Instructions to use michael-sigamani/llama2-7b-tat-convfinqa-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use michael-sigamani/llama2-7b-tat-convfinqa-fp16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="michael-sigamani/llama2-7b-tat-convfinqa-fp16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("michael-sigamani/llama2-7b-tat-convfinqa-fp16")
model = AutoModelForMultimodalLM.from_pretrained("michael-sigamani/llama2-7b-tat-convfinqa-fp16")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use michael-sigamani/llama2-7b-tat-convfinqa-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "michael-sigamani/llama2-7b-tat-convfinqa-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "michael-sigamani/llama2-7b-tat-convfinqa-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/michael-sigamani/llama2-7b-tat-convfinqa-fp16

SGLang

How to use michael-sigamani/llama2-7b-tat-convfinqa-fp16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "michael-sigamani/llama2-7b-tat-convfinqa-fp16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "michael-sigamani/llama2-7b-tat-convfinqa-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "michael-sigamani/llama2-7b-tat-convfinqa-fp16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "michael-sigamani/llama2-7b-tat-convfinqa-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use michael-sigamani/llama2-7b-tat-convfinqa-fp16 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for michael-sigamani/llama2-7b-tat-convfinqa-fp16 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for michael-sigamani/llama2-7b-tat-convfinqa-fp16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for michael-sigamani/llama2-7b-tat-convfinqa-fp16 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="michael-sigamani/llama2-7b-tat-convfinqa-fp16",
    max_seq_length=2048,
)

Docker Model Runner
How to use michael-sigamani/llama2-7b-tat-convfinqa-fp16 with Docker Model Runner:
```
docker model run hf.co/michael-sigamani/llama2-7b-tat-convfinqa-fp16
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

🦙 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.jsonl with 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

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

F16

Model tree for michael-sigamani/llama2-7b-tat-convfinqa-fp16

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NousResearch/Llama-2-7b-hf

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llama2-7b-tat-convfinqa-fp16