Instructions to use mehul79/Text2SQL-4B-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mehul79/Text2SQL-4B-Model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "mehul79/Text2SQL-4B-Model") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Add Spider SQL LoRA adapter
Browse files- .gitattributes +1 -0
- README.md +85 -0
- adapter_config.json +47 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +154 -0
- tokenizer.json +3 -0
- tokenizer_config.json +32 -0
- train_log.jsonl +95 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
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| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
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| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
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| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
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| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
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| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
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| 36 |
+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
README.md
ADDED
|
@@ -0,0 +1,85 @@
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| 1 |
+
---
|
| 2 |
+
base_model: Qwen/Qwen3.5-4B
|
| 3 |
+
library_name: peft
|
| 4 |
+
pipeline_tag: text-generation
|
| 5 |
+
datasets:
|
| 6 |
+
- spider
|
| 7 |
+
tags:
|
| 8 |
+
- base_model:adapter:Qwen/Qwen3.5-4B
|
| 9 |
+
- lora
|
| 10 |
+
- text-to-sql
|
| 11 |
+
- tool-calling
|
| 12 |
+
- unsloth
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
# Qwen3.5-4B Spider text-to-SQL LoRA
|
| 16 |
+
|
| 17 |
+
A LoRA adapter for [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) that writes SQLite queries
|
| 18 |
+
from English questions. It was trained on tool-calling traces built from the
|
| 19 |
+
[Spider](https://yale-lily.github.io/spider) training set.
|
| 20 |
+
|
| 21 |
+
Code to rebuild the data, retrain, and run the benchmarks:
|
| 22 |
+
[Self-healing-Text2SQL-Agent](https://github.com/mehul79/Self-healing-Text2SQL-Agent) (`training/` and `backend/eval/`).
|
| 23 |
+
|
| 24 |
+
## Results
|
| 25 |
+
|
| 26 |
+
Spider dev, execution accuracy (%), all 1,034 questions, official Spider evaluator.
|
| 27 |
+
Both models run inside the Self-healing Text2SQL harness (LangGraph: generate, validate, execute, repair), temperature 0.
|
| 28 |
+
|
| 29 |
+
| Model | Easy | Medium | Hard | Extra | All |
|
| 30 |
+
|---|---|---|---|---|---|
|
| 31 |
+
| Qwen3.5-4B base, first attempt | 87.1 | 78.0 | 56.9 | 45.2 | 71.4 |
|
| 32 |
+
| Qwen3.5-4B base, with repair | 86.7 | 80.5 | 57.5 | 50.0 | 73.2 |
|
| 33 |
+
| **This adapter, first attempt** | 89.9 | 77.4 | 74.1 | 51.8 | **75.7** |
|
| 34 |
+
| **This adapter, with repair** | 90.3 | 80.0 | 74.7 | 53.0 | **77.3** |
|
| 35 |
+
|
| 36 |
+
Most of the gain is on hard questions (+17 points).
|
| 37 |
+
|
| 38 |
+
## Training
|
| 39 |
+
|
| 40 |
+
Every training example has the same three-step tool-calling shape:
|
| 41 |
+
|
| 42 |
+
1. `get_schema` returns the database's `CREATE TABLE` statements.
|
| 43 |
+
2. `execute_sql` runs the gold query and returns up to 10 rows.
|
| 44 |
+
3. `submit_answer` submits the gold query.
|
| 45 |
+
|
| 46 |
+
The traces come from Spider train. Gold queries that failed to run were dropped, leaving 6,797 traces.
|
| 47 |
+
200 were held out for validation (seed 42). Loss is computed only on the assistant turns, after an empty
|
| 48 |
+
`<think></think>` block, so the model is trained with thinking off.
