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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
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+olmo-base.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,16 +1,171 @@
 ---
-license: cc-by-nc-4.0
-tags:
-- mhm
-- text-generation
+license: apache-2.0
+language:
+- en
 library_name: transformers
+datasets:
+- allenai/dolma3_mix-6T-1025
 ---
 
-# olmo_3_1025_7b
+## Model Details
+<img alt="Logo for Olmo 3 7B Base model" src="olmo-base.png" width="211px" style="margin-left:'auto' margin-right:'auto' display:'block'">
 
-Auto-uploaded from local output (MergeBench and LlamaFactory excluded).
 
-- Source path: `hf_models/Olmo-3-1025-7B`
-- Type: `full`
-- Uploaded at: `2026-05-20T06:49:20.303147`
-- Visibility: `public`
+# Model Card for Olmo 3 7B
+
+We introduce Olmo 3, a new family of 7B and 32B models. This suite includes Base, Instruct, and Think variants. The Base models were trained using a staged training approach.
+
+Olmo is a series of **O**pen **l**anguage **mo**dels designed to enable the science of language models. 
+These models are trained on the Dolma 3 dataset. We are releasing all code, checkpoints, and associated training details. 
+
+| Size   | Training Tokens | Layers | Hidden Size | Q Heads | KV Heads | Context Length |
+|--------|-----------------|--------|-------------|---------|----------|----------------|
+| [OLMo 3 7B](https://huggingface.co/allenai/Olmo-3-1025-7B) | 5.93 Trillion | 32 | 4096 | 32 | 32 | 65,536 |
+| [OLMo 3 32B](https://huggingface.co/allenai/Olmo-3-1125-32B) | 5.50 Trillion | 64 | 5120 | 40 | 8 | 65,536 |
+
+
+The core models released in this batch include the following:
+
+| **Stage**               | **Olmo 3 7B Think** | **Olmo 3 32B Think** | **Olmo 3 7B Instruct** |
+|--------------------------|-----------------------|------------------------|---------------------------|
+| **Base Model**           | [Olmo-3-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) | [Olmo-3-32B](https://huggingface.co/allenai/Olmo-3-1125-32B) | [Olmo-3-7B](https://huggingface.co/allenai/Olmo-3-1025-7B) |
+| **SFT**                  | [Olmo-3-7B-Think-SFT](https://huggingface.co/allenai/Olmo-3-7B-Think-SFT) | [Olmo-3-32B-Think-SFT](https://huggingface.co/allenai/Olmo-3-32B-Think-SFT) | [Olmo-3-7B-Instruct-SFT](https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT) |
+| **DPO**                  | [Olmo-3-7B-Think-DPO](https://huggingface.co/allenai/Olmo-3-7B-Think-DPO) | [Olmo-3-32B-Think-DPO](https://huggingface.co/allenai/Olmo-3-32B-Think-DPO) | [Olmo-3-7B-Instruct-DPO](https://huggingface.co/allenai/Olmo-3-7B-Instruct-DPO) |
+| **Final Models (RLVR)**  | [Olmo-3-7B-Think](https://huggingface.co/allenai/Olmo-3-7B-Think) | [Olmo-3-32B-Think](https://huggingface.co/allenai/Olmo-3-32B-Think) | [Olmo-3-7B-Instruct](https://huggingface.co/allenai/Olmo-3-7B-Instruct) |
+                                                 
+
+## Installation
+
+Olmo 3 is supported in transformers v4.57.0 or higher:
+```bash
+pip install transformers>=4.57.0
+```
+
+## Inference
+
+You can use OLMo with the standard HuggingFace transformers library:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1025-7B")
+tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-1025-7B")
+message = ["Language modeling is "]
+inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
+# optional verifying cuda
+# inputs = {k: v.to('cuda') for k,v in inputs.items()}
+# olmo = olmo.to('cuda')
+response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=0, temperature=1.0, top_p=0.7)
+print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
+>> 'Language modeling is  a key component of any text-based application, but its effectiveness...'
+```
+
+For faster performance, you can quantize the model using the following method:
+```python
+AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1025-7B", 
+    torch_dtype=torch.float16, 
+    load_in_8bit=True)  # Requires bitsandbytes
+```
+The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using:
+```python
+inputs.input_ids.to('cuda')
+```
+
+We have released checkpoints for these models. For pretraining, the naming convention is `stage1-stepXXX`. The conventions for midtraining and long context are `stage2-stepXXX` and `stage3-stepXXX`, respectively.
