahul7star
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
https://huggingface.co/spaces/rahul7star/claude-Qwen
text
# Use a pipeline as a high-level helperfrom transformers import pipeline pipe = pipeline("image-text-to-text", model="rahul7star/Qwen3.5-0.8B-Coder-Calude-Full")messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] },]pipe(text=messages) ## CODE ##write a python code to show llm and pytorch use case # Example 1: Basic LLM usagefrom langchain import chat # Create a simple chat modelchat_model = chat.ChatModel.from_chain_model("gpt-3.5-turbo") # Create a conversationmessages = [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Can you calculate the area of a circle with radius 5?" }] # Generate responseresponse = chat_model(messages)print(f"Response: {response}") ## another sample from transformers import pipeline pipe = pipeline("text-generation", model="rahul7star/Qwen3.5-0.8B-Coder-Calude-Full")messages = [ {"role": "user", "content": "write a python code for neural network"},]pipe(messages) -------output---- [{'generated_text': [{'role': 'user', 'content': 'write a python code for neural network'}, {'role': 'assistant', 'content': "Here's a beginner-friendly PyTorch neural network example that builds a simple feedforward neural network with multiple layers.\n\n# Neural Network with PyTorch\n\n```python\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom torch.optim.lr_scheduler import StepLR\nfrom torch.utils.data import TensorDataset, Subset\n\n# === Create the Neural Network ===\n\nclass NeuralNetwork(nn.Module):\n def __init__(self, input_size, hidden_sizes, output_size):\n super(NeuralNetwork, self).__init__()\n \n # Input layer\n self.input = nn.Linear(input_size, hidden_sizes)\n \n # Hidden layers\n for i in range(1, hidden_sizes):\n self.hidden_layer_i = nn.Linear(hidden_sizes, hidden_sizes)\n \n # Output layer\n self.output = nn.Linear(hidden_sizes, output_size)\n \n def forward(self, x):\n # Apply all linear layers\n x = self.input(x)\n for i in range(1, len(self.hidden_layer)):\n x = self.hidden_layer_i[i](x)\n x = self.output(x)\n return x\n\n# === Create Training Data ===\n\ndef create"}]}]This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.
# Use a pipeline as a high-level helperfrom transformers import pipelinepipe = pipeline("image-text-to-text", model="rahul7star/Qwen3.5-0.8B-Coder-Calude-Full")messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] },]pipe(text=messages)## CODE ##write a python code to show llm and pytorch use case# Example 1: Basic LLM usagefrom langchain import chat# Create a simple chat modelchat_model = chat.ChatModel.from_chain_model("gpt-3.5-turbo")# Create a conversationmessages = [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": "Can you calculate the area of a circle with radius 5?" }]# Generate responseresponse = chat_model(messages)print(f"Response: {response}")## another samplefrom transformers import pipelinepipe = pipeline("text-generation", model="rahul7star/Qwen3.5-0.8B-Coder-Calude-Full")messages = [ {"role": "user", "content": "write a python code for neural network"},]pipe(messages)-------output----[{'generated_text': [{'role': 'user', 'content': 'write a python code for neural network'}, {'role': 'assistant', 'content': "Here's a beginner-friendly PyTorch neural network example that builds a simple feedforward neural network with multiple layers.\n\n# Neural Network with PyTorch\n\n```python\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\nfrom torch.optim.lr_scheduler import StepLR\nfrom torch.utils.data import TensorDataset, Subset\n\n# === Create the Neural Network ===\n\nclass NeuralNetwork(nn.Module):\n def __init__(self, input_size, hidden_sizes, output_size):\n super(NeuralNetwork, self).__init__()\n \n # Input layer\n self.input = nn.Linear(input_size, hidden_sizes)\n \n # Hidden layers\n for i in range(1, hidden_sizes):\n self.hidden_layer_i = nn.Linear(hidden_sizes, hidden_sizes)\n \n # Output layer\n self.output = nn.Linear(hidden_sizes, output_size)\n \n def forward(self, x):\n # Apply all linear layers\n x = self.input(x)\n for i in range(1, len(self.hidden_layer)):\n x = self.hidden_layer_i[i](x)\n x = self.output(x)\n return x\n\n# === Create Training Data ===\n\ndef create"}]}]