This model is a fine-tuned version of allenai/OLMo-2-0425-1B-Instruct optimized for math tasks. It was trained using a subset of OpenAI's openai/gsm8k to improve logical reasoning and syntax accuracy.

Model Details

Developed by: Fu01978
Model type: Decoder-only Transformer
Language: English
License: Apache 2.0
Fine-tuned from model: allenai/OLMo-2-0425-1B-Instruct

Intended Uses & Limitations

Intended Use

Python code completion and generation.
Explaining programming logic.
Debugging small snippets of code.
Solving simple math problems.

Limitations

As a 1B model, it may struggle with highly complex architectural patterns or obscure libraries.
The model may still produce hallucinations if the prompt is ambiguous.

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

Method: LoRA (Low-Rank Adaptation)
Rank (r): 64
Alpha: 128
Target Modules: q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj
Learning Rate: 5e-5
Optimizer: AdamW
Precision: BF16 (Bfloat16)
Epochs: 1
Batch Size: 2 (Gradient Accumulation: 4)

Training Results

Final Loss: 1.2134
Training Time: ~8 minutes (on NVIDIA T4 GPU)
Dataset Size: 1,346 rows

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Fu01978/OLMo-2-1B-openai-gsm8k"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

prompt = "def find_primes(n):"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))