Instructions to use alfiannajih/g-retriever-resume-reviewer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alfiannajih/g-retriever-resume-reviewer with Transformers:

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

pipe = pipeline("text-generation", model="alfiannajih/g-retriever-resume-reviewer", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alfiannajih/g-retriever-resume-reviewer", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("alfiannajih/g-retriever-resume-reviewer", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alfiannajih/g-retriever-resume-reviewer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/alfiannajih/g-retriever-resume-reviewer

SGLang

How to use alfiannajih/g-retriever-resume-reviewer 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 "alfiannajih/g-retriever-resume-reviewer" \
    --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": "alfiannajih/g-retriever-resume-reviewer",
		"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 "alfiannajih/g-retriever-resume-reviewer" \
        --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": "alfiannajih/g-retriever-resume-reviewer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alfiannajih/g-retriever-resume-reviewer with Docker Model Runner:
```
docker model run hf.co/alfiannajih/g-retriever-resume-reviewer
```

g-retriever-resume-reviewer

File size: 1,042 Bytes

c3a8a48

from transformers import LlamaConfig

class GRetrieverConfig(LlamaConfig):
    model_type = "llama"
    
    def __init__(
        self,
        max_txt_len: int = 1024,
        max_new_tokens: int = 256,
        gnn_num_layers: int = 4,
        gnn_in_dim: int = 768,
        gnn_hidden_dim: int = 1024,
        gnn_num_heads: int = 4,
        gnn_dropout: int = 0,
        bos_id: list = [128000, 128000, 128006, 882, 128007],
        **kwargs
    ):
        pretrained_config = LlamaConfig.from_pretrained("NousResearch/Hermes-3-Llama-3.1-8B")
        pretrained_config.update(kwargs)
        
        self.max_txt_len = max_txt_len
        self.max_new_tokens = max_new_tokens
        self.gnn_num_layers = gnn_num_layers
        self.gnn_in_dim = gnn_in_dim
        self.gnn_hidden_dim = gnn_hidden_dim
        self.gnn_num_heads = gnn_num_heads
        self.gnn_dropout = gnn_dropout
        self.bos_id = bos_id
        
        super().__init__(**pretrained_config.to_dict())
        self.pad_token_id = pretrained_config.eos_token_id