|
| 49 |
+
|
| 50 |
+
| Setting | Value |
|
| 51 |
+
|---|---|
|
| 52 |
+
| Method | LoRA, bf16 (Unsloth) |
|
| 53 |
+
| Rank / alpha / dropout | 64 / 128 / 0 |
|
| 54 |
+
| Target modules | q, k, v, o, gate, up, down projections |
|
| 55 |
+
| Learning rate | 1e-4, cosine schedule, 3% warmup |
|
| 56 |
+
| Batch | 4 per device x 4 accumulation = 16 |
|
| 57 |
+
| Epochs | 2 (850 steps) |
|
| 58 |
+
| Max sequence length | 4096 |
|
| 59 |
+
| Hardware | 1 GPU, 13.7 GB peak, about 75 minutes |
|
| 60 |
+
| Final losses | train 0.009, validation 0.0165 |
|
| 61 |
+
|
| 62 |
+
## Usage
|
| 63 |
+
|
| 64 |
+
Serve the base model with the adapter in vLLM, with thinking off:
|
| 65 |
+
|
| 66 |
+
```bash
|
| 67 |
+
vllm serve Qwen/Qwen3.5-4B --enable-lora \
|
| 68 |
+
--lora-modules text2sql-sft=mehul79/Text2SQL-4B-Model \
|
| 69 |
+
--enable-auto-tool-choice --tool-call-parser qwen3_coder \
|
| 70 |
+
--reasoning-parser qwen3 \
|
| 71 |
+
--default-chat-template-kwargs '{"enable_thinking": false}' \
|
| 72 |
+
--language-model-only --max-model-len 16384
|
| 73 |
+
```
|
| 74 |
+
|
| 75 |
+
Or merge it into the base model with PEFT (`PeftModel.from_pretrained(base, "mehul79/Text2SQL-4B-Model").merge_and_unload()`)
|
| 76 |
+
and serve the merged weights.
|
| 77 |
+
|
| 78 |
+
## Limitations
|
| 79 |
+
|
| 80 |
+
- Trained only on Spider and SQLite. Other SQL dialects and real-world schemas are untested.
|
| 81 |
+
- The validation split comes from Spider train, so the validation loss says little about new databases;
|
| 82 |
+
the Spider dev results above are the real measure.
|
| 83 |
+
- Every training trace has one fixed shape and contains no errors, so the model never saw a failed query
|
| 84 |
+
being fixed during training.
|
| 85 |
+
- It sometimes writes Spider-style double-quoted string literals (`"value"`), which strict SQL parsers reject.
|
adapter_config.json
ADDED
|
@@ -0,0 +1,47 @@
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+
meh{
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| 2 |
+
"alora_invocation_tokens": null,
|
| 3 |
+
"alpha_pattern": {},
|
| 4 |
+
"arrow_config": null,
|
| 5 |
+
"auto_mapping": {
|
| 6 |
+
"base_model_class": "Qwen3_5ForConditionalGeneration",
|
| 7 |
+
"parent_library": "transformers.models.qwen3_5.modeling_qwen3_5",
|
| 8 |
+
"unsloth_fixed": true
|
| 9 |
+
},
|
| 10 |
+
"base_model_name_or_path": "Qwen/Qwen3.5-4B",
|
| 11 |
+
"bias": "none",
|
| 12 |
+
"corda_config": null,
|
| 13 |
+
"ensure_weight_tying": false,
|
| 14 |
+
"eva_config": null,
|
| 15 |
+
"exclude_modules": null,
|
| 16 |
+
"fan_in_fan_out": false,
|
| 17 |
+
"inference_mode": true,
|
| 18 |
+
"init_lora_weights": true,
|
| 19 |
+
"kasa_config": null,
|
| 20 |
+
"layer_replication": null,
|
| 21 |
+
"layers_pattern": null,
|
| 22 |
+
"layers_to_transform": null,
|
| 23 |
+
"loftq_config": {},
|
| 24 |
+
"lora_alpha": 128,
|
| 25 |
+
"lora_bias": false,
|
| 26 |
+
"lora_dropout": 0,
|
| 27 |
+
"lora_ga_config": null,
|
| 28 |
+
"megatron_config": null,
|
| 29 |
+
"megatron_core": "megatron.core",
|
| 30 |
+
"modules_to_save": null,
|
| 31 |
+
"monteclora_config": null,
|
| 32 |
+
"peft_type": "LORA",
|
| 33 |
+
"peft_version": "0.21.2",
|
| 34 |
+
"qalora_group_size": 16,
|
| 35 |
+
"r": 64,
|
| 36 |
+
"rank_pattern": {},
