Instructions to use IHaBiS/PiVoT-0.1-early-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IHaBiS/PiVoT-0.1-early-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="IHaBiS/PiVoT-0.1-early-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IHaBiS/PiVoT-0.1-early-exl2")
model = AutoModelForCausalLM.from_pretrained("IHaBiS/PiVoT-0.1-early-exl2")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IHaBiS/PiVoT-0.1-early-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IHaBiS/PiVoT-0.1-early-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IHaBiS/PiVoT-0.1-early-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IHaBiS/PiVoT-0.1-early-exl2

SGLang

How to use IHaBiS/PiVoT-0.1-early-exl2 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 "IHaBiS/PiVoT-0.1-early-exl2" \
    --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": "IHaBiS/PiVoT-0.1-early-exl2",
		"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 "IHaBiS/PiVoT-0.1-early-exl2" \
        --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": "IHaBiS/PiVoT-0.1-early-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IHaBiS/PiVoT-0.1-early-exl2 with Docker Model Runner:
```
docker model run hf.co/IHaBiS/PiVoT-0.1-early-exl2
```

Exl2 version of maywell/PiVoT-0.1-early

branch

main : 8bpw h8
6bh8 : 6bpw h8
4bh8 : 4bpw h8

Using VMware/open-instruct as dataset

Quantization settings : python convert.py -i models/maywell_PiVoT-0.1-early -o PiVoT-0.1-early-temp -cf PiVoT-0.1-early-8bpw-h8-exl2 -c 0000.parquet -l 4096 -b 8 -hb 8
python convert.py -i models/maywell_PiVoT-0.1-early -o PiVoT-0.1-early-temp2 -cf PiVoT-0.1-early-6bpw-h8-exl22 -c 0000.parquet -l 4096 -b 6 -hb 8 -m PiVoT-0.1-early-temp/measurement.json
python convert.py -i models/maywell_PiVoT-0.1-early -o PiVoT-0.1-early-temp3 -cf PiVoT-0.1-early-4bpw-h8-exl2 -c 0000.parquet -l 4096 -b 4 -hb 8 -m PiVoT-0.1-early-temp/measurement.json

below this line is original readme

PiVoT-0.1-early

Model Details

Description

PivoT is Finetuned model based on Mistral 7B. It is variation from Synatra v0.3 RP which has shown decent performance.

OpenOrca Dataset used when finetune PiVoT variation. Arcalive Ai Chat Chan log 7k, ko_wikidata_QA, kyujinpy/OpenOrca-KO and other datasets used on base model.

Follow me on twitter: https://twitter.com/stablefluffy

Consider Support me making these model alone: https://www.buymeacoffee.com/mwell or with Runpod Credit Gift 💕

Contact me on Telegram: https://t.me/AlzarTakkarsen

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