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