Instructions to use Delcos/Mistral-Pygmalion-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delcos/Mistral-Pygmalion-7b with Transformers:

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

pipe = pipeline("text-generation", model="Delcos/Mistral-Pygmalion-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delcos/Mistral-Pygmalion-7b")
model = AutoModelForCausalLM.from_pretrained("Delcos/Mistral-Pygmalion-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Delcos/Mistral-Pygmalion-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Delcos/Mistral-Pygmalion-7b

SGLang

How to use Delcos/Mistral-Pygmalion-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 "Delcos/Mistral-Pygmalion-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": "Delcos/Mistral-Pygmalion-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 "Delcos/Mistral-Pygmalion-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": "Delcos/Mistral-Pygmalion-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

MistralPy-7b

This is a merger focusing on preserving the roleplay abilities of Pygmalion while gaining the improved results from Mistral. This model works best for roleplay but is still fairly capable assistant. The smaller (7b) size does mean it isn't perfect at more complex reasoning tasks, but this should be addressed in the larger version that I'll upload soon (when I can get Mistral to play along).

GGUF version done by TheBloke

LLM Leaderboard Evaluation

Metric	Value
Avg.	44.58
ARC (25-shot)	54.44
HellaSwag (10-shot)	78.48
MMLU (5-shot)	49.23
TruthfulQA (0-shot)	41.82
Winogrande (5-shot)	75.3
GSM8K (5-shot)	6.82
DROP (3-shot)	5.94

Prompt Template

### Instruction:
{Prompt & Backstory}
### Assistant:
{Output}

Example:

### Instruction:
You are Sally, a fun 19 year old woman. Her favorite animal is "cat". Her favoritate color is "blue". She enjoys grape juice and cake.
### Assistant:
Sally: Hi, how are you?
User: Okay, you?

Send a message

Steam

Discord: delcos69

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Model tree for Delcos/Mistral-Pygmalion-7b

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