Add chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4

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chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/README.md

@@ -0,0 +1,128 @@
+---
+library_name: peft
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- llama-factory
+- lora
+- generated_from_trainer
+model-index:
+- name: Llama-3.1-8B-Instruct-PsyCourse-fold4
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Llama-3.1-8B-Instruct-PsyCourse-fold4
+
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the course-train-fold4 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0344
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5.0
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.5681        | 0.0763 | 50   | 0.3814          |
+| 0.122         | 0.1527 | 100  | 0.0796          |
+| 0.0541        | 0.2290 | 150  | 0.0592          |
+| 0.0502        | 0.3053 | 200  | 0.0522          |
+| 0.0666        | 0.3816 | 250  | 0.0539          |
+| 0.046         | 0.4580 | 300  | 0.0493          |
+| 0.0458        | 0.5343 | 350  | 0.0527          |
+| 0.0448        | 0.6106 | 400  | 0.0488          |
+| 0.0567        | 0.6870 | 450  | 0.0462          |
+| 0.0358        | 0.7633 | 500  | 0.0410          |
+| 0.0445        | 0.8396 | 550  | 0.0407          |
+| 0.0462        | 0.9159 | 600  | 0.0407          |
+| 0.0363        | 0.9923 | 650  | 0.0410          |
+| 0.0343        | 1.0686 | 700  | 0.0370          |
+| 0.0413        | 1.1449 | 750  | 0.0378          |
+| 0.0322        | 1.2213 | 800  | 0.0398          |
+| 0.0342        | 1.2976 | 850  | 0.0385          |
+| 0.0337        | 1.3739 | 900  | 0.0436          |
+| 0.0295        | 1.4502 | 950  | 0.0373          |
+| 0.0267        | 1.5266 | 1000 | 0.0386          |
+| 0.0287        | 1.6029 | 1050 | 0.0380          |
+| 0.0504        | 1.6792 | 1100 | 0.0388          |
+| 0.0317        | 1.7556 | 1150 | 0.0391          |
+| 0.0448        | 1.8319 | 1200 | 0.0366          |
+| 0.0278        | 1.9082 | 1250 | 0.0362          |
+| 0.0347        | 1.9845 | 1300 | 0.0344          |
+| 0.0201        | 2.0609 | 1350 | 0.0355          |
+| 0.0238        | 2.1372 | 1400 | 0.0357          |
+| 0.0299        | 2.2135 | 1450 | 0.0371          |
+| 0.0155        | 2.2899 | 1500 | 0.0384          |
+| 0.0157        | 2.3662 | 1550 | 0.0391          |
+| 0.0222        | 2.4425 | 1600 | 0.0370          |
+| 0.0245        | 2.5188 | 1650 | 0.0360          |
+| 0.0206        | 2.5952 | 1700 | 0.0376          |
+| 0.0198        | 2.6715 | 1750 | 0.0363          |
+| 0.0209        | 2.7478 | 1800 | 0.0370          |
+| 0.026         | 2.8242 | 1850 | 0.0362          |
+| 0.0197        | 2.9005 | 1900 | 0.0358          |
+| 0.0291        | 2.9768 | 1950 | 0.0355          |
+| 0.0091        | 3.0531 | 2000 | 0.0416          |
+| 0.0132        | 3.1295 | 2050 | 0.0421          |
+| 0.0115        | 3.2058 | 2100 | 0.0443          |
+| 0.0131        | 3.2821 | 2150 | 0.0459          |
+| 0.0132        | 3.3585 | 2200 | 0.0409          |
+| 0.0077        | 3.4348 | 2250 | 0.0445          |
+| 0.0156        | 3.5111 | 2300 | 0.0444          |
+| 0.0125        | 3.5874 | 2350 | 0.0480          |
+| 0.0089        | 3.6638 | 2400 | 0.0499          |
+| 0.0125        | 3.7401 | 2450 | 0.0467          |
+| 0.0115        | 3.8164 | 2500 | 0.0447          |
+| 0.0062        | 3.8928 | 2550 | 0.0449          |
+| 0.0112        | 3.9691 | 2600 | 0.0462          |
+| 0.005         | 4.0454 | 2650 | 0.0465          |
+| 0.0065        | 4.1217 | 2700 | 0.0502          |
+| 0.0021        | 4.1981 | 2750 | 0.0543          |
+| 0.0033        | 4.2744 | 2800 | 0.0556          |
+| 0.0068        | 4.3507 | 2850 | 0.0572          |
+| 0.0015        | 4.4271 | 2900 | 0.0599          |
+| 0.0036        | 4.5034 | 2950 | 0.0602          |
+| 0.0027        | 4.5797 | 3000 | 0.0615          |
+| 0.0013        | 4.6560 | 3050 | 0.0615          |
+| 0.0056        | 4.7324 | 3100 | 0.0618          |
+| 0.0028        | 4.8087 | 3150 | 0.0618          |
+| 0.0044        | 4.8850 | 3200 | 0.0620          |
+| 0.0061        | 4.9614 | 3250 | 0.0622          |
+
+
+### Framework versions
+
+- PEFT 0.12.0
+- Transformers 4.46.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
+- Tokenizers 0.20.3

chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
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+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "v_proj",
+    "o_proj",
+    "gate_proj",
+    "q_proj",
+    "down_proj",
+    "k_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/adapter_model.safetensors

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chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/all_results.json

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+    "epoch": 4.999522946283752,
+    "eval_loss": 0.03436192870140076,
+    "eval_runtime": 157.8575,
+    "eval_samples_per_second": 7.38,
+    "eval_steps_per_second": 7.38,
+    "total_flos": 8.22077121848279e+17,
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chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/eval_results.json

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+{
+    "epoch": 4.999522946283752,
+    "eval_loss": 0.03436192870140076,
+    "eval_runtime": 157.8575,
+    "eval_samples_per_second": 7.38,
+    "eval_steps_per_second": 7.38
+}

chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/llama3_lora_sft.yaml

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+### model
+model_name_or_path: meta-llama/Llama-3.1-8B-Instruct
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: course-train-fold4
+dataset_dir: data_private
+template: llama3
+cutoff_len: 1024
+# max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/psy-course/Llama-3.1-8B-Instruct/train/fold4
+logging_steps: 10
+save_steps: 50
+plot_loss: true
+overwrite_output_dir: true
+save_total_limit: 3
+push_to_hub: true
+hub_model_id: chchen/Llama-3.1-8B-Instruct-PsyCourse-fold4
+load_best_model_at_end: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 16
+learning_rate: 1.0e-4
+num_train_epochs: 5.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+enable_liger_kernel: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 50

chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/special_tokens_map.json

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+      "rstrip": false,
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+  },
+  "additional_special_tokens": [
+    "<|eot_id|>",
+    "<|eom_id|>"
+  ],
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/train_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 4.999522946283752,
+    "total_flos": 8.22077121848279e+17,
+    "train_loss": 0.04503766617872788,
+    "train_runtime": 36493.551,
+    "train_samples_per_second": 1.436,
+    "train_steps_per_second": 0.09
+}

chchen__Llama-3.1-8B-Instruct-PsyCourse-fold4/trainer_log.jsonl

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