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

Commit

02b752b

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@@ -8,202 +8,79 @@ tags:
 - sft
 - transformers
 - trl
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
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 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
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-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-## Glossary [optional]
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-## Model Card Contact
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-### Framework versions
-- PEFT 0.17.1

 - sft
 - transformers
 - trl
+- constitutional-ai
+- rlaif
+- legal-llm
+- 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.
+- **Developed by:** Gopi M
+- **Model type:** Causal Language Model (LLM)
+- **Language(s):** English (Legal Domain - Indian Context)
+- **License:** Apache 2.0 (inherits from base model)
+- **Finetuned from model:** ybelkada/falcon-7b-sharded-bf16
+---
 ## Uses
 ### Direct Use
+- Legal question answering
+- Legal document summarization
+- Safe text generation in sensitive domains
+- Educational tools for legal studies
+### Downstream Use
+- Integration into legal chatbots
+- AI-based legal assistants
+- Compliance and advisory systems
+- Safety-critical NLP applications
 ### Out-of-Scope Use
+- Providing official legal advice
+- Use in high-risk legal decision-making without human oversight
+- Generating harmful, biased, or illegal content
+---
 ## Bias, Risks, and Limitations
+- May still exhibit residual bias from training data
+- Performance may vary outside Indian legal domain
+- Not a substitute for professional legal consultation
+- Risk of hallucinations in complex legal scenarios
 ### Recommendations
+- Use with human oversight in legal applications
+- Validate outputs before deployment in critical systems
+- Avoid relying on model for final legal decisions
+---
+## How to Get Started
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = "your-username/LegalSafe-Falcon-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+prompt = "Explain the concept of fundamental rights in Indian law."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0]))