---
base_model: mlx-community/Qwen2.5-0.5B-Instruct-4bit
license: mit
tags:
- mlx
- lora
- apple-silicon
- cx
- crm
- lam
- large-action-model
- customer-experience
- tool-calling
- qwen2.5
- 0.5b
- local
- iterative
- agentic
- multi-turn
language:
- en
pipeline_tag: text-generation
---

# Qwen2.5-0.5B Iterative CX LAM

A **Large Action Model** fine-tuned for Customer Experience (CX) and CRM workflows with native **iterative / closed-loop** support. It runs entirely locally on Apple Silicon using MLX.

Unlike one-shot planners, this version is trained and prompted for **step-by-step** operation: the model proposes one action, receives execution results as feedback, and decides the next action until the interaction is complete.

**This is a research / proof-of-concept release.** It demonstrates local LAM construction with perception-planning-execution-adaptation (inspired by AI21 LAM ideas) using small models + deterministic pipeline.

## Model Details
- **Base**: Qwen2.5-0.5B-Instruct (4-bit mlx-community version)
- **Method**: LoRA (rank 8)
- **Training**: 300 iterations on Apple M4 Max
- **Adapter size**: ~11.7 MB
- **Training data**: Iterative step-by-step expansions (~9.2k examples) derived from 2,346 full trajectories
- **Total trajectories generated**: 2,346 (from two public sources)

## Datasets Used
Trajectories were synthesized from:
- [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset) (1,996 trajs)
- [knkarthick/dialogsum](https://huggingface.co/datasets/knkarthick/dialogsum) (350 trajs, used as transcript-style proxy)

See the companion dataset: [chendren/cx-lam-trajectories](https://huggingface.co/datasets/chendren/cx-lam-trajectories)

Iterative training data is generated on-the-fly or via `scripts/build_iterative_data.py`.

## How the LAM Works (Iterative Mode)
We use a **narrow contract** with two prompt styles:

- **Initial call**: Uses the classic full-plan prefix ending at `### Execute\n[`
- **Continuation / adaptation steps**: Uses `build_continuation_prompt` that surfaces:
  - Current observation + rich state summary ("contact loaded, case created, actions so far...")
  - "### Completed so far"
  - "### Next Action\n["

The runtime loop (`run_lam_session` / `--iterative`):
1. Model proposes (typically) **one action**.
2. CRMSimulator executes it and returns structured results.
3. Results are turned into a new observation via `build_adaptation_observation`.
4. Repeat (up to max_steps) until `log_call` succeeds or the model emits nothing.

A deterministic post-processing pipeline still handles perception derivation, arg filling, and validation.

## Usage (Recommended Iterative)

```python
from lam.inference import run_lam_session

sess = run_lam_session(
    "Customer Jordan Lee asks about renewal and add-on pricing for the contract",
    one_action_per_step=True,
    max_steps=6
)

print("Steps:", sess["num_steps"], "terminal:", sess["terminal"])
for s in sess["steps"]:
    print(s["actions"])
```

CLI (recommended):

```bash
PYTHONPATH=. python3 scripts/lam_infer.py --adaptive --iterative \
  "Customer reports $420 billing discrepancy on April invoice"
```

Expected behavior (example from retrained model):
```
Step 0: screenpop
Step 1: create_case
Step 2: screenpop (or list_cases)
Step 3: log_call  → terminal
```

## Evaluation
### Iterative behavior (10 diverse CX tests post-retrain)
- **Terminal success** (reached log_call): **10/10 (100%)**
- **Avg steps**: 4.0 (true one-action-per-step)
- **Final state**: always 1 case + 1 log created
- Typical flow: `screenpop` → `create_case` → ... → `log_call`

One ELD example used `list_cases` mid-sequence (sensible variation).

### Legacy full test set (previous single-shot checkpoint)
For reference only (on the 271-example hold-out):
- valid_rate (valid + ≥3 actions): 0.395
- Time for 271: ~161s on M4 Max

## Limitations
- Small base model (0.5B) → occasional redundant actions (e.g. re-screenpop) and limited long-horizon reasoning.
- Iterative behavior is strong on canonical CX flows but can still repeat early actions.
- Perception remains mostly derived (not model-generated).
- Research artifact. Not production-ready without more data, longer training, or larger base.

## Citation
If you use this work, please cite the original datasets and reference the AI21 Large Action Model concept.

## License
MIT (code and adapter). See source dataset cards for original data license notes.

---

**Trained and evaluated entirely locally on Apple M4 Max with MLX.**

**Key new scripts**: `scripts/build_iterative_data.py`, `lam/format.py:build_continuation_prompt`, `lam/inference.py:run_lam_session(..., one_action_per_step=True)`