+
+
+To load a specific model revision with HuggingFace, simply add the argument `revision`:
+```bash
+olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1025-7B", revision="stage1-step10000")
+```
+
+Or, you can access all the revisions for the models via the following code snippet:
+```python
+from huggingface_hub import list_repo_refs
+out = list_repo_refs("allenai/Olmo-3-1025-7B")
+branches = [b.name for b in out.branches]
+```
+
+### Fine-tuning
+Model fine-tuning can be done from the final checkpoint (the `main` revision of this model) or many intermediate checkpoints. Two recipes for tuning are available.
+1. Fine-tune with the OLMo-core repository:
+```bash
+torchrun --nproc-per-node=8 ./src/scripts/official/OLMo3/OLMo-3-1025-7B-pretrain-1.py run01
+```
+You can override most configuration options from the command-line. For example, to override the learning rate you could launch the script like this:
+
+```bash
+torchrun --nproc-per-node=8 ./src/scripts/official/OLMo3/OLMo-3-1025-7B-pretrain-1.py run01 --train_module.optim.lr=3e-4
+```
+For more documentation, see the [GitHub readme](https://github.com/allenai/OLMo-core).
+
+### Model Description
+
+- **Developed by:** Allen Institute for AI (Ai2)
+- **Model type:** a Transformer style autoregressive language model.
+- **Language(s) (NLP):** English
+- **License:** The code and model are released under Apache 2.0.
+- **Contact:** Technical inquiries: `olmo@allenai.org`. Press: `press@allenai.org`
+- **Date cutoff:** Dec 2024
+
+
+### Model Sources
+
+- **Project Page:** https://allenai.org/olmo
+- **Repositories:** 
+    - Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo-core
+    - Evaluation code: https://github.com/allenai/OLMo-Eval
+    - Further fine-tuning code: https://github.com/allenai/open-instruct
+- **W&B Report:** https://wandb.ai/ai2-llm/Olmo-3-1025-7B/reports/Olmo-3-7B-October-2025--VmlldzoxNDcwOTM0NA
+- **Paper:** https://allenai.org/papers/olmo3
+<!-- - **Technical blog post:** (URL)  -->
+
+
+## Evaluation
+Core model results for Olmo 3 7B are found below.
+
+| Model | Olmo 3-Eval Math | BigCodeBench | HumanEval | DeepSeek LeetCode | DS 1000 | MBPP | MultiPL HumanEval | MultiPL MBPPP | Olmo 3-Eval Code | ARC MC | MMLU STEM | MedMCQA MC | MedQA MC | SciQ MC | Olmo 3-Eval MC_STEM | MMLU Humanities | MMLU Social Sci. | MMLU Other | CSQA MC | PIQA MC | SocialIQA MC | CoQA Gen2MC MC | DROP Gen2MC MC | Jeopardy Gen2MC MC | NaturalQs Gen2MC MC | SQuAD Gen2MC MC | Olmo 3-Eval MC_Non-STEM | HellaSwag RC | Winogrande RC | Lambada | Basic Skills | DROP | Jeopardy | NaturalQs | SQuAD | CoQA | Olmo 3-Eval GenQA | BBH | MMLU Pro MC | Deepmind Math | LBPP |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| **Open-weight Models** | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
