Hugging Face's logo

Datasets:
SZLHOLDINGS
/
receipted-unsloth

Tasks:
Text Generation
Languages:
English
Tags:
szl-holdings
unsloth
qlora
receipts
doctrine-v11
proposal-only
License:
Dataset card Data Studio Files
xet
 Community
Dataset Preview
Duplicate
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
schema: string
artifact: string
split: string
opened_test_jsonl: bool
n: int64
correct: int64
draft: string
recovery: string
refuse: string
label: string
publication_eligible: bool
not_rosie_supervised: bool
train_loss_is_eval: bool
cases: list<item: struct<id: string, kind: string, ok: bool, reason: string, preview: string>>
  child 0, item: struct<id: string, kind: string, ok: bool, reason: string, preview: string>
      child 0, id: string
      child 1, kind: string
      child 2, ok: bool
      child 3, reason: string
      child 4, preview: string
formula_tax: struct<kind: string, registry_count: int64, locked_8: list<item: string>, lambda_aggregate: double,  (... 94 chars omitted)
  child 0, kind: string
  child 1, registry_count: int64
  child 2, locked_8: list<item: string>
      child 0, item: string
  child 3, lambda_aggregate: double
  child 4, lambda_label: string
  child 5, lambda_uniqueness: string
  child 6, khipu_merkle_sha256: string
  child 7, honesty: string
computed_at: string
trackio: bool
dataset_file: string
adapterSha256: string
reason: null
v: int64
optim: string
evals: string
base_model: string
learning_rate: double
lr_scheduler_type: string
finalTrainLoss: string
doctrine: string
raw_graph_nodes_admitted_to_gradients: int64
train_loss_label: string
public_chunk_count: int64
canonical_base: string
source: string
max_seq_length: int64
kind: string
load_in_4bit: bool
dataset_sha256: string
training_rows: int64
hub_put: bool
push_to_hub: bool
autonomy_eligible: bool
lambda: string
held_out_in_gradients: bool
does_not_overwrite: list<item: string>
  child 0, item: string
load_in_16bit: bool
report_to: string
response_only_loss: bool
weights: string
num_train_epochs: int64
quality: string
quant: string
lora_alpha: int64
proposal_only: bool
gpu: struct<platform: string, python: string, nvidia_smi: string, torch: string, cuda: bool, gpu_name: st (... 25 chars omitted)
  child 0, platform: string
  child 1, python: string
  child 2, nvidia_smi: string
  child 3, torch: string
  child 4, cuda: bool
  child 5, gpu_name: string
  child 6, gpu_mem_gb: double
seed: int64
sku: string
lora_r: int64
warmup_steps: int64
claim_boundary: string
qlora: bool
to
{'kind': Value('string'), 'schema': Value('string'), 'v': Value('int64'), 'artifact': Value('string'), 'sku': Value('string'), 'does_not_overwrite': List(Value('string')), 'canonical_base': Value('string'), 'base_model': Value('string'), 'qlora': Value('bool'), 'load_in_4bit': Value('bool'), 'load_in_16bit': Value('bool'), 'quant': Value('string'), 'lora_r': Value('int64'), 'lora_alpha': Value('int64'), 'seed': Value('int64'), 'num_train_epochs': Value('int64'), 'warmup_steps': Value('int64'), 'learning_rate': Value('float64'), 'lr_scheduler_type': Value('string'), 'optim': Value('string'), 'response_only_loss': Value('bool'), 'max_seq_length': Value('int64'), 'dataset_file': Value('string'), 'dataset_sha256': Value('string'), 'held_out_in_gradients': Value('bool'), 'raw_graph_nodes_admitted_to_gradients': Value('int64'), 'public_chunk_count': Value('int64'), 'push_to_hub': Value('bool'), 'trackio': Value('bool'), 'report_to': Value('string'), 'weights': Value('string'), 'adapterSha256': Value('string'), 'finalTrainLoss': Value('string'), 'train_loss_label': Value('string'), 'evals': Value('string'), 'quality': Value('string'), 'lambda': Value('string'), 'doctrine': Value('string'), 'proposal_only': Value('bool'), 'publication_eligible': Value('bool'), 'autonomy_eligible': Value('bool'), 'hub_put': Value('bool'), 'training_rows': Value('int64'), 'reason': Value('null'), 'gpu': {'platform': Value('string'), 'python': Value('string'), 'nvidia_smi': Value('string'), 'torch': Value('string'), 'cuda': Value('bool'), 'gpu_name': Value('string'), 'gpu_mem_gb': Value('float64')}, 'claim_boundary': Value('string'), 'computed_at': Value('string'), 'source': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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
              schema: string
              artifact: string
              split: string
              opened_test_jsonl: bool
              n: int64
              correct: int64
              draft: string
              recovery: string
              refuse: string
              label: string
              publication_eligible: bool
              not_rosie_supervised: bool
              train_loss_is_eval: bool
              cases: list<item: struct<id: string, kind: string, ok: bool, reason: string, preview: string>>
                child 0, item: struct<id: string, kind: string, ok: bool, reason: string, preview: string>
                    child 0, id: string
                    child 1, kind: string
                    child 2, ok: bool
                    child 3, reason: string
                    child 4, preview: string
              formula_tax: struct<kind: string, registry_count: int64, locked_8: list<item: string>, lambda_aggregate: double,  (... 94 chars omitted)
                child 0, kind: string
                child 1, registry_count: int64
                child 2, locked_8: list<item: string>
                    child 0, item: string
                child 3, lambda_aggregate: double
                child 4, lambda_label: string
                child 5, lambda_uniqueness: string
                child 6, khipu_merkle_sha256: string
                child 7, honesty: string
              computed_at: string
              trackio: bool
              dataset_file: string
              adapterSha256: string
              reason: null
              v: int64
              optim: string
              evals: string
              base_model: string
              learning_rate: double
              lr_scheduler_type: string
              finalTrainLoss: string
              doctrine: string
              raw_graph_nodes_admitted_to_gradients: int64
              train_loss_label: string
              public_chunk_count: int64
              canonical_base: string
              source: string
              max_seq_length: int64
              kind: string
              load_in_4bit: bool
              dataset_sha256: string
              training_rows: int64
              hub_put: bool
              push_to_hub: bool
              autonomy_eligible: bool
              lambda: string
              held_out_in_gradients: bool
              does_not_overwrite: list<item: string>
                child 0, item: string
              load_in_16bit: bool
              report_to: string
              response_only_loss: bool
              weights: string
              num_train_epochs: int64
              quality: string
              quant: string
              lora_alpha: int64
              proposal_only: bool
              gpu: struct<platform: string, python: string, nvidia_smi: string, torch: string, cuda: bool, gpu_name: st (... 25 chars omitted)
                child 0, platform: string
                child 1, python: string
                child 2, nvidia_smi: string
                child 3, torch: string
                child 4, cuda: bool
                child 5, gpu_name: string
                child 6, gpu_mem_gb: double
              seed: int64
              sku: string
              lora_r: int64
              warmup_steps: int64
              claim_boundary: string
              qlora: bool
              to
              {'kind': Value('string'), 'schema': Value('string'), 'v': Value('int64'), 'artifact': Value('string'), 'sku': Value('string'), 'does_not_overwrite': List(Value('string')), 'canonical_base': Value('string'), 'base_model': Value('string'), 'qlora': Value('bool'), 'load_in_4bit': Value('bool'), 'load_in_16bit': Value('bool'), 'quant': Value('string'), 'lora_r': Value('int64'), 'lora_alpha': Value('int64'), 'seed': Value('int64'), 'num_train_epochs': Value('int64'), 'warmup_steps': Value('int64'), 'learning_rate': Value('float64'), 'lr_scheduler_type': Value('string'), 'optim': Value('string'), 'response_only_loss': Value('bool'), 'max_seq_length': Value('int64'), 'dataset_file': Value('string'), 'dataset_sha256': Value('string'), 'held_out_in_gradients': Value('bool'), 'raw_graph_nodes_admitted_to_gradients': Value('int64'), 'public_chunk_count': Value('int64'), 'push_to_hub': Value('bool'), 'trackio': Value('bool'), 'report_to': Value('string'), 'weights': Value('string'), 'adapterSha256': Value('string'), 'finalTrainLoss': Value('string'), 'train_loss_label': Value('string'), 'evals': Value('string'), 'quality': Value('string'), 'lambda': Value('string'), 'doctrine': Value('string'), 'proposal_only': Value('bool'), 'publication_eligible': Value('bool'), 'autonomy_eligible': Value('bool'), 'hub_put': Value('bool'), 'training_rows': Value('int64'), 'reason': Value('null'), 'gpu': {'platform': Value('string'), 'python': Value('string'), 'nvidia_smi': Value('string'), 'torch': Value('string'), 'cuda': Value('bool'), 'gpu_name': Value('string'), 'gpu_mem_gb': Value('float64')}, 'claim_boundary': Value('string'), 'computed_at': Value('string'), 'source': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

