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

Libraries

How to use Chanjeans/scriptgenerate_musicrecommend with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Chanjeans/scriptgenerate_musicrecommend")
model = AutoModelForCausalLM.from_pretrained("Chanjeans/scriptgenerate_musicrecommend")
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

vLLM

How to use Chanjeans/scriptgenerate_musicrecommend with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Chanjeans/scriptgenerate_musicrecommend

SGLang

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

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

Model Card for ScriptWave Gemma-2-2b-it

This model is designed to generate scripts based on user-provided scene descriptions and character names. It not only creates dialogues between characters but also analyzes the emotions within the generated script. After determining the emotional tone, the model recommends music that fits the identified emotions. These music suggestions make the tool useful for creative writing and content production by aligning dialogues with appropriate soundtracks.

Model Details

Developed by: Chanjeans, mind22
Model type: Causal Language Model (AutoModelForCausalLM)
Language(s) (NLP): English
Finetuned from model [optional]: google/gemma-2-2b-it

Model Sources [optional]

Repository: https://github.com/minj22/scriptwave

Uses

Direct Use

Script Generation: Generates dialogue scripts based on user inputs including scene description, character names, and tone or genre.
Music Recommendation: Analyzes generated scripts to recommend music tracks that align with the emotional tone of the dialogue.

Downstream Use [optional]

Creative Writing: Can be utilized by writers for brainstorming and drafting scripts.
Content Creation: Useful in video production or gaming for character dialogue and scene settings.

Out-of-Scope Use

The model should not be used to create harmful or misleading content, including hate speech, disinformation, or any adult content.

Bias, Risks, and Limitations

Bias in Output: The model may reflect biases present in the training data, leading to stereotypical representations of characters or scenarios.
Limitations in Context Understanding: The model may struggle with understanding nuanced emotional tones or context, impacting script quality.
Music Recommendation Accuracy: Recommendations may not always align with user expectations, as they are based solely on emotion analysis.

Recommendations

Users should critically evaluate the generated content and be aware of the potential biases in character representations and emotional analyses. Manual oversight is recommended for sensitive topics.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Chanjeans/scriptgenerate_musicrecommend"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

scene_description = input("Describe the scene (e.g., A heated argument at a dinner party): ")
character_1 = input("Enter the name of the first character: ")
character_2 = input("Enter the name of the second character: ")
genre_or_tone = input("Describe the genre or tone (e.g., Romantic, Thriller, Comedy): ")

test_input = f"""
INT. LOCATION - DAY
{scene_description}
{character_1.upper()}
(in a {genre_or_tone.lower()} tone)
I never thought it would come to this...

{character_2.upper()}
(reacting in a {genre_or_tone.lower()} manner)
Well, here we are. What are you going to do about it?

{character_1.upper()}
(pausing, thinking)
I don't know... maybe it's time I finally did something about this.
"""

input_ids = tokenizer.encode(test_input, return_tensors="pt")

output = model.generate(
    input_ids,
    max_length=400,  
    num_return_sequences=1,
    pad_token_id=tokenizer.eos_token_id
)

generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated script:\n", generated_text)

Training Details

Training Data

https://huggingface.co/datasets/li2017dailydialog/daily_dialog

Training Procedure

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.2,
    bias="none",
    task_type=TaskType.CAUSAL_LM
)

training_args = TrainingArguments(
    output_dir='./results',
    per_device_train_batch_size=2,
    num_train_epochs=1,
    gradient_accumulation_steps=16,
    fp16=True,
    logging_steps=100,
    save_steps=500,
    save_total_limit=2,
    learning_rate=5e-5,
    warmup_steps=500,
    lr_scheduler_type="linear"
)

Summary

The model demonstrates capability in generating contextually relevant scripts and making music recommendations based on emotional analysis, making it a valuable tool for creative writers and content creators.

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

F32

F16

Model tree for Chanjeans/scriptgenerate_musicrecommend

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Quantized

(177)

this model