Update README.md

README.md

@@ -1,11 +1,101 @@
+Here is a complete, professional, and well-structured Hugging Face model card (README.md) designed specifically for WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled.
+You can copy and paste the raw Markdown block below directly into your repository’s README.md file.
 ---
+license: apache-2.0
+tags:
+- text-generation
+- code
+- reasoning
+- gemma
+- safe-tensors
+- text-generation-inference
+base_model: google/gemma-2-2b
+pipeline_tag: text-generation
 library_name: transformers
-datasets:
-- WithinUsAI/Gemini_3.2_Pro_Distilled
-- WithinUsAI/gemini_3.5_flash_distilled_25k
-- WithinUsAI/codegemma_gemini_pro_32_distilled_25k
-- WithinUsAI/GeminiPro3.2_max_distill_god_seed_25k
-- WithinUsAI/DEEPMIND_Alpha_Distilled
-base_model:
-- google/codegemma-1.1-2b
----
+language:
+- en
+---
+
+# Gemini3.5-Code.Reasoner-2b-Distilled
+
+Gemini3.5-Code.Reasoner-2b-Distilled is a lightweight, high-performance reasoning model designed for code generation, debugging, and complex logical workflows. Built on top of the robust **Gemma 2 2B (`GemmaForCausalLM`)** architecture, this model integrates a specialized low-rank adaptation (LoRA) layer to embed advanced chain-of-thought (CoT) reasoning paths.
+
+The model is distilled from larger frontier reasoning pipelines, bringing complex multi-step code planning capabilities into an efficient 2-billion parameter footprint that can easily run locally or on edge devices.
+
+## Model Details
+
+- **Developed by:** WithinUsAI
+- **Model Type:** Causal Language Model (Fine-tuned / Distilled)
+- **Base Model:** `google/gemma-2-2b`
+- **Architecture:** GemmaForCausalLM with LoRA Adapters
+- **Language(s):** English (Primary), Code languages (Python, JavaScript, C++, Go, etc.)
+- **License:** Apache 2.0
+
+## Intended Uses & Limitations
+
+### Ideal Use Cases
+- **Local Code Assistance:** Fast autocompletion, code translation, and docstring generation on consumer-grade hardware.
+- **Reasoning-focused Scripting:** Handling multi-step software logic tasks where the model must think step-by-step before writing out a block of code.
+- **Mobile & Edge Deployments:** Thanks to its 2B parameter scale, it is optimal for low-latency setups.
+
+### Limitations
+- **Parameter Constraints:** Being a 2B model, it may struggle with highly architectural software design patterns compared to massive 70B+ or closed-source models.
+- **Hallucinations:** Like all LLMs, it can occasionally generate syntax errors or deprecations. Always test generated code in a secure sandboxed environment.
+
+## Quickstart Guide
+
+### Using Transformers Pipeline
+
+```python
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled", device_map="auto")
+
+prompt = """<bos>Answer the following coding problem by thinking step-by-step:
+Write a Python function to find the longest palindromic substring in a string.
+"""
+
+outputs = pipe(prompt, max_new_tokens=512, temperature=0.3)
+print(outputs[0]["generated_text"])
+
+Direct Model Load (AutoModelForCausalLM)
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+inputs = tokenizer("<bos>Write a quick bash script to parse logs for 404 errors.", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+High-Performance Deployment via vLLM
+For production setups or fast local servers, you can serve the model using vLLM:
+# Install vLLM from pip
+pip install vllm
+
+# Start the OpenAI-compatible vLLM server
+vllm serve "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled"
+
+You can then query it via curl:
+curl -X POST "http://localhost:8000/v1/completions" \
+	-H "Content-Type: application/json" \
+	--data '{
+		"model": "WithinUsAI/Gemini3.5-Code.Reasoner-2b-Distilled",
+		"prompt": "<bos>Explain the time complexity of QuickSort.",
+		"max_tokens": 512,
+		"temperature": 0.2
+	}'
+
+Repository Structure
+The weight distribution includes both the base model layers and structural adapter targets for easy loading:
+ * model.safetensors (~5.01 GB) - The primary model weights.
+ * adapter_model.safetensors (~49.9 MB) & adapter_config.json - LoRA configuration fine-tuned for code extraction and reasoning properties.
+ * tokenizer.json & tokenizer_config.json - Tokenizer structures mapped to the base Gemma profile.
+---