kind
string
schema
string
v
int64
artifact
string
sku
string
does_not_overwrite
list
canonical_base
string
base_model
string
qlora
bool
load_in_4bit
bool
load_in_16bit
bool
quant
string
lora_r
int64
lora_alpha
int64
seed
int64
num_train_epochs
int64
warmup_steps
int64
learning_rate
float64
lr_scheduler_type
string
optim
string
response_only_loss
bool
max_seq_length
int64
dataset_file
string
dataset_sha256
string
held_out_in_gradients
bool
raw_graph_nodes_admitted_to_gradients
int64
public_chunk_count
int64
push_to_hub
bool
trackio
bool
report_to
string
weights
string
adapterSha256
string
finalTrainLoss
string
train_loss_label
string
evals
string
quality
string
lambda
string
doctrine
string
proposal_only
bool
publication_eligible
bool
autonomy_eligible
bool
hub_put
bool
training_rows
int64
reason
null
gpu
dict
claim_boundary
string
computed_at
string
source
string
szl-brain-navigator-r2-training-receipt
szl.frontier-training-run/v1
1
SZLHOLDINGS/brain-navigator-r2
BRAIN-NAVIGATOR-R2
[ "SZLHOLDINGS/SZL-Khipu-1.5B-BrainNavigator", "SZLHOLDINGS/SZL-Khipu-1.5B", "SZLHOLDINGS/SZL-Khipu-1.5B-GGUF" ]
Qwen/Qwen3.5-0.8B
Qwen/Qwen3.5-0.8B
false
false
true
bf16-lora
16
32
11
3
6
0.0002
constant_with_warmup
adamw_8bit
true
2,048
train/train.jsonl
198cae10d737fd651cfe15be1ed334ba94432c3873509ddd755254300946cd4a
false
0
575
false
false
none
LOCAL
cf227a67fd97bcf3cee1469ee18c491be2ae89fe89d1eefce2cd7b0556a8bec8
0.1805
MEASURED
none-this-run
UNAVAILABLE
Conjecture 1
v11 LOCKED 749/14/163
true
false
false
false
24
null
{ "platform": "Windows-10-10.0.26200-SP0", "python": "3.11.9", "nvidia_smi": "NVIDIA GeForce RTX 5050 Laptop GPU, 8151 MiB, 7910 MiB, 610.47", "torch": "2.10.0+cu128", "cuda": true, "gpu_name": "NVIDIA GeForce RTX 5050 Laptop GPU", "gpu_mem_gb": 7.96 }
Separate SKU SZLHOLDINGS/brain-navigator-r2. Does not overwrite the 1.5B BrainNavigator. Train loss is not eval. publication_eligible false until MEASURED generate. Curriculum is synthetic routing over PUBLIC 575-chunk handles. Raw 9464-node graph admitted to gradients = 0. Λ = Conjecture 1.
2026-08-29T13:15:26.502558+00:00
local-train

