Update README.md

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]
-- **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. -->
-
-[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]
+```python
+#!/usr/bin/env python
+import torch
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    LlamaForSequenceClassification,
+)
+# install torch, transformers, accelerate
+
+def main():
+    # Define the input and output repository names.
+    input_model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+    split_2 = input_model_id.split("/")[1]
+    output_model_id = f"baseten/example-{split_2}ForSequenceClassification"  
+
+    # Load the original configuration.
+    # (If needed, add trust_remote_code=True for custom implementations.)
+    config = AutoConfig.from_pretrained(input_model_id)
+    
+    # Update the config for a sequence classification task with 10 labels.
+    num_labels = 30
+    config.num_labels = num_labels
+    config.id2label = {i: f"token activation {i}" for i in range(num_labels)}
+    config.label2id = {f"token activation {i}": i for i in range(num_labels)}
+
+    # Download the tokenizer from the original model.
+    tokenizer = AutoTokenizer.from_pretrained(input_model_id)
+
+    # Load the original causal LM model.
+    lm_model = AutoModelForCausalLM.from_pretrained(input_model_id, config=config, device_map="auto", low_cpu_mem_usage=True)
+    config.architectures = ["LlamaForSequenceClassification"]
+    del lm_model.model
+    print("loaded lm model")
+    # Initialize the sequence classification model.
+    # NOTE: We are using the built-in LlamaForSequenceClassification,
+    # which uses a `.score` attribute as the output head.
+    seq_cls_model = LlamaForSequenceClassification.from_pretrained(input_model_id, config=config, device_map="auto", low_cpu_mem_usage=True)
+
+    # --- Initialize the Classification Head ---
+    # Here we re-use the first 10 rows from the original LM head
+    # (i.e. rows 0 to 9) to initialize the new classification head.
+    with torch.no_grad():
+        # lm_model.lm_head.weight has shape [vocab_size, hidden_size]
+        # We take the first 10 rows to form a [10, hidden_size] weight matrix.
+        seq_cls_model.score.weight.copy_(lm_model.lm_head.weight.data[:num_labels, :])
+        if lm_model.lm_head.bias is not None:
+            seq_cls_model.score.bias.copy_(lm_model.lm_head.bias.data[:num_labels])
+
+    # Optionally, save the new model locally.
+    # save_directory = f"./{output_model_id.replace('/','_')}"
+    # seq_cls_model.save_pretrained(save_directory)
+    # tokenizer.save_pretrained(save_directory)
+
+    # Push the new model and tokenizer to the Hub.
+    # (Ensure you are authenticated with Hugging Face Hub via `huggingface-cli login`.)
+    tokenizer.push_to_hub(output_model_id)
+    seq_cls_model.push_to_hub(output_model_id)
+    
+
+    print(f"New model pushed to the Hub: {output_model_id}")
+
+if __name__ == "__main__":
+    main()
+
+```