This model is a fine-tuned version of Snowflake/Arctic-Text2SQL-R1-7B on the NL2SQL++ v5.7.8.1_arctic_stage_2 dataset with code-with-thought reasoning.

Model Details

Base Model: Snowflake/Arctic-Text2SQL-R1-7B
Task: Text-to-SQL generation
Dataset: NL2SQL++ v5.7.8.1_arctic_stage_2 with code-with-thought reasoning
Fine-tuning Method: LoRA (Low-Rank Adaptation) with Unsloth
Quantization: 16-bit merged weights
Training Dataset Size: 38727 examples
Validation Dataset Size: 0 examples

Training Configuration

output_dir: saved_models/stage_2
per_device_train_batch_size: 8
num_train_epochs: 3
max_steps: -1
learning_rate: 2e-06
lr_scheduler_type: SchedulerType.COSINE
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: OptimizerNames.ADAMW_TORCH_FUSED
optim_args: None
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 2
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: True
fp16_full_eval: False
tf32: None
gradient_checkpointing: True
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
logging_strategy: IntervalStrategy.STEPS
logging_steps: 3
logging_first_step: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
report_to: ['wandb']
run_name: None
project: huggingface
trackio_space_id: trackio
eval_strategy: IntervalStrategy.STEPS
eval_steps: 50
eval_delay: 0
per_device_eval_batch_size: 5
prediction_loss_only: False
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: 10
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_strategy: SaveStrategy.BEST
save_steps: 50
save_on_each_node: False
save_total_limit: 1
enable_jit_checkpoint: False
push_to_hub: False
hub_token: None
hub_private_repo: None
hub_model_id: None
hub_strategy: HubStrategy.EVERY_SAVE
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
metric_for_best_model: eval_exec_accuracy
greater_is_better: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False)
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: None
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_train: False
do_eval: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: 0.1
logging_dir: None
local_rank: -1
model_init_kwargs: None
chat_template_path: None
dataset_text_field: text
dataset_kwargs: None
dataset_num_proc: None
eos_token: None
pad_token: None
max_length: 12000
packing: False
packing_strategy: bfd
padding_free: False
pad_to_multiple_of: None
eval_packing: None
completion_only_loss: None
assistant_only_loss: False
loss_type: nll
activation_offloading: False
vllm_sampling_params: None
unsloth_num_chunks: -1
unsloth_logit_chunk_multiplier: None
unsloth_grpo_mini_batch: None
max_seq_length: 12000
model_name: Snowflake/Arctic-Text2SQL-R1-7B
model_specs: ModelSpecs(instruction_part='<|im_start|>user', response_part='<|im_start|>assistant', target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'], chat_template=None)
lora_use_rslora: True
lora_r: 128
lora_alpha: 256
lora_dropout: 0.1
early_stopping_patience: 5

Train Dataset Example

<|im_start|>system
You are a Couchbase SQL++ query expert. Given a database schema and a natural language question, generate a syntactically valid SQL++ query that precisely answers the question.

Rules:
- SELECT only the columns explicitly asked for — nothing more, nothing less
- Use the exact bucket, scope, and collection names provided in the schema

Output Format:
In your answer, please enclose the generated SQL++ query in a code block:

