Datasets:

Rapnss
/

RapnssZ-compatibility-training

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
category: string
prompt: string
expected_tool: bool
total_samples: int64
formats: list<item: string>
  child 0, item: string
version: string
domains_covered: list<item: string>
  child 0, item: string
categories: struct<system_identity_and_arch: int64, desktop_rpa: int64, filesystem_and_code: int64, universal_vi (... 229 chars omitted)
  child 0, system_identity_and_arch: int64
  child 1, desktop_rpa: int64
  child 2, filesystem_and_code: int64
  child 3, universal_vision: int64
  child 4, web_browsing: int64
  child 5, root_architect: int64
  child 6, mcp_and_blender: int64
  child 7, tactical_auto_op: int64
  child 8, nexus_swarm: int64
  child 9, agentic_error_recovery: int64
  child 10, mobile_phone_pack: int64
  child 11, voice_and_audio: int64
  child 12, memory_and_rag: int64
generated_at: timestamp[s]
to
{'version': Value('string'), 'generated_at': Value('timestamp[s]'), 'total_samples': Value('int64'), 'formats': List(Value('string')), 'categories': {'system_identity_and_arch': Value('int64'), 'desktop_rpa': Value('int64'), 'filesystem_and_code': Value('int64'), 'universal_vision': Value('int64'), 'web_browsing': Value('int64'), 'root_architect': Value('int64'), 'mcp_and_blender': Value('int64'), 'tactical_auto_op': Value('int64'), 'nexus_swarm': Value('int64'), 'agentic_error_recovery': Value('int64'), 'mobile_phone_pack': Value('int64'), 'voice_and_audio': Value('int64'), 'memory_and_rag': Value('int64')}, 'domains_covered': List(Value('string'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              category: string
              prompt: string
              expected_tool: bool
              total_samples: int64
              formats: list<item: string>
                child 0, item: string
              version: string
              domains_covered: list<item: string>
                child 0, item: string
              categories: struct<system_identity_and_arch: int64, desktop_rpa: int64, filesystem_and_code: int64, universal_vi (... 229 chars omitted)
                child 0, system_identity_and_arch: int64
                child 1, desktop_rpa: int64
                child 2, filesystem_and_code: int64
                child 3, universal_vision: int64
                child 4, web_browsing: int64
                child 5, root_architect: int64
                child 6, mcp_and_blender: int64
                child 7, tactical_auto_op: int64
                child 8, nexus_swarm: int64
                child 9, agentic_error_recovery: int64
                child 10, mobile_phone_pack: int64
                child 11, voice_and_audio: int64
                child 12, memory_and_rag: int64
              generated_at: timestamp[s]
              to
              {'version': Value('string'), 'generated_at': Value('timestamp[s]'), 'total_samples': Value('int64'), 'formats': List(Value('string')), 'categories': {'system_identity_and_arch': Value('int64'), 'desktop_rpa': Value('int64'), 'filesystem_and_code': Value('int64'), 'universal_vision': Value('int64'), 'web_browsing': Value('int64'), 'root_architect': Value('int64'), 'mcp_and_blender': Value('int64'), 'tactical_auto_op': Value('int64'), 'nexus_swarm': Value('int64'), 'agentic_error_recovery': Value('int64'), 'mobile_phone_pack': Value('int64'), 'voice_and_audio': Value('int64'), 'memory_and_rag': Value('int64')}, 'domains_covered': List(Value('string'))}
              because column names don't match

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Install RapnssZ CLI in your device :- irm https://framework.rapnss.in/install.ps1 | iex

RapnssZ Specialized LLM Fine-Tuning Dataset (v5.7)

This dataset is specifically constructed to fine-tune an LLM to serve as the brain for the RapnssZ Agentic Operating System, unlocking its maximum autonomous capabilities across desktop automation, browser intelligence, vision inspection, and tool orchestration.

📊 Dataset Overview

Total Samples: 1,733
Multi-Turn Conversational Dataset: rapnssz_full_sft_sharegpt.jsonl
Single-Turn Instruction Dataset: rapnssz_full_sft_alpaca.jsonl
Benchmarking Curriculum: rapnssz_curriculum.json
Metadata Specification: dataset_metadata.json

🎯 Capability Distribution Across 13 Domains

{ "system_identity_and_arch": 104, "desktop_rpa": 652, "filesystem_and_code": 367, "universal_vision": 207, "web_browsing": 195, "root_architect": 26, "mcp_and_blender": 26, "tactical_auto_op": 39, "nexus_swarm": 26, "agentic_error_recovery": 26, "mobile_phone_pack": 26, "voice_and_audio": 26, "memory_and_rag": 13 }

🛠️ Fine-Tuning Instructions

1. Recommended Base Models

Qwen/Qwen2.5-7B-Instruct or Qwen/Qwen2.5-14B-Instruct (Best JSON formatting, instruction following, and tool calling discipline)
meta-llama/Llama-3.3-70B-Instruct / Llama-3.1-8B-Instruct
mistralai/Ministral-8B-Instruct-2410

2. Training with Unsloth / Axolotl / LLaMA-Factory

model_name: Qwen/Qwen2.5-7B-Instruct
dataset: dataset/rapnssz_full_sft_sharegpt.jsonl
dataset_format: sharegpt

# LoRA / QLoRA Hyperparameters
adapter: lora
lora_r: 64
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]

# Optimization
learning_rate: 2e-4
lr_scheduler_type: cosine
warmup_ratio: 0.03
epochs: 3
micro_batch_size: 2
gradient_accumulation_steps: 8
max_seq_length: 8192
bf16: true

# Stop Tokens
stop_tokens:
  - "Observation:"
  - "\nObservation:"

3. Deploying in RapnssZ

Export the fine-tuned model to GGUF (rapnssz-qwen2.5-7b-v5.7.Q4_K_M.gguf) and place it in %LOCALAPPDATA%\RapnssZ\Brain\models\. Launch RapnssZ with:

rapnssz --mode local

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Total file size:

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