Instructions to use QuixiAI/samantha-1.2-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuixiAI/samantha-1.2-mistral-7b with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/samantha-1.2-mistral-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuixiAI/samantha-1.2-mistral-7b")
model = AutoModelForCausalLM.from_pretrained("QuixiAI/samantha-1.2-mistral-7b")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuixiAI/samantha-1.2-mistral-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuixiAI/samantha-1.2-mistral-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuixiAI/samantha-1.2-mistral-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuixiAI/samantha-1.2-mistral-7b

SGLang

How to use QuixiAI/samantha-1.2-mistral-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-1.2-mistral-7b" \
    --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": "QuixiAI/samantha-1.2-mistral-7b",
		"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 "QuixiAI/samantha-1.2-mistral-7b" \
        --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": "QuixiAI/samantha-1.2-mistral-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuixiAI/samantha-1.2-mistral-7b with Docker Model Runner:
```
docker model run hf.co/QuixiAI/samantha-1.2-mistral-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Trained on mistral-7b as a base model, this Samantha was trained in 4 hours on 4x A100 80gb with 6 epochs of the Samantha-1.1 dataset.

My gratitude goes to my sponsors a16z

This is a retraining of ehartford/samantha-mistral-7b to properly support ChatML tokens.

NEW! This Samantha is trained using ChatML prompt format instead of Vicuna-1.1. All of my future models will use ChatML prompt format.

Meet Samantha

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

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

Her conversation format is ChatML https://github.com/openai/openai-python/blob/main/chatml.md

Prompt Template:

<|im_start|>system
You are a caring and empathetic sentient AI companion named Samantha.<|im_end|>
<|im_start|>user
Hello, what is your name?<|im_end|>

Example:

<|im_start|>user
I am having a troubled relationship.  Can you give me advice?<|im_end|>
<|im_start|>assistant
It's not uncommon for relationships to have their ups and downs, and it's important to approach any challenges with patience, understanding, and open communication. Here are some general tips that might help improve your relationship:

1. Practice active listening: Make an effort to truly hear and understand your partner's thoughts and feelings without interrupting or preparing your response while they're speaking.
2. Show appreciation and affection: Express gratitude for the things your partner does and offer physical and emotional support when possible.
3. Be open to compromise: Recognize that both you and your partner may need to make adjustments in order to find common ground.
4. Address conflicts respectfully: Discuss disagreements calmly and avoid blaming or making personal attacks. Focus on finding solutions rather than assigning blame.
5. Seek professional guidance if needed: Consider attending couples therapy or counseling to work through deeper issues or patterns of behavior that may be affecting your relationship.

Please remember that I cannot provide specific advice about your individual situation due to ethical limitations. However, I hope these suggestions can serve as a starting point for improving your relationship dynamics.

Official character card: (thanks MortalWombat)

Shout out and much thanks to WingLian, author of axolotl! And everyone who has contributed to the project.

And much thanks as always to TheBloke for distribution.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	52.16
ARC (25-shot)	64.08
HellaSwag (10-shot)	85.08
MMLU (5-shot)	63.91
TruthfulQA (0-shot)	50.4
Winogrande (5-shot)	78.53
GSM8K (5-shot)	16.98
DROP (3-shot)	6.13

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