yarenty
/

llama32-datafusion-instruct

@@ -1,199 +1,104 @@
 ---
-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]

 ---
+license: llama3.2
+base_model: meta-llama/Llama-3.2-8B-Instruct
+tags:
+- text-generation
+- instruction
+- datafusion
+- rust
+- code
 ---
+![transformers](https://img.shields.io/badge/transformers-yes-green)
+![Downloads](https://img.shields.io/endpoint?url=https://huggingface.co/api/models/yarenty/llama32-datafusion-instruct/badge/downloads)
+![Likes](https://img.shields.io/endpoint?url=https://huggingface.co/api/models/yarenty/llama32-datafusion-instruct/badge/likes)
+**Author:** yarenty
+**Model type:** Llama 3.2 (fine-tuned)
+**Task:** Instruction-following, code Q/A, DataFusion expert assistant
+**License:** Apache 2.0
+**Visibility:** Public
+---
+# Llama 3.2 DataFusion Instruct
+This model is a fine-tuned version of **meta-llama/Llama-3.2-8B-Instruct**, specialized for the [Apache Arrow DataFusion](https://arrow.apache.org/datafusion/) ecosystem. It's designed to be a helpful assistant for developers, answering technical questions, generating code, and explaining concepts related to DataFusion, Arrow.rs, Ballista, and the broader Rust data engineering landscape.
+**GGUF Version:** For quantized, low-resource deployment, you can find the GGUF version [here](<https://huggingface.co/yarenty/llama32-datafusion-instruct-gguf>).
+## Model Description
+This model was fine-tuned on a curated dataset of high-quality question-answer pairs and instruction-following examples sourced from the official DataFusion documentation, source code, mailing lists, and community discussions.
+- **Model Type:** Instruction-following Large Language Model (LLM)
+- **Base Model:** `meta-llama/Llama-3.2-8B-Instruct`
+- **Primary Use:** Developer assistant for the DataFusion ecosystem.
+## Prompt Template
+To get the best results, format your prompts using the following instruction template.
+```
+### Instruction:
+{Your question or instruction here}
+### Response:
+```
+## Example Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "yarenty/llama32-datafusion-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+# The model was trained with a specific instruction template.
+# For optimal performance, your prompt should follow this structure.
+prompt_template = """### Instruction:
+How do I register a Parquet file in DataFusion?
+### Response:"""
+inputs = tokenizer(prompt_template, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id)
+# Decode the output, skipping special tokens and the prompt
+prompt_length = inputs["input_ids"].shape[1]
+print(tokenizer.decode(outputs[0][prompt_length:], skip_special_tokens=True))
+```
+## Training Procedure
+- **Hardware:** Trained on 1x NVIDIA A100 GPU.
+- **Training Script:** Custom script using `transformers.SFTTrainer`.
+- **Key Hyperparameters:**
+  - Epochs: 3
+  - Learning Rate: 2e-5
+  - Batch Size: 4
+- **Dataset:** A curated dataset of ~5,000 high-quality QA pairs and instructions related to DataFusion. Data was cleaned and deduplicated as per the notes in `pitfalls.md`.
+## Intended Use & Limitations
+- **Intended Use:** This model is intended for developers and data engineers working with DataFusion. It can be used for code generation, debugging assistance, and learning the library. It can also serve as a strong base for further fine-tuning on more specialized data.
+- **Limitations:** The model's knowledge is limited to the data it was trained on. It may produce inaccurate or outdated information for rapidly evolving parts of the library. It is not a substitute for official documentation or expert human review.
+## Citation
+If you find this model useful in your work, please cite:
+```
+@misc{yarenty_2025_llama32_datafusion_instruct,
+  author = {yarenty},
+  title = {Llama 3.2 DataFusion Instruct},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face repository},
+  howpublished = {\url{https://huggingface.co/yarenty/llama32-datafusion-instruct}}
+}
+```
+## Contact
+For questions or feedback, please open an issue on the Hugging Face repository or the [source GitHub repository](https://github.com/yarenty/trainer).