Instructions to use Dapinsky/PIT-4B-FT-202212-math-reasoning-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dapinsky/PIT-4B-FT-202212-math-reasoning-sft with Transformers:

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

pipe = pipeline("text-generation", model="Dapinsky/PIT-4B-FT-202212-math-reasoning-sft", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Dapinsky/PIT-4B-FT-202212-math-reasoning-sft", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Dapinsky/PIT-4B-FT-202212-math-reasoning-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dapinsky/PIT-4B-FT-202212-math-reasoning-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dapinsky/PIT-4B-FT-202212-math-reasoning-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Dapinsky/PIT-4B-FT-202212-math-reasoning-sft

SGLang

How to use Dapinsky/PIT-4B-FT-202212-math-reasoning-sft 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 "Dapinsky/PIT-4B-FT-202212-math-reasoning-sft" \
    --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": "Dapinsky/PIT-4B-FT-202212-math-reasoning-sft",
		"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 "Dapinsky/PIT-4B-FT-202212-math-reasoning-sft" \
        --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": "Dapinsky/PIT-4B-FT-202212-math-reasoning-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Dapinsky/PIT-4B-FT-202212-math-reasoning-sft with Docker Model Runner:
```
docker model run hf.co/Dapinsky/PIT-4B-FT-202212-math-reasoning-sft
```

Dapinsky/PIT-4B-FT-202212-math-reasoning-sft

This repo contains a full merged model produced from Diamegs/PIT-4B-FT-202212 and a PEFT LoRA adapter. It is intended to be loadable with the same Transformers API used for the base Diamegs PIT model:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Dapinsky/PIT-4B-FT-202212-math-reasoning-sft", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Dapinsky/PIT-4B-FT-202212-math-reasoning-sft", trust_remote_code=True)

Training Summary

Base model: Diamegs/PIT-4B-FT-202212
Fine-tuning method: supervised fine-tuning with LoRA, merged into the base weights for upload.
Tokenizer files copied from: base model

LoRA Configuration

{
  "peft_type": "LORA",
  "task_type": "CAUSAL_LM",
  "r": 16,
  "lora_alpha": 32,
  "lora_dropout": 0.05,
  "target_modules": [
    "c_proj",
    "c_q",
    "c_k",
    "c_fc",
    "c_v"
  ],
  "base_model_name_or_path": "Diamegs/PIT-4B-FT-202212"
}

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Safetensors

Model size

4B params

Tensor type

F32

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Diamegs/PIT-4B-FT-202212

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