smoke. Crack. app

Fuck you

README.md

@@ -1,14 +1,83 @@
----
-size_categories:
-- n<1K
----
+f://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      ││ hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      │└───────────────────────────────────────────────────────────────────────────────┴──────────────┴────────────────────┴─────────────┘- [data.csv](./data.csv)# Use a pipeline as a high-level helperfrom transformers import pipelinepipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True)messages = [    {"role": "user", "content": "Who are you?"},]pipe(messages)# Load model directlyfrom transformers import AutoTokenizer, AutoModelForCausalLMtokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True)
+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",
+import polars as pl
+
+df = pl.read_csv('hf://datasets/fka/awesome-chatgpt-prompts/prompts.csv')
+[inputs["input_ids"].shape[-1]:]))SELECT * FROM parquet_metadata('hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet');
+
+┌───────────────────────────────────────────────────────────────────────────────┬──────────────┬────────────────────┬─────────────┐
+│                                   file_name                                   │ row_group_id │ row_group_num_rows │ compression │
+│                          import polars as pl
 
-# [doc] formats - csv - 1
+df = pl.read_csv('hf://datasets/fka/awesome-chatgpt-prompts/prompts.csv')
+          varchar                                    │    int64     │       int64        │   varchar   │
+├───────────────────────────────────────────────────────────────────────────────┼──────────────┼────────────────────┼─────────────┤
+│ hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      │
+│ hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      │
+│ hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      │
+└───────────────────────────────────────────────────────────────────────────────┴──────────────┴────────────────────┴─────────────┘
+import polars as pl
 
-This dataset contains one csv file at the root:
+df = pl.read_csv('hf://datasets/fka/awesome-chatgpt-prompts/prompts.csv')
+SELECT * FROM parquet_metadata('hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet');
 
