Update README.md

README.md

@@ -42,7 +42,78 @@ Utilized DeepSpeed (Stage 3), HF.Accelerator for distributed training and fused
 Для обучения использовались HuggingFace Accelerator с Microsoft DeepSpeed (Stage 3) для распределения параметров и стейта оптимизатора, а так же зафьюженный AdamW  
 **GPU часы**: 12 часов NVIDIA A100
 
-### Usage:
+### Model Config / Конфигурация обучения
+```toml
+[model]
+model_name_or_path = "Qwen/Qwen3-8B"
+
+[datasets]
+dataset = [
+    'attn-signs/kolmogorov-3',
+    'attn-signs/russian-code',
+]
+dataset_ratio = [
+    1,
+    1
+]
+test_size = 0.05
+conversation_field = "conversation"
+generate_eval_examples = false
+evaluation_strategy = "steps"
+eval_steps = 500
+dataloader_num_workers = 2
+remove_unused_columns = true
+
+[run]
+save_strategy = "steps"
+save_steps = 500
+save_total_limit = 3
+run_name = "sft-qwen3-8b"
+report_to = "wandb"
+logging_first_step = true
+logging_steps = 1
+output_dir = "models/attn-signs-qwen3-8b"
+project_name = "sft-qwen3"
+
+[training]
+train_only_on_completions = true
+per_device_train_batch_size = 1
+per_device_eval_batch_size = 1
+num_train_epochs = 1
+learning_rate = 0.00004
+gradient_accumulation_steps = 8
+gradient_checkpointing = true
+warmup_steps = 10
+bf16 = true
+seed = 42
+use_peft = true
+max_length = 4096
+
+[fusion]
+use_liger = true
+attn_implementation = "flash_attention_2"
+
+[lora]
+lora_target_modules = [
+    "k_proj",
+    "v_proj",
+    "q_proj",
+    "o_proj",
+    "gate_proj",
+    "up_proj",
+    "down_proj",
+]
+lora_r = 512
+lora_alpha = 512
+
+[tokenizer]
+assistant_message_template = "<|im_start|>assistant"
+pad_token = "<|endoftext|>"
+eos_token = "<|im_end|>"
+chat_template = "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\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    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first 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    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}"
+```
+
+### Usage / Использование модели
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer