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Model: jastorj/couchmind-v5.8_rl_cold_start-cw-26K-16bit Source: Original Platform
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242
README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- text-to-sql
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- code
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- sql
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- fine-tuned
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- unsloth
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- lora
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base_model: Snowflake/Arctic-Text2SQL-R1-7B
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---
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# Snowflake/Arctic-Text2SQL-R1-7B Fine-tuned for NL2SQL++ v5.8_rl_cold_start
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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.
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## Model Details
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- **Base Model**: Snowflake/Arctic-Text2SQL-R1-7B
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- **Task**: Text-to-SQL generation
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- **Dataset**: NL2SQL++ v5.8_rl_cold_start with code-with-thought reasoning
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- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) with Unsloth
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- **Quantization**: 16-bit merged weights
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- **Training Dataset Size**: 2104 examples
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||||
- **Validation Dataset Size**: 0 examples
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||||
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## Training Configuration
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||||
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- **output_dir**: ./saved_models
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||||
- **per_device_train_batch_size**: 2
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||||
- **num_train_epochs**: 3
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||||
- **max_steps**: -1
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- **learning_rate**: 1e-05
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||||
- **lr_scheduler_type**: SchedulerType.COSINE
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- **lr_scheduler_kwargs**: None
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||||
- **warmup_steps**: 0.1
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- **optim**: OptimizerNames.ADAMW_TORCH_FUSED
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- **optim_args**: None
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||||
- **weight_decay**: 0.01
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||||
- **adam_beta1**: 0.9
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||||
- **adam_beta2**: 0.999
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||||
- **adam_epsilon**: 1e-08
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||||
- **optim_target_modules**: None
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||||
- **gradient_accumulation_steps**: 8
|
||||
- **average_tokens_across_devices**: True
|
||||
- **max_grad_norm**: 1.0
|
||||
- **label_smoothing_factor**: 0.0
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||||
- **bf16**: True
|
||||
- **fp16**: False
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||||
- **bf16_full_eval**: True
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||||
- **fp16_full_eval**: False
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||||
- **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
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||||
- **logging_steps**: 3
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||||
- **logging_first_step**: False
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||||
- **log_on_each_node**: True
|
||||
- **logging_nan_inf_filter**: True
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||||
- **include_num_input_tokens_seen**: no
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||||
- **log_level**: passive
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||||
- **log_level_replica**: warning
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||||
- **disable_tqdm**: False
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||||
- **report_to**: ['wandb']
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||||
- **run_name**: None
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||||
- **project**: huggingface
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||||
- **trackio_space_id**: trackio
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||||
- **eval_strategy**: IntervalStrategy.STEPS
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||||
- **eval_steps**: 50
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||||
- **eval_delay**: 0
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||||
- **per_device_eval_batch_size**: 5
|
||||
- **prediction_loss_only**: False
|
||||
- **eval_on_start**: False
|
||||
- **eval_do_concat_batches**: True
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||||
- **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|>
|
||||
|
||||
```
|
||||
54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
61
config.json
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61
config.json
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|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": null,
|
||||
"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
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"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
147
train_conf.json
Normal file
147
train_conf.json
Normal file
@@ -0,0 +1,147 @@
|
||||
{
|
||||
"output_dir": "./saved_models",
|
||||
"per_device_train_batch_size": 2,
|
||||
"num_train_epochs": 3,
|
||||
"max_steps": -1,
|
||||
"learning_rate": 1e-05,
|
||||
"lr_scheduler_type": "cosine",
|
||||
"lr_scheduler_kwargs": null,
|
||||
"warmup_steps": 0.1,
|
||||
"optim": "adamw_torch_fused",
|
||||
"optim_args": null,
|
||||
"weight_decay": 0.01,
|
||||
"adam_beta1": 0.9,
|
||||
"adam_beta2": 0.999,
|
||||
"adam_epsilon": 1e-08,
|
||||
"optim_target_modules": null,
|
||||
"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": null,
|
||||
"gradient_checkpointing": true,
|
||||
"gradient_checkpointing_kwargs": null,
|
||||
"torch_compile": false,
|
||||
"torch_compile_backend": null,
|
||||
"torch_compile_mode": null,
|
||||
"use_liger_kernel": false,
|
||||
"liger_kernel_config": null,
|
||||
"use_cache": false,
|
||||
"neftune_noise_alpha": null,
|
||||
"torch_empty_cache_steps": null,
|
||||
"auto_find_batch_size": false,
|
||||
"logging_strategy": "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": null,
|
||||
"project": "huggingface",
|
||||
"trackio_space_id": "trackio",
|
||||
"eval_strategy": "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": "best",
|
||||
"save_steps": 50,
|
||||
"save_on_each_node": false,
|
||||
"save_total_limit": 1,
|
||||
"enable_jit_checkpoint": false,
|
||||
"push_to_hub": false,
|
||||
"hub_token": null,
|
||||
"hub_private_repo": null,
|
||||
"hub_model_id": null,
|
||||
"hub_strategy": "every_save",
|
||||
"hub_always_push": false,
|
||||
"hub_revision": null,
|
||||
"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": null,
|
||||
"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": null,
|
||||
"dataloader_drop_last": false,
|
||||
"dataloader_num_workers": 0,
|
||||
"dataloader_pin_memory": true,
|
||||
"dataloader_persistent_workers": false,
|
||||
"dataloader_prefetch_factor": null,
|
||||
"remove_unused_columns": true,
|
||||
"label_names": null,
|
||||
"train_sampling_strategy": "random",
|
||||
"length_column_name": "length",
|
||||
"ddp_find_unused_parameters": null,
|
||||
"ddp_bucket_cap_mb": null,
|
||||
"ddp_broadcast_buffers": null,
|
||||
"ddp_backend": null,
|
||||
"ddp_timeout": 1800,
|
||||
"fsdp": [],
|
||||
"fsdp_config": {
|
||||
"min_num_params": 0,
|
||||
"xla": false,
|
||||
"xla_fsdp_v2": false,
|
||||
"xla_fsdp_grad_ckpt": false
|
||||
},
|
||||
"deepspeed": null,
|
||||
"debug": [],
|
||||
"skip_memory_metrics": true,
|
||||
"do_train": false,
|
||||
"do_eval": true,
|
||||
"do_predict": false,
|
||||
"resume_from_checkpoint": null,
|
||||
"warmup_ratio": 0.1,
|
||||
"logging_dir": null,
|
||||
"local_rank": -1,
|
||||
"model_init_kwargs": null,
|
||||
"chat_template_path": null,
|
||||
"dataset_text_field": "text",
|
||||
"dataset_kwargs": null,
|
||||
"dataset_num_proc": null,
|
||||
"eos_token": null,
|
||||
"pad_token": null,
|
||||
"max_length": 26000,
|
||||
"packing": false,
|
||||
"packing_strategy": "bfd",
|
||||
"padding_free": false,
|
||||
"pad_to_multiple_of": null,
|
||||
"eval_packing": null,
|
||||
"completion_only_loss": null,
|
||||
"assistant_only_loss": false,
|
||||
"loss_type": "nll",
|
||||
"activation_offloading": false,
|
||||
"vllm_sampling_params": null,
|
||||
"unsloth_num_chunks": -1,
|
||||
"unsloth_logit_chunk_multiplier": null,
|
||||
"unsloth_grpo_mini_batch": null,
|
||||
"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
|
||||
}
|
||||
Reference in New Issue
Block a user