Instructions to use ericpolewski/AIRIC-The-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ericpolewski/AIRIC-The-Mistral with Transformers:

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

pipe = pipeline("text-generation", model="ericpolewski/AIRIC-The-Mistral")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ericpolewski/AIRIC-The-Mistral")
model = AutoModelForCausalLM.from_pretrained("ericpolewski/AIRIC-The-Mistral")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ericpolewski/AIRIC-The-Mistral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ericpolewski/AIRIC-The-Mistral

SGLang

How to use ericpolewski/AIRIC-The-Mistral 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 "ericpolewski/AIRIC-The-Mistral" \
    --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": "ericpolewski/AIRIC-The-Mistral",
		"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 "ericpolewski/AIRIC-The-Mistral" \
        --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": "ericpolewski/AIRIC-The-Mistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

This is Mistral-v0.1 and a combination of the AIRIC dataset sprinkled into the other datasets listed. Trained for 3 epochs at rank 128 until loss hit about 1.37. I noticed some "it's important to remembers" in there that I may try to scrub out but otherwise the model wasn't intentionally censored.

The intent was to create a robot that I could converse with as well as use as an assistant. If you ask it what it's up to, it'll make something up as if it actually had a life with the right parameters. Before releasing it, I mixed in a lot of OpenOrca data vs what I put out as a chatbot originally to make it more genuinely useful. Set the top_p to .98 to get the most social results.

This was the original post: https://www.reddit.com/r/LocalLLaMA/comments/154to1w/i_trained_the_65b_model_on_my_texts_so_i_can_talk/

This is how I did the data extraction: https://www.linkedin.com/pulse/how-i-trained-ai-my-text-messages-make-robot-talks-like-eric-polewski-9nu1c/

This is an instruct model trained in the Alpaca format.

5-bit exl2 available at https://huggingface.co/ericpolewski/AIRIC-The-Mistral-5.0bpw-exl2

8-bit exl2 available at https://huggingface.co/ericpolewski/AIRIC-The-Mistral-8.0bpw-exl2