---
library_name: peft
base_model: HuggingFaceM4/idefics-9b-instruct
---

# Model Card for Model ID

This is a IDEFICS 9B model trained with ppo on the frozenlake env.



## Model Details

### Trainer Hyperparameters

suppress_warnings: True
debug: True
seed: 9812
reseed_env: True
torch_deterministic: True
track: True
wandb_project_name: "frozenlake_idefics"
wandb_entity: null #'rl-team-unito' 
wandb_log_dir: "${now:%Y-%m-%d_%H-%M-%S}"
save_video: True
save_video_every: 20
save_stats: True
save_episode: False
env_size: 244
env_area: 8
num_prompt_images: 1
use_text_description: True 

# Algorithm specific arguments
model: "HuggingFaceM4/idefics-9b-instruct"
model_ckpt: null
lora_adapter_path: null
is_slippery: False 
fixed_orientation: True
no_step_description: False
first_person: True
fov: 1

total_timesteps: 400000
disable_training: False
from_accelerate_savestate_to_checkpoint: False
learning_rate: 1e-5
critic_learning_rate: 1e-5
local_num_envs: 4
num_steps: 128
anneal_lr: False
gamma: 0.99
gae_lambda: 0.95
num_minibatches: 128
update_epochs: 1
norm_adv: True
clip_coef: 0.1
clip_vloss: True
ent_coef: 0.01 #0.01
vf_coef: 0.5
max_grad_norm: 0.5
target_kl: null
save_every: 50
gradient_accumulation: 4
adam_epsilon: 1e-8
gradient_ckpt: False
lora: True
temperature: 'max_logit'
disable_adapters_for_generation: True
normalization_by_words: False
action_logits_from_whole_seq: True
advanced_action_matching: False
env_id: "FrozenLakeText-v0"  # MiniGrid-LavaGapS7-v0
generate_actions: False
value_prompt_template: "I am the agent in this minigrid world. {} Avoid the traps!\nWhat's the next best action?"
action_template: " Based on the information provided, the next best action would be to {}"
possible_actions_list: "forward pickup toggle opt_left opt_right opt_back"

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses

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### Direct Use

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### Downstream Use [optional]

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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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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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## Training Details

### Training Data

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### Training Procedure

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#### 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]

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## Evaluation

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### 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]

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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]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.10.0