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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,143 @@
+---
+language:
+  - en
+license: apache-2.0
+library_name: transformers
+tags:
+  - anomaly-detection
+  - observability
+  - distributed-systems
+  - crypto-exchange
+  - lora
+  - fine-tuned
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+---
+
+# KrystalineX Anomaly Analyzer
+
+A fine-tuned language model for analyzing performance anomalies in distributed crypto exchange systems. Built for the [KrystalineX](https://github.com/MoebiusX/KrystalineX) observability platform.
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| **Base Model** | [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) |
+| **Method** | LoRA (Low-Rank Adaptation) |
+| **Trainable Parameters** | 18.5M / 1.56B (1.18%) |
+| **Training Framework** | [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) |
+| **Precision** | BF16 with 8-bit quantized base |
+| **License** | Apache 2.0 |
+
+## Intended Use
+
+This model analyzes OpenTelemetry trace data and correlated system metrics to identify root causes of performance anomalies in microservice architectures. Given an anomaly report containing span attributes, latency deviations, and system metrics (CPU, memory, error rates), the model produces:
+
+- **Summary** of the likely cause
+- **Root causes** with reasoning based on actual metric values
+- **Actionable recommendations** for remediation
+- **Confidence level** assessment
+
+### Example Input
+
+```
+Analyze this performance anomaly:
+- Service: kx-exchange
+- Operation: pg-pool.connect
+- Duration: 286.94ms (expected: 0.44ms ± 10.66ms)
+- Deviation: 26.88σ
+- CPU Usage: 0.5%, Memory: 142MB, Error Rate: 0.0%
+```
+
+### Example Output
+
+```
+SUMMARY: The pg-pool.connect operation experienced extreme latency due to
+connection pool exhaustion requiring a new TCP connection to PostgreSQL.
+
+CAUSES:
+- Connection pool was empty, forcing a new connection establishment
+- TCP connect span of 264ms confirms network-level connection setup
+- Idle timeout (30s) likely evicted pooled connections
+
+RECOMMENDATIONS:
+- Increase minimum pool size to maintain warm connections
+- Reduce idle timeout or implement connection keepalive
+- Add connection pool metrics to monitoring
+
+CONFIDENCE: high
+```
+
+## Training Details
+
+### Dataset
+
+22 expert-curated examples of anomaly analysis from a production crypto exchange platform. Each example pairs real OpenTelemetry trace data with expert analysis that corrects common LLM hallucinations (e.g., citing "high CPU usage" when CPU is at 0.5%).
+
+### LoRA Configuration
+
+| Parameter | Value |
+|---|---|
+| Rank (r) | 16 |
+| Alpha | 32 |
+| Dropout | 0.05 |
+| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Epochs | 3 |
+| Learning Rate | 2e-4 |
+| Scheduler | Cosine |
+| Warmup Ratio | 0.1 |
+| Batch Size | 2 (micro) × 4 (grad accum) = 8 effective |
+| Optimizer | AdamW |
+| Sequence Length | 2048 |
+
+### Training Results
+
+- **Training Loss**: 2.41
+- **Training Time**: ~5 minutes on NVIDIA Turing GPU (sm_75)
+- **VRAM Usage**: ~1.9GB training, ~6.8GB cache
+
+## Usage
+
+### With Ollama
+
+```bash
+ollama run anomaly-analyzer "Analyze: service latency 500ms, expected 50ms, CPU 0.1%"
+```
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("XavierThibaudon/anomaly-analyzer")
+tokenizer = AutoTokenizer.from_pretrained("XavierThibaudon/anomaly-analyzer")
+
+prompt = "Analyze anomaly: kx-exchange GET 500ms, expected 50ms, CPU 0.1%"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- Trained on a small dataset (22 examples) — results improve significantly with more training data
+- Optimized for the KrystalineX platform's specific service topology
+- Best results when prompts include correlated system metrics alongside trace data
+- May hallucinate metric interpretations for scenarios not represented in training data
+
+## Citation
+
+```bibtex
+@misc{krystalinex-anomaly-analyzer,
+  title={KrystalineX Anomaly Analyzer},
+  author={Xavier Thibaudon},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/XavierThibaudon/anomaly-analyzer}
+}
+```

added_tokens.json

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
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+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
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+    "Qwen2ForCausalLM"
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+}

generation_config.json

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