theprint/AuthorsAssistant
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How to use theprint/CreativeWriter-Llama3.2-3B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="theprint/CreativeWriter-Llama3.2-3B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("theprint/CreativeWriter-Llama3.2-3B")
model = AutoModelForCausalLM.from_pretrained("theprint/CreativeWriter-Llama3.2-3B")
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]:]))How to use theprint/CreativeWriter-Llama3.2-3B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/CreativeWriter-Llama3.2-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "theprint/CreativeWriter-Llama3.2-3B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/theprint/CreativeWriter-Llama3.2-3B
How to use theprint/CreativeWriter-Llama3.2-3B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "theprint/CreativeWriter-Llama3.2-3B" \
--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": "theprint/CreativeWriter-Llama3.2-3B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "theprint/CreativeWriter-Llama3.2-3B" \
--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": "theprint/CreativeWriter-Llama3.2-3B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use theprint/CreativeWriter-Llama3.2-3B with Unsloth Studio:
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 theprint/CreativeWriter-Llama3.2-3B to start chatting
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 theprint/CreativeWriter-Llama3.2-3B to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for theprint/CreativeWriter-Llama3.2-3B to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="theprint/CreativeWriter-Llama3.2-3B",
max_seq_length=2048,
)How to use theprint/CreativeWriter-Llama3.2-3B with Docker Model Runner:
docker model run hf.co/theprint/CreativeWriter-Llama3.2-3B
This Llama 3.2 model was fine tuned on two combined data sets, one full of writing coach conversations, the other containing actual creative writing samples. Both data sets used were synthetic.
The model was fine-tuned for 2 epochs.
You can find a more in-depth description of this model and its intended use in this blog post.
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.