Update README.md

README.md

@@ -74,105 +74,6 @@ The dataset was preprocessed to fit the model's input requirements, with special
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Data 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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-[More Information Needed]
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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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-[More Information Needed]
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-### Results
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[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 -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### 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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-<!-- 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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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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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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 ## Training procedure
@@ -193,4 +94,26 @@ The following `bitsandbytes` quantization config was used during training:
 ### Framework versions
 
 
-- PEFT 0.6.2.dev0
+- PEFT 0.6.2.dev0
+
+### Code
+
+```python
+from peft import PeftModel, PeftConfig
+
+peft_model_id='ckandemir/idefics-9b-pokemon-peft'
+quantization_config = BitsAndBytesConfig(
+                                load_in_4bit=True,
+                                bnb_4bit_use_double_quant=True,
+                                bnb_4bit_quant_type="nf4",
+                                bnb_4bit_compute_dtype=torch.float16,
+                                llm_int8_skip_modules=["lm_head", "embed_tokens"],
+                            )
+config = PeftConfig.from_pretrained(peft_model_id)
+# Load the model with the appropriate configuration for inference
+model = IdeficsForVisionText2Text.from_pretrained(config.base_model_name_or_path, quantization_config=quantization_config, device_map="auto")
+processor = AutoProcessor.from_pretrained(config.base_model_name_or_path)
+
+model = PeftModel.from_pretrained(model, peft_model_id, device_map={"":0})
+
+```