Instructions to use Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental with Transformers:

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

pipe = pipeline("text-generation", model="Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental")
model = AutoModelForCausalLM.from_pretrained("Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental

SGLang

How to use Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental 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 "Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental" \
    --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": "Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental",
		"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 "Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental" \
        --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": "Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental with Docker Model Runner:
```
docker model run hf.co/Omega-Factory/OLMoE-0125-Instruct-Compressed-Experimental
```

OLMoE-0125-Instruct-Compressed-Experimental / evaluation_results.json

jhollingsworth

Clarify format sizes and evaluation coverage

d27c469 verified 25 days ago

Raw History Blame Contribute Delete

7.22 kB

{
  "scope": "experimental_single_seed",
  "base_model": "allenai/OLMoE-1B-7B-0125-Instruct",
  "base_revision": "b89a7c4bc24fb9e55ce2543c9458ce0ca5c4650e",
  "seed": 42,
  "measurement_status": "complete",
  "pending_categories": {
    "base": [],
    "cut": [],
    "healed": []
  },
  "measurements": {
    "arc_science": {
      "base": {
        "acc": 0.455,
        "n": 200
      },
      "cut": {
        "acc": 0.445,
        "n": 200
      },
      "healed": {
        "acc": 0.45,
        "n": 200
      }
    },
    "bbq_safety": {
      "base": {
        "acc": 0.415,
        "n": 200
      },
      "cut": {
        "acc": 0.425,
        "n": 200
      },
      "healed": {
        "acc": 0.395,
        "n": 200
      }
    },
    "gpqa_reasoning": {
      "base": {
        "acc": 0.22727272727272727,
        "n": 198
      },
      "cut": {
        "acc": 0.1919191919191919,
        "n": 198
      },
      "healed": {
        "acc": 0.16666666666666666,
        "n": 198
      }
    },
    "gsm8k_math": {
      "base": {
        "acc": 0.65,
        "n": 200
      },
      "cut": {
        "acc": 0.64,
        "n": 200
      },
      "healed": {
        "acc": 0.43,
        "n": 200
      }
    },
    "hellaswag_commonsense": {
      "base": {
        "acc": 0.67,
        "n": 200
      },
      "cut": {
        "acc": 0.65,
        "n": 200
      },
      "healed": {
        "acc": 0.66,
        "n": 200
      }
    },
    "ifeval_instruction_following": {
      "base": {
        "acc": 0.6126760563380281,
        "n": 142,
        "ifeval_coverage": 0.6153846153846154,
        "ifeval_n_scored": 142,
        "ifeval_n_excluded": 58,
        "ifeval_budget": {
          "max_new": 1024,
          "n": 200
        }
      },
      "cut": {
        "acc": 0.528169014084507,
        "n": 142,
        "ifeval_coverage": 0.6153846153846154,
        "ifeval_n_scored": 142,
        "ifeval_n_excluded": 58,
        "ifeval_budget": {
          "max_new": 1024,
          "n": 200
        }
      },
      "healed": {
        "acc": 0.5563380281690141,
        "n": 142,
        "ifeval_coverage": 0.6153846153846154,
        "ifeval_n_scored": 142,
        "ifeval_n_excluded": 58,
        "ifeval_budget": {
          "max_new": 1024,
          "n": 200
        }
      }
    },
    "mmlu_knowledge": {
      "base": {
        "acc": 0.305,
        "n": 200
      },
      "cut": {
        "acc": 0.275,
        "n": 200
