Instructions to use Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb with Transformers:

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

pipe = pipeline("text-generation", model="Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb")
model = AutoModelForCausalLM.from_pretrained("Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb

SGLang

How to use Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb 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 "Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb" \
    --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": "Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb",
		"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 "Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb" \
        --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": "Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb with Docker Model Runner:
```
docker model run hf.co/Fizzarolli/OLMoE-1B-7B-0924-extended-pos-emb
```

Fizzarolli

Muennighoff commited on Sep 21, 2024

Commit

ae4af1b

verified ·

0 Parent(s):

Duplicate from allenai/OLMoE-1B-7B-0924

Browse files

Co-authored-by: Niklas Muennighoff <Muennighoff@users.noreply.huggingface.co>

Files changed (12) hide show

.gitattributes +44 -0
README.md +97 -0
config.json +31 -0
generation_config.json +6 -0
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README.md ADDED Viewed

	@@ -0,0 +1,97 @@

+---
+license: apache-2.0
+language:
+- en
+tags:
+- moe
+- olmo
+- olmoe
+co2_eq_emissions: 1
+datasets:
+- allenai/OLMoE-mix-0924
+library_name: transformers
+---
+<img alt="OLMoE Logo." src="olmoe-logo.png" width="250px">
+# Model Summary
+> OLMoE-1B-7B is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in September 2024 (0924). It yields state-of-the-art performance among models with a similar cost (1B) and is competitive with much larger models like Llama2-13B. OLMoE is 100% open-source.
+This information and more can also be found on the [**OLMoE GitHub repository**](https://github.com/allenai/OLMoE).
+- **Paper**: https://arxiv.org/abs/2409.02060
+- **Pretraining** [Checkpoints](https://hf.co/allenai/OLMoE-1B-7B-0924), [Code](https://github.com/allenai/OLMo/tree/Muennighoff/MoE), [Data](https://huggingface.co/datasets/allenai/OLMoE-mix-0924) and [Logs](https://wandb.ai/ai2-llm/olmoe/reports/OLMoE-1B-7B-0924--Vmlldzo4OTcyMjU3).
+- **SFT (Supervised Fine-Tuning)** [Checkpoints](https://huggingface.co/allenai/OLMoE-1B-7B-0924-SFT), [Code](https://github.com/allenai/open-instruct/tree/olmoe-sft), [Data](https://hf.co/datasets/allenai/tulu-v3.1-mix-preview-4096-OLMoE) and [Logs](https://github.com/allenai/OLMoE/blob/main/logs/olmoe-sft-logs.txt).
+- **DPO/KTO (Direct Preference Optimization/Kahneman-Tversky Optimization)**, [Checkpoints](https://huggingface.co/allenai/OLMoE-1B-7B-0924-Instruct), [Preference Data](https://hf.co/datasets/allenai/ultrafeedback_binarized_cleaned), [DPO code](https://github.com/allenai/open-instruct/tree/olmoe-sft), [KTO code](https://github.com/Muennighoff/kto/blob/master/kto.py) and [Logs](https://github.com/allenai/OLMoE/blob/main/logs/olmoe-dpo-logs.txt).
+# Use
+Install `transformers` **from source** until a release after [this PR](https://github.com/huggingface/transformers/pull/32406) & `torch` and run:
+```python
+from transformers import OlmoeForCausalLM, AutoTokenizer
+import torch
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+# Load different ckpts via passing e.g. `revision=step10000-tokens41B`
+model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924").to(DEVICE)
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924")
+inputs = tokenizer("Bitcoin is", return_tensors="pt")
+inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
+out = model.generate(**inputs, max_length=64)
+print(tokenizer.decode(out[0]))
+# > # Bitcoin is a digital currency that is created and held electronically. No one controls it. Bitcoins aren’t printed, like dollars or euros – they’re produced by people and businesses running computers all around the world, using software that solves mathematical
+```
+You can list all revisions/branches by installing `huggingface-hub` & running:
+```python
+from huggingface_hub import list_repo_refs
+out = list_repo_refs("OLMoE/OLMoE-1B-7B-0924")
+branches = [b.name for b in out.branches]
+```
+Important branches:
+- `step1200000-tokens5033B`: Pretraining checkpoint used for annealing. There are a few more checkpoints after this one but we did not use them.
+- `main`: Checkpoint annealed from `step1200000-tokens5033B` for an additional 100B tokens (23,842 steps). We use this checkpoint for our adaptation (https://huggingface.co/allenai/OLMoE-1B-7B-0924-SFT & https://huggingface.co/allenai/OLMoE-1B-7B-0924-Instruct).
