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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+checkpoint-410/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,146 @@
+---
+library_name: peft
+license: other
+base_model: unsloth/Llama-3.2-3B-Instruct
+tags:
+- llama-factory
+- lora
+- unsloth
+- generated_from_trainer
+model-index:
+- name: llm3br256
+  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. -->
+
+# llm3br256
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the brasingh_publicis_f5f dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0205
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.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 |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.1038        | 0.0606 | 5    | 0.0979          |
+| 0.0759        | 0.1212 | 10   | 0.0765          |
+| 0.069         | 0.1818 | 15   | 0.0683          |
+| 0.0729        | 0.2424 | 20   | 0.0620          |
+| 0.0545        | 0.3030 | 25   | 0.0571          |
+| 0.0589        | 0.3636 | 30   | 0.0528          |
+| 0.0461        | 0.4242 | 35   | 0.0501          |
+| 0.0522        | 0.4848 | 40   | 0.0493          |
+| 0.052         | 0.5455 | 45   | 0.0483          |
+| 0.0459        | 0.6061 | 50   | 0.0458          |
+| 0.0363        | 0.6667 | 55   | 0.0434          |
+| 0.0553        | 0.7273 | 60   | 0.0418          |
+| 0.0444        | 0.7879 | 65   | 0.0403          |
+| 0.0469        | 0.8485 | 70   | 0.0397          |
+| 0.0417        | 0.9091 | 75   | 0.0386          |
+| 0.0388        | 0.9697 | 80   | 0.0372          |
+| 0.0309        | 1.0303 | 85   | 0.0358          |
+| 0.0487        | 1.0909 | 90   | 0.0354          |
+| 0.0348        | 1.1515 | 95   | 0.0340          |
+| 0.0308        | 1.2121 | 100  | 0.0334          |
+| 0.0318        | 1.2727 | 105  | 0.0330          |
+| 0.028         | 1.3333 | 110  | 0.0322          |
+| 0.0311        | 1.3939 | 115  | 0.0321          |
+| 0.0382        | 1.4545 | 120  | 0.0315          |
+| 0.0316        | 1.5152 | 125  | 0.0304          |
+| 0.0278        | 1.5758 | 130  | 0.0299          |
+| 0.0285        | 1.6364 | 135  | 0.0292          |
+| 0.0257        | 1.6970 | 140  | 0.0285          |
+| 0.0244        | 1.7576 | 145  | 0.0281          |
+| 0.0256        | 1.8182 | 150  | 0.0278          |
+| 0.0338        | 1.8788 | 155  | 0.0270          |
+| 0.0309        | 1.9394 | 160  | 0.0262          |
+| 0.0378        | 2.0    | 165  | 0.0261          |
+| 0.0275        | 2.0606 | 170  | 0.0263          |
+| 0.0225        | 2.1212 | 175  | 0.0259          |
+| 0.0232        | 2.1818 | 180  | 0.0256          |
+| 0.0193        | 2.2424 | 185  | 0.0255          |
+| 0.0251        | 2.3030 | 190  | 0.0253          |
+| 0.0228        | 2.3636 | 195  | 0.0249          |
+| 0.0195        | 2.4242 | 200  | 0.0249          |
+| 0.0219        | 2.4848 | 205  | 0.0241          |
+| 0.0184        | 2.5455 | 210  | 0.0238          |
+| 0.0199        | 2.6061 | 215  | 0.0236          |
+| 0.023         | 2.6667 | 220  | 0.0232          |
+| 0.0227        | 2.7273 | 225  | 0.0234          |
+| 0.0206        | 2.7879 | 230  | 0.0230          |
+| 0.0217        | 2.8485 | 235  | 0.0225          |
+| 0.0186        | 2.9091 | 240  | 0.0224          |
+| 0.0201        | 2.9697 | 245  | 0.0220          |
+| 0.0147        | 3.0303 | 250  | 0.0220          |
+| 0.0142        | 3.0909 | 255  | 0.0226          |
+| 0.0149        | 3.1515 | 260  | 0.0218          |
+| 0.0151        | 3.2121 | 265  | 0.0215          |
+| 0.0174        | 3.2727 | 270  | 0.0217          |
+| 0.0172        | 3.3333 | 275  | 0.0213          |
+| 0.017         | 3.3939 | 280  | 0.0211          |
+| 0.0223        | 3.4545 | 285  | 0.0212          |
+| 0.0144        | 3.5152 | 290  | 0.0211          |
+| 0.0125        | 3.5758 | 295  | 0.0208          |
+| 0.0163        | 3.6364 | 300  | 0.0207          |
+| 0.015         | 3.6970 | 305  | 0.0207          |
+| 0.0154        | 3.7576 | 310  | 0.0206          |
+| 0.0186        | 3.8182 | 315  | 0.0203          |
+| 0.0135        | 3.8788 | 320  | 0.0202          |
+| 0.0159        | 3.9394 | 325  | 0.0201          |
+| 0.0211        | 4.0    | 330  | 0.0200          |
+| 0.0134        | 4.0606 | 335  | 0.0202          |
+| 0.0113        | 4.1212 | 340  | 0.0206          |
+| 0.0117        | 4.1818 | 345  | 0.0208          |
+| 0.0108        | 4.2424 | 350  | 0.0209          |
+| 0.012         | 4.3030 | 355  | 0.0207          |
+| 0.0111        | 4.3636 | 360  | 0.0206          |
+| 0.0118        | 4.4242 | 365  | 0.0205          |
+| 0.0099        | 4.4848 | 370  | 0.0206          |
+| 0.0118        | 4.5455 | 375  | 0.0206          |
+| 0.0119        | 4.6061 | 380  | 0.0206          |
+| 0.0114        | 4.6667 | 385  | 0.0206          |
+| 0.0109        | 4.7273 | 390  | 0.0206          |
+| 0.0124        | 4.7879 | 395  | 0.0205          |
+| 0.0111        | 4.8485 | 400  | 0.0206          |
+| 0.012         | 4.9091 | 405  | 0.0206          |
+| 0.0104        | 4.9697 | 410  | 0.0205          |
+
+
+### Framework versions
+
+- PEFT 0.12.0
+- Transformers 4.46.1
+- Pytorch 2.4.0+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/Llama-3.2-3B-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": 256,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 256,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "up_proj",
+    "o_proj",
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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all_results.json

