my-fined-tuned-qwen3b

README.md

@@ -1,6 +1,10 @@
 ---
-base_model: aboonaji/llama2finetune-v2
+base_model: Qwen/Qwen2.5-3B-Instruct
+library_name: peft
+license: other
 tags:
+- trl
+- sft
 - generated_from_trainer
 model-index:
 - name: results
@@ -12,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # results
 
-This model is a fine-tuned version of [aboonaji/llama2finetune-v2](https://huggingface.co/aboonaji/llama2finetune-v2) on an unknown dataset.
+This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on an unknown dataset.
 
 ## Model description
 
@@ -31,14 +35,17 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 0.0002
-- train_batch_size: 4
-- eval_batch_size: 4
+- learning_rate: 5e-05
+- train_batch_size: 1
+- eval_batch_size: 8
 - seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.03
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 10
 - training_steps: 100
+- mixed_precision_training: Native AMP
 
 ### Training results
 
@@ -46,7 +53,8 @@ The following hyperparameters were used during training:
 
 ### Framework versions
 
-- Transformers 4.31.0
+- PEFT 0.12.0
+- Transformers 4.44.2
 - Pytorch 2.4.1+cu121
 - Datasets 3.0.0
-- Tokenizers 0.13.3
+- Tokenizers 0.19.1

adapter_config.json

@@ -1,21 +1,35 @@
 {
+  "alpha_pattern": {},
   "auto_mapping": null,
-  "base_model_name_or_path": "aboonaji/llama2finetune-v2",
+  "base_model_name_or_path": "Qwen/Qwen2.5-3B-Instruct",
   "bias": "none",
   "fan_in_fan_out": false,
   "inference_mode": true,
   "init_lora_weights": true,
+  "layer_replication": null,
   "layers_pattern": null,
   "layers_to_transform": null,
+  "loftq_config": {},
   "lora_alpha": 16,
   "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
   "r": 64,
+  "rank_pattern": {},
   "revision": null,
   "target_modules": [
+    "down_proj",
+    "k_proj",
+    "lm_head",
+    "gate_proj",
+    "v_proj",
     "q_proj",
-    "v_proj"
+    "up_proj",
+    "o_proj"
   ],
-  "task_type": "CAUSAL_LM"
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
 }

adapter_model.safetensors

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added_tokens.json

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 {
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+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
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+  "<|vision_pad|>": 151654,
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+  "[PAD]": 151665
 }

runs/Sep22_10-25-20_c5187df0f914/events.out.tfevents.1727000848.c5187df0f914.574.0

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special_tokens_map.json

@@ -1,23 +1,30 @@
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     "lstrip": false,
-    "normalized": true,
+    "normalized": false,
     "rstrip": false,
     "single_word": false
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tokenizer_config.json

@@ -1,32 +1,215 @@
 {
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+  "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",
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+  "pad_token": "[PAD]",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
 }

training_args.bin

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