|
| 37 |
+
"revision": null,
|
| 38 |
+
"target_modules": "(?:.*?(?:language|text).*?(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?\\.(?:q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj))|(?:\\bmodel\\.layers\\.[\\d]{1,}(?!.*\\.experts\\.\\d)\\..*?(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?\\.(?:(?:q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj)))",
|
| 39 |
+
"target_parameters": null,
|
| 40 |
+
"task_type": "CAUSAL_LM",
|
| 41 |
+
"trainable_token_indices": null,
|
| 42 |
+
"use_bdlora": null,
|
| 43 |
+
"use_dora": false,
|
| 44 |
+
"use_qalora": false,
|
| 45 |
+
"use_rslora": false,
|
| 46 |
+
"velora_config": null
|
| 47 |
+
}
|
adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:627be94a79260f2f7fec10738292b4e29d71a38b6fa2129db7eb931a08ff080d
|
| 3 |
+
size 339776632
|
chat_template.jinja
ADDED
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@@ -0,0 +1,154 @@
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| 1 |
+
{%- set image_count = namespace(value=0) %}
|
| 2 |
+
{%- set video_count = namespace(value=0) %}
|
| 3 |
+
{%- macro render_content(content, do_vision_count, is_system_content=false) %}
|
| 4 |
+
{%- if content is string %}
|
| 5 |
+
{{- content }}
|
| 6 |
+
{%- elif content is iterable and content is not mapping %}
|
| 7 |
+
{%- for item in content %}
|
| 8 |
+
{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
|
| 9 |
+
{%- if is_system_content %}
|
| 10 |
+
{{- raise_exception('System message cannot contain images.') }}
|
| 11 |
+
{%- endif %}
|
| 12 |
+
{%- if do_vision_count %}
|
| 13 |
+
{%- set image_count.value = image_count.value + 1 %}
|
| 14 |
+
{%- endif %}
|
| 15 |
+
{%- if add_vision_id %}
|
| 16 |
+
{{- 'Picture ' ~ image_count.value ~ ': ' }}
|
| 17 |
+
{%- endif %}
|
| 18 |
+
{{- '<|vision_start|><|image_pad|><|vision_end|>' }}
|
| 19 |
+
{%- elif 'video' in item or item.type == 'video' %}
|
| 20 |
+
{%- if is_system_content %}
|
| 21 |
+
{{- raise_exception('System message cannot contain videos.') }}
|
| 22 |
+
{%- endif %}
|
| 23 |
+
{%- if do_vision_count %}
|
| 24 |
+
{%- set video_count.value = video_count.value + 1 %}
|
| 25 |
+
{%- endif %}
|
| 26 |
+
{%- if add_vision_id %}
|
| 27 |
+
{{- 'Video ' ~ video_count.value ~ ': ' }}
|
| 28 |
+
{%- endif %}
|
| 29 |
+
{{- '<|vision_start|><|video_pad|><|vision_end|>' }}
|
| 30 |
+
{%- elif 'text' in item %}
|
| 31 |
+
{{- item.text }}
|
| 32 |
+
{%- else %}
|
| 33 |
+
{{- raise_exception('Unexpected item type in content.') }}
|
| 34 |
+
{%- endif %}
|
| 35 |
+
{%- endfor %}
|
| 36 |
+
{%- elif content is none or content is undefined %}
|
| 37 |
+
{{- '' }}
|
| 38 |
+
{%- else %}
|
| 39 |
+
{{- raise_exception('Unexpected content type.') }}
|
| 40 |
+
{%- endif %}
|
| 41 |
+
{%- endmacro %}
|
| 42 |
+
{%- if not messages %}
|
| 43 |
+
{{- raise_exception('No messages provided.') }}
|
| 44 |
+
{%- endif %}
|
| 45 |
+
{%- if tools and tools is iterable and tools is not mapping %}
|
| 46 |
+
{{- '<|im_start|>system\n' }}
|
| 47 |
+
{{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
|
| 48 |
+
{%- for tool in tools %}
|
| 49 |
+
{{- "\n" }}
|
| 50 |
+
{{- tool | tojson }}
|
| 51 |
+
{%- endfor %}
|
| 52 |
+
{{- "\n</tools>" }}
|
| 53 |
+
{{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
