Solana NVIDIA Trading Factory LoRA
LoRA adapter trained from NousResearch/Hermes-3-Llama-3.1-8B on solanaclawd/solana-clawd-nvidia-trading-factory-instruct.
Hub model ID: solanaclawd/solana-nvidia-trading-factory-8b-lora
Training
- Train rows: 127
- Eval rows: 7
- Max sequence length: 4096
- LoRA rank/alpha: r=32, alpha=64
- LoRA target modules:
q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj - Output directory:
/data/outputs/solana-nvidia-trading-factory-8b-lora
Metrics
| Metric | Value |
|---|---|
epoch |
3 |
total_flos |
1.03422e+16 |
train_loss |
1.17217 |
train_runtime |
212.916 |
train_samples_per_second |
1.789 |
train_steps_per_second |
0.225 |
Intended Use
This adapter is intended for Solana-native research and execution agents that need paper-first strategy planning over Phoenix/Vulcan perps, Rise read plans, cuFOLIO/cuOpt Mean-CVaR portfolio handoffs, and risk-gated spot/perps trading-factory workflows.
Loading
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = "NousResearch/Hermes-3-Llama-3.1-8B"
adapter_id = "solanaclawd/solana-nvidia-trading-factory-8b-lora"
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto", trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter_id)
Safety
The dataset builder runs in public-safe mode by default and excludes common secret filenames, private key/token patterns, binary artifacts, dependency folders, lockfiles, and high-risk security records that are not suitable for public dataset release.
This adapter is a research/developer artifact. Live trading or wallet actions must remain behind separate execution clients, simulation, explicit operator approval, and pre-trade risk gates.
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Model tree for solanaclawd/solana-nvidia-trading-factory-8b-lora
Base model
meta-llama/Llama-3.1-8B