Welcome to ElasticExpert-7B-Coder, a specialized Assistant fine-tuned explicitly for Elasticsearch (ES|QL & Painless) and general Site Reliability Engineering (SRE) tasks.

Instead of being a jack-of-all-trades, this model was surgically trained to act as a Tier-3 DevOps engineer. It excels at formulating advanced Elasticsearch queries, diagnosing cluster health vectors, and resolving complex API payloads.

🚀 The Mission

The goal of this project was to understand the boundaries of "In-House Foundation Model Training". We wanted to compress the vast, scattered documentation of Elasticsearch (spanning complex Markdown and Asciidoc formats) into a highly efficient 7-Billion-Parameter brain that could run locally, fully offline, and blisteringly fast on consumer hardware.

🏗️ Architecture & Training Pipeline

This model wasn't trained on random web-scrapes. It is the product of a highly orchestrated Teacher-Student Synthetic Pipeline, executed entirely on a single NVIDIA DGX Spark (Grace Blackwell GB10).

1. Data Engineering (The "Teacher")

We built an autonomous scraper to extract the official Elasticsearch _docs/reference repository. To transform raw manuals into intelligent Q&A formats, we orchestrated a massive Teacher Model (gpt-oss:120b). Operating under strict VRAM orchestration (keep_alive=0), the 120-Billion-Parameter teacher digested the raw technical data and distilled 1,828 high-precision, logically robust instruction pairs, focusing heavily on the new ES|QL syntax and Painless scripting.

2. Supervised Fine-Tuning (The "Student")

We took Qwen2.5-Coder-7B as our base cortex. Utilizing PEFT / LoRA (r=16, alpha=32), we trained the model purely in Bfloat16 (bypassing 4-bit quantization degradation) directly leveraging the GB10's Unified Memory pool.

Real-Time Observability

Rather than relying on external SaaS tools like Weights & Biases, we built a native PyTorch TrainerCallback that streamed the Loss, Learning Rate, and Epoch data directly into our own Elasticsearch logging cluster via API-Key authentication. This gave us a millisecond-precision dashboard in Kibana to monitor the convergence (which hit a beautiful "Sweet Spot" Loss of 0.56).

💥 Overcoming Silicon-Level Challenges (Didactics)

Training a model on bleeding-edge hardware is never a straight line. We encountered and resolved two critical architectural challenges that we are sharing for the Open Source community:

Flash Attention 2 Architecture Constraints: The NVIDIA NGC PyTorch container (24.01) was compiled before the GB10 (sm_121 architecture) existed in mainline. Enforcing strict flash-attn v2 dependencies caused the container to crash. We gracefully bypassed this by falling back to PyTorch 2.x native SDPA (Scaled Dot Product Attention), which perfectly maps to the Blackwell framework, delivering mathematically identical speedups.
Thermal Throttling & Register Reads: Deep into Epoch 1.37, the massive 114GB Unified Memory bandwidth provoked an intermittent hardware anomaly (gpuHandleSanityCheckRegReadError_GH100 / 0xbadf5600). The CPU failed to fetch a register over the C2C NVLink layer due to high computational saturation early in the Blackwell silicon lifecycle. The Fix: We implemented extreme Idempotent Checkpointing (save_steps=20). When the Linux Kernel dropped the GPU, Docker executed an unless-stopped reboot, PyTorch parsed the local disk, detected the latest checkpoint-150, and seamlessly resumed training. Zero data lost.
Inference Architecture (Tokenizer Corruption via GGML): Qwen2.5 utilizes a massive 152,000-token BPE vocabulary. When attempting to natively quantize the Safetensors directly to q8_0 via legacy llama.cpp tools (convert_hf_to_gguf.py), the tensor alignment broke, resulting in infinite context hallucination loops. The VLLM Standard: We abandoned llama.cpp quantization entirely. By feeding the raw, unquantized Float16 LoRA-merged Safetensors directly into a vLLM container on the DGX, the engine recognized the GB10 (sm_121) architecture and successfully compiled Flash Attention 2 in-flight, dropping inference latency to microseconds while preserving 100% of the SRE diagnostic brilliance.

🧪 Validation & Benchmark (Real-World Stress Testing)

To validate its SRE diagnostic capabilities, we fed the model a massive, raw, and unparsed JSON payload from an enterprise Elasticsearch _health_report endpoint indicating a yellow cluster status.

The Model's Output:

markdown

**Diagnóstico SRE**

* **Estado global**: *yellow* – el clúster tiene una alerta de *shards_availability* (121 shards sin asignar).
* **Almacenamiento**: *green* – no hay índices con bloqueo de solo lectura, todos los nodos tienen suficiente espacio.
* **Disponibilidad de shards**:
  * **Impacto**: 121 shards sin asignar afecta la capacidad de búsqueda y persistencia HA.
  * **Causa**: Los índices tienen un número de réplicas mayor que la capacidad de los tiers de datos disponibles. Por ejemplo, si un índice tiene 3 réplicas y solo hay 2 nodos en el tier *data_hot*, Elasticsearch no puede asignar todas las réplicas a nodos distintos.
  * **Acción Táctica**: 
    1. **Verificar la capacidad de los tiers** – ejecuta `GET _nodes?filter_path=nodes.*.attributes.tier`.
    2. **Reducir el número de réplicas** – si no es necesario, actualiza la política de ILM para reducir `number_of_replicas`.

(Result: Flawless abstraction. Zero hallucination. Actionable endpoints provided instantly).

⚠️ Current Status & Limitations (Alpha v1.0)

This is my very first iteration diving into the deep end of LLM Fine-Tuning and Hardware Management on a DGX GB10. While the diagnostic reasoning of the model is extremely sharp (as seen above), the model is currently in an Alpha state and is NOT 100% Plug-and-Play for generic Ollama containers out-of-the-box (OOB).

The "EOS Amnesia" Bug: Because the SFT phase was trained strictly on Qwen2.5-Coder-7B (a Base model) without explicitly injecting <|im_end|> ChatML termination tokens into the synthetic dataset, the model occasionally forgets how to "hang up the phone" once it finishes its brilliant SRE report. It will complete the diagnosis perfectly, but may trail off hallucinating random tokens or punctuation.

How to run it today: Do not use llama.cpp to natively quantize this to q8_0 (it will break the 152k BPE vocabulary). The only official recommendation for v1.0 is to run the uncompressed PyTorch/Bfloat16 weights natively via vLLM and enforce stop tokens from the Python client API:

"stop": ["<|im_end|>", "<|endoftext|>", "user\n"]

Roadmap for v2.0 (The True OOB Experience):

Re-format the synthetic dataset to explicitly append ChatML sequence terminators.
Fine-Tune over an Instruct base model instead of a pure Base completion model.
Merge LoRA weights utilizing unsloth for mathematically flawless Hugging Face ecosystem preservation.

💻 How to Use (Ollama / Llama.cpp)

(Próximamente... Enlazaremos aquí cómo descargarlo en GGUF o acoplarlo directamente vía LoRA)

Desarrollado in-house en un ecosistema Elastic y NVIDIA DGX. 2026.

ocuil/ElasticExpert-7B-Coder

🧠 ElasticExpert-7B-Coder: An SRE Foundation Model