Receipted Unsloth

How SZL Holdings actually trains. Silhouette from Unsloth QLoRA. Cut is original SZL. We do not republish Unsloth Studio, Desktop, copy, code, or someone else's tensors.

Collection: Receipted Unsloth — LIVE

The house loop

  1. Disclose the Apache base (Qwen/Qwen2.5-* or Qwen/Qwen3.5-0.8B).
  2. Train with Unsloth FastLanguageModel QLoRA on owner metal or HF Jobs (uv run + HF_TOKEN).
  3. Bind dataset SHA-256, LoRA knobs, seed, and loss into a training receipt.
  4. Push adapter or merge only with that receipt. No receipt = ROADMAP, not a model.
  5. Serve GGUF on szl-model-inference-lab. szl-forge-lab is a SNAPSHOT evidence console, not a trainer.
  6. Weights propose (decision=DRAFT, approvalRequired=true, executed=false). A controller outside the weights gates. A human approves.

Repeated knobs (MEASURED in scripts, not a performance claim)

Knob House value
seed 11
LoRA r / alpha 16 / 32
lr 2e-4
optim adamw_8bit
loss response-only CE (Chaski)
doctrine rows 41 MEASURED in SZLHOLDINGS/szl-1-doctrine-sft
Λ Conjecture 1, never a theorem
doctrine v11 LOCKED 749/14/163

Source kit: szl-holdings/szl-forge (train_szl.py, Unsloth QLoRA, GGUF rebirth). Hub Jobs scripts: SZLHOLDINGS/chaski/train_chaski.py.

LIVE vs CUTTING (2026-08-28)

Artifact Class Evidence
SZL-Forge-1.5B-ReceiptAgent LIVE weights owner-signed train/eval receipts
SZL-Khipu-1.5B LIVE weights trained 2026-07-14 on betterwithage, loss 0.0245 REPORTED
SZL-Khipu-1.5B-GGUF LIVE derivative served on inference-lab, sha256 matched
szl-receiptagent-qwen35-0.8b-v2 LIVE adapter Unsloth tag; 5/5 + 6/6 on a tiny owner gate MEASURED for that gate only
chaski CUTTING Jobs retry 6a91ba00 RUNNING on a10g-small. Adapter not uploaded. No eval this run.
KHIPU-R2 / A11OY-MINI / WILLAY Hub model CUTTING / ROADMAP cards, not weights

HF Jobs exist under org SZLHOLDINGS. A job id is not MEASURED training. Trackio on the personal free account returned HTTP 402; logs fell back local. Next runs should pin Trackio to the org Team plan, not @betterwithage.

What we refuse

  • Unsloth Studio / Desktop UI, Tauri wrapper, dual-dialect /v1/messages clone.
  • Relabeling Khipu GGUF as Chaski.
  • Fake tokens/s. ReceiptAgent 28 tok / 16.274s stays on that card only.
  • Bricklayer / claude.ai / NIM console chrome.
  • Invented evals. evals: none-this-run until a held-out k/n exists.

Famous on purpose

Hub visitors should land on receipted weights they can run, not 30 empty cards. Pin this collection. Pin killinchu and a11oy as the two shipping flagships. When Chaski finishes with a receipt, add it here. Until then it stays CUTTING.
Downloads last month
44
Total file size:
42.6 kB

Collection including SZLHOLDINGS/receipted-unsloth