Upload fine-tuned model based on Qwen/Qwen3-1.7B. Run: placeholder_run_1748694791

README.md

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+---
+license: apache-2.0
+tags:
+- fine-tuned
+- text-generation
+- qwen 
+# Add your base model tag e.g., - qwen3-1.7b
+- oneplusaries2 
+# Add task-specific tags:
+# - math-reasoning
+# - tree-of-thoughts 
+# - custom-pipeline
+pipeline_tag: text-generation
+---
+
+# Fine-tuned Model: ziadrone/oneplusaries2
+
+This model is a fine-tuned version of `Qwen/Qwen3-1.7B`.
+It has undergone a custom fine-tuning process which may include techniques like Tree-of-Thoughts data generation and/or specific policy optimization methods (e.g., GRPO).
+
+## Fine-tuning Details
+- **Base Model**: `Qwen/Qwen3-1.7B`
+- **Fine-tuning Data Source**: Data was likely generated or selected based on problems from sources like `HuggingFaceH4/MATH-500` and/or other custom datasets, processed to align with a structured reasoning format.
+  - The SFT/generated dataset associated with this model (if pushed) might be found at: [huggingface.co/datasets/ziadrone/dataset-for-oneplusaries2](https://huggingface.co/datasets/ziadrone/dataset-for-oneplusaries2)
+- **Training Objective**: To improve performance on tasks requiring step-by-step reasoning and to adhere to specific structured output formats (e.g., involving `<reasoning>` and `<answer>` tags).
+
+## Intended Uses & Limitations
+This model is the result of an experimental fine-tuning process. Its performance should be carefully evaluated for your specific use case. It is primarily aimed at tasks that benefit from detailed, structured reasoning.
+
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "ziadrone/oneplusaries2"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# To use with a GPU:
+# model.to("cuda")
+
+# Example prompt structure (adapt to your model's training):
+# SYSTEM_PROMPT = "Your system prompt here..." # The system prompt used during training
+# user_problem = "Your problem statement here..."
+# messages = [
+#     {"role": "system", "content": SYSTEM_PROMPT},
+#     {"role": "user", "content": user_problem}
+# ]
+# input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+# inputs = tokenizer(input_text, return_tensors="pt") # .to("cuda" if using GPU)
+
+# outputs = model.generate(**inputs, max_new_tokens=512, pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id)
+# response_text = tokenizer.decode(outputs, skip_special_tokens=True) 
+# # Note: The response_text might include the prompt depending on generation settings.
+# # You might need to slice it to get only the generated part.
+# # generated_output = response_text[len(input_text):] if response_text.startswith(input_text) else response_text
+# print(response_text) ```