Instructions to use danish-foundation-models/DFM-Mimir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danish-foundation-models/DFM-Mimir with Transformers:

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

pipe = pipeline("text-generation", model="danish-foundation-models/DFM-Mimir")
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("danish-foundation-models/DFM-Mimir")
model = AutoModelForCausalLM.from_pretrained("danish-foundation-models/DFM-Mimir", 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 danish-foundation-models/DFM-Mimir with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/danish-foundation-models/DFM-Mimir

SGLang

How to use danish-foundation-models/DFM-Mimir 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 "danish-foundation-models/DFM-Mimir" \
    --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": "danish-foundation-models/DFM-Mimir",
		"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 "danish-foundation-models/DFM-Mimir" \
        --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": "danish-foundation-models/DFM-Mimir",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use danish-foundation-models/DFM-Mimir with Docker Model Runner:
```
docker model run hf.co/danish-foundation-models/DFM-Mimir
```

Jacob Nielsen commited on Aug 13

Commit

f318288

1 Parent(s): f2c5955

more results

Browse files

Files changed (3) hide show

README.md +47 -24
dfm8-datasets.md +0 -222
plots/subject_avg_scores.png +2 -2

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ tags:
 # Mimir v1
-Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture, that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data. Trained on a mixture of 161 datasets, Mimir v1 outperforms the original HRM-Text 1B and competes with larger frontier models like Qwen 3.5 2B and Gemma 4 E2B, tested across 20 benchmarks for English, Math & Code and Danish.
@@ -31,52 +31,75 @@ Mimir v1 is evaluated across Danish, English, and Math & Code benchmarks. The fi
 ![Average scores by subject area](plots/subject_avg_scores.png)
-### English benchmark results
 | Model | BoolQ (Acc) | Winogrande (Acc) | Hellaswag (Acc) | MMLU (Acc) | ARC-C (Acc) | DROP (F1) | GovRep. (R1) | Avg. |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **1B models** | | | | | | | | |
-| Mimir 1B | **87.8** | **73.5** | **67.3** | 57.5 | 81.6 | **83.1** | 32.0 | **69.0** |
-| HRM-Text 1B | 87.5 | 70.4 | 60.4 | **58.7** | 82.2 | 78.1 | 25.4 | 66.1 |
-| **2–5B models** | | | | | | | | |
-| Qwen 3.5 2B | 78.4 | 51.8 | 28.7 | 27.4 | 61.7 | 30.5 | 31.6 | 44.3 |
-| Gemma 4 E2B 5B | 64.1 | 54.1 | 46.0 | 44.1 | 69.7 | 39.3 | 33.6 | 50.1 |
-| Gemma 4 E2B 5B (think) | 82.5 | 63.0 | 54.7 | 57.2 | **86.2** | 42.1 | **34.7** | 60.1 |
-### Math & Code benchmark results
 | Model | GSM8K (Acc) | MATH (Acc) | HumanEval (Acc) | Avg. |
 | :--- | :---: | :---: | :---: | :---: |
-| **1B models** | | | | |
-| Mimir 1B | **89.9** | 45.8 | 56.7 | 64.1 |
 | HRM-Text 1B | 84.8 | 56.0 | 0.0 | 46.9 |
-| **2–5B models** | | | | |
-| Qwen 3.5 2B | 67.1 | 48.9 | 48.8 | 54.9 |
-| Gemma 4 E2B 5B | 74.1 | 55.2 | **76.2** | 68.5 |
-| Gemms 4 E2B 5B (think) | 74.8 | **59.1** | 73.2 | **69.0** |
-### Danish benchmark results
 | Model | Angry Tweets (Acc) | DaLA (F1) | GEC (EM) | PIQA (Acc) | Daisy (EM) | WikiQA (EM) | WMT (chrF) | N.News (chrF) | IFEval (Acc) | Hellaswag-DA (Acc) | Avg. |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **1B models** | | | | | | | | | | | |
 | Mimir 1B | 67.4 | **96.1** | **85.6** | 53.7 | 9.6 | **66.8** | 53.9 | 35.87 | 63.9 | 35.3 | **56.8** |
 | HRM-Text 1B | 42.4 | 26.7 | 0.5 | 13.0 | 0.0 | 34.9 | 25.4 | 26.76 | 18.5 | 28.8 | 21.7 |
-| **2–5B models** | | | | | | | | | | | |
 | Qwen 3.5 2B | 61.6 | 36.4 | 8.0 | 25.0 | 2.5 | 49.4 | 45.6 | 34.85 | 56.1 | 24.7 | 34.4 |
-| Gemma 4 E2B 5B | 64.6 | 56.7 | 36.9 | 46.3 | 5.6 | 44.1 | 55.2 | 35.67 | 75.5 | 25.6 | 44.6 |
-| Gemma 4 E2B 5B (think) | 67.7 | 66.8 | 23.4 | 63.9 | 5.1 | 59.3 | 56.0 | **36.30** | **81.2** | **39.0** | 49.9 |
-| **8–9B models** | | | | | | | | | | | |
-| Munin-Apertus 8B | 60.6 | 46.1 | 42.1 | **81.5** | **12.5** | 49.9 | 55.8 | 30.30 | 53.0 | 24.5 | 45.6 |
 | Munin-Mistral 8B | 61.3 | 48.8 | 26.4 | 76.9 | 8.4 | 48.4 | 51.8 | 32.92 | 67.8 | 33.6 | 45.6 |
 | Munin-Qwen 9B | **69.1** | 60.6 | 11.4 | 38.9 | 5.4 | 55.7 | **56.1** | 35.89 | 71.8 | 34.3 | 43.9 |
 ## Model details
 | Architecture | Parameters | Hidden size | Layers | Attention heads | Vocab size | Context length | Training steps | Tokens per epoch | License |
@@ -87,7 +110,7 @@ Mimir v1 is evaluated across Danish, English, and Math & Code benchmarks. The fi
 ## Technical Report
 Mimir v1 is trained from scratch a corpus, a curated mixture of 161 dataset repositories comprising approximately **70.479 billion tokens per epoch**.
-Training was performed using a fork of [HRM-Text](https://github.com/schneiderkamplab/HRM-Text). We will publish a technical report detailing everything.
 ## Limitations

