georgiyozhegov/g.arithmetic
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How to use georgiyozhegov/calculator-8m with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="georgiyozhegov/calculator-8m") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("georgiyozhegov/calculator-8m")
model = AutoModelForCausalLM.from_pretrained("georgiyozhegov/calculator-8m")How to use georgiyozhegov/calculator-8m with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "georgiyozhegov/calculator-8m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "georgiyozhegov/calculator-8m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/georgiyozhegov/calculator-8m
How to use georgiyozhegov/calculator-8m with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "georgiyozhegov/calculator-8m" \
--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": "georgiyozhegov/calculator-8m",
"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 "georgiyozhegov/calculator-8m" \
--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": "georgiyozhegov/calculator-8m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use georgiyozhegov/calculator-8m with Docker Model Runner:
docker model run hf.co/georgiyozhegov/calculator-8m
Model-calculator.
Works well with simple calculations, but fails with complex ones.
Here's a 6-million parameters model.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("georgiyozhegov/calculator-8m")
model = AutoModelForCausalLM.from_pretrained("georgiyozhegov/calculator-8m")
prompt = "find 2 + 3\nstep"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)
with torch.no_grad():
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=32,
do_sample=True,
top_k=50,
top_p=0.98
)
# Cut the rest
count = 0
for index, token in enumerate(outputs[0]):
if token == 6: count += 1
if count >= 2: break
output = tokenizer.decode(outputs[0][:index])
print(output)