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---
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]
<!-- 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 0.10.0