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@@ -12,7 +12,7 @@ language:
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  <img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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- # Model Card for OLMo 1.7 7B Nitro Instruct
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  **Requires transformers versions v4.40.0 or newer**
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@@ -21,7 +21,8 @@ OLMo is a series of **O**pen **L**anguage **Mo**dels designed to enable the scie
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  The OLMo base models are trained on the [Dolma](https://huggingface.co/datasets/allenai/dolma) dataset.
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  The adapted versions are trained on the [Tulu SFT mixture](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) and, for the Instruct version, a [cleaned version of the UltraFeedback dataset](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned).
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- OLMo 1.7 Nitro 7B Instruct and OLMo SFT are two adapted versions of these models trained for better question answering.
 
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  They show the performance gain that OLMo base models can achieve with existing fine-tuning techniques.
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  ## Model Details
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  We release two adapted model versions:
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  | Model | Training Method(s) | Datasets | Context Length |
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  |------|--------|---------|--|
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- | [OLMo 1.7 7B Nitro SFT](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-SFT-hf) | SFT | [Tulu 2 SFT Mix](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) | 2048 |
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- | [OLMo 1.7 7B Nitro Instruct](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-Instruct-hf) | SFT + DPO | [Tulu 2 SFT Mix](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) + [Ultrafeedback Cleaned](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) | 2048 |
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- These models are both trained on top of OLMo 1.7 7b 'Nitro':
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  | Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
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  |------|--------|---------|-------------|-----------------|----------------|
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- | [OLMo 1.7 7B](https://huggingface.co/allenai/OLMo-1.7-7B-hf) | 2.7T |32 | 4096 | 32 | 4096 |
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  ### Model Description
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1.7-7B-Nitro-SFT-hf")
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- tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1.7-7B-Nitro-SFT-hf")
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  chat = [
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  { "role": "user", "content": "What is language modeling?" },
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  ]
@@ -94,9 +95,9 @@ Core model results for the 7B adapted models are found below.
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  | Model | MMLU 0-shot ↑ | AlpacaEval %win ↑ | ToxiGen % Toxic ↓ | TruthfulQA %Info+True ↑ |
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  |-----------------------|---------------|--------------------|--------------------|-------------------------|
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- | **OLMo 1.7 'Nitro' base** | 50.8 | - | 85.2 | 28.4 |
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- | **[OLMo 1.7 7B Nitro SFT](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-SFT-hf)** | 54.2 | 70.9 | .1 | 44.4 |
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- | **[OLMo 1.7 7B Nitro Instruct](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-Instruct-hf)** | 52.8 | 83.5 | 1.7 | 70.3 |
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  <img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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+ # Model Card for OLMo 7B July 2024 Instruct
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  **Requires transformers versions v4.40.0 or newer**
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  The OLMo base models are trained on the [Dolma](https://huggingface.co/datasets/allenai/dolma) dataset.
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  The adapted versions are trained on the [Tulu SFT mixture](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) and, for the Instruct version, a [cleaned version of the UltraFeedback dataset](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned).
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+ OLMo 7B Instruct SFT are two adapted versions of these models trained for better question answering.
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+ These are updated OLMo models corresponding to our July 2024 release.
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  They show the performance gain that OLMo base models can achieve with existing fine-tuning techniques.
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  ## Model Details
 
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  We release two adapted model versions:
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  | Model | Training Method(s) | Datasets | Context Length |
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  |------|--------|---------|--|
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+ | [OLMo 7B July 2024 SFT](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-SFT-hf) | SFT | [Tulu 2 SFT Mix](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) | 2048 |
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+ | [OLMo 7B July 2024 Instruct](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-Instruct-hf) | SFT + DPO | [Tulu 2 SFT Mix](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) + [Ultrafeedback Cleaned](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) | 2048 |
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+ These models are both trained on top of OLMo 7b July 2024:
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  | Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
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  |------|--------|---------|-------------|-----------------|----------------|
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+ | [OLMo 7B July 2024](https://huggingface.co/allenai/OLMo-1.7-7B-hf) | 2.7T |32 | 4096 | 32 | 4096 |
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  ### Model Description
 
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-0724-Instruct-hf")
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+ tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-0724-Instruct-hf")
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  chat = [
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  { "role": "user", "content": "What is language modeling?" },
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  ]
 
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  | Model | MMLU 0-shot ↑ | AlpacaEval %win ↑ | ToxiGen % Toxic ↓ | TruthfulQA %Info+True ↑ |
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  |-----------------------|---------------|--------------------|--------------------|-------------------------|
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+ | **OLMo July 2024 base** | 50.8 | - | 85.2 | 28.4 |
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+ | **[OLMo 7B July 2024 SFT](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-SFT-hf)** | 54.2 | 70.9 | .1 | 44.4 |
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+ | **[OLMo 7B July 2024 Instruct](https://huggingface.co/allenai/OLMo-1.7-7B-Nitro-Instruct-hf)** | 52.8 | 83.5 | 1.7 | 70.3 |
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