|
| 54 |
+
{%- if messages[0].role == 'system' %}
|
| 55 |
+
{%- set content = render_content(messages[0].content, false, true)|trim %}
|
| 56 |
+
{%- if content %}
|
| 57 |
+
{{- '\n\n' + content }}
|
| 58 |
+
{%- endif %}
|
| 59 |
+
{%- endif %}
|
| 60 |
+
{{- '<|im_end|>\n' }}
|
| 61 |
+
{%- else %}
|
| 62 |
+
{%- if messages[0].role == 'system' %}
|
| 63 |
+
{%- set content = render_content(messages[0].content, false, true)|trim %}
|
| 64 |
+
{{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
|
| 65 |
+
{%- endif %}
|
| 66 |
+
{%- endif %}
|
| 67 |
+
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
| 68 |
+
{%- for message in messages[::-1] %}
|
| 69 |
+
{%- set index = (messages|length - 1) - loop.index0 %}
|
| 70 |
+
{%- if ns.multi_step_tool and message.role == "user" %}
|
| 71 |
+
{%- set content = render_content(message.content, false)|trim %}
|
| 72 |
+
{%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
|
| 73 |
+
{%- set ns.multi_step_tool = false %}
|
| 74 |
+
{%- set ns.last_query_index = index %}
|
| 75 |
+
{%- endif %}
|
| 76 |
+
{%- endif %}
|
| 77 |
+
{%- endfor %}
|
| 78 |
+
{%- if ns.multi_step_tool %}
|
| 79 |
+
{{- raise_exception('No user query found in messages.') }}
|
| 80 |
+
{%- endif %}
|
| 81 |
+
{%- for message in messages %}
|
| 82 |
+
{%- set content = render_content(message.content, true)|trim %}
|
| 83 |
+
{%- if message.role == "system" %}
|
| 84 |
+
{%- if not loop.first %}
|
| 85 |
+
{{- raise_exception('System message must be at the beginning.') }}
|
| 86 |
+
{%- endif %}
|
| 87 |
+
{%- elif message.role == "user" %}
|
| 88 |
+
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
| 89 |
+
{%- elif message.role == "assistant" %}
|
| 90 |
+
{%- set reasoning_content = '' %}
|
| 91 |
+
{%- if message.reasoning_content is string %}
|
| 92 |
+
{%- set reasoning_content = message.reasoning_content %}
|
| 93 |
+
{%- else %}
|
| 94 |
+
{%- if '</think>' in content %}
|
| 95 |
+
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
| 96 |
+
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
| 97 |
+
{%- endif %}
|
| 98 |
+
{%- endif %}
|
| 99 |
+
{%- set reasoning_content = reasoning_content|trim %}
|
| 100 |
+
{%- if loop.index0 > ns.last_query_index %}
|
| 101 |
+
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
|
| 102 |
+
{%- else %}
|
| 103 |
+
{{- '<|im_start|>' + message.role + '\n' + content }}
|
| 104 |
+
{%- endif %}
|
| 105 |
+
{%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
|
| 106 |
+
{%- for tool_call in message.tool_calls %}
|
| 107 |
+
{%- if tool_call.function is defined %}
|
| 108 |
+
{%- set tool_call = tool_call.function %}
|
| 109 |
+
{%- endif %}
|
| 110 |
+
{%- if loop.first %}
|
| 111 |
+
{%- if content|trim %}
|
| 112 |
+
{{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
|
| 113 |
+
{%- else %}
|
| 114 |
+
{{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
|
| 115 |
+
{%- endif %}
|
| 116 |
+
{%- else %}
|
| 117 |
+
{{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
|
| 118 |
+
{%- endif %}
|
| 119 |
+
{%- if tool_call.arguments is defined %}
|
| 120 |
+
{%- for args_name, args_value in tool_call.arguments|items %}
|
| 121 |
+
{{- '<parameter=' + args_name + '>\n' }}
|
| 122 |
+
{%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
|
