Instructions to use gopi30/phase-1-sft-legal-alligned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gopi30/phase-1-sft-legal-alligned with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ybelkada/falcon-7b-sharded-bf16")
model = PeftModel.from_pretrained(base_model, "gopi30/phase-1-sft-legal-alligned")

Transformers

How to use gopi30/phase-1-sft-legal-alligned with Transformers:

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

pipe = pipeline("text-generation", model="gopi30/phase-1-sft-legal-alligned")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gopi30/phase-1-sft-legal-alligned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gopi30/phase-1-sft-legal-alligned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gopi30/phase-1-sft-legal-alligned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gopi30/phase-1-sft-legal-alligned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gopi30/phase-1-sft-legal-alligned

SGLang

How to use gopi30/phase-1-sft-legal-alligned 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 "gopi30/phase-1-sft-legal-alligned" \
    --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": "gopi30/phase-1-sft-legal-alligned",
		"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 "gopi30/phase-1-sft-legal-alligned" \
        --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": "gopi30/phase-1-sft-legal-alligned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gopi30/phase-1-sft-legal-alligned with Docker Model Runner:
```
docker model run hf.co/gopi30/phase-1-sft-legal-alligned
```

gopi30 commited on Mar 19

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@@ -14,10 +14,6 @@ tags:
 - safety-alignment
 ---
-# Model Card for LegalSafe-Falcon-7B
-## Model Details
 ### Model Description
 LegalSafe-Falcon-7B is a safety-aligned large language model fine-tuned for the Indian legal domain. The model focuses on reducing toxic, biased, and unsafe outputs while maintaining high accuracy in legal reasoning and responses. It integrates Constitutional AI principles and Reinforcement Learning from AI Feedback (RLAIF) to improve ethical alignment.

 - safety-alignment
 ---
 ### Model Description
 LegalSafe-Falcon-7B is a safety-aligned large language model fine-tuned for the Indian legal domain. The model focuses on reducing toxic, biased, and unsafe outputs while maintaining high accuracy in legal reasoning and responses. It integrates Constitutional AI principles and Reinforcement Learning from AI Feedback (RLAIF) to improve ethical alignment.