---
license: apache-2.0
language:
- en
tags:
- text-to-sql
- code
- sql
- fine-tuned
- unsloth
- lora
base_model: Snowflake/Arctic-Text2SQL-R1-7B
---

# Snowflake/Arctic-Text2SQL-R1-7B Fine-tuned for NL2SQL++ v5.8_rl_cold_start

This model is a fine-tuned version of [Snowflake/Arctic-Text2SQL-R1-7B](https://huggingface.co/Snowflake/Arctic-Text2SQL-R1-7B) on the NL2SQL++ v5.8_rl_cold_start dataset with code-with-thought reasoning.

## Model Details

- **Base Model**: Snowflake/Arctic-Text2SQL-R1-7B
- **Task**: Text-to-SQL generation
- **Dataset**: NL2SQL++ v5.8_rl_cold_start with code-with-thought reasoning
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) with Unsloth
- **Quantization**: 16-bit merged weights
- **Training Dataset Size**: 2104 examples
- **Validation Dataset Size**: 0 examples

## Training Configuration

- **output_dir**: ./saved_models
- **per_device_train_batch_size**: 2
- **num_train_epochs**: 3
- **max_steps**: -1
- **learning_rate**: 1e-05
- **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**: 8
- **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**: 26000
- **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**: 26000
- **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**: 64
- **lora_alpha**: 128
- **lora_dropout**: 0.1
- **early_stopping_patience**: 5


## Train Dataset Example
```
<|im_start|>system
You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first
think about the reasoning process as an internal monologue and then provide the user with
the answer. Respond in the following format: <think>
...
</think>
<answer>
...
</answer>
<|im_end|>
<|im_start|>user
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 database schema

Bucket Name:
`nan`

Scope Name:
`nan`

Database Schema:
{}

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:
Build the special_flights list with two objects and return the values from each object.
<|im_end|>
<|im_start|>assistant
<think>
I start by noticing there is no schema at all. So this is not a normal “read from a collection” question. There are no buckets, scopes, collections, or existing fields to join together. That means I need to construct the data inline inside the query itself.

The wording is: “Build the special_flights list with two objects and return the values from each object.”

A few pieces matter here.

First, “build the special_flights list” suggests I should create a named list, not just throw an anonymous array into the SELECT. In SQL++ / N1QL, a clean way to do that is a WITH binding. So I want a common expression named special_flights.

Second, it says “with two objects.” So special_flights should be an array containing exactly two object literals.

Since there is no schema, I have to invent the object structure and values myself. The list name special_flights makes flight-oriented fields feel natural. I need a clear identifier for each object, so using a field named flight makes sense. I also want some time-like field; utc is a natural label for that. Then I can add one more descriptive field to each object to make them feel like richer objects rather than just two identical key sets.

Now I need actual literal values. Because these are made-up demo rows, I want them to be memorable and obviously different. For the first one, a flight value like AI444 is easy to recognize, and I can pair it with a matching time-like string 4:44:44. For the extra descriptive field, codename with value green works well. For the second object, I can mirror that pattern with flight AI333 and utc 3:33:33, then use a different status-style key alert with value red. That also shows that the two objects do not have to share every non-core field, which is interesting for an object-values operation.

So the array I am binding to special_flights is two objects:
- one with flight AI444, utc 4:44:44, codename green
- one with flight AI333, utc 3:33:33, alert red

Next I need to “return the values from each object.” I should think about what that means in N1QL terms.

One possibility is to UNNEST the array and call OBJECT_VALUES on each object individually, which would give one row per object. But the question says “build the special_flights list” first, and then “return the values from each object,” which sounds more like operating on the list as a whole rather than exploding it into separate rows. So I lean toward applying an object-values function to the list’s contents directly.

The N1QL function for extracting an object’s values is OBJECT_VALUES. To apply it to the objects inside the array, I need the array-star form special_flights[*]. That notation refers to the objects in the list. Using OBJECT_VALUES on that expression gives me the values drawn from the objects in the list.

Since the SELECT is just returning that one derived expression, I should give it an alias. A name like outer_values fits, because I am returning the values produced from the outer list binding.

There is also no need for a FROM clause, because I am not querying any stored collection; everything comes from the WITH-defined array literal.

So the final shape is: a WITH clause naming special_flights and assigning it the two-object array, then a SELECT that applies OBJECT_VALUES to special_flights[*] and aliases the result as outer_values.
</think>

<answer>
WITH special_flights AS ([{"flight":"AI444","utc":"4:44:44","codename":"green"},{"flight":"AI333","utc":"3:33:33","alert":"red"}]) SELECT OBJECT_VALUES(special_flights[*]) AS outer_values;
</answer>
<|im_end|>

```