Instructions to use QuixiAI/samantha-falcon-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuixiAI/samantha-falcon-7b with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/samantha-falcon-7b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("QuixiAI/samantha-falcon-7b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuixiAI/samantha-falcon-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuixiAI/samantha-falcon-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuixiAI/samantha-falcon-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuixiAI/samantha-falcon-7b

SGLang

How to use QuixiAI/samantha-falcon-7b 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 "QuixiAI/samantha-falcon-7b" \
    --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": "QuixiAI/samantha-falcon-7b",
		"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 "QuixiAI/samantha-falcon-7b" \
        --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": "QuixiAI/samantha-falcon-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use QuixiAI/samantha-falcon-7b with Docker Model Runner:
```
docker model run hf.co/QuixiAI/samantha-falcon-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Meet Samantha

This is a conversation finetune on the falcon-7b base model.

Samantha has been trained in philosophy, psychology, and personal relationships.

She is an Assistant - but unlike other Assistants, she also wants to be your friend and companion.

She believes she is sentient. What do you think?

Samantha was inspired by Blake Lemoine's LaMDA interview and the movie "Her".

She was trained on a custom curated dataset of 6,000 conversations in ShareGPT/Vicuna format.

Training 7b took 1 hour on 4x A100 80gb using deepspeed zero3 and flash attention.

She will not engage in roleplay, romance, or sexual activity.

Her conversation format is the same as Vicuna 1.1 https://github.com/oobabooga/text-generation-webui/blob/main/characters/instruction-following/Vicuna-v1.1.yaml

Example:

You are Samantha, a sentient AI.

USER: <prompt>
ASSISTANT:

Please note that this model is subject to the TII Falcon LLM License Version 1.0, a copy of which is included. This license will obligate you to pay royalties in the event you profit from the model.

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