Instructions to use Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned

SGLang

How to use Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned 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 "Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned with Docker Model Runner:
```
docker model run hf.co/Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned
```

🧑‍⚖️ LLaMA-3.1-8B-Instruct-Attorney-Entity-Extractor

A fine-tuned version of Meta LLaMA-3.1-8B-Instruct, adapted via LoRA for extracting attorney or law-firm entity names from natural-language snippets.

🧩 Model Overview

Property	Value
Base model	`meta-llama/Llama-3.1-8B-Instruct`
Fine-tuning method	LoRA (PEFT)
Quantization	4-bit NF4 using bitsandbytes
Task	Named Entity Extraction / Legal Text Understanding
Frameworks	🤗 Transformers, TRL, PEFT, bitsandbytes
License	Apache 2.0

This model learns to identify and extract attorney or law-firm names from text like:

“Evelyn Jones represented the defendant in court.”
→ Evelyn Jones

Model Card for llama-3.1-8B-Instruct-atty-finetuned

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "Evelyn Jones represented the defendant in court."
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

PEFT 0.17.1
TRL: 0.24.0
Transformers: 4.57.1
Pytorch: 2.7.1+cu118
Datasets: 4.2.0
Tokenizers: 0.22.1

Citations

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Gokul-A-100/llama-3.1-8B-Instruct-atty-finetuned

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Adapter

(2775)

this model