Publish initial SparkBotLLM Q4 preview and adaptation report

.gitattributes

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+SparkBotLLM-1.7B-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

@@ -0,0 +1,35 @@
+---
+library_name: peft
+base_model: LiquidAI/LFM3-1.7B
+tags: [dgx-spark, sparkbot, gguf, lora, text-generation]
+license: other
+license_name: lfm-open-license-v1
+license_link: LICENSE
+language: [en]
+pipeline_tag: text-generation
+---
+# SparkBotLLM 1.7B · initial preview
+
+A local assistant for DGX Spark setup and local inference, adapted from LiquidAI LFM3 1.7B. Sparkbot supplies the live inspection tools, reviewed actions and dated support references. Model inference can run privately on CPU with a 4K context and one request at a time.
+
+This is a small first adaptation, not a complete or continuously updated authority on NVIDIA hardware or new models. Use hardware tools for measurements and current primary sources for releases. Keep command review enabled.
+
+## Files and use
+
+`SparkBotLLM-1.7B-Q4_0.gguf` is the merged, CPU-ready Q4_0 model with Q6_K token embeddings. The adapter files require the exact BF16 base revision `ac2586a34f1a1c03329dbdcdefabd8b6478ddaa7`; do not apply them to LiquidAI’s separate QAD quantized model. The GGUF embeds the original chat template. Disable thinking for short helper turns.
+
+Run with a recent LFM3-capable llama.cpp server, for example:
+
+```sh
+llama-server --model SparkBotLLM-1.7B-Q4_0.gguf --alias SparkBotLLM --host 127.0.0.1 --port 10004 --ctx-size 4096 --parallel 1 --threads 4 --n-gpu-layers 0 --jinja --chat-template-kwargs '{"enable_thinking":false}'
+```
+
+## Adaptation and checks
+
+242 authored synthetic support examples, 44 dated support references, LoRA rank 8/alpha 16, assistant-only loss, AdamW learning rate 0.00005. A 192-step run selected step 128 by lowest validation loss. Six domain validation examples were used for selection: mean loss fell from 4.62 to 3.32. They are not an independent test set. Five independent Q4 tool-routing smoke checks passed after export. These narrow checks do not establish broad factual accuracy, safety or reliability. Peak training CUDA allocation was 3.59 GiB. Reports are included.
+
+The adaptation covers setup, troubleshooting, tool use and local inference concepts. Training examples do not contain the owner’s private chats or credentials. Knowledge can be stale; web retrieval is supplied by the application, not the weights. The 4K serving limit is intentional; long-context quality was not evaluated.
+
+## License and credits
+
+Based on LiquidAI LFM3 1.7B. The retained LFM Open License v1.0 applies, including its commercial conditions. SparkBotLLM is an adaptation, not a model trained from scratch. LiquidAI supplies the base weights and architecture; PyTorch, Transformers and PEFT support training; llama.cpp supplies GGUF conversion and the CPU runtime. Reference summaries credit NVIDIA, Docker, TensorFold and model publishers in the Sparkbot dataset. Sparkbot is an independent project by x00byte.

SparkBotLLM-1.7B-Q4_0.gguf

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adapter_config.json

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+  "r": 8,
+  "rank_pattern": {},
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+  "use_rslora": false,
+  "velora_config": null
+}

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quantized-checks.json

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+    "expected": "inspect_sparks",
+    "passed": true,
+    "message": {
+      "role": "assistant",
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training-report.json

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+  "limits": "Small synthetic pilot. Held-out loss and five routing checks do not establish broad factual accuracy or production quality."
+}