Instructions to use Fredithefish/ScarletPajama-3B-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fredithefish/ScarletPajama-3B-HF with Transformers:

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

pipe = pipeline("text-generation", model="Fredithefish/ScarletPajama-3B-HF")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fredithefish/ScarletPajama-3B-HF")
model = AutoModelForCausalLM.from_pretrained("Fredithefish/ScarletPajama-3B-HF")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Fredithefish/ScarletPajama-3B-HF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fredithefish/ScarletPajama-3B-HF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fredithefish/ScarletPajama-3B-HF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Fredithefish/ScarletPajama-3B-HF

SGLang

How to use Fredithefish/ScarletPajama-3B-HF 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 "Fredithefish/ScarletPajama-3B-HF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fredithefish/ScarletPajama-3B-HF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Fredithefish/ScarletPajama-3B-HF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fredithefish/ScarletPajama-3B-HF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Fredithefish/ScarletPajama-3B-HF with Docker Model Runner:
```
docker model run hf.co/Fredithefish/ScarletPajama-3B-HF
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ScarletPajama

Introducing ScarletPajama: a language model that has been finetuned on the ShareGPT dataset. Built upon the robust RedPajama-INCITE-Chat-3b architecture.
The original ShareGPT dataset consisted of 53k pairs of conversational exchanges. In order to optimize the training process, the dataset was converted to the appropriate format and filtered to remove long texts. The resulting filtered version of ShareGPT contains 22k pairs, ensuring a more focused and efficient training process.

Model Details

Model Name: ScarletPajama
Base Model: RedPajama-INCITE-Chat-3b
Dataset: ShareGPT-22K
Fine-tuning Epochs: 2

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