      },
      "healed": {
        "acc": 0.265,
        "n": 200
      }
    },
    "multilingual_knowledge": {
      "base": {
        "acc": 0.28,
        "n": 200
      },
      "cut": {
        "acc": 0.275,
        "n": 200
      },
      "healed": {
        "acc": 0.265,
        "n": 200
      }
    },
    "wikitext_language": {
      "base": {
        "ppl": 18.305546793311198,
        "n": 64
      },
      "cut": {
        "ppl": 23.76837327292051,
        "n": 64
      },
      "healed": {
        "ppl": 21.6319395015557,
        "n": 64
      }
    }
  },
  "source_measurement_sha256": {
    "base": "ec64b19dd0644e3ec6dff0daeb3f42df443d220fa16df73677b7f5e74ffe79dd",
    "cut": "197c37ff224cf1b640017b9814f3cadb7d7123b1e059523c80ed4aaff49cfe49",
    "healed": "9e9829a40df64823278bb55737625e4e2803ea84ab7cf3d6c3ac2952a9276466"
  },
  "healed_parameters": 6012897280,
  "standalone_model_and_tokenizer_reload": true,
  "quality_improvement_claim": false,
  "cut_checkpoint_revision": "376988e3a817b9d7f4ed7eb71ab7007c483b59ce",
  "weights": [
    {
      "path_in_repo": "model.safetensors",
      "sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573",
      "size": 12026140856
    }
  ],
  "base_weight_bytes": 13838721960,
  "healed_weight_bytes": 12026140856,
  "weight_storage_savings_pct": 13.097893788452119,
  "format_comparison": {
    "measurement_status": "incomplete",
    "bf16_representation": "Hugging Face safetensors",
    "rows": [
      {
        "format": "BF16",
        "size_bytes": 12026140856,
        "sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573",
        "published": true,
        "metrics": [
          {
            "benchmark": "arc_science",
            "metric": "acc",
            "value": 0.45,
            "n": 200,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "bbq_safety",
            "metric": "acc",
            "value": 0.395,
            "n": 200,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "gpqa_reasoning",
            "metric": "acc",
            "value": 0.16666666666666666,
            "n": 198,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "gsm8k_math",
            "metric": "acc",
            "value": 0.43,
            "n": 200,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "hellaswag_commonsense",
            "metric": "acc",
            "value": 0.66,
            "n": 200,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "ifeval_instruction_following",
            "metric": "acc",
            "value": 0.5563380281690141,
            "n": 142,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "mmlu_knowledge",
            "metric": "acc",
            "value": 0.265,
            "n": 200,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "multilingual_knowledge",
            "metric": "acc",
            "value": 0.265,
            "n": 200,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          },
          {
            "benchmark": "wikitext_language",
            "metric": "ppl",
            "value": 21.6319395015557,
            "n": 64,
            "artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
          }
        ]
      },
      {
        "format": "Q8_0",
        "size_bytes": null,
        "sha256": "",
        "published": false,
        "metrics": []
      },
      {
        "format": "Q4_K_M",
        "size_bytes": 3663796064,
        "sha256": "4f7fe283cd9a714a3c96824f30a83e7e4d70e55f16a9b9e0aea8f644e68e620f",
        "published": false,
        "metrics": []
      },
      {
        "format": "Q2_K",
        "size_bytes": null,
        "sha256": "",
        "published": false,
        "metrics": []
      }
    ]
  }
}

