Training in progress, step 130, checkpoint

checkpoint-130/README.md

@@ -0,0 +1,208 @@
+---
+base_model: meta-llama/Llama-3.2-3B-Instruct
+library_name: peft
+tags:
+- base_model:adapter:meta-llama/Llama-3.2-3B-Instruct
+- lora
+- sft
+- transformers
+- trl
+---
+
+# 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 0.19.1

checkpoint-130/adapter_config.json

@@ -0,0 +1,51 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama"
+  },
+  "base_model_name_or_path": "meta-llama/Llama-3.2-3B-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "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": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "q_proj",
+    "v_proj",
+    "up_proj",
+    "o_proj",
+    "k_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

checkpoint-130/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5dfe1b679a397d737e29b828be6a2221411c8836b50bf687bc519f394b967f63
+size 97307936

checkpoint-130/chat_template.jinja

@@ -0,0 +1,93 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

checkpoint-130/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bea4fd79b46d3874356756966f58ab4fa959d0e8da46ee2c604d669ff1fe6a6
+size 99244805

checkpoint-130/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:074cae679846fe7a56577312e09afe80a12f0fe4f6c4bc80d789460b96e98f4f
+size 14773

checkpoint-130/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e426e06e55104a4dea4a5397ed51fdc172b0681f2c8a6b2defb1686376ae3656
+size 1465

checkpoint-130/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ad537f502cb4887e79ec677aadf59fc5601551711efcda3826ad17d4541e422
+size 17209467

checkpoint-130/tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": false,
+  "map_device": "auto",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "TokenizersBackend"
+}

