TabbyML: Self-hosted AI coding assistant. #642
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Related issues#625: unsloth/README.md at main · unslothai/unslothDetailsSimilarity score: 0.87 - [ ] [unsloth/README.md at main · unslothai/unsloth](https://github.com/unslothai/unsloth/blob/main/README.md?plain=1)unsloth/README.md at main · unslothai/unsloth
Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory!✨ Finetune for FreeAll notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
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Suggested labels#640: README.md · defog/sqlcoder-7b-2 at mainDetailsSimilarity score: 0.85 - [ ] [README.md · defog/sqlcoder-7b-2 at main](https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true)README.md · defog/sqlcoder-7b-2 at mainDESCRIPTION: license: cc-by-sa-4.0
library_name: transformers
pipeline_tag: text-generationUpdate noticeThe model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins. If you downloaded the model before that, please redownload the weights for best performance. Model Card for SQLCoder-7B-2A capable large language model for natural language to SQL generation. Model DetailsModel DescriptionThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Model Sources [optional]UsesThis model is intended to be used by non-technical users to understand data inside their SQL databases. It is meant as an analytics tool, and not as a database admin tool. This model has not been trained to reject malicious requests from users with write access to databases, and should only be used by users with read-only access. How to Get Started with the ModelUse the code here to get started with the model. PromptPlease use the following prompt for optimal results. Please remember to use EvaluationThis model was evaluated on SQL-Eval, a PostgreSQL based evaluation framework developed by Defog for testing and alignment of model capabilities. You can read more about the methodology behind SQLEval here. ResultsWe classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.
Model Card ContactContact us on X at @defogdata, or on email at founders@defog.ai URL: https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true Suggested labels#498: CodeGPTPlus/deepseek-coder-1.3b-typescript · Hugging Face### DetailsSimilarity score: 0.85 - [ ] [CodeGPTPlus/deepseek-coder-1.3b-typescript · Hugging Face](https://huggingface.co/CodeGPTPlus/deepseek-coder-1.3b-typescript)CodeGPTPlus/deepseek-coder-1.3b-typescriptThis is a fine-tuned model by the CodeGPT team, specifically crafted for generating expert code in TypeScript. It is fine-tuned from The model uses a 16K window size and an additional fill-in-the-middle task for project-level code completion. How to UseThis model is for completion purposes only. Here are some examples of how to use the model: Running the model on a GPUfrom transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("CodeGPTPlus/deepseek-coder-1.3b-typescript", trust_remote_code=True).cuda()
input_text = """<|fim begin|>function quickSort(arr: number[]): number[] {
if (arr.length <= 1) {
return arr;
}
const pivot = arr[0];
const left = [];
const right = [];
<|fim hole|>
return [...quickSort(left), pivot, ...quickSort(right)];
}<|fim end|>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Running with Ollama
Running with Ollama and CodeGPT Autocomplete in VSCode
Fill In the Middle (FIM)<|fim begin|>function quickSort(arr: number[]): number[] {
if (arr.length <= 1) {
return arr;
}
const pivot = arr[0];
const left = [];
const right = [];
<|fim hole|>
return [...quickSort(left), pivot, ...quickSort(right)];
}<|fim end|>Training ProcedureThe model was trained using the following hyperparameters:
For more information, visit the model page. Suggested labels{ "label-name": "TypeScript-Code-Generation", "description": "Model for generating TypeScript code", "repo": "CodeGPTPlus/deepseek-coder-1.3b-typescript", "confidence": 70.59 }#309: openai/human-eval: Code for the paper "Evaluating Large Language Models Trained on Code"### DetailsSimilarity score: 0.85 - [ ] [openai/human-eval: Code for the paper "Evaluating Large Language Models Trained on Code"](https://github.com/openai/human-eval)HumanEval: Hand-Written Evaluation Set This is an evaluation harness for the HumanEval problem solving dataset described in the paper "Evaluating Large Language Models Trained on Code". Installation Make sure to use python 3.7 or later: $ conda create -n codex python=3.7 $ git clone https://github.com/openai/human-eval This program exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. The execution call in execution.py is deliberately commented out to ensure users read this disclaimer before running code in a potentially unsafe manner. See the comment in execution.py for more information and instructions. After following the above instructions to enable execution, generate samples and save them in the following JSON Lines (jsonl) format, where each sample is formatted into a single line like so: {"task_id": "Corresponding HumanEval task ID", "completion": "Completion only without the prompt"} Here is nearly functional example code (you just have to provide generate_one_completion to make it work) that saves generated completions to samples.jsonl. from human_eval.data import write_jsonl, read_problems problems = read_problems() num_samples_per_task = 200 $ evaluate_functional_correctness samples.jsonl As a quick sanity-check, the example samples should yield 0.5 pass@1. $ evaluate_functional_correctness data/example_samples.jsonl --problem_file=data/example_problem.jsonl $ evaluate_functional_correctness --help Known Issues While evaluation uses very little memory, you might see the following error message when the system is running out of RAM. Since this may cause some correct programs to fail, we recommend that you free some memory and try again. malloc: can't allocate region Please cite using the following bibtex entry: @Article{chen2021codex, Suggested labels{ "key": "llm-evaluation", "value": "Evaluating Large Language Models performance and behavior through human-written evaluation sets" }#628: LLaVA/README.md at main · haotian-liu/LLaVA### DetailsSimilarity score: 0.85 - [ ] [LLaVA/README.md at main · haotian-liu/LLaVA](https://github.com/haotian-liu/LLaVA/blob/main/README.md?plain=1)LLaVA/README.md at main · haotian-liu/LLaVA🌋 LLaVA: Large Language and Vision AssistantVisual instruction tuning towards large language and vision models with GPT-4 level capabilities. 📢 LLaVA-NeXT Blog Project Page Demo Data Model Zoo 🤝Community Contributions: llama.cpp Colab 🤗Space Replicate AutoGen BakLLaVA Improved Baselines with Visual Instruction Tuning Paper HF Visual Instruction Tuning (NeurIPS 2023, Oral) Paper HF Release
More
Usage and License Notices: This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the OpenAI Terms of Use for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. Llama community license for LLaMA-2 and Vicuna-v1.5). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations. ContentsSuggested labels |
irthomasthomas
changed the title
tabby/README.md at main · TabbyML/tabby
🐾 Tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features:
🔥 What's New
Archived
👋 Getting Started
You can find our documentation here.
Run Tabby in 1 Minute
The easiest way to start a Tabby server is by using the following Docker command:
```bash
docker run -it \
--gpus all -p 8080:8080 -v $HOME/.tabby:/data \
tabbyml/tabby \
serve --model TabbyML/StarCoder-1B --device cuda
```
For additional options (e.g inference type, parallelism), please refer to the documentation page.
🤝 Contributing
Full guide at CONTRIBUTING.md;
Get the Code
```bash
git clone --recurse-submodules https://github.com/TabbyML/tabby
cd tabby
```
If you have already cloned the repository, you could run the `git submodule update --recursive --init` command to fetch all submodules.
Build
Set up the Rust environment by following this tutorial.
Install the required dependencies:
```bash
For MacOS
brew install protobuf
For Ubuntu / Debian
apt-get install protobuf-compiler libopenblas-dev
```
Start Hacking!
... and don't forget to submit a Pull Request
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URL: tabby/README.md
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