Instructions to use mjf-su/New-Edge-Critic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mjf-su/New-Edge-Critic with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-4B-Instruct")
model = PeftModel.from_pretrained(base_model, "mjf-su/New-Edge-Critic")

Transformers

How to use mjf-su/New-Edge-Critic with Transformers:

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

pipe = pipeline("text-generation", model="mjf-su/New-Edge-Critic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mjf-su/New-Edge-Critic", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mjf-su/New-Edge-Critic with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mjf-su/New-Edge-Critic

SGLang

How to use mjf-su/New-Edge-Critic 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 "mjf-su/New-Edge-Critic" \
    --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": "mjf-su/New-Edge-Critic",
		"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 "mjf-su/New-Edge-Critic" \
        --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": "mjf-su/New-Edge-Critic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mjf-su/New-Edge-Critic with Docker Model Runner:
```
docker model run hf.co/mjf-su/New-Edge-Critic
```

New-Edge-Critic / train_config.json

mjf-su

Upload train_config.json with huggingface_hub

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	{
	"train_dataset": "datasets/Faithfulness-Critic-Dataset/train_dataset.jsonl",
	"val_dataset": "datasets/Faithfulness-Critic-Dataset/val_dataset.jsonl",
	"dataset_dir": "datasets/Faithfulness-Critic-Dataset",
	"max_length": 4096,
	"seed": 0,
	"eval_max_new_tokens": 32,
	"eval_batch_size": 8,
	"eval_steps": 531,
	"scene_oversample_factor": 1.8,
	"e1_oversample_factor": 3.2,
	"base_model": "Qwen/Qwen3-VL-4B-Instruct",
	"use_lora": true,
	"lora_rank": 256,
	"lora_alpha": null,
	"lora_dropout": 0.05,
	"lora_target_modules": "q_proj,k_proj,v_proj,o_proj",
	"flash_attn": false,
	"gradient_checkpointing": false,
	"output_dir": "results/scene-critic-lora",
	"epochs": 2.0,
	"per_device_batch_size": 8,
	"grad_accum_steps": 1,
	"lr": 0.0001,
	"warmup_ratio": 0.03,
	"weight_decay": 0.0,
	"logging_steps": 10,
	"save_steps": 2656,
	"save_total_limit": 2,
	"max_steps": -1,
	"report_to": "wandb",
	"wandb_project": "Faithfulness-Critic",
	"wandb_run_name": "completion-verdict"
	}