Instructions to use tomasonjo/text2cypher-demo-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tomasonjo/text2cypher-demo-16bit with Transformers:

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

pipe = pipeline("text-generation", model="tomasonjo/text2cypher-demo-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tomasonjo/text2cypher-demo-16bit")
model = AutoModelForCausalLM.from_pretrained("tomasonjo/text2cypher-demo-16bit")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tomasonjo/text2cypher-demo-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tomasonjo/text2cypher-demo-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tomasonjo/text2cypher-demo-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tomasonjo/text2cypher-demo-16bit

SGLang

How to use tomasonjo/text2cypher-demo-16bit 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 "tomasonjo/text2cypher-demo-16bit" \
    --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": "tomasonjo/text2cypher-demo-16bit",
		"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 "tomasonjo/text2cypher-demo-16bit" \
        --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": "tomasonjo/text2cypher-demo-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use tomasonjo/text2cypher-demo-16bit 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 tomasonjo/text2cypher-demo-16bit 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 tomasonjo/text2cypher-demo-16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tomasonjo/text2cypher-demo-16bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="tomasonjo/text2cypher-demo-16bit",
    max_seq_length=2048,
)

Docker Model Runner
How to use tomasonjo/text2cypher-demo-16bit with Docker Model Runner:
```
docker model run hf.co/tomasonjo/text2cypher-demo-16bit
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

Developed by: tomasonjo
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-Instruct

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

For more information visit this link

Example usage:

Install dependencies. Check Unsloth documentation for specific installation for other environments.

%%capture
# Installs Unsloth, Xformers (Flash Attention) and all other packages!
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes

Then you can load the model and use it as inference

from unsloth.chat_templates import get_chat_template

tokenizer = get_chat_template(
    tokenizer,
    chat_template = "llama-3", 
    map_eos_token = True, 
)

FastLanguageModel.for_inference(model) # Enable native 2x faster inference

schema = """Node properties: - **Question** - `favorites`: INTEGER Example: "0" - `answered`: BOOLEAN - `text`: STRING Example: "### This is: Bug ### Specifications OS: Win10" - `link`: STRING Example: "https://stackoverflow.com/questions/62224586/playg" - `createdAt`: DATE_TIME Min: 2020-06-05T16:57:19Z, Max: 2020-06-05T21:49:16Z - `title`: STRING Example: "Playground is not loading with apollo-server-lambd" - `id`: INTEGER Min: 62220505, Max: 62224586 - `upVotes`: INTEGER Example: "0" - `score`: INTEGER Example: "-1" - `downVotes`: INTEGER Example: "1" - **Tag** - `name`: STRING Example: "aws-lambda" - **User** - `image`: STRING Example: "https://lh3.googleusercontent.com/-NcFYSuXU0nk/AAA" - `link`: STRING Example: "https://stackoverflow.com/users/10251021/alexandre" - `id`: INTEGER Min: 751, Max: 13681006 - `reputation`: INTEGER Min: 1, Max: 420137 - `display_name`: STRING Example: "Alexandre Le" Relationship properties: The relationships: (:Question)-[:TAGGED]->(:Tag) (:User)-[:ASKED]->(:Question)"""
question = "Identify the top 5 questions with the most downVotes."

messages = [
      {"role": "system", "content": "Given an input question, convert it to a Cypher query. No pre-amble."},
      {"role": "user", "content": f"""Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
{schema}

Question: {question}
Cypher query:"""}
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

outputs = model.generate(input_ids = inputs, max_new_tokens = 128, use_cache = True)
tokenizer.batch_decode(outputs)