 # Mimir v1
+Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture, that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data. Trained on a mixture of 161 datasets, Mimir v1 outperforms the original HRM-Text 1B and competes with larger frontier models like Qwen 3.5 4B and Gemma 4 E2B, tested across 20 benchmarks for English, Math & Code and Danish.
 ![Average scores by subject area](plots/subject_avg_scores.png)
+### English benchmark results (Best scores in **bold**.)
+English benchmark results (full datasets).
 | Model | BoolQ (Acc) | Winogrande (Acc) | Hellaswag (Acc) | MMLU (Acc) | ARC-C (Acc) | DROP (F1) | GovRep. (R1) | Avg. |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **\(\sim\)1B models** | | | | | | | | |
+| Mimir 1B | **87.8** | **73.5** | 67.3 | 57.5 | 81.6 | **83.1** | 32.0 | 69.0 |
+| HRM-Text 1B | 87.5 | 70.4 | 60.4 | 58.7 | 82.2 | 78.1 | 25.4 | 66.1 |
+| Qwen 3.5 0.8B | 69.8 | 48.9 | 37.0 | 51.5 | 68.4 | 45.2 | 32.5 | 50.5 |
+| Gemma 3 1B | 62.4 | 49.1 | 30.6 | 37.5 | 43.5 | 7.0 | 29.5 | 37.1 |
+| OLMo 2 1B | 67.2 | 51.0 | 42.4 | 41.6 | 48.1 | 12.4 | 37.7 | 42.9 |
+| **2--3B models** | | | | | | | | |
+| Qwen 3.5 2B | 80.8 | 53.4 | 64.6 | 62.8 | 82.7 | 31.3 | 31.5 | 58.2 |
+| SmolLM3 3B | 84.3 | 60.3 | 65.1 | 60.2 | 79.5 | 54.0 | **38.1** | 63.1 |
+| **4--5B models** | | | | | | | | |
+| Qwen 3.5 4B | 87.0 | 70.0 | **83.2** | **75.8** | **92.9** | 48.0 | 27.9 | **69.3** |
+| Gemma 4 E2B | 64.1 | 56.7 | 55.6 | 59.3 | 69.8 | 57.3 | 33.6 | 56.6 |
+| Gemma 4 E2B (think) | 83.4 | 63.0 | 55.8 | 72.0 | 86.8 | 70.8 | 34.7 | 66.6 |
+### Math & Code benchmark results  (Best scores in **bold**.)
 | Model | GSM8K (Acc) | MATH (Acc) | HumanEval (Acc) | Avg. |
 | :--- | :---: | :---: | :---: | :---: |
+| **\(\sim\)1B models** | | | | |
+| Mimir 1B | 89.9 | 45.8 | 56.7 | 64.1 |
 | HRM-Text 1B | 84.8 | 56.0 | 0.0 | 46.9 |
+| Qwen 3.5 0.8B | 49.1 | 36.1 | 30.5 | 38.6 |
+| Gemma 3 1B | 49.7 | 37.2 | 42.7 | 43.2 |
+| OLMo 2 1B | 59.4 | 18.8 | 15.9 | 31.4 |
+| **2--3B models** | | | | |
+| Qwen 3.5 2B | 73.7 | 55.7 | 47.6 | 59.0 |
+| SmolLM3 3B | 80.0 | 62.2 | 61.6 | 67.9 |
+| **4--5B models** | | | | |
+| Qwen 3.5 4B | 60.5 | 56.5 | 78.0 | 65.0 |
+| Gemma 4 E2B | 88.3 | **64.2** | **73.8** | **75.4** |
+| Gemma 4 E2B (think) | **90.3** | 49.1 | 72.0 | 70.5 |
+### Danish benchmark results (Best scores in **bold**.)
 | Model | Angry Tweets (Acc) | DaLA (F1) | GEC (EM) | PIQA (Acc) | Daisy (EM) | WikiQA (EM) | WMT (chrF) | N.News (chrF) | IFEval (Acc) | Hellaswag-DA (Acc) | Avg. |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **\(\sim\)1B models** | | | | | | | | | | | |
 | Mimir 1B | 67.4 | **96.1** | **85.6** | 53.7 | 9.6 | **66.8** | 53.9 | 35.87 | 63.9 | 35.3 | **56.8** |
 | HRM-Text 1B | 42.4 | 26.7 | 0.5 | 13.0 | 0.0 | 34.9 | 25.4 | 26.76 | 18.5 | 28.8 | 21.7 |
+| Qwen 3.5 0.8B | 53.8 | 51.0 | 0.7 | 56.5 | 0.7 | 41.6 | 37.8 | 35.30 | 39.6 | 25.0 | 34.2 |
+| Gemma 3 1B | 54.4 | 41.0 | 3.3 | 72.2 | 1.4 | 42.6 | 45.1 | 35.56 | 47.2 | 24.8 | 36.8 |
+| OLMo 2 1B | 33.6 | 48.7 | 0.2 | 75.0 | 0.0 | 8.4 | 30.0 | 33.77 | 32.5 | 26.7 | 28.9 |
+| **2--3B models** | | | | | | | | | | | |
 | Qwen 3.5 2B | 61.6 | 36.4 | 8.0 | 25.0 | 2.5 | 49.4 | 45.6 | 34.85 | 56.1 | 24.7 | 34.4 |
+| SmolLM3 3B | 63.2 | 33.5 | 3.3 | 51.9 | 2.2 | 0.3 | 37.3 | 35.98 | 49.8 | 40.1 | 31.7 |
+| **4--5B models** | | | | | | | | | | | |
+| Qwen 3.5 4B | 69.1 | 50.1 | 42.6 | 70.4 | 4.7 | 57.1 | 52.1 | **37.03** | 73.7 | 34.7 | 49.2 |
+| Gemma 4 E2B | 64.6 | 56.7 | 36.9 | 46.3 | 5.6 | 44.1 | 55.2 | 35.67 | 75.5 | 25.6 | 44.6 |
+| Gemma 4 E2B (think) | 67.7 | 66.8 | 23.4 | 63.9 | 5.1 | 59.3 | 56.0 | 36.30 | **81.2** | **39.0** | 49.9 |
+| **8--9B models** | | | | | | | | | | | |
+| Munin-Apertus 8B | 60.6 | 46.1 | 42.1 | 81.5 | **12.5** | 49.9 | 55.8 | 30.30 | 53.0 | 24.5 | 45.6 |
 | Munin-Mistral 8B | 61.3 | 48.8 | 26.4 | 76.9 | 8.4 | 48.4 | 51.8 | 32.92 | 67.8 | 33.6 | 45.6 |
 | Munin-Qwen 9B | **69.1** | 60.6 | 11.4 | 38.9 | 5.4 | 55.7 | **56.1** | 35.89 | 71.8 | 34.3 | 43.9 |
 ## Model details
 | Architecture | Parameters | Hidden size | Layers | Attention heads | Vocab size | Context length | Training steps | Tokens per epoch | License |
 ## Technical Report
 Mimir v1 is trained from scratch a corpus, a curated mixture of 161 dataset repositories comprising approximately **70.479 billion tokens per epoch**.
+Training was performed using a fork of [HRM-Text](https://github.com/schneiderkamplab/HRM-Text). We have published a technical report with more details and insights.
 ## Limitations

