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license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- finance
- crypto
- trading
- lora
- qwen3
- gguf
---
# FinGPT-Crypto v5
LoRA fine-tune of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for structured crypto trading verdict generation, trained on Apple Silicon via [MLX-LM](https://github.com/ml-explore/mlx-lm).
Given a news headline plus market context (price, funding rate, volatility regime, trend, BTC alignment, etc.), the model outputs a structured JSON verdict:
```json
{
"refined_conviction": 0.74,
"conviction_adjustment": -0.04,
"reasoning_summary": "Funding building positive, no negative catalysts in 24h, BTC NORMAL_STABLE.",
"concerns": ["ls_extremes_approaching_crowded_60pct"],
"supportive_factors": ["funding_curve_steepening", "no_event_risk_in_horizon"],
"would_recommend_skip": false,
"confidence": 0.82,
"horizons": {
"1h": {"direction": "LONG", "confidence": 75},
"4h": {"direction": "LONG", "confidence": 70},
"24h": {"direction": "SKIP", "confidence": 60}
}
}
```
## Files
- `fingpt-crypto-v5-full-test.q8_0.gguf` — Q8_0 quantized GGUF (8.7GB), for use with [Ollama](https://ollama.com) or [llama.cpp](https://github.com/ggml-org/llama.cpp).
## Training
- Base: Qwen3-8B, 4-bit quantized during training (MLX)
- Method: LoRA (rank 8, scale 20), resumed incrementally across v1→v5, 16 of 36 layers
- v5 stage: 5000 iterations, batch size 1, lr 1e-5, max_seq_length 1024
- Final val loss: 0.311
## Usage (Ollama)
```bash
ollama create fingpt-crypto:v5 -f Modelfile
ollama run fingpt-crypto:v5
```
Modelfile:
```
FROM fingpt-crypto-v5-full-test.q8_0.gguf
PARAMETER temperature 0.1
PARAMETER top_p 0.9
PARAMETER num_ctx 4096
```
## Notes
This is a research/experimental fine-tune for a personal crypto trading project. Not financial advice. Use at your own risk.
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