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Model: divahno/cx-SmolLM2-360M-Q8_0 Source: Original Platform
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license: apache-2.0
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base_model: HuggingFaceTB/SmolLM2-360M-Instruct
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tags:
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- llama-cpp
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- gguf
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- cx-analytics
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- fine-tuned
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- lora
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model_type: llama
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quantized_by: llama.cpp
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pipeline_tag: text-generation
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---
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# CX SmolLM2-360M Q8_0 GGUF
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Fine-tuned [SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) for **CX (Customer Experience) analytics insights** — part of the Action-XM AI Guide system.
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## Model Details
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| Property | Value |
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|----------|-------|
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| Base model | HuggingFaceTB/SmolLM2-360M-Instruct |
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| Architecture | LlamaForCausalLM |
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| Parameters | 360M |
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| Quantization | Q8_0 (GGUF) |
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| File size | 369 MB |
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| Context length | 8192 tokens |
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| Training framework | MLX-LM (Apple Silicon) |
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## Training
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- **Method**: LoRA (r=16, alpha=32, targets: q/k/v/o projections)
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- **Dataset**: 9,828 synthetic CX analytics examples (ChatML format)
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- **Iterations**: 1,000
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- **Learning rate**: 2e-5
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- **Batch size**: 2
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- **LoRA layers**: 16 (of 32)
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- **Peak memory**: 3.2 GB
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- **Hardware**: Apple Silicon (MLX)
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### Training Data
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Synthetic CX insight pairs generated via Claude Sonnet 4.6, covering:
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- Funnel analysis and drop-off diagnosis
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- Rage click / dead click interpretation
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- Session replay pattern analysis
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- Core Web Vitals optimization
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- Scroll depth and engagement insights
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- Heatmap and click pattern analysis
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- Quick-back / bounce diagnosis
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- Segment comparison and cohort analysis
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Quality-gated: each example passed JSON structure, length, hallucination, and actionability checks.
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## Usage
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### llama.cpp
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```bash
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llama-cli -m cx-SmolLM2-360M-Q8_0.gguf -n 256 --temp 0.7 --chat-template chatml
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```
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### Python (llama-cpp-python)
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="cx-SmolLM2-360M-Q8_0.gguf", n_ctx=2048)
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response = llm.create_chat_completion(messages=[
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{"role": "system", "content": "You are a CX analytics assistant."},
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{"role": "user", "content": "Cart abandonment is 71%. Average payment page duration is 12s. Insights?"},
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])
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print(response["choices"][0]["message"]["content"])
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```
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## Performance
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On Apple Silicon (M1 Pro):
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- Prompt processing: ~579 tokens/sec
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- Generation: ~164 tokens/sec
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## License
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Apache 2.0 (same as base model)
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