Erland
/

gemma-2b-JAX

@@ -1,199 +1,269 @@
 ---
-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]

 ---
+language: en
+tags:
+- jax
+- flax
+- text-generation
+- transformers
+- google/gemma-2b # Add the specific model name as a tag
 ---
+# google/gemma-2b - JAX/Flax
+This repository contains the JAX/Flax version of the google/gemma-2b model, originally a PyTorch model from google. This conversion enables efficient inference and training on TPUs and GPUs using the JAX/Flax framework.
+## Model Description
+google/gemma-2b is a transformer-based language model developed by google.
+## Conversion Details
+This model was converted from the original PyTorch implementation to JAX/Flax. The conversion process involved the following steps:
+1. **Loading the PyTorch model and configuration:** The pretrained PyTorch model and its configuration were loaded using the Hugging Face Transformers library.
+2. **Creating an equivalent Flax model architecture:** A Flax model with the same architecture as the original PyTorch model was created.
+3. **Converting the PyTorch weights to Flax format:** The weights from the PyTorch model were converted to the Flax format using the `convert_pytorch_state_dict_to_flax` utility function provided by Hugging Face.
+4. **Verifying the converted weights:** The converted Flax weights were compared against the original PyTorch weights to ensure that the conversion process was performed accurately.
+### Important Note about `max_position_embeddings`
+During the conversion process, it was necessary to modify the `max_position_embeddings` parameter in the model's configuration. The original value of {original_max_pos_embed} led to out-of-memory (OOM) errors on the hardware used for conversion. To resolve this, `max_position_embeddings` was adjusted to {new_max_pos_embed}.
+**Implications of this change:**
+*   The model may not be able to handle sequences longer than 8192 tokens without truncation or other modifications.
+*   If you fine-tune this model, keep in mind the revised `max_position_embeddings` when preparing your training data.
+## Weight Comparison
+The following table summarizes the comparison between the weights of the original PyTorch model and the converted JAX/Flax model. This detailed verification confirms that the conversion was accurate and that both models should produce (approximately) the same outputs given the same inputs.
+| Layer | PyTorch Shape | Flax Shape | Allclose | Max Diff | Mean Diff | Std Diff |
+| :---- | :------------ | :--------- | :------- | :------- | :-------- | :------- |
+| model.embed_tokens.weight | (256000, 2048) | (256000, 2048) | True | 0 | 0 | 0 |
+| model.layers.0.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.0.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.0.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.0.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.0.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.0.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.0.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.0.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.0.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.1.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.1.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.1.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.1.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.1.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.1.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.1.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.1.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.1.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.2.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.2.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.2.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.2.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.2.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.2.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.2.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.2.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.2.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.3.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.3.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.3.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.3.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.3.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.3.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.3.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.3.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.3.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.4.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.4.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.4.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.4.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.4.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.4.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.4.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.4.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.4.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.5.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.5.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.5.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.5.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.5.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.5.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.5.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.5.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.5.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.6.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.6.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.6.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.6.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.6.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.6.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.6.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.6.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.6.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.7.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.7.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.7.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.7.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.7.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.7.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.7.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.7.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.7.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.8.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.8.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.8.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.8.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.8.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.8.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.8.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.8.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.8.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.9.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.9.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.9.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.9.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.9.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.9.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.9.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.9.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.9.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.10.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.10.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.10.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.10.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.10.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.10.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.10.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.10.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.10.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.11.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.11.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.11.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.11.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.11.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.11.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.11.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.11.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.11.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.12.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.12.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.12.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.12.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.12.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.12.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.12.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.12.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.12.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.13.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.13.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.13.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.13.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.13.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.13.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.13.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.13.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.13.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.14.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.14.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.14.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.14.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.14.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.14.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.14.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.14.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.14.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.15.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.15.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.15.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.15.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.15.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.15.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.15.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.15.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.15.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.16.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.16.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.16.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.16.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.16.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.16.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.16.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.16.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.16.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.17.self_attn.q_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.17.self_attn.k_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.17.self_attn.v_proj.weight | (2048, 256) | (2048, 256) | True | 0 | 0 | 0 |
+| model.layers.17.self_attn.o_proj.weight | (2048, 2048) | (2048, 2048) | True | 0 | 0 | 0 |
+| model.layers.17.mlp.gate_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.17.mlp.up_proj.weight | (2048, 16384) | (2048, 16384) | True | 0 | 0 | 0 |
+| model.layers.17.mlp.down_proj.weight | (16384, 2048) | (16384, 2048) | True | 0 | 0 | 0 |
+| model.layers.17.input_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.layers.17.post_attention_layernorm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| model.norm.weight | (2048,) | (2048,) | True | 0 | 0 | 0 |
+| lm_head.weight | (2048, 256000) | (2048, 256000) | True | 0 | 0 | 0 |
+**Note:**
+*   `Allclose` indicates whether the weights are approximately equal within the specified relative (`rtol=1e-5`) and absolute (`atol=1e-3`) tolerances using `jnp.allclose()`.
+*   `Max Diff`, `Mean Diff`, and `Std Diff` provide further details on the differences between the weights if `Allclose` is `False`, which might be expected for some layers due to numerical precision differences between frameworks.
+## Hardware Used for Conversion
+The conversion process was performed on the following hardware configuration:
+*   **CPU:**
+*   **RAM:** 251.67 GB
+*   **OS:** Linux-5.15.0-107-generic-x86_64-with-glibc2.36
+*   **JAX version:** 0.3.22
+*   **Flax version:** 0.6.2
+*   **Transformers version:** 4.47.0
+*   **GPU:** NVIDIA A100-SXM4-40GB
+This conversion took approximately 184.13 seconds to complete.
+## Usage
+Here's how you can use the converted model in JAX/Flax for text generation:
+```python
+import jax
+import jax.numpy as jnp
+from transformers import FlaxAutoModelForCausalLM, AutoTokenizer
+model_name = "Erland/gemma-2b-JAX"  # Replace with your repository name
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = FlaxAutoModelForCausalLM.from_pretrained(model_name, from_pt=False) # from_pt should be False since it's already flax
+# Example prompt
+prompt = "The quick brown fox"
+# Tokenize the prompt
+tokenized_prompt = tokenizer(prompt, return_tensors="np")
+# Generate text
+output_ids = model.generate(tokenized_prompt.input_ids, max_length=50)
+# Decode the generated text
+generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+```
+## Limitations
+Sequence Length: As mentioned earlier, the max_position_embeddings has been modified to 8192. Be mindful of this limitation when working with long sequences.
+Numerical Precision: Minor differences in outputs compared to the original PyTorch model might be observed due to numerical precision variations between PyTorch and JAX/Flax, particularly on different hardware.
+## Acknowledgements
+We thank the original authors of google/gemma-2b at `google` for their groundbreaking work in developing this powerful language model.
+We acknowledge the Hugging Face Transformers library for providing the essential tools and infrastructure that made this conversion possible.
+Thanks to the JAX and Flax teams for developing such performant and flexible frameworks for numerical computation and deep learning.
+## License
+This JAX/Flax model is released under the original model license.