| 123 |
+
{{- args_value }}
|
| 124 |
+
{{- '\n</parameter>\n' }}
|
| 125 |
+
{%- endfor %}
|
| 126 |
+
{%- endif %}
|
| 127 |
+
{{- '</function>\n</tool_call>' }}
|
| 128 |
+
{%- endfor %}
|
| 129 |
+
{%- endif %}
|
| 130 |
+
{{- '<|im_end|>\n' }}
|
| 131 |
+
{%- elif message.role == "tool" %}
|
| 132 |
+
{%- if loop.previtem and loop.previtem.role != "tool" %}
|
| 133 |
+
{{- '<|im_start|>user' }}
|
| 134 |
+
{%- endif %}
|
| 135 |
+
{{- '\n<tool_response>\n' }}
|
| 136 |
+
{{- content }}
|
| 137 |
+
{{- '\n</tool_response>' }}
|
| 138 |
+
{%- if not loop.last and loop.nextitem.role != "tool" %}
|
| 139 |
+
{{- '<|im_end|>\n' }}
|
| 140 |
+
{%- elif loop.last %}
|
| 141 |
+
{{- '<|im_end|>\n' }}
|
| 142 |
+
{%- endif %}
|
| 143 |
+
{%- else %}
|
| 144 |
+
{{- raise_exception('Unexpected message role.') }}
|
| 145 |
+
{%- endif %}
|
| 146 |
+
{%- endfor %}
|
| 147 |
+
{%- if add_generation_prompt %}
|
| 148 |
+
{{- '<|im_start|>assistant\n' }}
|
| 149 |
+
{%- if enable_thinking is defined and enable_thinking is false %}
|
| 150 |
+
{{- '<think>\n\n</think>\n\n' }}
|
| 151 |
+
{%- else %}
|
| 152 |
+
{{- '<think>\n' }}
|
| 153 |
+
{%- endif %}
|
| 154 |
+
{%- endif %}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
|
| 3 |
+
size 19989325
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"audio_bos_token": "<|audio_start|>",
|
| 4 |
+
"audio_eos_token": "<|audio_end|>",
|
| 5 |
+
"audio_token": "<|audio_pad|>",
|
| 6 |
+
"backend": "tokenizers",
|
| 7 |
+
"bos_token": null,
|
| 8 |
+
"clean_up_tokenization_spaces": false,
|
| 9 |
+
"eos_token": "<|im_end|>",
|
| 10 |
+
"errors": "replace",
|
| 11 |
+
"image_token": "<|image_pad|>",
|
| 12 |
+
"is_local": true,
|
| 13 |
+
"local_files_only": false,
|
| 14 |
+
"model_max_length": 262144,
|
| 15 |
+
"model_specific_special_tokens": {
|
| 16 |
+
"audio_bos_token": "<|audio_start|>",
|
| 17 |
+
"audio_eos_token": "<|audio_end|>",
|
| 18 |
+
"audio_token": "<|audio_pad|>",
|
| 19 |
+
"image_token": "<|image_pad|>",
|
| 20 |
+
"video_token": "<|video_pad|>",
|
| 21 |
+
"vision_bos_token": "<|vision_start|>",
|
| 22 |
+
"vision_eos_token": "<|vision_end|>"
|
| 23 |
+
},
|
| 24 |
+
"pad_token": "<|endoftext|>",
|
| 25 |
+
"pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
| 26 |
+
"split_special_tokens": false,
|
| 27 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 28 |
+
"unk_token": null,
|
| 29 |
+
"video_token": "<|video_pad|>",
|
| 30 |
+
"vision_bos_token": "<|vision_start|>",
|
| 31 |
+
"vision_eos_token": "<|vision_end|>"
|
| 32 |
+
}
|
train_log.jsonl
ADDED
|
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"step": 10, "epoch": 0.023529411764705882, "time": 1793036132.370058, "loss": 0.22702066898345946, "grad_norm": 0.6640090942382812, "learning_rate": 3.6e-05, "gpu_mem_gb": 13.36}
|
| 2 |
+
{"step": 20, "epoch": 0.047058823529411764, "time": 1793036185.654046, "loss": 0.06938545107841491, "grad_norm": 0.47914600372314453, "learning_rate": 7.6e-05, "gpu_mem_gb": 13.5}
|
| 3 |
+
{"step": 30, "epoch": 0.07058823529411765, "time": 1793036236.498706, "loss": 0.0575491726398468, "grad_norm": 0.27023765444755554, "learning_rate": 9.999419979367214e-05, "gpu_mem_gb": 13.5}
|
| 4 |
+
