Instructions to use Fu01978/OLMo-2-1B-openai-gsm8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fu01978/OLMo-2-1B-openai-gsm8k with Transformers:

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

pipe = pipeline("text-generation", model="Fu01978/OLMo-2-1B-openai-gsm8k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fu01978/OLMo-2-1B-openai-gsm8k")
model = AutoModelForCausalLM.from_pretrained("Fu01978/OLMo-2-1B-openai-gsm8k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Fu01978/OLMo-2-1B-openai-gsm8k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fu01978/OLMo-2-1B-openai-gsm8k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fu01978/OLMo-2-1B-openai-gsm8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fu01978/OLMo-2-1B-openai-gsm8k

SGLang

How to use Fu01978/OLMo-2-1B-openai-gsm8k 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 "Fu01978/OLMo-2-1B-openai-gsm8k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fu01978/OLMo-2-1B-openai-gsm8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Fu01978/OLMo-2-1B-openai-gsm8k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fu01978/OLMo-2-1B-openai-gsm8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fu01978/OLMo-2-1B-openai-gsm8k with Docker Model Runner:
```
docker model run hf.co/Fu01978/OLMo-2-1B-openai-gsm8k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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))