Create README.md

README.md

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+---
+license: apache-2.0
+base_model: meta-llama/Llama-3.2-3B-Instruct
+tags:
+- text-generation
+- fine-tuned
+- conversational
+- lora
+- peft
+- trl
+- sft
+- enterprise
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Model Card for KielMind-Pro
+
+KielMind-Pro is an enterprise-grade, high-performance conversational language model fine-tuned specifically to anchor the premium tier of the KielTech AI production ecosystem. Built upon Meta's `Llama-3.2-3B-Instruct` architecture, the model undergoes an intensive parameter-efficient adaptation process targeting both attention mechanism layers and core multi-layer perceptron (MLP) blocks. 
+
+This model balances dense reasoning capacities and highly structured instruction-following capabilities, making it ideal for robust enterprise automation, complex multi-turn API workflows, and rapid serverless deployment via engines like vLLM.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** KielTech
+- **Shared by:** kiel2
+- **Model type:** Causal Language Model (Transformer Architecture)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** `meta-llama/Llama-3.2-3B-Instruct`
+
+### Model Sources
+
+- **Repository:** https://huggingface.co/kiel2/KielMind-Pro
+
+---
+
+## Uses
+
+### Direct Use
+
+KielMind-Pro is engineered for deployment within high-volume production setups. It directly services complex systemic tasks including:
+- Long-context multi-turn dialogue management.
+- Multi-step reasoning and algorithmic problem-solving.
+- Strict structural compliance (JSON parsing, code syntax outputting, and precise API calling schemas).
+
+### Out-of-Scope Use
+
+This model is not intended for unmonitored critical safety systems, malicious text generation, or downstream applications that lack safety guardrails or validation layers.
+
+---
+
+## Training Details
+
+### Training Data
+
+The intelligence profile of KielMind-Pro is derived from a highly curated 10,000-sample strategic mixture ingested via real-time cloud streaming (`streaming=True`):
+1. **`mlabonne/FineTome-100k`**: Optimized to maximize natural conversational pacing, verbal crispness, and conversational alignment.
+2. **`Arcee-AI/Llama-3.1-SuperNova-Lite`**: A heavily distilled dataset used to inject advanced multi-step reasoning patterns and complex instruction-following capabilities.
+
+### Training Procedure
+
+Training was completed within a highly optimized 4-bit NormalFloat (`nf4`) workspace, applying the official Llama 3 structural chat template tokens during streaming ingestion to guarantee exact template cohesion.
+
+#### Training Hyperparameters
+
+- **Fine-Tuning Method:** Parameter-Efficient Fine-Tuning (PEFT / LoRA)
+- **LoRA Target Modules:** `q_proj`, `v_proj`, `k_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+- **LoRA Rank (r):** 16
+- **LoRA Alpha:** 32 (Extended context and scaling capability)
+- **Optimization Target:** `paged_adamw_8bit`
+- **Learning Rate:** 2e-4 (with Cosine Decay scheduler)
+- **Batch Configurations:** Per-device batch size of 2, with a Gradient Accumulation Steps configuration of 4.
+- **Max Steps:** 100
+
+---
+
+## Technical Specifications
+
+### Compute Infrastructure
+
+#### Hardware
+- **GPU Type:** NVIDIA T4 Tensor Core GPU (Single Instance Cloud Container)
+- **Memory Optimization:** 4-bit weight loading with `bfloat16` compute precision to maximize gradient calculation throughput within a standard 16GB VRAM constraint.
+
+#### Software
+- **Frameworks:** Hugging Face `transformers`, `peft`, `trl` (Supervised Fine-Tuning Trainer), and `bitsandbytes`.
+
+---
+
+## How to Get Started with the Model
+
+### High-Throughput Production Setup (vLLM)
+For business backend pipelines, loading KielMind-Pro into a vLLM offline engine or server instance provides optimal throughput:
+
+```python
+from vllm import LLM, SamplingParams
+
+# Load the premium enterprise model directly from the Hub
+llm = LLM(
+    model="kiel2/KielMind-Pro",
+    quantization="bitsandbytes",
+    load_format="bitsandbytes",
+    max_model_len=2048
+)
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=256)
+
+prompts = ["<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nGenerate an enterprise system-architecture report summary for KielTech AI.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"]
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    print(output.outputs[0].text)