+- `fp32`: FP32 version of `main`. The model weights were stored in FP32 during training but we did not observe any performance drop from casting them to BF16 after training so we upload all weights in BF16. If you want the original FP32 checkpoint for `main` you can use this one. You will find that it yields slightly different results but should perform around the same on benchmarks.
+# Evaluation Snapshot
+| Model                       | Active Params | Open Data | MMLU | HellaSwag | ARC-Chall. | ARC-Easy | PIQA | WinoGrande |
+|-----------------------------|---------------|-----------|------|-----------|------------|----------|------|------------|
+| **LMs with ~1B active parameters** |               |           |      |           |            |          |      |            |
+| **OLMoE-1B-7B**              | **1.3B**      | **✅**    | **54.1** | **80.0** | **62.1**   | **84.2** | **79.8** | **70.2**  |
+| DCLM-1B                     | 1.4B          | ✅        | 48.5 | 75.1      | 57.6       | 79.5     | 76.6 | 68.1       |
+| TinyLlama-1B                | 1.1B          | ✅        | 33.6 | 60.8      | 38.1       | 69.5     | 71.7 | 60.1       |
+| OLMo-1B (0724)              | 1.3B          | ✅        | 32.1 | 67.5      | 36.4       | 53.5     | 74.0 | 62.9       |
+| Pythia-1B                   | 1.1B          | ✅        | 31.1 | 48.0      | 31.4       | 63.4     | 68.9 | 52.7       |
+| **LMs with ~2-3B active parameters** |               |           |      |           |            |          |      |            |
+| Qwen1.5-3B-14B              | 2.7B          | ❌        | **62.4** | 80.0      | **77.4**   | **91.6** | **81.0** | 72.3 |
+| Gemma2-3B                   | 2.6B          | ❌        | 53.3 | 74.6      | 67.5       | 84.3     | 78.5 | 71.8       |
+| JetMoE-2B-9B                | 2.2B          | ❌        | 49.1 | **81.7**  | 61.4       | 81.9     | 80.3 | 70.7       |
+| DeepSeek-3B-16B             | 2.9B          | ❌        | 45.5 | 80.4      | 53.4       | 82.7     | 80.1 | **73.2**   |
+| StableLM-2B                 | 1.6B          | ❌        | 40.4 | 70.3      | 50.6       | 75.3     | 75.6 | 65.8       |
+| OpenMoE-3B-9B               | 2.9B          | ✅        | 27.4 | 44.4      | 29.3       | 50.6     | 63.3 | 51.9       |
+| **LMs with ~7-9B active parameters** |               |           |      |           |            |          |      |            |
+| Gemma2-9B                   | 9.2B          | ❌        | **70.6** | **87.3**  | **89.5**   | **95.5** | **86.1** | **78.8** |
+| Llama3.1-8B                 | 8.0B          | ❌        | 66.9 | 81.6      | 79.5       | 91.7     | 81.1 | 76.6       |
+| DCLM-7B                     | 6.9B          | ✅        | 64.4 | 82.3      | 79.8       | 92.3     | 80.1 | 77.3       |
+| Mistral-7B                  | 7.3B          | ❌        | 64.0 | 83.0      | 78.6       | 90.8     | 82.8 | 77.9       |
+| OLMo-7B (0724)              | 6.9B          | ✅        | 54.9 | 80.5      | 68.0       | 85.7     | 79.3 | 73.2       |
+| Llama2-7B                   | 6.7B          | ❌        | 46.2 | 78.9      | 54.2       | 84.0     | 77.5 | 71.7       |
+# Citation
+```bibtex
+@misc{muennighoff2024olmoeopenmixtureofexpertslanguage,
+      title={OLMoE: Open Mixture-of-Experts Language Models},
+      author={Niklas Muennighoff and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Jacob Morrison and Sewon Min and Weijia Shi and Pete Walsh and Oyvind Tafjord and Nathan Lambert and Yuling Gu and Shane Arora and Akshita Bhagia and Dustin Schwenk and David Wadden and Alexander Wettig and Binyuan Hui and Tim Dettmers and Douwe Kiela and Ali Farhadi and Noah A. Smith and Pang Wei Koh and Amanpreet Singh and Hannaneh Hajishirzi},
+      year={2024},
+      eprint={2409.02060},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.02060},
+}
+```

config.json ADDED Viewed

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+{
+  "architectures": [
+    "OlmoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "clip_qkv": null,
+  "eos_token_id": 50279,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "max_position_embeddings": 4096,
+  "model_type": "olmoe",
+  "norm_topk_prob": false,
+  "num_attention_heads": 16,
+  "num_experts": 64,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 16,
+  "output_router_logits": false,
+  "pad_token_id": 1,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "router_aux_loss_coef": 0.01,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.0.dev0",
+  "use_cache": true,
+  "vocab_size": 50304
+}

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