dfm8-datasets.md DELETED Viewed

@@ -1,222 +0,0 @@
-# DFM8 Dataset Inventory
-This is the exhaustive repository-level inventory of datasets with nonzero
-coverage in the sampled DFM8 corpus. There is exactly one row per Hugging
-Face dataset repository; no row represents an unexpanded repository group.
-## Measurement and scope
-- Contributions are measured from the six-epoch coverage report in
-  data/show_analytics_dfm8.md. Per-epoch values are six-epoch means.
-- Source rows/tokens describe the tokenized derivative visible to the DFM8
-  sampler, not necessarily every row in the upstream HF repository.
-- Sampled values include DFM8 caps and repetition.
-- The report accounts for **70,479,308,606 tokens per epoch**. Direct sums
-  from sampled epoch index arrays range from 70.479B to 70.480B.
-- Canonical upstream IDs are used for sources restored through the
-  consolidated Sapient mirror. Generated derivatives use their published
-  schneiderkamplab repository IDs.
-## Hugging Face datasets (159 repositories)
-| Hugging Face dataset ID | DFM8 prefix/category | Form used | Source rows | Source tokens | Sampled rows/epoch | Sampled tokens/epoch | DFM8 share |
-|---|---|---|---:|---:|---:|---:|---:|
-| [allenai/big-reasoning-traces](https://huggingface.co/datasets/allenai/big-reasoning-traces) | allenai_big_reasoning_traces | converted | 676,947 | 1,656,898,949 | 676,947 | 1,656,898,949 | 2.35090% |
-| [allenai/Dolci-Instruct-SFT](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT) | dolci_instruct_sft | converted | 3,026,926 | 2,236,249,049 | 3,026,926 | 2,236,249,049 | 3.17292% |
-| [allenai/Dolci-Instruct-SFT-No-Tools](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT-No-Tools) | dolci_instruct_sft_no_tools | converted | 1,922,348 | 1,162,167,502 | 5,767,044 | 3,486,502,506 | 4.94685% |
-| [allenai/Dolci-Instruct-SFT-Tool-Use](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT-Tool-Use) | dolci_native_tool_use/dolci_instruct_sft_tool_use | native-tool conversion | 1,076,314 | 807,406,444 | 2,152,628 | 1,614,812,888 | 2.29119% |
-| [allenai/Dolci-Instruct-SFT-Tool-Use-SA](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT-Tool-Use-SA) | dolci_native_tool_use/dolci_instruct_sft_tool_use_sa | native-tool conversion | 4,542 | 2,907,414 | 9,084 | 5,814,828 | 0.00825% |
-| [allenai/IF_sft_data_verified](https://huggingface.co/datasets/allenai/IF_sft_data_verified) | allenai_if_sft_verified | converted | 31,747 | 19,940,266 | 31,747 | 19,940,266 | 0.02829% |
-| [allenai/open_math_2_50k_r1-original](https://huggingface.co/datasets/allenai/open_math_2_50k_r1-original) | allenai_open_math_2_50k_r1 | converted | 49,829 | 71,816,694 | 49,829 | 71,816,694 | 0.10190% |
-| [allenai/RLVR-GSM](https://huggingface.co/datasets/allenai/RLVR-GSM) | allenai_rlvr_gsm | converted | 7,473 | 682,485 | 74,730 | 6,824,850 | 0.00968% |
-| [allenai/RLVR-MATH](https://huggingface.co/datasets/allenai/RLVR-MATH) | allenai_rlvr_math | converted | 7,498 | 806,707 | 74,980 | 8,067,070 | 0.01145% |
-| [allenai/SciRIFF-train-mix](https://huggingface.co/datasets/allenai/SciRIFF-train-mix) | allenai_sciriff_train_mix | converted | 118,517 | 132,295,148 | 118,517 | 132,295,148 | 0.18771% |
-| [allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) | allenai_tulu_3_sft_mixture | converted | 1,050,994 | 785,281,086 | 2,101,988 | 1,570,562,172 | 2.22840% |
-| [allenai/tulu-3-sft-personas-algebra](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-algebra) | allenai_tulu_3_personas_algebra | converted | 20,000 | 21,019,876 | 20,000 | 21,019,876 | 0.02982% |
-| [allenai/tulu-3-sft-personas-code](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-code) | allenai_tulu_3_personas_code | converted | 34,999 | 14,638,779 | 34,999 | 14,638,779 | 0.02077% |
-| [allenai/tulu-3-sft-personas-instruction-following](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following) | allenai_tulu_3_personas_if | converted | 29,980 | 11,519,076 | 29,980 | 11,519,076 | 0.01634% |
-| [allenai/tulu-3-sft-personas-math](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-math) | allenai_tulu_3_personas_math | converted | 149,960 | 203,838,413 | 149,960 | 203,838,413 | 0.28922% |
-| [allenai/tulu-v2-sft-long-mixture](https://huggingface.co/datasets/allenai/tulu-v2-sft-long-mixture) | allenai_tulu_v2_sft_long_mixture | converted | 560,846 | 605,433,930 | 560,846 | 605,433,930 | 0.85902% |
-| [allenai/tulu-v2-sft-mixture](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) | allenai_tulu_v2_sft_mixture | converted | 752,868 | 839,604,204 | 752,868 | 839,604,204 | 1.19128% |
-| [allenai/verifiable-reasoning-filtered-gpt-41](https://huggingface.co/datasets/allenai/verifiable-reasoning-filtered-gpt-41) | allenai_verifiable_reasoning_gpt41 | converted | 284,811 | 604,700,862 | 284,811 | 604,700,862 | 0.85798% |
-| [allenai/verifiable-reasoning-filtered-o4-mini](https://huggingface.co/datasets/allenai/verifiable-reasoning-filtered-o4-mini) | allenai_verifiable_reasoning_o4mini | converted | 190,217 | 88,684,802 | 172,000 | 80,190,190 | 0.11378% |
-| [ccdv/govreport-summarization](https://huggingface.co/datasets/ccdv/govreport-summarization) | dfm4_govreport_summarization | converted | 4,152 | 2,206,151 | 8,304 | 4,412,302 | 0.00626% |
-| [common-pile/arxiv_papers_filtered](https://huggingface.co/datasets/common-pile/arxiv_papers_filtered) | dfm4_arxiv_paper_summarization | derived task | 213,354 | 129,586,132 | 213,354 | 129,586,132 | 0.18386% |
-| [danish-foundation-models/ai_arena_udtraek](https://huggingface.co/datasets/danish-foundation-models/ai_arena_udtraek) | ai_arena_udtraek | converted | 4,255 | 4,569,060 | 42,550 | 45,690,600 | 0.06483% |
-| [danish-foundation-models/dfm-dyna-instruct](https://huggingface.co/datasets/danish-foundation-models/dfm-dyna-instruct) | dfm_dyna_instruct | converted | 5,502,941 | 3,543,871,662 | 5,502,941 | 3,543,871,662 | 5.02824% |
-| [danish-foundation-models/kaenguruen](https://huggingface.co/datasets/danish-foundation-models/kaenguruen) | kaenguruen | converted | 212 | 31,889 | 4,240 | 637,780 | 0.00090% |
-| [danish-foundation-models/laerebogen](https://huggingface.co/datasets/danish-foundation-models/laerebogen) | laerebogen_with_followups | converted | 5,163,468 | 2,080,173,232 | 20,653,872 | 8,320,692,928 | 11.80587% |
-| [GEM/wiki_cat_sum](https://huggingface.co/datasets/GEM/wiki_cat_sum) | dfm4_wiki_cat_sum_summarization | converted | 153,911 | 102,608,687 | 307,822 | 205,217,374 | 0.29117% |
-| [giannor/dala_tv2r_it](https://huggingface.co/datasets/giannor/dala_tv2r_it) | giannor_dala_tv2r_it | converted | 984,126 | 68,526,212 | 984,126 | 68,526,212 | 0.09723% |
-| [giannor/gec_dala_tv2r_it](https://huggingface.co/datasets/giannor/gec_dala_tv2r_it) | giannor_gec_dala_tv2r_it | converted | 930,565 | 192,947,845 | 930,565 | 192,947,845 | 0.27377% |
-| [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | glaive_native_tool_use | native-tool conversion | 218,270 | 51,840,904 | 654,810 | 155,522,712 | 0.22066% |
-| [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots) | no_robots | converted | 23,610 | 6,285,648 | 118,050 | 31,428,240 | 0.04459% |
