Create README.md

README.md

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+---
+license: mit
+datasets:
+- CreitinGameplays/Raiden-DeepSeek-R1-llama3.1-v1
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Llama 3.1 8B R1 Experimental
+
+Chat template format:
+```
+<|start_header_id|>system<|end_header_id|>
+You are focused on providing systematic, well-reasoned responses. Response Structure: - Format: <think>{{reasoning}}</think>{{answer}} - Reasoning: Minimum 6 logical steps only when it required in <think> block - Process: Think first, then answer.
+
+You are a helpful AI assistant named Llama, made by Meta AI.
+<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+How many r's are in strawberry?<|eot_id|><|start_header_id|>assistant<|end_header_id|><think>
+```
+
+Run this model:
+```python
+# test the model
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+
+def main():
+    model_id = "CreitinGameplays/Llama-3.2-3B-Instruct-R1-v1"
+
+    # Load the tokenizer.
+    tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True)
+
+    # Load the model using bitsandbytes 8-bit quantization if CUDA is available.
+    if torch.cuda.is_available():
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            load_in_8bit=True,
+            device_map="auto"
+        )
+        device = torch.device("cuda")
+    else:
+        model = AutoModelForCausalLM.from_pretrained(model_id)
+        device = torch.device("cpu")
+
+    # Define the generation parameters.
+    generation_kwargs = {
+        "max_new_tokens": 2048,
+        "do_sample": True,
+        "temperature": 0.5,
+        "top_p": 0.9,
+        "repetition_penalty": 1.1,
+        "num_return_sequences": 1,
+        "forced_eos_token_id": tokenizer.eos_token_id,
+        "pad_token_id": tokenizer.eos_token_id
+    }
+
+    print("Enter your prompt (type 'exit' to quit):")
+    while True:
+        # Get user input.
+        user_input = input("Input> ")
+        if user_input.lower().strip() in ("exit", "quit"):
+            break
+
+        # Construct the prompt in your desired format.
+        prompt = f"""
+<|start_header_id|>system<|end_header_id|>
+You are focused on providing systematic, well-reasoned responses. Response Structure: - Format: <think>{{reasoning}}</think>{{answer}} - Reasoning: Minimum 6 logical steps only when it required in <think> block - Process: Think first, then answer.
+
+You are a helpful AI assistant named Llama, made by Meta AI.
+<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+How many r's are in strawberry?<|eot_id|><|start_header_id|>assistant<|end_header_id|><think>
+"""
+
+        # Tokenize the prompt and send to the selected device.
+        input_ids = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=True).to(device)
+
+        # Create a new TextStreamer instance for streaming responses.
+        streamer = TextStreamer(tokenizer)
+        generation_kwargs["streamer"] = streamer
+
+        print("\nAssistant Response:")
+        # Generate the text (tokens will stream to stdout via the streamer).
+        outputs = model.generate(input_ids, **generation_kwargs)
+
+if __name__ == "__main__":
+    main()
+```
+
+Or alternatively:
+```python
+import torch
+from transformers import pipeline
+
+model_id = "CreitinGameplays/Llama-3.2-3B-Instruct-R1-v1"
+
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [{"role": "user", "content": "hello there!"}]
+
+outputs = pipe(
+    messages,
+    temperature=0.5,
+    repetition_penalty=1.1,
+    max_new_tokens=2048
+)
+
+print(outputs[0]["generated_text"][-1])
+```