Instructions to use shanearora/i-am-a-good-big-instruct-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shanearora/i-am-a-good-big-instruct-model with Transformers:

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

pipe = pipeline("text-generation", model="shanearora/i-am-a-good-big-instruct-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("shanearora/i-am-a-good-big-instruct-model", dtype="auto")

llama-cpp-python

How to use shanearora/i-am-a-good-big-instruct-model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shanearora/i-am-a-good-big-instruct-model",
	filename="OLMo-2-1124-13B-Instruct-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use shanearora/i-am-a-good-big-instruct-model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shanearora/i-am-a-good-big-instruct-model:F16
# Run inference directly in the terminal:
llama-cli -hf shanearora/i-am-a-good-big-instruct-model:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shanearora/i-am-a-good-big-instruct-model:F16
# Run inference directly in the terminal:
llama-cli -hf shanearora/i-am-a-good-big-instruct-model:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf shanearora/i-am-a-good-big-instruct-model:F16
# Run inference directly in the terminal:
./llama-cli -hf shanearora/i-am-a-good-big-instruct-model:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf shanearora/i-am-a-good-big-instruct-model:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shanearora/i-am-a-good-big-instruct-model:F16

Use Docker

docker model run hf.co/shanearora/i-am-a-good-big-instruct-model:F16

LM Studio
Jan

vLLM

How to use shanearora/i-am-a-good-big-instruct-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shanearora/i-am-a-good-big-instruct-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shanearora/i-am-a-good-big-instruct-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shanearora/i-am-a-good-big-instruct-model:F16

SGLang

How to use shanearora/i-am-a-good-big-instruct-model 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 "shanearora/i-am-a-good-big-instruct-model" \
    --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": "shanearora/i-am-a-good-big-instruct-model",
		"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 "shanearora/i-am-a-good-big-instruct-model" \
        --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": "shanearora/i-am-a-good-big-instruct-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use shanearora/i-am-a-good-big-instruct-model with Ollama:
```
ollama run hf.co/shanearora/i-am-a-good-big-instruct-model:F16
```

Unsloth Studio

How to use shanearora/i-am-a-good-big-instruct-model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shanearora/i-am-a-good-big-instruct-model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shanearora/i-am-a-good-big-instruct-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shanearora/i-am-a-good-big-instruct-model to start chatting

Docker Model Runner
How to use shanearora/i-am-a-good-big-instruct-model with Docker Model Runner:
```
docker model run hf.co/shanearora/i-am-a-good-big-instruct-model:F16
```

Lemonade

How to use shanearora/i-am-a-good-big-instruct-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shanearora/i-am-a-good-big-instruct-model:F16

Run and chat with the model

lemonade run user.i-am-a-good-big-instruct-model-F16

List all available models

lemonade list

OLMo-2-1124-13B-Instruct

OLMo-2 13B Instruct November 2024 is post-trained variant of the OLMo-2 13B November 2024 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset and further DPO training on this dataset, and finally RLVR training using this data. Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval. Check out the OLMo 2 paper (forthcoming) or Tülu 3 paper for more details!

OLMo is a series of Open Language Models designed to enable the science of language models. These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details. The core models released in this batch include the following:

Stage	OLMo-2 7B	OLMo 2 13B
Base Model	allenai/OLMo2-7B-1124	allenai/OLMo-2-13B-1124
SFT	allenai/OLMo-2-1124-7B-SFT	allenai/OLMo-2-1124-13B-SFT
DPO	allenai/OLMo-2-1124-7B-DPO	allenai/OLMo-2-1124-13B-DPO
Final Models (RLVR)	allenai/OLMo-2-1124-7B-Instruct	allenai/OLMo-2-1124-13B-Instruct
Reward Model (RM)	allenai/OLMo-2-1124-7B-RM	(Same as 8B)

Model description

Model type: A model trained on a mix of publicly available, synthetic and human-created datasets.
Language(s) (NLP): Primarily English
License: Apache 2.0
Finetuned from model: allenai/OLMo-2-13B-1124-DPO

Model Sources

Project Page: https://allenai.org/olmo
Repositories:
- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
- Evaluation code: https://github.com/allenai/olmes
- Further fine-tuning code: https://github.com/allenai/open-instruct
Paper: Coming soon!
Demo: https://playground.allenai.org/

Using the model

Loading with HuggingFace

To load the model with HuggingFace, use the following snippet:

from transformers import AutoModelForCausalLM

olmo_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-13B-Instruct")

Chat template

The chat template for our models is formatted as:

<|endoftext|><|user|>\nHow are you doing?\n<|assistant|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

Or with new lines expanded:

<|endoftext|><|user|>
How are you doing?
<|assistant|>
I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

It is embedded within the tokenizer as well, for tokenizer.apply_chat_template.

System prompt

In Ai2 demos, we use this system prompt by default:

You are OLMo 2, a helpful and harmless AI Assistant built by the Allen Institute for AI.

The model has not been trained with a specific system prompt in mind.

Bias, Risks, and Limitations

The OLMo 2 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). See the Falcon 180B model card for an example of this.

Performance

Model	Average	AlpacaEval	BBH	DROP	GSM8k	IFEval	MATH	MMLU	Safety	PopQA	TruthQA
Open weights models
Gemma-2-9B-it	51.9	43.7	2.5	58.8	79.7	69.9	29.8	69.1	75.5	28.3	61.4
Ministral-8B-Instruct	52.1	31.4	56.2	56.2	80.0	56.4	40.0	68.5	56.2	20.2	55.5
Mistral-Nemo-Instruct-2407	51.1	45.8	56.0	23.6	81.4	64.5	31.9	70.0	52.7	26.9	57.7
Qwen-2.5-7B-Instruct	57.1	29.7	25.3	54.4	83.8	74.7	69.9	76.6	75.0	18.1	63.1
Llama-3.1-8B-Instruct	58.9	25.8	69.7	61.7	83.4	80.6	42.5	71.3	70.2	28.4	55.1
Tülu 3 8B	60.4	34.0	66.0	62.6	87.6	82.4	43.7	68.2	75.4	29.1	55.0
Qwen-2.5-14B-Instruct	61.0	34.6	35.4	50.5	83.9	82.4	70.6	81.1	79.3	21.1	70.8
Fully open models
OLMo-7B-Instruct	28.2	5.2	35.3	30.7	14.3	32.2	2.1	46.3	54.0	17.1	44.5
OLMo-7B-0424-Instruct	33.2	8.5	35.2	47.9	23.2	39.2	5.2	48.9	49.3	18.9	55.2
OLMoE-1B-7B-0924-Instruct	35.5	8.5	37.2	34.3	47.2	46.2	8.4	51.6	51.6	20.6	49.1
MAP-Neo-7B-Instruct	42.9	17.6	26.4	48.2	69.4	35.9	31.5	56.5	73.7	18.4	51.6
OLMo-2-7B-SFT	50.0	9.3	50.7	58.2	71.2	68.0	25.1	62.0	82.4	25.0	47.8
OLMo-2-7B-DPO	55.0	29.9	47.0	58.8	82.4	74.5	31.2	63.4	81.5	24.5	57.2
OLMo-2-13B-SFT	55.7	12.0	58.8	71.8	75.7	71.5	31.1	67.3	82.8	29.3	56.2
OLMo-2-13B-DPO	61.0	38.3	58.5	71.9	84.2	80.6	35.0	68.5	80.6	28.9	63.9
OLMo-2-7B-1124–Instruct	55.7	31.0	48.9	58.9	85.2	75.6	31.3	63.9	81.2	24.6	56.3
OLMo-2-13B-1124-Instruct	61.4	37.5	58.4	72.1	87.4	80.4	39.7	68.6	77.5	28.8	63.9

Hyperparameters

PPO settings for RLVR:

Learning Rate: 4 × 10⁻⁷
Discount Factor (gamma): 1.0
General Advantage Estimation (lambda): 0.95
Mini-batches (N_mb): 1
PPO Update Iterations (K): 4
PPO's Clipping Coefficient (epsilon): 0.2
Value Function Coefficient (c1): 0.1
Gradient Norm Threshold: 1.0
Learning Rate Schedule: Linear
Generation Temperature: 1.0
Batch Size (effective): 512
Max Token Length: 2,048
Max Prompt Token Length: 2,048
Penalty Reward Value for Responses without an EOS Token: -10.0
Response Length: 2,048
Total Episodes: 100,000 (this checkpoint is training step 360)
KL penalty coefficient (beta): 0.03
Warm up ratio (omega): 0.0

License and use

OLMo 2 is licensed under the Apache 2.0 license. OLMo 2 is intended for research and educational use. For more information, please see our Responsible Use Guidelines. This model has been fine-tuned using a dataset mix with outputs generated from third party models and are subject to additional terms: Gemma Terms of Use.