Instructions to use Anish13/rl_arbiter_e14_checkpoint_25480 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anish13/rl_arbiter_e14_checkpoint_25480 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("checkpoints/web_arbiter_rl_web-wmrm-ep2-warm-start_lora_r32_a32_lr7e-6_bsz1_ga8_g8_lam0.2_epochs10_seed42_ddp6_vllm")
model = PeftModel.from_pretrained(base_model, "Anish13/rl_arbiter_e14_checkpoint_25480")

Transformers

How to use Anish13/rl_arbiter_e14_checkpoint_25480 with Transformers:

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

pipe = pipeline("text-generation", model="Anish13/rl_arbiter_e14_checkpoint_25480")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Anish13/rl_arbiter_e14_checkpoint_25480", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anish13/rl_arbiter_e14_checkpoint_25480 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anish13/rl_arbiter_e14_checkpoint_25480"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anish13/rl_arbiter_e14_checkpoint_25480",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anish13/rl_arbiter_e14_checkpoint_25480

SGLang

How to use Anish13/rl_arbiter_e14_checkpoint_25480 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 "Anish13/rl_arbiter_e14_checkpoint_25480" \
    --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": "Anish13/rl_arbiter_e14_checkpoint_25480",
		"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 "Anish13/rl_arbiter_e14_checkpoint_25480" \
        --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": "Anish13/rl_arbiter_e14_checkpoint_25480",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anish13/rl_arbiter_e14_checkpoint_25480 with Docker Model Runner:
```
docker model run hf.co/Anish13/rl_arbiter_e14_checkpoint_25480
```

rl_arbiter_e14_checkpoint_25480 / trainer_state.json

Anish13

Upload folder using huggingface_hub

f14cc70 verified 24 days ago

Download

History Blame Contribute Delete

36.7 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: fe3d7c242e86cc27903f0aa63d90860f1f0ed7e63813728ab6f115a79821fe57
Size of remote file:: 36.7 MB
SHA256:: 22b7d0fba8de16823a390e22b8e930bc0c7a65dfb8dbd535d27947c1f21d3ed7

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.