---
license: apache-2.0
datasets:
- yahma/alpaca-cleaned
model-index:
- name: hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 23.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 25.47
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 23.5
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 50.65
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 50.91
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mzio/hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3
      name: Open LLM Leaderboard
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

Configs
```
name: llama
model:
  pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1'
  cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/'
  return_dict: true
  quantization: false
  device_map: auto  # null
  low_cpu_mem_usage: true  # false
  torch_dtype: bfloat16
  attn_implementation: eager  # so we can load attention weights
  rope_theta: 10000.0

attention:
  attention_type: hedgehog_llama
  feature_map: softmax_dim
  feature_map_kwargs:
    input_dim: 128
    eps: 1e-12
    # mlp: null  # to set
    fullspace: true
  layer_idx: null  # to set
  learned_kernel: untied_head
  learned_kernel_kwargs:
    feature_dim: 128
    skip_connection: false
    bias: false
    zero_init: false
  tie_qk_kernels: false
  train_qk: true
  peft:
    method: lora
    kwargs:
      r: 8  # 256
      lora_alpha: 16  # 512
      lora_dropout: 0.1  # 0.05
      target_modules: ['self_attn.q_proj', 'self_attn.k_proj']

dataset:
  name: alpaca_clean
  dataset_config:
    name: alpaca
    path: yahma/alpaca-cleaned
    chunk_size: 1024  # 2048
    concat_data: true
    cache_dir: '/u/scr/nlp/data/alpaca'
  pretrained_model_config:
    pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1'
    cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/'
  preprocess_config: null

dataloader:
  batch_size: 1
  num_workers: 2
  drop_last: false
  pin_memory: true

optimizer:
  optim: adamw_torch_fused
  lr: 0.001
  weight_decay: 0.0

lr_scheduler:
  lr_scheduler_type: reduce_lr_on_plateau
  mode: min
  factor: 0.1
  patience: 10
  min_lr: 0.00001

trainer:  # HuggingFace Trainer-like arguments  
  name: distill_attention
  token_reduce: true
  bottom_attention_only: false
  reverse_kl: false
  
  bf16: true
  train_split: train
  val_split: validation
  num_train_epochs: 2
  gradient_accumulation_steps: 8
  seed: 42
  batch_size: 1
  load_best_model_at_end: true
  greater_is_better: false
  metric_for_best_model: distill/eval/loss
  logging_steps: 100
  evaluation_strategy: steps
  max_steps: -1
  eval_steps: 100
  max_eval_batches: null

dataset:
  name: alpaca_clean
  dataset_config:
    name: alpaca
    path: yahma/alpaca-cleaned
    chunk_size: 1024  # 2048
    concat_data: true
    cache_dir: '/u/scr/nlp/data/alpaca'
  pretrained_model_config:
    pretrained_model_name_or_path: 'mistralai/Mistral-7B-v0.1'
    cache_dir: '/juice/scr/scr110/scr/nlp/data/neo/hub/'
  preprocess_config: null    

dataloader:
  batch_size: 1
  num_workers: 2
  drop_last: false
  pin_memory: true

optimizer:
  optim: adamw_torch_fused
  lr: 1e-4
  weight_decay: 0.0

lr_scheduler:
  lr_scheduler_type: reduce_lr_on_plateau
  mode: min
  factor: 0.1
  patience: 10
  min_lr: 0.00001

trainer:  # HuggingFace Trainer-like arguments  
  name: default
  bf16: true
  train_split: train
  val_split: validation
  num_train_epochs: 2
  gradient_accumulation_steps: 8
  seed: 42
  batch_size: 1
  load_best_model_at_end: true
  greater_is_better: false
  metric_for_best_model: eval/loss  # eval/rouge/geometric_mean
  logging_steps: 100
  evaluation_strategy: steps
  max_steps: -1
  eval_steps: 100
  max_eval_batches: null

finetune:
  method: lora
  kwargs:
    r: 8
    lora_alpha: 16  # 32
    lora_dropout: 0  # 0.05
    target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']
```


### 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. -->

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### 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]

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#### 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. -->

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## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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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]
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## Technical Specifications [optional]

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## Citation [optional]

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## Glossary [optional]

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## Model Card Contact

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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mzio__hedgehog-mistral_7b-alpaca_clean-smd_lora_1e_3)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |28.97|
|AI2 Reasoning Challenge (25-Shot)|23.29|
|HellaSwag (10-Shot)              |25.47|
|MMLU (5-Shot)                    |23.50|
|TruthfulQA (0-shot)              |50.65|
|Winogrande (5-shot)              |50.91|
|GSM8k (5-shot)                   | 0.00|