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🦙 LLaMA 2 7B + TAT FinQA Adapter (Merged, Pre-CoT)
Repo: michael-sigamani/llama2-7b-tat-lora-fp16
Base: NousResearch/Llama-2-7b-hf
Adapter: next-tat/tat-llm-7b-lora
Merged: ✅ Yes
Fine-tuned: ❌ Not yet (this is the pre-CoT stage)
Format: Float16 (fp16)
📖 Overview
This model merges a FinQA-tuned adapter (TAT) into LLaMA 2 7B, producing a standalone checkpoint ready for further fine-tuning or inference on financial reasoning tasks.
- 📈 Finetuned LoRA (TAT) captures scalar reasoning from FinQA
- 🔁 Merged via
peft.merge_and_unload()into the full model - 🧵 Next step: fine-tune on
train_turn.jsonlwith chain-of-thought (CoT) supervision
🔁 Intended Usage
Use this model as the starting point for:
- 🧠 Fine-tuning on CoT financial datasets (e.g. ConvFinQA turn-based reasoning)
- 🧪 Evaluation on scalar, program, and reasoning benchmarks
- 🦙 Export to GGUF for Ollama / llama.cpp
🚧 Not a Final Model
This checkpoint has not been CoT fine-tuned yet. It is the output of:
Base: NousResearch/Llama-2-7b-hf
LoRA: next-tat/tat-llm-7b-lora (FinQA-style)
Merged: Yes (fp16, no adapter required)
Next step: Train on chain-of-thought examples (train_turn.jsonl) using Unsloth or PEFT + TRL.
🧠 Merge Script
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-hf")
adapter = PeftModel.from_pretrained(base_model, "next-tat/tat-llm-7b-lora")
merged = adapter.merge_and_unload()
merged.save_pretrained("llama2-7b-tat-lora-fp16")
AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf").save_pretrained("llama2-7b-tat-lora-fp16")
🧑💻 Maintainer
Michael Sigamani
github.com/sigamani
📜 License
- Base: Meta LLaMA 2 license (via NousResearch)
- Adapter: Apache 2.0
- Merged model: Inherits original LLaMA 2 license – requires HF auth
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Base model
NousResearch/Llama-2-7b-hf