-- Your SQL query

<|im_end|>
<|im_start|>user
Bucket Name:
`bird_training_bucket`

Scope Name:
`books`

Database Schema:
{"`bird_training_bucket`.`books`.`order_line`": {"properties": {"book_id": {"samples": [3103], "type": "number"}, "line_id": {"samples": [1849], "type": "number"}, "order_id": {"samples": [306], "type": "number"}, "price": {"samples": [3.68], "type": "number"}}, "type": "object"}, "`bird_training_bucket`.`books`.`order_history`": {"properties": {"history_id": {"samples": [1203], "type": "number"}, "order_id": {"samples": [515], "type": "number"}, "status_date": {"samples": ["2020-04-15 00:45:35"], "type": "string"}, "status_id": {"samples": [1], "type": "number"}}, "type": "object"}, "`bird_training_bucket`.`books`.`sqlite_sequence`": {"properties": {"name": {"samples": ["cust_order"], "type": "string"}, "seq": {"samples": [8573], "type": "number"}}, "type": "object"}, "`bird_training_bucket`.`books`.`cust_order`": {"properties": {"customer_id": {"samples": [184], "type": "number"}, "dest_address_id": {"samples": [50], "type": "number"}, "order_date": {"samples": ["2019-12-10 07:09:28"], "type": "string"}, "order_id": {"samples": [1465], "type": "number"}, "shipping_method_id": {"samples": [1], "type": "number"}}, "type": "object"}, "`bird_training_bucket`.`books`.`shipping_method`": {"properties": {"cost": {"samples": [5.9], "type": "number"}, "method_id": {"samples": [1], "type": "number"}, "method_name": {"samples": ["Express"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`book_author`": {"properties": {"author_id": {"samples": [290], "type": "number"}, "book_id": {"samples": [680], "type": "number"}}, "type": "object"}, "`bird_training_bucket`.`books`.`book`": {"properties": {"book_id": {"samples": [1267], "type": "number"}, "isbn13": {"samples": ["9780140620122"], "type": "string"}, "language_id": {"samples": [1], "type": "number"}, "num_pages": {"samples": [279], "type": "number"}, "publication_date": {"samples": ["1982-05-06"], "type": "string"}, "publisher_id": {"samples": [282], "type": "number"}, "title": {"samples": ["Must Love Dragons (Immortally ..."], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`publisher`": {"properties": {"publisher_id": {"samples": [1304], "type": "number"}, "publisher_name": {"samples": ["Museum of Modern Art"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`order_status`": {"properties": {"status_id": {"samples": [1], "type": "number"}, "status_value": {"samples": ["Cancelled"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`customer_address`": {"properties": {"address_id": {"samples": [130], "type": "number"}, "customer_id": {"samples": [460], "type": "number"}, "status_id": {"samples": [1], "type": "number"}}, "type": "object"}, "`bird_training_bucket`.`books`.`customer`": {"properties": {"customer_id": {"samples": [143], "type": "number"}, "email": {"samples": ["fbachellier7p@wired.com"], "type": "string"}, "first_name": {"samples": ["Fonzie"], "type": "string"}, "last_name": {"samples": ["Bachellier"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`address`": {"properties": {"address_id": {"samples": [121], "type": "number"}, "city": {"samples": ["Dokolo"], "type": "string"}, "country_id": {"samples": [42], "type": "number"}, "street_name": {"samples": ["Del Mar Trail"], "type": "string"}, "street_number": {"samples": ["0"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`country`": {"properties": {"country_id": {"samples": [1], "type": "number"}, "country_name": {"samples": ["Afghanistan"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`book_language`": {"properties": {"language_code": {"samples": ["fre"], "type": "string"}, "language_id": {"samples": [3], "type": "number"}, "language_name": {"samples": ["French"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`author`": {"properties": {"author_id": {"samples": [732], "type": "number"}, "author_name": {"samples": ["Ben Shahn"], "type": "string"}}, "type": "object"}, "`bird_training_bucket`.`books`.`address_status`": {"properties": {"address_status": {"samples": ["Active"], "type": "string"}, "status_id": {"samples": [1], "type": "number"}}, "type": "object"}}

This schema describes the structure of the data in the specified bucket and scope. It includes information about the collections, fields, and their data types.

Question:

How many books were ordered by customer Kandy Adamec?
<|im_end|>
<|im_start|>assistant

SELECT COUNT(*) FROM bird_training_bucket.books.order_line AS T1 INNER JOIN bird_training_bucket.books.cust_order AS T2 ON T2.order_id = T1.order_id INNER JOIN bird_training_bucket.books.customer AS T3 ON T3.customer_id = T2.customer_id WHERE T3.first_name = 'Kandy' AND T3.last_name = 'Adamec'

<|im_end|>

jastorj/couchmind-v5.7.8.1_arctic_stage_2-cw-12K-16bit

Snowflake/Arctic-Text2SQL-R1-7B Fine-tuned for NL2SQL++ v5.7.8.1_arctic_stage_2

Model Details

Training Configuration

Train Dataset Example

jastorj/
couchmind-v5.7.8.1_arctic_stage_2-cw-12K-16bit