+| Marin-8B | 39.6 | 21.5 | 31.6 | 0.5 | 16.5 | 36.5 | 15.6 | 27.6 | 21.4 | 89.2 | 58.1 | 52.7 | 47.3 | 93.2 | 68.1 | 71.4 | 77.4 | 68.3 | 75.3 | 85.7 | 79.8 | 86.2 | 63.7 | 90.8 | 71.5 | 96.5 | 78.8 | 84.0 | 88.6 | 73.9 | 85.6 | 73.0 | 72.7 | 42.6 | 93.4 | 69.5 | 75.9 | 55.6 | 38.8 | 20.2 | 5.8 |
+| Apertus-8B | 29.2 | 20.9 | 21.6 | 0.6 | 11.8 | 33.5 | 15.5 | 29.2 | 19.0 | 87.9 | 52.4 | 51.7 | 47.6 | 91.9 | 66.3 | 67.8 | 74.7 | 66.1 | 72.1 | 80.5 | 76.3 | 82.8 | 47.5 | 90.3 | 66.7 | 91.3 | 74.2 | 81.0 | 85.8 | 70.9 | 83.8 | 37.1 | 70.1 | 35.0 | 89.6 | 67.4 | 69.0 | 48.1 | 33.9 | 17.1 | 7.1 |
+| OLMo 2-7B | 41.7 | 8.8 | 16.3 | 0.2 | 10.1 | 21.2 | 4.2 | 12.2 | 10.4 | 85.7 | 53.2 | 49.2 | 43.8 | 90.9 | 64.6 | 67.9 | 73.1 | 65.2 | 72.0 | 80.1 | 77.5 | 85.0 | 55.6 | 89.5 | 66.3 | 95.3 | 75.2 | 82.2 | 87.4 | 70.5 | 82.2 | 61.5 | 70.8 | 37.4 | 91.5 | 68.3 | 72.4 | 49.6 | 33.1 | 16.3 | 3.1 |
+| Qwen3-8B | 67.2 | 42.5 | 71.7 | 8.3 | 33.1 | 66.2 | 52.3 | 48.4 | 46.1 | 95.4 | 76.7 | 63.5 | 62.1 | 96.1 | 78.8 | 78.6 | 84.8 | 76.8 | 84.1 | 89.9 | 83.3 | 93.7 | 78.3 | 92.3 | 74.1 | 97.5 | 84.8 | 80.5 | 86.4 | 73.0 | 93.5 | 57.2 | 65.1 | 33.8 | 89.2 | 61.6 | 71.1 | 76.5 | 50.3 | 47.7 | 25.7 |
+| Nemotron MiniD 8B | 49.8 | 43.2 | 71.7 | 6.8 | 30.3 | 62.3 | 40.0 | 47.5 | 43.1 | 94.1 | 71.1 | 54.5 | 53.5 | 94.3 | 73.5 | 78.0 | 82.2 | 73.8 | 74.4 | 86.0 | 78.7 | 92.2 | 70.0 | 90.7 | 71.1 | 97.4 | 81.3 | 80.2 | 86.2 | 67.9 | 91.4 | 71.4 | 64.9 | 31.2 | 92.3 | 60.4 | 71.8 | 77.0 | 50.2 | 31.4 | 31.7 |
+| Gemma-2-9B | 48.8 | 30.9 | 40.0 | 1.9 | 28.4 | 49.1 | 27.9 | 38.2 | 30.2 | 92.7 | 62.8 | 58.9 | 55.4 | 94.4 | 72.8 | 74.5 | 82.9 | 74.2 | 75.3 | 85.7 | 80.3 | 92.7 | 65.8 | 92.8 | 72.5 | 97.3 | 81.3 | 81.8 | 88.8 | 76.3 | 89.3 | 68.2 | 75.1 | 40.4 | 88.8 | 71.5 | 75.6 | 68.8 | 44.7 | 23.0 | 12.4 |
+| Qwen-2.5-7B | 60.7 | 39.7 | 66.1 | 5.1 | 35.2 | 55.4 | 40.3 | 45.4 | 41.0 | 93.4 | 67.6 | 60.3 | 56.6 | 95.4 | 74.7 | 76.2 | 83.0 | 74.4 | 85.0 | 88.5 | 82.9 | 93.5 | 69.1 | 92.1 | 70.5 | 96.4 | 82.9 | 81.0 | 86.0 | 70.3 | 91.4 | 56.7 | 63.0 | 31.2 | 87.0 | 40.5 | 67.5 | 54.7 | 48.1 | 32.8 | 22.1 |
+| Llama-3.1-8B | 36.9 | 30.7 | 40.4 | 0.1 | 22.2 | 12.1 | 14.5 | 28.3 | 21.2 | 86.4 | 55.7 | 56.5 | 53.7 | 92.7 | 69.0 | 70.1 | 75.5 | 69.1 | 72.9 | 78.3 | 77.0 | 89.9 | 53.3 | 88.9 | 68.0 | 94.4 | 76.1 | 81.5 | 87.3 | 75.5 | 88.0 | 59.5 | 70.9 | 36.7 | 89.2 | 69.0 | 73.1 | 63.0 | 37.4 | 24.1 | 9.1 |
+| Granite-3.3-8B | 41.5 | 0.4 | 0.0 | 0.0 | 22.6 | 48.5 | 22.3 | 32.3 | 18.0 | 86.2 | 55.6 | 49.6 | 43.0 | 90.8 | 65.0 | 67.6 | 71.8 | 64.5 | 82.3 | 81.5 | 83.1 | 87.6 | 55.0 | 88.4 | 69.2 | 94.5 | 76.9 | 83.7 | 89.4 | 76.0 | 88.7 | 38.4 | 69.7 | 37.0 | 89.6 | 37.8 | 67.8 | 61.5 | 33.9 | 32.2 | 18.5 |
+| MiMo-7B | 54.3 | 38.3 | 57.0 | 1.2 | 28.1 | 48.3 | 34.5 | 42.5 | 35.7 | 91.7 | 63.5 | 56.2 | 53.0 | 93.5 | 71.6 | 73.6 | 80.8 | 72.7 | 76.1 | 87.2 | 80.7 | 91.4 | 64.1 | 89.5 | 72.2 | 96.7 | 80.5 | 80.6 | 86.5 | 73.1 | 89.7 | 69.3 | 65.6 | 33.1 | 90.3 | 54.4 | 71.4 | 75.1 | 44.3 | 25.4 | 21.5 |