{"step": 40, "epoch": 0.09411764705882353, "time": 1793036285.0721498, "loss": 0.05584830045700073, "grad_norm": 0.3506012260913849, "learning_rate": 9.992896292597229e-05, "gpu_mem_gb": 13.5}
|
| 5 |
+
{"step": 50, "epoch": 0.11764705882352941, "time": 1793036338.9749413, "loss": 0.046492868661880495, "grad_norm": 0.30387771129608154, "learning_rate": 9.97913338352859e-05, "gpu_mem_gb": 13.5}
|
| 6 |
+
{"step": 60, "epoch": 0.1411764705882353, "time": 1793036387.087952, "loss": 0.05704301595687866, "grad_norm": 0.5013608336448669, "learning_rate": 9.958151207063704e-05, "gpu_mem_gb": 13.5}
|
| 7 |
+
{"step": 70, "epoch": 0.16470588235294117, "time": 1793036437.4532344, "loss": 0.04895034432411194, "grad_norm": 0.2968132197856903, "learning_rate": 9.929980185352526e-05, "gpu_mem_gb": 13.5}
|
| 8 |
+
{"step": 80, "epoch": 0.18823529411764706, "time": 1793036485.3591592, "loss": 0.044801142811775205, "grad_norm": 0.2740367650985718, "learning_rate": 9.894661163683361e-05, "gpu_mem_gb": 13.5}
|
| 9 |
+
{"step": 90, "epoch": 0.21176470588235294, "time": 1793036540.420378, "loss": 0.04286765456199646, "grad_norm": 0.13535021245479584, "learning_rate": 9.852245351261099e-05, "gpu_mem_gb": 13.52}
|
| 10 |
+
{"step": 100, "epoch": 0.23529411764705882, "time": 1793036588.9007075, "loss": 0.047144800424575806, "grad_norm": 0.2513267695903778, "learning_rate": 9.802794246958759e-05, "gpu_mem_gb": 13.52}
|
| 11 |
+
{"step": 100, "epoch": 0.23529411764705882, "time": 1793036604.6842003, "eval_loss": 0.035334281623363495, "eval_runtime": 15.7815, "eval_samples_per_second": 6.337, "eval_steps_per_second": 1.584, "gpu_mem_gb": 13.52}
|
| 12 |
+
{"step": 110, "epoch": 0.25882352941176473, "time": 1793036651.1330924, "loss": 0.03909997045993805, "grad_norm": 0.2486964613199234, "learning_rate": 9.746379550150009e-05, "gpu_mem_gb": 13.54}
|
| 13 |
+
{"step": 120, "epoch": 0.2823529411764706, "time": 1793036699.6009562, "loss": 0.038081982731819154, "grad_norm": 0.23092591762542725, "learning_rate": 9.68308305675192e-05, "gpu_mem_gb": 13.54}
|
| 14 |
+
{"step": 130, "epoch": 0.3058823529411765, "time": 1793036749.0410173, "loss": 0.03753519952297211, "grad_norm": 0.2653861939907074, "learning_rate": 9.612996540628718e-05, "gpu_mem_gb": 13.54}
|
| 15 |
+
{"step": 140, "epoch": 0.32941176470588235, "time": 1793036797.9871385, "loss": 0.03749722242355347, "grad_norm": 0.21498343348503113, "learning_rate": 9.536221620528442e-05, "gpu_mem_gb": 13.54}
|
| 16 |
+
{"step": 150, "epoch": 0.35294117647058826, "time": 1793036845.2672334, "loss": 0.031845000386238095, "grad_norm": 0.21117058396339417, "learning_rate": 9.452869612745483e-05, "gpu_mem_gb": 13.54}
|
| 17 |
+
{"step": 160, "epoch": 0.3764705882352941, "time": 1793036896.005399, "loss": 0.033359569311141965, "grad_norm": 0.20872750878334045, "learning_rate": 9.363061369722595e-05, "gpu_mem_gb": 13.54}
|
| 18 |
+
{"step": 170, "epoch": 0.4, "time": 1793036952.2341025, "loss": 0.036821970343589784, "grad_norm": 0.24200093746185303, "learning_rate": 9.266927104826409e-05, "gpu_mem_gb": 13.54}
|
| 19 |
+
{"step": 180, "epoch": 0.4235294117647059, "time": 1793037005.828352, "loss": 0.030702540278434755, "grad_norm": 0.18374107778072357, "learning_rate": 9.164606203550497e-05, "gpu_mem_gb": 13.54}
|
| 20 |
+
