Pallas
Collection
4 items • Updated
How to use Mihaiii/Pallas-0.2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Mihaiii/Pallas-0.2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Mihaiii/Pallas-0.2")
model = AutoModelForCausalLM.from_pretrained("Mihaiii/Pallas-0.2")How to use Mihaiii/Pallas-0.2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mihaiii/Pallas-0.2"
# 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-0.2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Mihaiii/Pallas-0.2
How to use Mihaiii/Pallas-0.2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Mihaiii/Pallas-0.2" \
--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-0.2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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-0.2" \
--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-0.2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Mihaiii/Pallas-0.2 with Docker Model Runner:
docker model run hf.co/Mihaiii/Pallas-0.2
An instruct based fine tune of migtissera/Tess-34B-v1.4.
It works well with long system prompts.
It works well for reasoning tasks.
This model is trained on a private dataset. The high GSM8K score is NOT because of the MetaMath dataset.
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
Base model
migtissera/Tess-34B-v1.4