-| [kobprof/skolegpt-instruct](https://huggingface.co/datasets/kobprof/skolegpt-instruct) | kobprof_skolegpt_instruct | converted | 21,548 | 10,800,037 | 172,384 | 86,400,296 | 0.12259% |
-| [laion/Scientific-Summaries](https://huggingface.co/datasets/laion/Scientific-Summaries) | dfm4_laion_scientific_summaries | converted | 2,288,807 | 1,271,200,636 | 2,288,807 | 1,271,200,636 | 1.80365% |
-| [MegaScience/TextbookReasoning](https://huggingface.co/datasets/MegaScience/TextbookReasoning) | textbookreasoning | converted | 1,178,449 | 374,316,487 | 1,178,449 | 374,316,487 | 0.53110% |
-| [nvidia/AceReason-1.1-SFT](https://huggingface.co/datasets/nvidia/AceReason-1.1-SFT) | acereason | converted | 2,668,741 | 1,945,044,893 | 2,668,741 | 1,945,044,893 | 2.75974% |
-| [nvidia/Nemotron-SFT-Agentic-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2) | nemotron_agentic | converted | 1,297,262 | 4,269,165,904 | 1,297,262 | 4,269,165,904 | 6.05733% |
-| [nvidia/Nemotron-SFT-Instruction-Following-Chat-v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v2) | nemotron_instruction_reasoning_off | converted | 1,402,420 | 1,603,274,447 | 1,402,420 | 1,603,274,447 | 2.27482% |
-| [nvidia/Nemotron-SFT-Multilingual-v1](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Multilingual-v1) | nemotron_multilingual | converted | 3,045,322 | 2,714,286,008 | 396,000 | 412,760,277 | 0.58565% |
-| [nvidia/OpenMathInstruct-2](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2) | openmathinstruct2 | converted | 25,023,009 | 6,602,901,165 | 25,023,009 | 6,602,901,165 | 9.36857% |
-| [oliverkinch/autodata-da-sft](https://huggingface.co/datasets/oliverkinch/autodata-da-sft) | oliverkinch_autodata_da_sft | converted | 2,963 | 1,886,618 | 29,630 | 18,866,180 | 0.02677% |
-| [oliverkinch/da-instruct-dynaword](https://huggingface.co/datasets/oliverkinch/da-instruct-dynaword) | oliverkinch_da_instruct_dynaword | converted | 39,847 | 25,710,401 | 159,388 | 102,841,604 | 0.14592% |
-| [oliverkinch/da-instruct-dynaword-contemporary](https://huggingface.co/datasets/oliverkinch/da-instruct-dynaword-contemporary) | oliverkinch_da_instruct_dynaword_contemporary | converted | 8,271 | 4,388,813 | 33,084 | 17,555,252 | 0.02491% |
-| [oliverkinch/da-instruct-dynaword-contemporary-hq](https://huggingface.co/datasets/oliverkinch/da-instruct-dynaword-contemporary-hq) | oliverkinch_da_instruct_dynaword_contemporary_hq | converted | 11,303 | 6,388,128 | 45,212 | 25,552,512 | 0.03626% |
-| [oliverkinch/da-instruct-dynaword-hq](https://huggingface.co/datasets/oliverkinch/da-instruct-dynaword-hq) | oliverkinch_da_instruct_dynaword_hq | converted | 10,660 | 6,016,020 | 42,640 | 24,064,080 | 0.03414% |
-| [oliverkinch/danish-qa](https://huggingface.co/datasets/oliverkinch/danish-qa) | oliverkinch_danish_qa | converted | 306,444 | 14,365,634 | 1,225,776 | 57,462,536 | 0.08153% |
-| [oliverkinch/danish-summarization](https://huggingface.co/datasets/oliverkinch/danish-summarization) | oliverkinch_danish_summarization | converted | 63,883 | 42,089,747 | 255,532 | 168,358,988 | 0.23888% |
-| [oliverkinch/danish-university-portals-bt](https://huggingface.co/datasets/oliverkinch/danish-university-portals-bt) | oliverkinch_danish_university_portals_bt | converted | 4,505 | 2,176,575 | 45,050 | 21,765,750 | 0.03088% |
-| [oliverkinch/danmarks-statistik-bt](https://huggingface.co/datasets/oliverkinch/danmarks-statistik-bt) | oliverkinch_danmarks_statistik_bt | converted | 7,154 | 1,886,248 | 71,540 | 18,862,480 | 0.02676% |
-| [oliverkinch/doab-da-bt](https://huggingface.co/datasets/oliverkinch/doab-da-bt) | oliverkinch_doab_da_bt | converted | 123 | 69,388 | 1,230 | 693,880 | 0.00098% |
-| [oliverkinch/dst-table-prompts-bt](https://huggingface.co/datasets/oliverkinch/dst-table-prompts-bt) | oliverkinch_dst_table_prompts_bt | converted | 3,042 | 5,108,516 | 30,420 | 51,085,160 | 0.07248% |
-| [oliverkinch/dynaword-bt](https://huggingface.co/datasets/oliverkinch/dynaword-bt) | oliverkinch_dynaword_bt | converted | 31,100 | 17,152,501 | 62,200 | 34,305,002 | 0.04867% |
-| [oliverkinch/eur-lex-bt](https://huggingface.co/datasets/oliverkinch/eur-lex-bt) | oliverkinch_eur_lex_bt | converted | 2,657 | 1,669,455 | 26,570 | 16,694,550 | 0.02369% |
-| [oliverkinch/eur-lex-sum-instruct](https://huggingface.co/datasets/oliverkinch/eur-lex-sum-instruct) | oliverkinch_eur_lex_sum_instruct | converted | 69 | 262,879 | 690 | 2,628,790 | 0.00373% |
-| [oliverkinch/instruct-bt](https://huggingface.co/datasets/oliverkinch/instruct-bt) | oliverkinch_instruct_bt | converted | 3,463 | 1,346,057 | 34,630 | 13,460,570 | 0.01910% |
-| [oliverkinch/machine-translation-da-ar](https://huggingface.co/datasets/oliverkinch/machine-translation-da-ar) | oliverkinch_machine_translation_da_ar | converted | 5,589,698 | 390,040,756 | 2,000,000 | 139,562,830 | 0.19802% |
-| [oliverkinch/machine-translation-da-en](https://huggingface.co/datasets/oliverkinch/machine-translation-da-en) | oliverkinch_machine_translation_da_en | converted | 26,095,982 | 2,473,704,186 | 3,000,000 | 284,366,481 | 0.40348% |
-| [oliverkinch/machine-translation-da-uk](https://huggingface.co/datasets/oliverkinch/machine-translation-da-uk) | oliverkinch_machine_translation_da_uk | converted | 3,452,880 | 239,956,804 | 1,000,000 | 69,487,719 | 0.09859% |
-| [oliverkinch/multi-wiki-qa-high-quality-subset](https://huggingface.co/datasets/oliverkinch/multi-wiki-qa-high-quality-subset) | oliverkinch_multi_wiki_qa_high_quality | converted | 4,594 | 4,191,165 | 45,940 | 41,911,650 | 0.05947% |
-| [oliverkinch/tidsskrift-dk-bt](https://huggingface.co/datasets/oliverkinch/tidsskrift-dk-bt) | oliverkinch_tidsskrift_dk_bt | converted | 62,934 | 38,334,152 | 125,868 | 76,668,304 | 0.10878% |
-| [open-thoughts/OpenThoughts2-1M](https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M) | openthoughts2 | converted | 892,160 | 534,474,638 | 892,160 | 534,474,638 | 0.75834% |
-| [Salesforce/xlam-function-calling-60k](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | xlam_native_tool_use | native-tool conversion | 60,000 | 22,322,656 | 180,000 | 66,967,968 | 0.09502% |
-| [sapientinc/HRM-Text-data-io-cleaned-20260515](https://huggingface.co/datasets/sapientinc/HRM-Text-data-io-cleaned-20260515) | {Platypus|SYNTH|ampsmathematica|dmmath|flan|sudoku_extreme|tasksource} | selected/converted | 288,857,089 | 75,842,844,658 | 77,394,413 | 11,920,658,275 | 16.91370% |
-| [schneiderkamplab/common-pile-denoising](https://huggingface.co/datasets/schneiderkamplab/common-pile-denoising) | common-pile-denoising | generated + audited | 254,565 | 294,539,346 | 763,695 | 883,618,038 | 1.25373% |
-| [schneiderkamplab/common-pile-paragraph-reordering](https://huggingface.co/datasets/schneiderkamplab/common-pile-paragraph-reordering) | common-pile-paragraph-reordering | generated + audited | 86,328 | 55,568,373 | 258,984 | 166,705,119 | 0.23653% |
-| [schneiderkamplab/common-pile-prefix-continuation](https://huggingface.co/datasets/schneiderkamplab/common-pile-prefix-continuation) | common-pile-prefix-continuation | generated + audited | 619,911 | 384,554,526 | 1,859,733 | 1,153,663,578 | 1.63688% |
-| [schneiderkamplab/common-pile-span-filling](https://huggingface.co/datasets/schneiderkamplab/common-pile-span-filling) | common-pile-span-filling | generated + audited | 253,715 | 274,165,333 | 761,145 | 822,495,999 | 1.16700% |
