Instructions to use appvoid/graphite-006 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/graphite-006 with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/graphite-006")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/graphite-006")
model = AutoModelForCausalLM.from_pretrained("appvoid/graphite-006", device_map="auto")
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 appvoid/graphite-006 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/graphite-006"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/graphite-006",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/appvoid/graphite-006

SGLang

How to use appvoid/graphite-006 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 "appvoid/graphite-006" \
    --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": "appvoid/graphite-006",
		"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 "appvoid/graphite-006" \
        --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": "appvoid/graphite-006",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use appvoid/graphite-006 with Docker Model Runner:
```
docker model run hf.co/appvoid/graphite-006
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

graphite-001

Private fine-tune of appvoid/graphite-005 on appvoid/rewrite.

Training

Method: full supervised fine-tuning
Epochs: 1
Dataset fields: instruction, text, output
Format: native chat template from the base tokenizer
Train rows used: 1044723

Prompt format

The model was trained with one user message:

{instruction}

Text:
{text}

Return only the final rewritten output. Do not explain.

The assistant message contains only:

{output}

Intended use

Text rewriting, paraphrasing, tone transfer, grammar-style editing, and instruction-following text transformations.

Downloads last month: 5

Safetensors

Model size

91.1M params

Tensor type

BF16

Model tree for appvoid/graphite-006

Base model

appvoid/graphite-004

Finetuned

appvoid/palmer-005

Finetuned

(2)

this model

Quantizations

2 models