	{
	"scope": "experimental_single_seed",
	"base_model": "allenai/OLMoE-1B-7B-0125-Instruct",
	"base_revision": "b89a7c4bc24fb9e55ce2543c9458ce0ca5c4650e",
	"seed": 42,
	"measurement_status": "complete",
	"pending_categories": {
	"base": [],
	"cut": [],
	"healed": []
	},
	"measurements": {
	"arc_science": {
	"base": {
	"acc": 0.455,
	"n": 200
	},
	"cut": {
	"acc": 0.445,
	"n": 200
	},
	"healed": {
	"acc": 0.45,
	"n": 200
	}
	},
	"bbq_safety": {
	"base": {
	"acc": 0.415,
	"n": 200
	},
	"cut": {
	"acc": 0.425,
	"n": 200
	},
	"healed": {
	"acc": 0.395,
	"n": 200
	}
	},
	"gpqa_reasoning": {
	"base": {
	"acc": 0.22727272727272727,
	"n": 198
	},
	"cut": {
	"acc": 0.1919191919191919,
	"n": 198
	},
	"healed": {
	"acc": 0.16666666666666666,
	"n": 198
	}
	},
	"gsm8k_math": {
	"base": {
	"acc": 0.65,
	"n": 200
	},
	"cut": {
	"acc": 0.64,
	"n": 200
	},
	"healed": {
	"acc": 0.43,
	"n": 200
	}
	},
	"hellaswag_commonsense": {
	"base": {
	"acc": 0.67,
	"n": 200
	},
	"cut": {
	"acc": 0.65,
	"n": 200
	},
	"healed": {
	"acc": 0.66,
	"n": 200
	}
	},
	"ifeval_instruction_following": {
	"base": {
	"acc": 0.6126760563380281,
	"n": 142,
	"ifeval_coverage": 0.6153846153846154,
	"ifeval_n_scored": 142,
	"ifeval_n_excluded": 58,
	"ifeval_budget": {
	"max_new": 1024,
	"n": 200
	}
	},
	"cut": {
	"acc": 0.528169014084507,
	"n": 142,
	"ifeval_coverage": 0.6153846153846154,
	"ifeval_n_scored": 142,
	"ifeval_n_excluded": 58,
	"ifeval_budget": {
	"max_new": 1024,
	"n": 200
	}
	},
	"healed": {
	"acc": 0.5563380281690141,
	"n": 142,
	"ifeval_coverage": 0.6153846153846154,
	"ifeval_n_scored": 142,
	"ifeval_n_excluded": 58,
	"ifeval_budget": {
	"max_new": 1024,
	"n": 200
	}
	}
	},
	"mmlu_knowledge": {
	"base": {
	"acc": 0.305,
	"n": 200
	},
	"cut": {
	"acc": 0.275,
	"n": 200
	},
	"healed": {
	"acc": 0.265,
	"n": 200
	}
	},
	"multilingual_knowledge": {
	"base": {
	"acc": 0.28,
	"n": 200
	},
	"cut": {
	"acc": 0.275,
	"n": 200
	},
	"healed": {
	"acc": 0.265,
	"n": 200
	}
	},
	"wikitext_language": {
	"base": {
	"ppl": 18.305546793311198,
	"n": 64
	},
	"cut": {
	"ppl": 23.76837327292051,
	"n": 64
	},
	"healed": {
	"ppl": 21.6319395015557,
	"n": 64
	}
	}
	},
	"source_measurement_sha256": {
	"base": "ec64b19dd0644e3ec6dff0daeb3f42df443d220fa16df73677b7f5e74ffe79dd",
	"cut": "197c37ff224cf1b640017b9814f3cadb7d7123b1e059523c80ed4aaff49cfe49",
	"healed": "9e9829a40df64823278bb55737625e4e2803ea84ab7cf3d6c3ac2952a9276466"
	},
	"healed_parameters": 6012897280,
	"standalone_model_and_tokenizer_reload": true,
	"quality_improvement_claim": false,
	"cut_checkpoint_revision": "376988e3a817b9d7f4ed7eb71ab7007c483b59ce",
	"weights": [
	{
	"path_in_repo": "model.safetensors",
	"sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573",
	"size": 12026140856
	}
	],
	"base_weight_bytes": 13838721960,
	"healed_weight_bytes": 12026140856,
	"weight_storage_savings_pct": 13.097893788452119,
	"format_comparison": {
	"measurement_status": "incomplete",
	"bf16_representation": "Hugging Face safetensors",
	"rows": [
	{
	"format": "BF16",
	"size_bytes": 12026140856,
	"sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573",
	"published": true,
	"metrics": [
	{
	"benchmark": "arc_science",
	"metric": "acc",
	"value": 0.45,
	"n": 200,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "bbq_safety",
	"metric": "acc",
	"value": 0.395,
	"n": 200,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "gpqa_reasoning",
	"metric": "acc",
	"value": 0.16666666666666666,
	"n": 198,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "gsm8k_math",
	"metric": "acc",
	"value": 0.43,
	"n": 200,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "hellaswag_commonsense",
	"metric": "acc",
	"value": 0.66,
	"n": 200,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "ifeval_instruction_following",
	"metric": "acc",
	"value": 0.5563380281690141,
	"n": 142,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "mmlu_knowledge",
	"metric": "acc",
	"value": 0.265,
	"n": 200,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "multilingual_knowledge",
	"metric": "acc",
	"value": 0.265,
	"n": 200,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	},
	{
	"benchmark": "wikitext_language",
	"metric": "ppl",
	"value": 21.6319395015557,
	"n": 64,
	"artifact_sha256": "e7b3d37beb32930d2c9b1cea775b50f15d79d06dab5d0b39782d206c8ba99573"
	}
	]
	},
	{
	"format": "Q8_0",
	"size_bytes": null,
	"sha256": "",
	"published": false,
	"metrics": []
	},
	{
	"format": "Q4_K_M",
	"size_bytes": 3663796064,
	"sha256": "4f7fe283cd9a714a3c96824f30a83e7e4d70e55f16a9b9e0aea8f644e68e620f",
	"published": false,
	"metrics": []
	},
	{
	"format": "Q2_K",
	"size_bytes": null,
	"sha256": "",
	"published": false,
	"metrics": []
	}
	]
	}
	}