{"step": 190, "epoch": 0.4470588235294118, "time": 1793037051.6120374, "loss": 0.027758315205574036, "grad_norm": 0.21995128691196442, "learning_rate": 9.056247021419735e-05, "gpu_mem_gb": 13.54}
|
| 21 |
+
{"step": 200, "epoch": 0.47058823529411764, "time": 1793037096.3004305, "loss": 0.028007945418357848, "grad_norm": 0.1685820370912552, "learning_rate": 8.942006668888972e-05, "gpu_mem_gb": 13.54}
|
| 22 |
+
{"step": 200, "epoch": 0.47058823529411764, "time": 1793037106.284887, "eval_loss": 0.031003303825855255, "eval_runtime": 9.9768, "eval_samples_per_second": 10.023, "eval_steps_per_second": 2.506, "gpu_mem_gb": 13.54}
|
| 23 |
+
{"step": 210, "epoch": 0.49411764705882355, "time": 1793037157.2594066, "loss": 0.030912512540817262, "grad_norm": 0.18973490595817566, "learning_rate": 8.822050783547889e-05, "gpu_mem_gb": 13.54}
|
| 24 |
+
{"step": 220, "epoch": 0.5176470588235295, "time": 1793037207.2781014, "loss": 0.03183888196945191, "grad_norm": 0.13943719863891602, "learning_rate": 8.696553289962339e-05, "gpu_mem_gb": 13.54}
|
| 25 |
+
{"step": 230, "epoch": 0.5411764705882353, "time": 1793037257.1834896, "loss": 0.030890381336212157, "grad_norm": 0.20441843569278717, "learning_rate": 8.565696147500337e-05, "gpu_mem_gb": 13.54}
|
| 26 |
+
{"step": 240, "epoch": 0.5647058823529412, "time": 1793037304.5701325, "loss": 0.02990294396877289, "grad_norm": 0.31319087743759155, "learning_rate": 8.42966908650839e-05, "gpu_mem_gb": 13.54}
|
| 27 |
+
{"step": 250, "epoch": 0.5882352941176471, "time": 1793037355.6071248, "loss": 0.027320706844329835, "grad_norm": 0.19986073672771454, "learning_rate": 8.288669333220616e-05, "gpu_mem_gb": 13.54}
|
| 28 |
+
{"step": 260, "epoch": 0.611764705882353, "time": 1793037405.4839106, "loss": 0.031134438514709473, "grad_norm": 0.2151864469051361, "learning_rate": 8.142901323799578e-05, "gpu_mem_gb": 13.54}
|
| 29 |
+
{"step": 270, "epoch": 0.6352941176470588, "time": 1793037455.2459846, "loss": 0.02885761559009552, "grad_norm": 0.2212103009223938, "learning_rate": 7.992576407923373e-05, "gpu_mem_gb": 13.54}
|
| 30 |
+
{"step": 280, "epoch": 0.6588235294117647, "time": 1793037505.3912323, "loss": 0.029626962542533875, "grad_norm": 0.25277453660964966, "learning_rate": 7.837912542348818e-05, "gpu_mem_gb": 13.54}
|
| 31 |
+
{"step": 290, "epoch": 0.6823529411764706, "time": 1793037550.6336727, "loss": 0.02786014974117279, "grad_norm": 0.2689838409423828, "learning_rate": 7.679133974894983e-05, "gpu_mem_gb": 13.54}
|
| 32 |
+
{"step": 300, "epoch": 0.7058823529411765, "time": 1793037596.1785831, "loss": 0.022744971513748168, "grad_norm": 0.15749776363372803, "learning_rate": 7.516470919305298e-05, "gpu_mem_gb": 13.54}
|
| 33 |
+
{"step": 300, "epoch": 0.7058823529411765, "time": 1793037606.1419787, "eval_loss": 0.02623208984732628, "eval_runtime": 9.9614, "eval_samples_per_second": 10.039, "eval_steps_per_second": 2.51, "gpu_mem_gb": 13.54}
|
| 34 |
+
{"step": 310, "epoch": 0.7294117647058823, "time": 1793037657.1449766, "loss": 0.027777934074401857, "grad_norm": 0.3017520308494568, "learning_rate": 7.350159221459621e-05, "gpu_mem_gb": 13.54}
|
| 35 |
+
{"step": 320, "epoch": 0.7529411764705882, "time": 1793037715.1739187, "loss": 0.02481487989425659, "grad_norm": 0.11701423674821854, "learning_rate": 7.180440017420277e-05, "gpu_mem_gb": 13.59}
|
| 36 |
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