Instructions to use allenai/OLMo-2-1124-13B-Instruct-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/OLMo-2-1124-13B-Instruct-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="allenai/OLMo-2-1124-13B-Instruct-preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-1124-13B-Instruct-preview") model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-13B-Instruct-preview", device_map="auto") 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 Settings
- vLLM
How to use allenai/OLMo-2-1124-13B-Instruct-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/OLMo-2-1124-13B-Instruct-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "allenai/OLMo-2-1124-13B-Instruct-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/allenai/OLMo-2-1124-13B-Instruct-preview
- SGLang
How to use allenai/OLMo-2-1124-13B-Instruct-preview 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 "allenai/OLMo-2-1124-13B-Instruct-preview" \ --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": "allenai/OLMo-2-1124-13B-Instruct-preview", "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 "allenai/OLMo-2-1124-13B-Instruct-preview" \ --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": "allenai/OLMo-2-1124-13B-Instruct-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use allenai/OLMo-2-1124-13B-Instruct-preview with Docker Model Runner:
docker model run hf.co/allenai/OLMo-2-1124-13B-Instruct-preview
something is wrong with this model
Something is wrong with this model. I'm getting great outputs from the 7b model but not this one, and I'm using the same script. Please check the tokenizer or other configuration files...Not sure what it is.
Sure, when I run it using the same exact script as the 7b version, it says it can't find the answer to a question. I'm posing a RAG type of question...single question and answer script, to test for my RAG application. No change in the parameters, inference logic or anything. With that being said, I am using the bitsandbytes library to do 4-bit quantization...that's the only possible thing I can think of that might make a difference...but it's strange that it would only affect the 13b model. Here is the prompt format I'm using:
prompt = f"""<|endoftext|><|user|>
{user_message}
<|assistant|>
"""
Notice I'm not using the annoying apply_chat_template, which is because I just like seeing the formatting.
Anyways, here's the configuration information as well. As you can see, I've tried commenting/uncommenting doubleq_quant and flash attention 2...same result
bnb_bfloat16_settings = {
'tokenizer_settings': {
'torch_dtype': torch.bfloat16,
'trust_remote_code': True,
},
'model_settings': {
'torch_dtype': torch.bfloat16,
'quantization_config': BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
# bnb_4bit_use_double_quant=True,
),
'low_cpu_mem_usage': True,
'trust_remote_code': True,
'attn_implementation': "sdpa"
# 'attn_implementation': "flash_attention_2"
}
}