Instructions to use Mihaiii/Pallas-0.5-LASER-0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mihaiii/Pallas-0.5-LASER-0.4 with Transformers:

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

pipe = pipeline("text-generation", model="Mihaiii/Pallas-0.5-LASER-0.4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mihaiii/Pallas-0.5-LASER-0.4")
model = AutoModelForCausalLM.from_pretrained("Mihaiii/Pallas-0.5-LASER-0.4")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mihaiii/Pallas-0.5-LASER-0.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mihaiii/Pallas-0.5-LASER-0.4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mihaiii/Pallas-0.5-LASER-0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mihaiii/Pallas-0.5-LASER-0.4

SGLang

How to use Mihaiii/Pallas-0.5-LASER-0.4 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 "Mihaiii/Pallas-0.5-LASER-0.4" \
    --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": "Mihaiii/Pallas-0.5-LASER-0.4",
		"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 "Mihaiii/Pallas-0.5-LASER-0.4" \
        --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": "Mihaiii/Pallas-0.5-LASER-0.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mihaiii/Pallas-0.5-LASER-0.4 with Docker Model Runner:
```
docker model run hf.co/Mihaiii/Pallas-0.5-LASER-0.4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model has a LASER intervention on Mihaiii/Pallas-0.5-LASER-0.3 .

Configs used:

lnum: 55
lnames: mlp (meaning: ["mlp.gate_proj.weight", "mlp.up_proj.weight", "mlp.down_proj.weight"])
rate: 9
dataset: bigbench (subset: causal_judgement)
intervention type: rank-reduction

Name	Validation acc (higher is better)	Validation logloss (lower is better)	Test acc (higher is better)	Test logloss (lower is better)
Pallas-0.5	55.263	1.650	60.526	1.463
Pallas-0.5-LASER-0.1	55.263	1.639	61.184	1.451
Pallas-0.5-LASER-0.2	55.263	1.646	61.184	1.458
Pallas-0.5-LASER-0.3	55.263	1.575	61.842	1.382
Pallas-0.5-LASER-0.4	55.263	1.525	61.842	1.326
Pallas-0.5-LASER-0.5	55.263	1.484	61.842	1.297
Pallas-0.5-LASER-0.6	55.263	1.455	61.184	1.283