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README.md

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+
+---
+
+base_model: EpistemeAI/Athena-codegemma-2-9b
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- gemma2
+- trl
+pipeline_tag: text-generation
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/Athena-codegemma-2-9b-v1-GGUF
+This is quantized version of [EpistemeAI/Athena-codegemma-2-9b-v1](https://huggingface.co/EpistemeAI/Athena-codegemma-2-9b-v1) created using llama.cpp
+
+# Original Model Card
+
+
+# How to use
+This repository contains Athena-codegemma-2-9b-v1, for use with transformers and with the original llama codebase.
+
+Use with transformers
+Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
+
+Make sure to update your transformers installation via pip install --upgrade transformers.
+
+## Best use to test or prompt:
+
+You need to prepare prompt in **alpaca** format to generate properly:
+```python
+def format_test(x):
+
+  if x['input']:
+    formatted_text = f"""Below is an instruction that describes a task. \
+    Write a response that appropriately completes the request.
+
+    ### Instruction:
+    {x['instruction']}
+
+    ### Input:
+    {x['input']}
+
+    ### Response:
+    """
+
+  else:
+    formatted_text = f"""Below is an instruction that describes a task. \
+    Write a response that appropriately completes the request.
+
+    ### Instruction:
+    {x['instruction']}
+
+    ### Response:
+    """
+
+  return formatted_text
+
+# using code_instructions_122k_alpaca dataset
+Prompt = format_test(data[155])
+print(Prompt)
+
+```
+- huggingface transformers method:
+```python
+from transformers import TextStreamer
+
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+inputs = tokenizer(
+[
+    Prompt
+], return_tensors = "pt").to("cuda")
+
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 512)
+```
+
+
+- unsloth method 
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "EpistemeAI/Athena-codegemma-2-9b-v1", # YOUR MODEL YOU USED FOR TRAINING
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+# alpaca_prompt = You MUST copy from above!
+
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "Create a function to calculate the sum of a sequence of integers.", # instruction
+        "", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
+tokenizer.batch_decode(outputs)
+```
+
+--
+
+### Inputs and outputs
+
+*   **Input:** Text string, such as a question, a prompt, or a document to be
+    summarized.
+*   **Output:** Generated English-language text in response to the input, such
+    as an answer to a question, or a summary of a document.
+### Citation
+
+```none
+@article{gemma_2024,
+    title={Gemma},
+    url={https://www.kaggle.com/m/3301},
+    DOI={10.34740/KAGGLE/M/3301},
+    publisher={Kaggle},
+    author={Gemma Team},
+    year={2024}
+}
+```
+
+# Uploaded  model
+
+- **Developed by:** EpistemeAI
+- **License:** apache-2.0
+- **Finetuned from model :** EpistemeAI/Athena-codegemma-2-9b
+
+This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)