-| [schneiderkamplab/danish-dynaword-denoising](https://huggingface.co/datasets/schneiderkamplab/danish-dynaword-denoising) | danish-dynaword-denoising | generated + audited | 65,518 | 80,806,697 | 262,072 | 323,226,788 | 0.45861% |
-| [schneiderkamplab/danish-dynaword-paragraph-reordering](https://huggingface.co/datasets/schneiderkamplab/danish-dynaword-paragraph-reordering) | danish-dynaword-paragraph-reordering | generated + audited | 55,340 | 40,118,295 | 221,360 | 160,473,180 | 0.22769% |
-| [schneiderkamplab/danish-dynaword-prefix-continuation](https://huggingface.co/datasets/schneiderkamplab/danish-dynaword-prefix-continuation) | danish-dynaword-prefix-continuation | generated + audited | 105,317 | 62,951,557 | 421,268 | 251,806,228 | 0.35728% |
-| [schneiderkamplab/danish-dynaword-span-filling](https://huggingface.co/datasets/schneiderkamplab/danish-dynaword-span-filling) | danish-dynaword-span-filling | generated + audited | 54,968 | 62,714,624 | 219,872 | 250,858,496 | 0.35593% |
-| [schneiderkamplab/dfm8-openhermes-da](https://huggingface.co/datasets/schneiderkamplab/dfm8-openhermes-da) | dfm8-openhermes-da | repaired/translated + audited | 972,744 | 461,016,646 | 1,945,488 | 922,033,292 | 1.30823% |
-| [schneiderkamplab/dfm8-openhermes-en](https://huggingface.co/datasets/schneiderkamplab/dfm8-openhermes-en) | dfm8-openhermes-en | repaired/translated + audited | 922,655 | 336,065,766 | 1,845,310 | 672,131,532 | 0.95366% |
-| [schneiderkamplab/dfm8-synthetic-code-debugging](https://huggingface.co/datasets/schneiderkamplab/dfm8-synthetic-code-debugging) | dfm8-synthetic-code-debugging | generated + audited | 341,106 | 246,646,769 | 341,106 | 246,646,769 | 0.34996% |
-| [schneiderkamplab/dfm8-synthetic-constrained-format-following](https://huggingface.co/datasets/schneiderkamplab/dfm8-synthetic-constrained-format-following) | dfm8-synthetic-constrained-format-following | generated + audited | 838,097 | 171,088,193 | 838,097 | 171,088,193 | 0.24275% |
-| [schneiderkamplab/dfm8-synthetic-danish-summarization-rewrite-controls](https://huggingface.co/datasets/schneiderkamplab/dfm8-synthetic-danish-summarization-rewrite-controls) | dfm8-synthetic-danish-summarization-rewrite-controls | generated + audited | 825,710 | 395,960,288 | 825,710 | 395,960,288 | 0.56181% |
-| [schneiderkamplab/dfm8-synthetic-multiturn-danish-english-chat](https://huggingface.co/datasets/schneiderkamplab/dfm8-synthetic-multiturn-danish-english-chat) | dfm8-synthetic-multiturn-danish-english-chat | generated + audited | 962,230 | 366,469,258 | 962,230 | 366,469,258 | 0.51997% |
-| [schneiderkamplab/dfm8-synthetic-native-tool-calling](https://huggingface.co/datasets/schneiderkamplab/dfm8-synthetic-native-tool-calling) | dfm8-synthetic-native-tool-calling | generated + audited | 1,810,418 | 513,088,304 | 1,810,418 | 513,088,304 | 0.72800% |
-| [schneiderkamplab/dfm8-synthetic-strict-math-answer-contract](https://huggingface.co/datasets/schneiderkamplab/dfm8-synthetic-strict-math-answer-contract) | dfm8-synthetic-strict-math-answer-contract | generated + audited | 552,956 | 173,411,485 | 552,956 | 173,411,485 | 0.24605% |
-| [schneiderkamplab/opus-da-en-permissive](https://huggingface.co/datasets/schneiderkamplab/opus-da-en-permissive) | opus | converted | 58,522,145 | 5,663,970,955 | 30,000,000 | 2,903,506,446 | 4.11966% |
-| [schneiderkamplab/sapient-synth-flan-dialog-fsopt-data-qrecc](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-dialog-fsopt-data-qrecc) | sapient-synth-flan-dialog-fsopt-data-qrecc | generated + audited | 20,748 | 13,106,102 | 20,748 | 13,106,102 | 0.01860% |
-| [schneiderkamplab/sapient-synth-flan-dialog-fsopt-data-qrecc-ii](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-dialog-fsopt-data-qrecc-ii) | sapient-synth-flan-dialog-fsopt-data-qrecc-ii | generated + audited | 5,679 | 2,912,505 | 5,679 | 2,912,505 | 0.00413% |
-| [schneiderkamplab/sapient-synth-flan-dialog-zsopt-data-qrecc](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-dialog-zsopt-data-qrecc) | sapient-synth-flan-dialog-zsopt-data-qrecc | generated + audited | 10,663 | 1,728,299 | 10,663 | 1,728,299 | 0.00245% |
-| [schneiderkamplab/sapient-synth-flan-dialog-zsopt-data-qrecc-ii](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-dialog-zsopt-data-qrecc-ii) | sapient-synth-flan-dialog-zsopt-data-qrecc-ii | generated + audited | 3,090 | 512,987 | 3,090 | 512,987 | 0.00073% |
-| [schneiderkamplab/sapient-synth-flan-flan-fsnoopt-data-aeslc-1.0.0](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-fsnoopt-data-aeslc-1.0.0) | sapient-synth-flan-flan-fsnoopt-data-aeslc-1.0.0 | generated + audited | 11,220 | 4,789,274 | 11,220 | 4,789,274 | 0.00680% |
-| [schneiderkamplab/sapient-synth-flan-flan-fsnoopt-data-opinion-abstracts-idebate](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-fsnoopt-data-opinion-abstracts-idebate) | sapient-synth-flan-flan-fsnoopt-data-opinion-abstracts-idebate | generated + audited | 831 | 527,024 | 831 | 527,024 | 0.00075% |
-| [schneiderkamplab/sapient-synth-flan-flan-fsnoopt-data-opinion-abstracts-rotten-tomatoes](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-fsnoopt-data-opinion-abstracts-rotten-tomatoes) | sapient-synth-flan-flan-fsnoopt-data-opinion-abstracts-rotten-tomatoes | generated + audited | 2,404 | 1,739,367 | 2,404 | 1,739,367 | 0.00247% |
-| [schneiderkamplab/sapient-synth-flan-flan-fsopt-data-aeslc-1.0.0](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-fsopt-data-aeslc-1.0.0) | sapient-synth-flan-flan-fsopt-data-aeslc-1.0.0 | generated + audited | 20,094 | 10,159,455 | 20,094 | 10,159,455 | 0.01441% |
-| [schneiderkamplab/sapient-synth-flan-flan-fsopt-data-opinion-abstracts-idebate](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-fsopt-data-opinion-abstracts-idebate) | sapient-synth-flan-flan-fsopt-data-opinion-abstracts-idebate | generated + audited | 1,217 | 745,866 | 1,217 | 745,866 | 0.00106% |
-| [schneiderkamplab/sapient-synth-flan-flan-fsopt-data-opinion-abstracts-rotten-tomatoes](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-fsopt-data-opinion-abstracts-rotten-tomatoes) | sapient-synth-flan-flan-fsopt-data-opinion-abstracts-rotten-tomatoes | generated + audited | 4,649 | 4,313,282 | 4,649 | 4,313,282 | 0.00612% |
-| [schneiderkamplab/sapient-synth-flan-flan-zsnoopt-data-aeslc-1.0.0](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-zsnoopt-data-aeslc-1.0.0) | sapient-synth-flan-flan-zsnoopt-data-aeslc-1.0.0 | generated + audited | 13,031 | 1,909,893 | 13,031 | 1,909,893 | 0.00271% |
-| [schneiderkamplab/sapient-synth-flan-flan-zsnoopt-data-opinion-abstracts-idebate](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-zsnoopt-data-opinion-abstracts-idebate) | sapient-synth-flan-flan-zsnoopt-data-opinion-abstracts-idebate | generated + audited | 1,302 | 222,807 | 1,302 | 222,807 | 0.00032% |
-| [schneiderkamplab/sapient-synth-flan-flan-zsnoopt-data-opinion-abstracts-rotten-tomatoes](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-zsnoopt-data-opinion-abstracts-rotten-tomatoes) | sapient-synth-flan-flan-zsnoopt-data-opinion-abstracts-rotten-tomatoes | generated + audited | 2,895 | 616,034 | 2,895 | 616,034 | 0.00087% |