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+{
+    "epoch": 4.96969696969697,
+    "eval_loss": 0.020535212010145187,
+    "eval_runtime": 6.2115,
+    "eval_samples_per_second": 8.05,
+    "eval_steps_per_second": 2.093,
+    "total_flos": 6.27193584892674e+17,
+    "train_loss": 0.027697906577278202,
+    "train_runtime": 5078.5288,
+    "train_samples_per_second": 2.598,
+    "train_steps_per_second": 0.081
+}

checkpoint-410/README.md

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+---
+base_model: unsloth/Llama-3.2-3B-Instruct
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.12.0

checkpoint-410/adapter_config.json

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+  "base_model_name_or_path": "unsloth/Llama-3.2-3B-Instruct",
+  "bias": "none",
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+  "target_modules": [
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+    "up_proj",
+    "o_proj",
+    "q_proj",
+    "k_proj",
+    "v_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
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+  "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    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\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 #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n    {{- \"Environment: ipython\\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        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\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"
+}

train_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 4.96969696969697,
+    "total_flos": 6.27193584892674e+17,
+    "train_loss": 0.027697906577278202,
+    "train_runtime": 5078.5288,
+    "train_samples_per_second": 2.598,
+    "train_steps_per_second": 0.081
+}

trainer_log.jsonl

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+{"current_steps": 2, "total_steps": 410, "loss": 0.11, "lr": 4.8780487804878055e-06, "epoch": 0.024242424242424242, "percentage": 0.49, "elapsed_time": "0:00:39", "remaining_time": "2:15:54"}
+{"current_steps": 3, "total_steps": 410, "loss": 0.1104, "lr": 7.317073170731707e-06, "epoch": 0.03636363636363636, "percentage": 0.73, "elapsed_time": "0:00:55", "remaining_time": "2:05:28"}
+{"current_steps": 4, "total_steps": 410, "loss": 0.1076, "lr": 9.756097560975611e-06, "epoch": 0.048484848484848485, "percentage": 0.98, "elapsed_time": "0:01:06", "remaining_time": "1:51:59"}
+{"current_steps": 5, "total_steps": 410, "loss": 0.1038, "lr": 1.2195121951219513e-05, "epoch": 0.06060606060606061, "percentage": 1.22, "elapsed_time": "0:01:17", "remaining_time": "1:44:23"}
+{"current_steps": 5, "total_steps": 410, "eval_loss": 0.09789121896028519, "epoch": 0.06060606060606061, "percentage": 1.22, "elapsed_time": "0:01:25", "remaining_time": "1:55:20"}
+{"current_steps": 6, "total_steps": 410, "loss": 0.0995, "lr": 1.4634146341463415e-05, "epoch": 0.07272727272727272, "percentage": 1.46, "elapsed_time": "0:01:35", "remaining_time": "1:47:37"}
+{"current_steps": 7, "total_steps": 410, "loss": 0.0833, "lr": 1.707317073170732e-05, "epoch": 0.08484848484848485, "percentage": 1.71, "elapsed_time": "0:01:47", "remaining_time": "1:42:40"}
+{"current_steps": 8, "total_steps": 410, "loss": 0.0756, "lr": 1.9512195121951222e-05, "epoch": 0.09696969696969697, "percentage": 1.95, "elapsed_time": "0:01:58", "remaining_time": "1:39:19"}
+{"current_steps": 9, "total_steps": 410, "loss": 0.0681, "lr": 2.1951219512195124e-05, "epoch": 0.10909090909090909, "percentage": 2.2, "elapsed_time": "0:02:08", "remaining_time": "1:35:14"}
+{"current_steps": 10, "total_steps": 410, "loss": 0.0759, "lr": 2.4390243902439026e-05, "epoch": 0.12121212121212122, "percentage": 2.44, "elapsed_time": "0:02:18", "remaining_time": "1:32:15"}
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