README.md

---
license: gemma
base_model: mlx-community/gemma-4-12B-it-bf16
base_model_relation: finetune
library_name: mlx
pipeline_tag: text-generation
inference: false
language:
- en
metrics:
- accuracy
tags:
- mlx
- gemma
- gemma4
- finance
- financial-reasoning
- crypto
- grpo
- fin-r1
- reasoning
model-index:
- name: gemma-4-12b-fin-grpo-v4
  results:
  - task:
      type: text-generation
      name: Financial Reasoning (FinQA)
    dataset:
      type: ChanceFocus/flare-finqa
      name: FinQA — FLARE test (N=100)
    metrics:
    - type: accuracy
      value: 64.0
      name: accuracy
  - task:
      type: text-generation
      name: Conversational Financial Reasoning (ConvFinQA)
    dataset:
      type: ChanceFocus/flare-convfinqa
      name: ConvFinQA — FLARE test (N=100)
    metrics:
    - type: accuracy
      value: 60.0
      name: accuracy
---

# gemma-4-12b-fin-grpo-v4

A **financial-reasoning + crypto** model that reproduces the **Fin-R1** recipe
(*[Fin-R1](https://arxiv.org/abs/2503.16252)*: SFT → GRPO on financial chain-of-thought data)
on a **larger base** — **Gemma 4 12B-it** — trained **locally on Apple Silicon** with
[`mlx-lm-lora`](https://github.com/Goekdeniz-Guelmez/mlx-lm-lora).

It reasons step-by-step inside `<think> </think>` tags, then gives a clear final answer.
Coverage spans the whole investment sector — fiat markets, equities, derivatives, macro,
accounting, and **crypto / DeFi / trading (SMC, ICT, FVG, etc.)**.

- **Base model:** [`mlx-community/gemma-4-12B-it-bf16`](https://huggingface.co/mlx-community/gemma-4-12B-it-bf16) (← `google/gemma-4-12b-it`)
- **Method:** LoRA SFT (rank 16) → GRPO (Fin-R1 stage 2), then fused to full bf16 weights
- **Params / dtype:** ~12B, bf16 (~22 GB)
- **Framework:** MLX (`mlx-lm`). `model_type: gemma4`.

## Training recipe

| Stage | Data | Notes |
|-------|------|-------|
| **SFT (v4)** | ~30k examples: financial CoT (Fino1 FinQA+CoT, fin-alpaca-r1, Finance-Instruct, TAT-QA CoT), crypto trading, an industry/security corpus, and FalseReject de-refusal | broad + balanced |
| **GRPO** | 12k verifiable items (FinQA-style numerics + crypto BUY/HOLD/SELL labels) | rewards: `think_format` + `numeric_or_label_accuracy`, 200 iters |

## Evaluation

FinQA / ConvFinQA accuracy (N=100, FLARE test split):

| Model | FinQA | ConvFinQA |
|-------|:-----:|:---------:|
| base `gemma-4-12B-it` | 27.5% | — (often refuses) |
| SFT v4 | 61% | 59% |
| **this model (SFT→GRPO v4)** | **64%** | **60%** |
| *Fin-R1 (paper reference)* | *76%* | *85%* |

GRPO added **+3 / +1** over SFT, matching the paper's reported lift. De-refusal eval:
100% of legitimate finance/investment questions answered, illegitimate ones still refused.

> The model is intentionally **broad** rather than benchmark-maximized: it trades a few
> FinQA points versus a narrow FinQA-tuned model (Fin-R1) for crypto/trading coverage that
> Fin-R1 does not have (e.g. Fin-R1 does not know SMC/ICT terms like *Fair Value Gap*).

## Usage (MLX)

```python
from mlx_lm import load, generate

model, tokenizer = load("z0n3x/gemma-4-12b-fin-grpo-v4")

system = ("You are a financial reasoning assistant covering the whole investment "
          "sector — fiat markets, equities, derivatives, macro, accounting, and "
          "crypto/DeFi. Reason step by step inside <think> </think> tags, then give "
          "a clear, correct final answer.")
messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": "What is a Fair Value Gap (FVG) and how do traders use it?"},
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=800))
```

### Thinking / reasoning

The chat template **defaults to a reasoning system prompt**, so the model produces
`<think> … </think>` then the answer **out of the box** — you don't need to pass a system
message (pass your own to override it). The reasoning markers are `<think>` / `</think>`
(this model was trained on those tags, not Gemma's native `<|channel>thought` format).

- **LM Studio:** set the reasoning / "thinking" section tags to `<think>` (start) and
  `</think>` (end) to fold the chain-of-thought into a collapsible block.
- **Text-only:** the base Gemma 4 vision/audio weights were dropped during fine-tuning, so
  this checkpoint is **not multimodal** — by design (Fin-R1 is a text recipe).

> **Note on loading:** these are brand-new Gemma 4 "unified" weights. With some `mlx-lm`
> versions you may need a small load-time shim that (a) resolves `model_type: gemma4` and
> (b) drops unused multimodal tensors. This is a **text-only** checkpoint.

## Limitations & disclosures

- **Not financial advice.** Outputs are model-generated and can be wrong; verify numbers
  and do your own research before acting on any market view.
- **De-refusal calibration.** Trained (with the FalseReject dataset) to answer legitimate
  finance/investment questions candidly instead of over-refusing. The boundary kept during
  training was **not** optimizing for fraud, money-laundering, or market-manipulation use.
- **Includes a private corpus.** Part of the SFT mix is the author's own industry/security
  ("OAK") corpus; outputs may reflect its style/content.
- **MLX checkpoint.** Built and tested with `mlx-lm`; not validated under `transformers`.

## License

This is a derivative of Google **Gemma** and is distributed under the
[**Gemma Terms of Use**](https://ai.google.dev/gemma/terms). By using these weights you
agree to those terms and to Google's
[Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).

## Citation

Built following the Fin-R1 recipe:

```bibtex
@article{liu2025finr1,
  title={Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning},
  author={Liu, Zhaowei and others},
  journal={arXiv preprint arXiv:2503.16252},
  year={2025}
}
```