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Model: yunjae-won/llama8b_sft
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# 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. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+### Model Sources [optional]
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+<!-- Provide the basic links for the model. -->
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+- **Repository:** [More Information Needed]
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+## 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. -->
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+### Direct Use
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+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+### Out-of-Scope Use
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+## Bias, Risks, and Limitations
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+### Recommendations
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+<!-- 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.
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+[More Information Needed]
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+## Training Details
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+### Training Data
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+<!-- 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. -->
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+[More Information Needed]
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+### Training Procedure
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+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
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+
+#### Training Hyperparameters
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+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+#### Speeds, Sizes, Times [optional]
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+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+[More Information Needed]
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+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
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+#### Testing Data
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+<!-- This should link to a Dataset Card if possible. -->
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+[More Information Needed]
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+#### Factors
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+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
+### Results
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+#### Summary
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+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
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+[More Information Needed]
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+## 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]
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+## Technical Specifications [optional]
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+### Model Architecture and Objective
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+[More Information Needed]
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+### Compute Infrastructure
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+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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chat_template.jinja

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+{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
+
+'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
+
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.0.0"
+}

model.safetensors

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+size 16060556616

scheduler.pt

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tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "is_local": true,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "model_specific_special_tokens": {},
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

training_config.yaml

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+seed: 1
+exp_name: llama3-8B-sft
+train_datasets:
+- yunjae-won/Qwen3-30B-MagpieLM-SFT-Outputs-v0.1-shard0
+test_datasets:
+- yunjae-won/Qwen3-30B-MagpieLM-SFT-Outputs-v0.1-shard0
+debug: false
+wandb:
+  enabled: true
+  entity: null
+  project: KD
+cache_dir: .cache/
+local_run_dir: .cache//llama3-8B-sft
+do_first_eval: true
+minimum_log_interval_secs: 1.0
+intermediate_checkpoints: false
+trainer: BasicTrainer
+template_tokens: []
+lr: 5.0e-06
+n_epochs: 1
+n_examples: null
+n_eval_examples: 512
+eval_every: 19968
+save_every: 5120
+step_scheduler_with_optimizer: false
+optimizer: RMSprop
+weight_decay: 0
+beta1: 0.9
+beta2: 0.999
+eps: 1.0e-05
+warmup: 0.1
+cache_reference_logprobs: false
+load_reference_logprobs: null
+humanline: false
+log_epsilon_P: -1.0
+log_epsilon_R: 1.5
+online: false
+frac_unique_desirable: 1.0
+frac_unique_undesirable: 1.0
+model:
+  name_or_path: meta-llama/Llama-3.1-8B
+  tokenizer_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+  load_from: null
+  from_checkpoint: null
+  block_name: LlamaDecoderLayer
+  policy_dtype: bfloat16
+  reference_dtype: bfloat16
+  max_grad_norm: 10.0
+  v_head_max_grad_norm: 0.1
+  max_length: 4096
+  max_prompt_length: 2048
+  activation_checkpointing: false
+  batch_size: 256
+  microbatch_size: 2.0
+  gradient_accumulation_steps: 32
+  eval_batch_size: 256
+  eval_microbatch_size: 2.0
+  attn_implementation: flash_attention_2
+  use_peft: false
+  load_lora_from: null
+  peft:
+    lora_r: 64
+    lora_alpha: 256
+    lora_dropout: 0.05
+    target_modules: all-linear
+  reward_model:
+    path: null
+    model_class: AutoModelForBradleyTerry
+    dtype: float32
+    attn_implementation: flash_attention_2
+loss:
+  trainer: SFTTrainer
+  dataloader: SFTDataLoader
+  sync_reference: false
+num_epochs: 1