checkpoint-130/trainer_state.json

@@ -0,0 +1,307 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.9860935524652339,
+  "eval_steps": 10,
+  "global_step": 130,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "entropy": 2.116106641292572,
+      "epoch": 0.07585335018963338,
+      "grad_norm": 1.4921875,
+      "learning_rate": 5.6250000000000005e-05,
+      "loss": 2.914295959472656,
+      "mean_token_accuracy": 0.5410080701112747,
+      "num_tokens": 36823.0,
+      "step": 10
+    },
+    {
+      "epoch": 0.07585335018963338,
+      "eval_entropy": 2.552819073200226,
+      "eval_loss": 2.6676909923553467,
+      "eval_mean_token_accuracy": 0.5321294479072094,
+      "eval_num_tokens": 36823.0,
+      "eval_runtime": 14.7674,
+      "eval_samples_per_second": 0.542,
+      "eval_steps_per_second": 0.542,
+      "step": 10
+    },
+    {
+      "entropy": 1.9473248382409414,
+      "epoch": 0.15170670037926676,
+      "grad_norm": 0.87890625,
+      "learning_rate": 9.999152908979117e-05,
+      "loss": 1.9090084075927733,
+      "mean_token_accuracy": 0.6584314892689387,
+      "num_tokens": 65881.0,
+      "step": 20
+    },
+    {
+      "epoch": 0.15170670037926676,
+      "eval_entropy": 1.8724936991930008,
+      "eval_loss": 1.9479429721832275,
+      "eval_mean_token_accuracy": 0.6351580396294594,
+      "eval_num_tokens": 65881.0,
+      "eval_runtime": 14.8467,
+      "eval_samples_per_second": 0.539,
+      "eval_steps_per_second": 0.539,
+      "step": 20
+    },
+    {
+      "entropy": 1.5991886417071024,
+      "epoch": 0.22756005056890014,
+      "grad_norm": 1.4765625,
+      "learning_rate": 9.984101496455829e-05,
+      "loss": 1.5578083038330077,
+      "mean_token_accuracy": 0.7192381203174592,
+      "num_tokens": 89977.0,
+      "step": 30
+    },
+    {
+      "epoch": 0.22756005056890014,
+      "eval_entropy": 1.6338046044111252,
+      "eval_loss": 1.694503903388977,
+      "eval_mean_token_accuracy": 0.6637412160634995,
+      "eval_num_tokens": 89977.0,
+      "eval_runtime": 14.8298,
+      "eval_samples_per_second": 0.539,
+      "eval_steps_per_second": 0.539,
+      "step": 30
+    },
+    {
+      "entropy": 1.6071883370478948,
+      "epoch": 0.3034134007585335,
+      "grad_norm": 0.71484375,
+      "learning_rate": 9.950291051311487e-05,
+      "loss": 1.6236091613769532,
+      "mean_token_accuracy": 0.6928597639004389,
+      "num_tokens": 122672.0,
+      "step": 40
+    },
+    {
+      "epoch": 0.3034134007585335,
+      "eval_entropy": 1.5961390733718872,
+      "eval_loss": 1.5684512853622437,
+      "eval_mean_token_accuracy": 0.6787843257188797,
+      "eval_num_tokens": 122672.0,
+      "eval_runtime": 14.7973,
+      "eval_samples_per_second": 0.541,
+      "eval_steps_per_second": 0.541,
+      "step": 40
+    },
+    {
+      "entropy": 1.3055346330006918,
+      "epoch": 0.37926675094816686,
+      "grad_norm": 0.9453125,
+      "learning_rate": 9.897848828427202e-05,
+      "loss": 1.2936326026916505,
+      "mean_token_accuracy": 0.7341216415166855,
+      "num_tokens": 152626.0,
+      "step": 50
+    },
+    {
+      "epoch": 0.37926675094816686,
+      "eval_entropy": 1.4331321269273758,
+      "eval_loss": 1.410101294517517,
+      "eval_mean_token_accuracy": 0.7011745795607567,
+      "eval_num_tokens": 152626.0,
+      "eval_runtime": 14.7788,
+      "eval_samples_per_second": 0.541,
+      "eval_steps_per_second": 0.541,
+      "step": 50
+    },
+    {
+      "entropy": 1.11328877111276,
+      "epoch": 0.45512010113780027,
+      "grad_norm": 0.82421875,
+      "learning_rate": 9.826972208488447e-05,
+      "loss": 1.0861340522766114,
+      "mean_token_accuracy": 0.766385022799174,
+      "num_tokens": 178156.0,
+      "step": 60
+    },
+    {
+      "epoch": 0.45512010113780027,
+      "eval_entropy": 1.3111007660627365,
+      "eval_loss": 1.2872167825698853,
+      "eval_mean_token_accuracy": 0.7273842021822929,
+      "eval_num_tokens": 178156.0,
+      "eval_runtime": 14.7936,
+      "eval_samples_per_second": 0.541,
+      "eval_steps_per_second": 0.541,
+      "step": 60
+    },
+    {
+      "entropy": 1.3175925850868224,
+      "epoch": 0.5309734513274337,
+      "grad_norm": 0.625,
+      "learning_rate": 9.737927955088705e-05,
+      "loss": 1.2941490173339845,
+      "mean_token_accuracy": 0.7372792253891627,
+      "num_tokens": 209487.0,
+      "step": 70
+    },
+    {
+      "epoch": 0.5309734513274337,
+      "eval_entropy": 1.2998864650726318,
+      "eval_loss": 1.2285542488098145,
+      "eval_mean_token_accuracy": 0.7347995787858963,
+      "eval_num_tokens": 209487.0,
+      "eval_runtime": 14.8531,
+      "eval_samples_per_second": 0.539,
+      "eval_steps_per_second": 0.539,
+      "step": 70
+    },
+    {
+      "entropy": 1.1506140291690827,
+      "epoch": 0.606826801517067,
+      "grad_norm": 0.828125,
+      "learning_rate": 9.631051210691558e-05,
+      "loss": 1.134031391143799,
+      "mean_token_accuracy": 0.760423094034195,
+      "num_tokens": 239555.0,
+      "step": 80
+    },
+    {
+      "epoch": 0.606826801517067,
+      "eval_entropy": 1.2010595947504044,
+      "eval_loss": 1.1256182193756104,
+      "eval_mean_token_accuracy": 0.7571268677711487,
+      "eval_num_tokens": 239555.0,
+      "eval_runtime": 15.6324,
+      "eval_samples_per_second": 0.512,
+      "eval_steps_per_second": 0.512,
+      "step": 80
+    },
+    {
+      "entropy": 0.9923018544912339,
+      "epoch": 0.6826801517067004,
+      "grad_norm": 1.0078125,
+      "learning_rate": 9.50674423523011e-05,
+      "loss": 0.9289534568786622,
+      "mean_token_accuracy": 0.7951417863368988,
+      "num_tokens": 265306.0,
+      "step": 90
+    },
+    {
+      "epoch": 0.6826801517067004,
+      "eval_entropy": 1.0992150977253914,
+      "eval_loss": 1.0379621982574463,
+      "eval_mean_token_accuracy": 0.7777058333158493,
+      "eval_num_tokens": 265306.0,
+      "eval_runtime": 15.7361,
+      "eval_samples_per_second": 0.508,
+      "eval_steps_per_second": 0.508,
+      "step": 90
+    },
+    {
+      "entropy": 1.0375868437190852,
+      "epoch": 0.7585335018963337,
+      "grad_norm": 0.609375,
+      "learning_rate": 9.365474892091451e-05,
+      "loss": 1.0106287956237794,
+      "mean_token_accuracy": 0.7893585612376531,
+      "num_tokens": 292993.0,
+      "step": 100
+    },
+    {
+      "epoch": 0.7585335018963337,
+      "eval_entropy": 1.072925515472889,
+      "eval_loss": 1.0008392333984375,
+      "eval_mean_token_accuracy": 0.7823155149817467,
+      "eval_num_tokens": 292993.0,
+      "eval_runtime": 15.602,
+      "eval_samples_per_second": 0.513,
+      "eval_steps_per_second": 0.513,
+      "step": 100
+    },
+    {
+      "entropy": 1.0960371459523837,
+      "epoch": 0.8343868520859671,
+      "grad_norm": 0.7890625,
+      "learning_rate": 9.207774887184493e-05,
+      "loss": 1.0759888648986817,
+      "mean_token_accuracy": 0.7768891970316569,
+      "num_tokens": 324472.0,
+      "step": 110
+    },
+    {
+      "epoch": 0.8343868520859671,
+      "eval_entropy": 0.9983096048235893,
+      "eval_loss": 0.965083658695221,
+      "eval_mean_token_accuracy": 0.7886351868510246,
+      "eval_num_tokens": 324472.0,
+      "eval_runtime": 15.6552,
+      "eval_samples_per_second": 0.511,
+      "eval_steps_per_second": 0.511,
+      "step": 110
+    },
+    {
+      "entropy": 0.8683194468418757,
+      "epoch": 0.9102402022756005,
+      "grad_norm": 0.859375,
+      "learning_rate": 9.034237767718997e-05,
+      "loss": 0.839997386932373,
+      "mean_token_accuracy": 0.8105442355076472,
+      "num_tokens": 350423.0,
+      "step": 120
+    },
+    {
+      "epoch": 0.9102402022756005,
+      "eval_entropy": 0.9654994606971741,
+      "eval_loss": 0.9282829761505127,
+      "eval_mean_token_accuracy": 0.8008697852492332,
+      "eval_num_tokens": 350423.0,
+      "eval_runtime": 15.7206,
+      "eval_samples_per_second": 0.509,
+      "eval_steps_per_second": 0.509,
+      "step": 120
+    },
+    {
+      "entropy": 0.8921634897589683,
+      "epoch": 0.9860935524652339,
+      "grad_norm": 0.66015625,
+      "learning_rate": 8.8455166882279e-05,
+      "loss": 0.8713577270507813,
+      "mean_token_accuracy": 0.805043849349022,
+      "num_tokens": 374888.0,
+      "step": 130
+    },
+    {
+      "epoch": 0.9860935524652339,
+      "eval_entropy": 0.925379566848278,
+      "eval_loss": 0.9301751852035522,
+      "eval_mean_token_accuracy": 0.8013799041509628,
+      "eval_num_tokens": 374888.0,
+      "eval_runtime": 15.7302,
+      "eval_samples_per_second": 0.509,
+      "eval_steps_per_second": 0.509,
+      "step": 130
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 528,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 4,
+  "save_steps": 10,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 6449667107930112.0,
+  "train_batch_size": 1,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-130/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de885d042fc883bdb1746107d1f7f854d09c4109e6e2b887d27994f901abb697
+size 5713