Instructions to use CodeGamers/legal-document-analysis-llama2-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeGamers/legal-document-analysis-llama2-qlora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
model = PeftModel.from_pretrained(base_model, "CodeGamers/legal-document-analysis-llama2-qlora")

Transformers

How to use CodeGamers/legal-document-analysis-llama2-qlora with Transformers:

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

pipe = pipeline("text-generation", model="CodeGamers/legal-document-analysis-llama2-qlora")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CodeGamers/legal-document-analysis-llama2-qlora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeGamers/legal-document-analysis-llama2-qlora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CodeGamers/legal-document-analysis-llama2-qlora

SGLang

How to use CodeGamers/legal-document-analysis-llama2-qlora 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 "CodeGamers/legal-document-analysis-llama2-qlora" \
    --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": "CodeGamers/legal-document-analysis-llama2-qlora",
		"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 "CodeGamers/legal-document-analysis-llama2-qlora" \
        --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": "CodeGamers/legal-document-analysis-llama2-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeGamers/legal-document-analysis-llama2-qlora with Docker Model Runner:
```
docker model run hf.co/CodeGamers/legal-document-analysis-llama2-qlora
```

Model Card for Model ID

Model Details

Model Description

Developed by: Harsha Deep Joga
Funded by [optional]: Self Project
Shared by [optional]: HarshaDeep2006
- Model type: Llama-2-7B QLoRa with RAFT
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: meta-llama/Llama-2-7b-hf

Model Sources [optional]

Repository: [More Information Needed]
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Demo [optional]: [More Information Needed]

Uses

Direct Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Model Card Contact

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Framework versions

PEFT 0.19.1

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Model tree for CodeGamers/legal-document-analysis-llama2-qlora

Base model

meta-llama/Llama-2-7b-hf

Adapter

(2455)

this model

Dataset used to train CodeGamers/legal-document-analysis-llama2-qlora

Paper for CodeGamers/legal-document-analysis-llama2-qlora

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61