Instructions to use joyboseroy/nyayasaar-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joyboseroy/nyayasaar-lora with Transformers:

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

pipe = pipeline("text-generation", model="joyboseroy/nyayasaar-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("joyboseroy/nyayasaar-lora", dtype="auto")

PEFT
How to use joyboseroy/nyayasaar-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use joyboseroy/nyayasaar-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/joyboseroy/nyayasaar-lora

SGLang

How to use joyboseroy/nyayasaar-lora 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 "joyboseroy/nyayasaar-lora" \
    --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": "joyboseroy/nyayasaar-lora",
		"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 "joyboseroy/nyayasaar-lora" \
        --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": "joyboseroy/nyayasaar-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use joyboseroy/nyayasaar-lora 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 joyboseroy/nyayasaar-lora 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 joyboseroy/nyayasaar-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for joyboseroy/nyayasaar-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="joyboseroy/nyayasaar-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use joyboseroy/nyayasaar-lora with Docker Model Runner:
```
docker model run hf.co/joyboseroy/nyayasaar-lora
```

joyboseroy commited on 9 days ago

Commit

8f67ac4

verified ·

1 Parent(s): a88368b

Update README

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README.md +42 -0

README.md CHANGED Viewed

@@ -127,6 +127,48 @@ This project is intended for:
 ---
 # Limitations
 This model:

 ---
+# Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base_model = "Qwen/Qwen2.5-0.5B-Instruct"
+adapter_model = "joyboseroy/nyayasaar-lora"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(model, adapter_model)
+prompt = """
+Issue: Whether the detention order violates Article 22.
+Rule: Preventive detention laws require procedural safeguards.
+Application: The petitioner argued safeguards were not followed.
+Conclusion: The detention order is quashed.
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+````
+```
+```
 # Limitations
 This model: