Instructions to use niranjanh123/lfm-grid-multi-turn-ppo-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use niranjanh123/lfm-grid-multi-turn-ppo-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("niranjanh123/lfm-sft-hotter-colder")
model = PeftModel.from_pretrained(base_model, "niranjanh123/lfm-grid-multi-turn-ppo-adapter")

Transformers

How to use niranjanh123/lfm-grid-multi-turn-ppo-adapter with Transformers:

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

pipe = pipeline("text-generation", model="niranjanh123/lfm-grid-multi-turn-ppo-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("niranjanh123/lfm-grid-multi-turn-ppo-adapter", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use niranjanh123/lfm-grid-multi-turn-ppo-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "niranjanh123/lfm-grid-multi-turn-ppo-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "niranjanh123/lfm-grid-multi-turn-ppo-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/niranjanh123/lfm-grid-multi-turn-ppo-adapter

SGLang

How to use niranjanh123/lfm-grid-multi-turn-ppo-adapter 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 "niranjanh123/lfm-grid-multi-turn-ppo-adapter" \
    --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": "niranjanh123/lfm-grid-multi-turn-ppo-adapter",
		"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 "niranjanh123/lfm-grid-multi-turn-ppo-adapter" \
        --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": "niranjanh123/lfm-grid-multi-turn-ppo-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use niranjanh123/lfm-grid-multi-turn-ppo-adapter with Docker Model Runner:
```
docker model run hf.co/niranjanh123/lfm-grid-multi-turn-ppo-adapter
```

lfm-grid-multi-turn-ppo-adapter / tokenizer.json

Commit History

Upload Hybrid Async vLLM + Multi-Turn PPO adapter

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niranjanh123 commited on 16 days ago

Commit History

Upload Hybrid Async vLLM + Multi-Turn PPO adapter d9acd8a verified

Upload Hybrid Async vLLM + Multi-Turn PPO adapter

d9acd8a
verified