Instructions to use kkk0123k/gremlin_gwen_14B_lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kkk0123k/gremlin_gwen_14B_lora_model with Transformers:

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

pipe = pipeline("text-generation", model="kkk0123k/gremlin_gwen_14B_lora_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kkk0123k/gremlin_gwen_14B_lora_model", device_map="auto")

PEFT
How to use kkk0123k/gremlin_gwen_14B_lora_model with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kkk0123k/gremlin_gwen_14B_lora_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kkk0123k/gremlin_gwen_14B_lora_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kkk0123k/gremlin_gwen_14B_lora_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kkk0123k/gremlin_gwen_14B_lora_model

SGLang

How to use kkk0123k/gremlin_gwen_14B_lora_model 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 "kkk0123k/gremlin_gwen_14B_lora_model" \
    --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": "kkk0123k/gremlin_gwen_14B_lora_model",
		"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 "kkk0123k/gremlin_gwen_14B_lora_model" \
        --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": "kkk0123k/gremlin_gwen_14B_lora_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use kkk0123k/gremlin_gwen_14B_lora_model 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 kkk0123k/gremlin_gwen_14B_lora_model 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 kkk0123k/gremlin_gwen_14B_lora_model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kkk0123k/gremlin_gwen_14B_lora_model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kkk0123k/gremlin_gwen_14B_lora_model",
    max_seq_length=2048,
)

Docker Model Runner
How to use kkk0123k/gremlin_gwen_14B_lora_model with Docker Model Runner:
```
docker model run hf.co/kkk0123k/gremlin_gwen_14B_lora_model
```

Gremlin Qwen2.5-Coder 14B LoRA Adapter

This is a LoRA adapter trained on Gremlin graph query tasks using the base model unsloth/qwen2.5-coder-14b-instruct-bnb-4bit.
It uses PEFT (LoRA) for parameter-efficient fine-tuning.

🧠 Model Details

Base Model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
LoRA Adapter Size: Lightweight adapter (~100MB)
Trained for: Graph query generation using the Gremlin query language
Format: PEFT / LoRA compatible with transformers + peft

🔧 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = "unsloth/qwen2.5-coder-14b-instruct-bnb-4bit"
adapter_model = "kkk0123k/gremlin_gwen_14B_lora_model"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto", torch_dtype=torch.float16)
model = PeftModel.from_pretrained(model, adapter_model)

inputs = tokenizer("Convert natural language to a Gremlin query: Get all people who know someone named Alice.", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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