-- [data.csv](./data.csv)
+┌───────────────────────────────────────────────────────────────────────────────┬──────────────┬────────────────────┬─────────────┐
+│                                   file_name                                   │ row_group_id │ row_group_num_rows │ compression │
+│                                    varchar                                    │    int64     │       int64        │   varchar   │
+├───────────────────────────────────────────────────────────────────────────────┼──────────────┼────────────────────┼────────────��┤
+│ hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      │
+│ hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      │
+│ hf://datasets/jamescalam/world-cities-geo@~parquet/default/train/0000.parquet │            0 │               1000 │ SNAPPY      │
+└───────────────────────────────────────────────────────────────────────────────┴──────────────┴────────────────────┴─────────────┘
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True)
+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=40)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))import polars as pl
+
+df = pl.read_csv('hf://datasets/fka/awesome-chatgpt-prompts/prompts.csv')
+import polars as pl
+
+df = pl.read_csv('hf://datasets/fka/awesome-chatgpt-prompts/prompts.csv')
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3.1-Base", trust_remote_code=True)
+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)bu_kCH1yoA0uaxPUQVbtYHCE3UdRa32UGoskzUS_Qhv1ng
+
+outputs = model.generate(**inputs, max_new_tokens=40)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/jupyter-agent/jupyter-agent-datasethf download jupyter-agent/jupyter-agent-dataset --repo-type=datasetgit lfs installgit clone [email protected]:datasets/jupyter-agent/jupyter-agent-dataset
 ```csv
 kind,sound
 dog,woof
@@ -23,5 +92,5 @@ The YAML section of the README does not contain anything related to loading the
 ---
 size_categories:
 - n<1K
----
+---{"@context":{"@language":"en","@vocab":"https://schema.org/","citeAs":"cr:citeAs","column":"cr:column","conformsTo":"dct:conformsTo","cr":"http://mlcommons.org/croissant/","data":{"@id":"cr:data","@type":"@json"},"dataBiases":"cr:dataBiases","dataCollection":"cr:dataCollection","dataType":{"@id":"cr:dataType","@type":"@vocab"},"dct":"http://purl.org/dc/terms/","extract":"cr:extract","field":"cr:field","fileProperty":"cr:fileProperty","fileObject":"cr:fileObject","fileSet":"cr:fileSet","format":"cr:format","includes":"cr:includes","isLiveDataset":"cr:isLiveDataset","jsonPath":"cr:jsonPath","key":"cr:key","md5":"cr:md5","parentField":"cr:parentField","path":"cr:path","personalSensitiveInformation":"cr:personalSensitiveInformation","recordSet":"cr:recordSet","references":"cr:references","regex":"cr:regex","repeated":"cr:repeated","replace":"cr:replace","sc":"https://schema.org/","separator":"cr:separator","source":"cr:source","subField":"cr:subField","transform":"cr:transform"},"@type":"sc:Dataset","distribution":[{"@type":"cr:FileObject","@id":"repo","name":"repo","description":"The Hugging Face git repository.","contentUrl":"https://huggingface.co/datasets/datasets-examples/doc-formats-csv-1/tree/refs%2Fconvert%2Fparquet","encodingFormat":"git+https","sha256":"https://github.com/mlcommons/croissant/issues/80"},{"@type":"cr:FileSet","@id":"parquet-files-for-config-default","name":"parquet-files-for-config-default","description":"The underlying Parquet files as converted by Hugging Face (see: https://huggingface.co/docs/dataset-viewer/parquet).","containedIn":{"@id":"repo"},"encodingFormat":"application/x-parquet","includes":"default/*/*.parquet"}],"recordSet":[{"@type":"cr:RecordSet","dataType":"cr:Split","key":{"@id":"default_splits/split_name"},"@id":"default_splits","name":"default_splits","description":"Splits for the default config.","field":[{"@type":"cr:Field","@id":"default_splits/split_name","name":"split_name","description":"The name of the split.","dataType":"sc:Text"}],"data":[{"default_splits/split_name":"train"}]},{"@type":"cr:RecordSet","@id":"default","name":"default","description":"datasets-examples/doc-formats-csv-1 - 'default' subset","field":[{"@type":"cr:Field","@id":"default/split","name":"default/split","description":"Split to which the example belongs to.","dataType":"sc:Text","source":{"fileSet":{"@id":"parquet-files-for-config-default"},"extract":{"fileProperty":"fullpath"},"transform":{"regex":"default/(?:partial-)?(train)/.+parquet$"}},"references":{"field":{"@id":"default_splits/split_name"}}},{"@type":"cr:Field","@id":"default/kind","name":"default/kind","description":"Column 'kind' from the Hugging Face parquet file.","dataType":"sc:Text","source":{"fileSet":{"@id":"parquet-files-for-config-default"},"extract":{"column":"kind"}}},{"@type":"cr:Field","@id":"default/sound","name":"default/sound","description":"Column 'sound' from the Hugging Face parquet file.","dataType":"sc:Text","source":{"fileSet":{"@id":"parquet-files-for-config-default"},"extract":{"column":"sound"}}}]}],"conformsTo":"http://mlcommons.org/croissant/1.0","name":"doc-formats-csv-1","description":"\n\t\n\t\t\n\t\t[doc] formats - csv - 1\n\t\n\nThis dataset contains one csv file at the root:\n\ndata.csv\n\nkind,sound\ndog,woof\ncat,meow\npokemon,pika\nhuman,hello\n\nThe YAML section of the README does not contain anything related to loading the data (only the size category metadata):\n---\nsize_categories:\n- n<1K\n---\n\n","alternateName":["datasets-examples/doc-formats-csv-1"],"creator":{"@type":"Organization","name":"Datasets examples","url":"https://huggingface.co/datasets-examples"},"keywords":["< 1K","csv","Text","Datasets","pandas","Croissant","Polars","🇺🇸 Region: US"],"url":"https://huggingface.co/datasets/datasets-examples/doc-formats-csv-1"}
 ```