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README.md

@@ -10,7 +10,7 @@ tags:
   - crypto-exchange
   - lora
   - fine-tuned
-base_model: Qwen/Qwen2.5-1.5B-Instruct
+base_model: meta-llama/Llama-3.2-1B-Instruct
 pipeline_tag: text-generation
 ---
 
@@ -22,9 +22,9 @@ A fine-tuned language model for analyzing performance anomalies in distributed c
 
 | Property | Value |
 |---|---|
-| **Base Model** | [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |
+| **Base Model** | [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |
 | **Method** | LoRA (Low-Rank Adaptation) |
-| **Trainable Parameters** | 1.56M / 1.56B (1.18%) |
+| **Trainable Parameters** | 1.56M / 1.24B (0.13%) |
 | **Training Framework** | [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) |
 | **Precision** | BF16 with 8-bit quantized base |
 | **License** | Apache 2.0 |
@@ -142,7 +142,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 - Trained on 222 examples (22 real + 200 synthetic) — results continue to improve with more real-world data
 - Optimized for the KrystalineX platform's specific service topology (kx-exchange, kx-wallet, api-gateway, order-matcher)
 - Best results when prompts include correlated system metrics alongside trace data
-- Small 1.5B model may not always follow strict output formatting — the parser handles free-form responses gracefully
+- Small 1B model may not always follow strict output formatting — the parser handles free-form responses gracefully
 - May hallucinate metric interpretations for scenarios not represented in training data
 
 ## Citation