+| **Olmo 3 7B** | 54.7 | 34.1 | 49.1 | 1.4 | 20.2 | 43.6 | 28.7 | 38.2 | 30.7 | 89.2 | 59.7 | 48.3 | 41.8 | 92.8 | 66.4 | 68.9 | 75.0 | 66.9 | 75.3 | 80.2 | 80.3 | 92.5 | 67.3 | 86.9 | 69.4 | 96.9 | 78.2 | 77.7 | 85.7 | 68.9 | 89.5 | 71.5 | 60.4 | 32.6 | 93.5 | 72.8 | 72.5 | 63.5 | 37.3 | 23.7 | 17.1 |
+## Model Details
+
+#### Stage 1: Initial Pretraining
+- Dataset: [dolma3_6T-mix-1025](https://huggingface.co/datasets/allenai/dolma3_mix-6T-1025)
+- 5.93T tokens
+- Coverage: 97.53%+ of total pretraining budget
+
+#### Stage 2: Mid-training
+- Dataset: [dolma3-dolmino-mix-1025](https://huggingface.co/datasets/allenai/dolma3_dolmino_mix-100B-1025)
+- 100B tokens
+- Mix composition: 20% code, 28% web pages, 19% math, 14% QA, 8% thinking, 6% instruction, and 5% PDFs
+- Note: We also include the three checkpoints reported in Table 7 of the Olmo 3 paper showing domain tradeoffs: Gen-QA Mix, Math-code-thinking mix, and Round 5 (final) mix. These experiments were run earlier in the Stage 1 pretraining process, and therefore represent training on 100B midtraining tokens starting from a Stage 1 checkpoint that had been trained to 2T tokens.
+
+#### Stage 3: Long Context
+- Dataset: [dolma3-longmino-mix-1025](https://huggingface.co/datasets/allenai/dolma3_longmino_mix-50B-1025)
+- 50B tokens
+- Mix composition: 66% midtraining data, 34% PDFs
+
+#### Model Merging
+- 7B Model: No merging
+- 32B Model: 2 versions on 100B mix, merged before starting long context run.  Final checkpoint is merged 4 final checkpoints.
+
+## Bias, Risks, and Limitations
+Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified.
+
+## License
+This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use).
+
+## Citation
+Find the paper at: https://allenai.org/papers/olmo3
+
+## Model Card Contact
+For errors in this model card, contact `olmo@allenai.org`.

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+' -}}{%- endif -%}{%- endif -%}{%- for message in messages -%}{%- if message['role'] == 'system' -%}{{- '<|im_start|>system
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config.json

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+  ],
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+    "sliding_attention",
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+    "sliding_attention",
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+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
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+    "beta_slow": 1,
+    "factor": 8.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "yarn"
+  },
+  "rope_theta": 500000,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0",
+  "use_cache": true,
+  "vocab_size": 100278
+}

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+  "_from_model_config": true,
+  "transformers_version": "4.57.0"
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