Kiel-2-Sol

Kiel-2-Sol

Kiel-2-Sol 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 meta-llama/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.

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Model Details

Model Description

• Developed by: KielTech
• Model Type: Causal Language Model (Transformer Architecture)
• Base Model: meta-llama/Llama-3.2-3B-Instruct
• Language(s): English
• License: Apache 2.0
• Hugging Face Hub: kiel2/Kiel-2-Sol

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Uses

Direct Use

Kiel-2-Sol 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.

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Training Details

Training Data

The intelligence profile of Kiel-2-Sol 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

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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.

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How to Get Started with the Model

High-Throughput Production Setup (vLLM)

For business backend pipelines, loading Kiel-2-Sol into a vLLM offline engine or server instance provides optimal throughput:

from vllm import LLM, SamplingParams

# Load the premium enterprise model directly from the Hub
llm = LLM(
    model="kiel2/Kiel-2-Sol",
    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)