-| [schneiderkamplab/sapient-synth-flan-flan-zsopt-data-aeslc-1.0.0](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-zsopt-data-aeslc-1.0.0) | sapient-synth-flan-flan-zsopt-data-aeslc-1.0.0 | generated + audited | 11,820 | 1,729,747 | 11,820 | 1,729,747 | 0.00245% |
-| [schneiderkamplab/sapient-synth-flan-flan-zsopt-data-opinion-abstracts-idebate](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-zsopt-data-opinion-abstracts-idebate) | sapient-synth-flan-flan-zsopt-data-opinion-abstracts-idebate | generated + audited | 1,307 | 222,628 | 1,307 | 222,628 | 0.00032% |
-| [schneiderkamplab/sapient-synth-flan-flan-zsopt-data-opinion-abstracts-rotten-tomatoes](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-flan-zsopt-data-opinion-abstracts-rotten-tomatoes) | sapient-synth-flan-flan-zsopt-data-opinion-abstracts-rotten-tomatoes | generated + audited | 2,919 | 622,031 | 2,919 | 622,031 | 0.00088% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1309-amazonreview-summary-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1309-amazonreview-summary-classification) | sapient-synth-flan-niv2-fsopt-data-task1309-amazonreview-summary-classification | generated + audited | 9,614 | 1,948,021 | 9,614 | 1,948,021 | 0.00276% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1370-newscomm-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1370-newscomm-classification) | sapient-synth-flan-niv2-fsopt-data-task1370-newscomm-classification | generated + audited | 4,137 | 720,660 | 4,137 | 720,660 | 0.00102% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1371-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1371-newscomm-translation) | sapient-synth-flan-niv2-fsopt-data-task1371-newscomm-translation | generated + audited | 3,535 | 703,194 | 3,535 | 703,194 | 0.00100% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1373-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1373-newscomm-translation) | sapient-synth-flan-niv2-fsopt-data-task1373-newscomm-translation | generated + audited | 5,212 | 1,060,440 | 5,212 | 1,060,440 | 0.00150% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1374-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1374-newscomm-translation) | sapient-synth-flan-niv2-fsopt-data-task1374-newscomm-translation | generated + audited | 1,616 | 598,761 | 1,616 | 598,761 | 0.00085% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1375-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1375-newscomm-translation) | sapient-synth-flan-niv2-fsopt-data-task1375-newscomm-translation | generated + audited | 5,084 | 1,168,532 | 5,084 | 1,168,532 | 0.00166% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1376-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1376-newscomm-translation) | sapient-synth-flan-niv2-fsopt-data-task1376-newscomm-translation | generated + audited | 3,116 | 974,302 | 3,116 | 974,302 | 0.00138% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1377-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task1377-newscomm-translation) | sapient-synth-flan-niv2-fsopt-data-task1377-newscomm-translation | generated + audited | 3,491 | 679,180 | 3,491 | 679,180 | 0.00096% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task264-paper-reviews-accept-reject](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task264-paper-reviews-accept-reject) | sapient-synth-flan-niv2-fsopt-data-task264-paper-reviews-accept-reject | generated + audited | 252 | 139,871 | 252 | 139,871 | 0.00020% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task265-paper-reviews-language-identification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task265-paper-reviews-language-identification) | sapient-synth-flan-niv2-fsopt-data-task265-paper-reviews-language-identification | generated + audited | 1,341 | 395,317 | 1,341 | 395,317 | 0.00056% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task266-paper-reviews-reviewer-perspective](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task266-paper-reviews-reviewer-perspective) | sapient-synth-flan-niv2-fsopt-data-task266-paper-reviews-reviewer-perspective | generated + audited | 676 | 235,696 | 676 | 235,696 | 0.00033% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task589-amazonfood-summary-text-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task589-amazonfood-summary-text-generation) | sapient-synth-flan-niv2-fsopt-data-task589-amazonfood-summary-text-generation | generated + audited | 11,860 | 2,814,833 | 11,860 | 2,814,833 | 0.00399% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task590-amazonfood-summary-correction-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task590-amazonfood-summary-correction-classification) | sapient-synth-flan-niv2-fsopt-data-task590-amazonfood-summary-correction-classification | generated + audited | 10,857 | 2,539,909 | 10,857 | 2,539,909 | 0.00360% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task618-amazonreview-summary-text-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task618-amazonreview-summary-text-generation) | sapient-synth-flan-niv2-fsopt-data-task618-amazonreview-summary-text-generation | generated + audited | 11,879 | 2,177,827 | 11,879 | 2,177,827 | 0.00309% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task634-allegro-reviews-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task634-allegro-reviews-classification) | sapient-synth-flan-niv2-fsopt-data-task634-allegro-reviews-classification | generated + audited | 2,546 | 816,301 | 2,546 | 816,301 | 0.00116% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task635-allegro-reviews-answer-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task635-allegro-reviews-answer-generation) | sapient-synth-flan-niv2-fsopt-data-task635-allegro-reviews-answer-generation | generated + audited | 3,186 | 816,257 | 3,186 | 816,257 | 0.00116% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task672-amazon-yelp-summarization](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task672-amazon-yelp-summarization) | sapient-synth-flan-niv2-fsopt-data-task672-amazon-yelp-summarization | generated + audited | 259 | 198,111 | 259 | 198,111 | 0.00028% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task870-msmarco-answer-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task870-msmarco-answer-generation) | sapient-synth-flan-niv2-fsopt-data-task870-msmarco-answer-generation | generated + audited | 1,226 | 598,966 | 1,226 | 598,966 | 0.00085% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task871-msmarco-question-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task871-msmarco-question-generation) | sapient-synth-flan-niv2-fsopt-data-task871-msmarco-question-generation | generated + audited | 158 | 71,057 | 158 | 71,057 | 0.00010% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task902-deceptive-opinion-spam-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task902-deceptive-opinion-spam-classification) | sapient-synth-flan-niv2-fsopt-data-task902-deceptive-opinion-spam-classification | generated + audited | 2,935 | 877,555 | 2,935 | 877,555 | 0.00125% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task903-deceptive-opinion-spam-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task903-deceptive-opinion-spam-classification) | sapient-synth-flan-niv2-fsopt-data-task903-deceptive-opinion-spam-classification | generated + audited | 1,703 | 534,985 | 1,703 | 534,985 | 0.00076% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task906-dialogre-identify-names](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task906-dialogre-identify-names) | sapient-synth-flan-niv2-fsopt-data-task906-dialogre-identify-names | generated + audited | 459 | 235,077 | 459 | 235,077 | 0.00033% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task907-dialogre-identify-relationships](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task907-dialogre-identify-relationships) | sapient-synth-flan-niv2-fsopt-data-task907-dialogre-identify-relationships | generated + audited | 101 | 46,025 | 101 | 46,025 | 0.00007% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task908-dialogre-identify-familial-relationships](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task908-dialogre-identify-familial-relationships) | sapient-synth-flan-niv2-fsopt-data-task908-dialogre-identify-familial-relationships | generated + audited | 163 | 68,410 | 163 | 68,410 | 0.00010% |
-| [schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task909-dialogre-prevalent-speakers](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-fsopt-data-task909-dialogre-prevalent-speakers) | sapient-synth-flan-niv2-fsopt-data-task909-dialogre-prevalent-speakers | generated + audited | 753 | 295,703 | 753 | 295,703 | 0.00042% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1309-amazonreview-summary-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1309-amazonreview-summary-classification) | sapient-synth-flan-niv2-zsopt-data-task1309-amazonreview-summary-classification | generated + audited | 6,249 | 795,481 | 6,249 | 795,481 | 0.00113% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1370-newscomm-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1370-newscomm-classification) | sapient-synth-flan-niv2-zsopt-data-task1370-newscomm-classification | generated + audited | 20 | 1,929 | 20 | 1,929 | 0.00000% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1371-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1371-newscomm-translation) | sapient-synth-flan-niv2-zsopt-data-task1371-newscomm-translation | generated + audited | 1,577 | 170,549 | 1,577 | 170,549 | 0.00024% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1373-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1373-newscomm-translation) | sapient-synth-flan-niv2-zsopt-data-task1373-newscomm-translation | generated + audited | 2,243 | 267,691 | 2,243 | 267,691 | 0.00038% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1374-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1374-newscomm-translation) | sapient-synth-flan-niv2-zsopt-data-task1374-newscomm-translation | generated + audited | 716 | 173,515 | 716 | 173,515 | 0.00025% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1375-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1375-newscomm-translation) | sapient-synth-flan-niv2-zsopt-data-task1375-newscomm-translation | generated + audited | 2,180 | 265,049 | 2,180 | 265,049 | 0.00038% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1376-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1376-newscomm-translation) | sapient-synth-flan-niv2-zsopt-data-task1376-newscomm-translation | generated + audited | 1,369 | 273,620 | 1,369 | 273,620 | 0.00039% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1377-newscomm-translation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task1377-newscomm-translation) | sapient-synth-flan-niv2-zsopt-data-task1377-newscomm-translation | generated + audited | 1,531 | 181,717 | 1,531 | 181,717 | 0.00026% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task264-paper-reviews-accept-reject](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task264-paper-reviews-accept-reject) | sapient-synth-flan-niv2-zsopt-data-task264-paper-reviews-accept-reject | generated + audited | 141 | 49,270 | 141 | 49,270 | 0.00007% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task265-paper-reviews-language-identification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task265-paper-reviews-language-identification) | sapient-synth-flan-niv2-zsopt-data-task265-paper-reviews-language-identification | generated + audited | 710 | 154,418 | 710 | 154,418 | 0.00022% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task266-paper-reviews-reviewer-perspective](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task266-paper-reviews-reviewer-perspective) | sapient-synth-flan-niv2-zsopt-data-task266-paper-reviews-reviewer-perspective | generated + audited | 302 | 71,856 | 302 | 71,856 | 0.00010% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task589-amazonfood-summary-text-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task589-amazonfood-summary-text-generation) | sapient-synth-flan-niv2-zsopt-data-task589-amazonfood-summary-text-generation | generated + audited | 5,311 | 705,459 | 5,311 | 705,459 | 0.00100% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task590-amazonfood-summary-correction-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task590-amazonfood-summary-correction-classification) | sapient-synth-flan-niv2-zsopt-data-task590-amazonfood-summary-correction-classification | generated + audited | 5,723 | 811,411 | 5,723 | 811,411 | 0.00115% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task618-amazonreview-summary-text-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task618-amazonreview-summary-text-generation) | sapient-synth-flan-niv2-zsopt-data-task618-amazonreview-summary-text-generation | generated + audited | 4,896 | 532,494 | 4,896 | 532,494 | 0.00076% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task634-allegro-reviews-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task634-allegro-reviews-classification) | sapient-synth-flan-niv2-zsopt-data-task634-allegro-reviews-classification | generated + audited | 1,235 | 214,780 | 1,235 | 214,780 | 0.00030% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task635-allegro-reviews-answer-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task635-allegro-reviews-answer-generation) | sapient-synth-flan-niv2-zsopt-data-task635-allegro-reviews-answer-generation | generated + audited | 2,120 | 319,795 | 2,120 | 319,795 | 0.00045% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task672-amazon-yelp-summarization](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task672-amazon-yelp-summarization) | sapient-synth-flan-niv2-zsopt-data-task672-amazon-yelp-summarization | generated + audited | 159 | 85,279 | 159 | 85,279 | 0.00012% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task870-msmarco-answer-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task870-msmarco-answer-generation) | sapient-synth-flan-niv2-zsopt-data-task870-msmarco-answer-generation | generated + audited | 581 | 212,021 | 581 | 212,021 | 0.00030% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task871-msmarco-question-generation](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task871-msmarco-question-generation) | sapient-synth-flan-niv2-zsopt-data-task871-msmarco-question-generation | generated + audited | 89 | 35,607 | 89 | 35,607 | 0.00005% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task902-deceptive-opinion-spam-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task902-deceptive-opinion-spam-classification) | sapient-synth-flan-niv2-zsopt-data-task902-deceptive-opinion-spam-classification | generated + audited | 1,234 | 198,319 | 1,234 | 198,319 | 0.00028% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task903-deceptive-opinion-spam-classification](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task903-deceptive-opinion-spam-classification) | sapient-synth-flan-niv2-zsopt-data-task903-deceptive-opinion-spam-classification | generated + audited | 865 | 149,122 | 865 | 149,122 | 0.00021% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task906-dialogre-identify-names](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task906-dialogre-identify-names) | sapient-synth-flan-niv2-zsopt-data-task906-dialogre-identify-names | generated + audited | 221 | 57,511 | 221 | 57,511 | 0.00008% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task907-dialogre-identify-relationships](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task907-dialogre-identify-relationships) | sapient-synth-flan-niv2-zsopt-data-task907-dialogre-identify-relationships | generated + audited | 50 | 13,407 | 50 | 13,407 | 0.00002% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task908-dialogre-identify-familial-relationships](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task908-dialogre-identify-familial-relationships) | sapient-synth-flan-niv2-zsopt-data-task908-dialogre-identify-familial-relationships | generated + audited | 72 | 17,327 | 72 | 17,327 | 0.00002% |
-| [schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task909-dialogre-prevalent-speakers](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-flan-niv2-zsopt-data-task909-dialogre-prevalent-speakers) | sapient-synth-flan-niv2-zsopt-data-task909-dialogre-prevalent-speakers | generated + audited | 364 | 82,014 | 364 | 82,014 | 0.00012% |
-| [schneiderkamplab/sapient-synth-platypus-reclor](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-platypus-reclor) | sapient-synth-platypus-reclor | generated + audited | 5,131 | 921,983 | 5,131 | 921,983 | 0.00131% |
-| [schneiderkamplab/sapient-synth-platypus-scibench](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-platypus-scibench) | sapient-synth-platypus-scibench | generated + audited | 744 | 103,655 | 744 | 103,655 | 0.00015% |
-| [schneiderkamplab/sapient-synth-tasksource-pragmeval-sarcasm](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-tasksource-pragmeval-sarcasm) | sapient-synth-tasksource-pragmeval-sarcasm | generated + audited | 3,753 | 542,414 | 3,753 | 542,414 | 0.00077% |
-| [schneiderkamplab/sapient-synth-tasksource-reclor](https://huggingface.co/datasets/schneiderkamplab/sapient-synth-tasksource-reclor) | sapient-synth-tasksource-reclor | generated + audited | 4,633 | 900,568 | 4,633 | 900,568 | 0.00128% |
-| [schneiderkamplab/transformations-danish-danish](https://huggingface.co/datasets/schneiderkamplab/transformations-danish-danish) | transformations-danish-danish | generated + audited | 208,117 | 165,397,479 | 624,351 | 496,192,437 | 0.70403% |
-| [schneiderkamplab/transformations-danish-english](https://huggingface.co/datasets/schneiderkamplab/transformations-danish-english) | transformations-danish-english | generated + audited | 211,401 | 156,297,268 | 634,203 | 468,891,804 | 0.66529% |
-| [schneiderkamplab/transformations-english-danish](https://huggingface.co/datasets/schneiderkamplab/transformations-english-danish) | transformations-english-danish | generated + audited | 246,288 | 139,253,177 | 738,864 | 417,759,531 | 0.59274% |
-| [schneiderkamplab/transformations-english-english](https://huggingface.co/datasets/schneiderkamplab/transformations-english-english) | transformations-english-english | generated + audited | 248,474 | 117,774,730 | 993,896 | 471,098,920 | 0.66842% |
-| [synquid/danish-verifiable-reasoning](https://huggingface.co/datasets/synquid/danish-verifiable-reasoning) | synquid_danish_verifiable_reasoning | converted | 2,800 | 9,065,680 | 5,600 | 18,131,360 | 0.02573% |
-| [synquid/ifbench-train](https://huggingface.co/datasets/synquid/ifbench-train) | synquid_ifbench_train | converted | 2,356 | 1,268,913 | 23,560 | 12,689,130 | 0.01800% |
-| [synquid/mt-da-deepseek](https://huggingface.co/datasets/synquid/mt-da-deepseek) | synquid_mt_da_deepseek | converted | 3,016 | 8,885,734 | 3,016 | 8,885,734 | 0.01261% |
-| [synquid/translation-100k](https://huggingface.co/datasets/synquid/translation-100k) | synquid_translation_100k | converted | 99,943 | 98,234,612 | 99,943 | 98,234,612 | 0.13938% |
-| [synquid/wiki-instruct-da](https://huggingface.co/datasets/synquid/wiki-instruct-da) | synquid_wiki_instruct_da | converted | 226,213 | 164,686,585 | 1,357,278 | 988,119,510 | 1.40200% |
-| [synquid/wildchat-100k-qwen-messages](https://huggingface.co/datasets/synquid/wildchat-100k-qwen-messages) | synquid_wildchat_100k_qwen_messages | converted | 128,860 | 189,868,452 | 128,860 | 189,868,452 | 0.26940% |
-| [Team-ACE/ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) | toolace_native_tool_use | native-tool conversion | 13,097 | 8,409,302 | 39,291 | 25,227,906 | 0.03579% |
-## Datasets supplied under Danish Foundation Model agreements
-These datasets are not hosted on Hugging Face. They are supplied through
-the Danish Foundation Model's data agreements and are deliberately placed
-after the complete HF inventory.
-| Dataset | DFM8 prefix/category | Source rows | Source tokens | Sampled rows/epoch | Sampled tokens/epoch | DFM8 share |
-|---|---|---:|---:|---:|---:|---:|
-| DBC {abstracts_*|reviews|faktalink|farfatterweb} | dbc | 11,886,840 | 1,606,640,077 | 2,338,220 | 356,125,374 | 0.50529% |
-| Lex.dk articles | lexdk | 108,711 | 62,679,490 | 543,555 | 313,397,450 | 0.44467% |
-## Checksum
-| Scope | Dataset repositories/groups | Sampled tokens/epoch | Share |
-|---|---:|---:|---:|
-| Hugging Face datasets | 159 | 69,809,785,782 | 99.05004% |
-| Agreement-supplied datasets | 2 | 669,522,824 | 0.94996% |
-| **DFM8 total** | **161** | **70,479,308,606** | **100.00000%** |
-## Specification cross-check
-The table includes every category with nonzero coverage in
-data/show_analytics_dfm8.md. The following entries occur in
-data_io/prefix_config_dfm8.yaml but are not DFM8 datasets in this sampled
-build:
-- Explicitly capped to zero: danish_wildchat4_8m, nemotron_swe,
-  dolci_instruct_sft_tool_use, and dolci_instruct_sft_tool_use_sa.
-  The last two raw Dolci prefixes are replaced by the corrected
-  dolci_native_tool_use derivatives reported against their two source HF
-  repositories above.
-- Configured but with no matching sampled coverage: ai_arenaen_conversations,
-  allenai_code_meta_reasoning, allenai_if_multi_constraints_upto5,
-  allenai_rlvr_ifeval, omnimath.jsonl, gsm8k_train.jsonl,
-  math_train.jsonl, webinstruct_verified.jsonl, and no_robots.jsonl.
-Therefore none of those zero/unmatched entries is counted in the repository
-total or token checksum.

plots/subject_avg_scores.png CHANGED Viewed

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