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625: unsloth/README.md at main · unslothai/unsloth
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Similarity 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!
**Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)|
| 🥇 **Benchmarking** | [Performance Tables](https://github.com/unslothai/unsloth/tree/main#-performance-benchmarking)
| 🌐 **Released Models** | [Unsloth Releases](https://huggingface.co/unsloth)|
| ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog)|
## ⭐ Key Features
- All kernels written in [OpenAI's Triton](https://openai.com/research/triton) language. **Manual backprop engine**.
- **0% loss in accuracy** - no approximation methods - all exact.
- No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
- Works on **Linux** and **Windows** via WSL.
- Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
- Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for **30x faster training**!
- If you trained a model with 🦥Unsloth, you can use this cool sticker! 
## 🥇 Performance Benchmarking
- For the full list of **reproducable** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
| 1 A100 40GB | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) |
|--------------|--------------|-----------------|---------------------|-----------------|
| Alpaca | 1x | 1.04x | 1.98x | **15.64x** |
| LAION Chip2 | 1x | 0.92x | 1.61x | **20.73x** |
| OASST | 1x | 1.19x | 2.17x | **14.83x** |
| Slim Orca | 1x | 1.18x | 2.22x | **14.82x** |
- Benchmarking table below was conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).
| Free Colab T4 | Dataset | 🤗Hugging Face | Pytorch 2.1.1 | 🦥Unsloth | 🦥 VRAM reduction |
| --- | --- | --- | --- | --- | --- |
| Llama-2 7b | OASST | 1x | 1.19x | 1.95x | -43.3% |
| Mistral 7b | Alpaca | 1x | 1.07x | 1.56x | -13.7% |
| Tiny Llama 1.1b | Alpaca | 1x | 2.06x | 3.87x | -73.8% |
| DPO with Zephyr | Ultra Chat | 1x | 1.09x | 1.55x | -18.6% |
[View on GitHub](https://github.com/unslothai/unsloth/blob/main/README.md?plain=1)
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#### 640: README.md · defog/sqlcoder-7b-2 at main
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Similarity 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 main **DESCRIPTION:** ```yaml license: cc-by-sa-4.0 library_name: transformers pipeline_tag: text-generation ``` ## Update notice The 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-2 A capable large language model for natural language to SQL generation.  ### Model Details #### Model Description 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:** [Defog, Inc](https://defog.ai) - **Model type:** [Text to SQL] - **License:** [CC-by-SA-4.0] - **Finetuned from model:** [CodeLlama-7B] #### Model Sources [optional] - [**HuggingFace:**](https://huggingface.co/defog/sqlcoder-70b-alpha) - [**GitHub:**](https://github.com/defog-ai/sqlcoder) - [**Demo:**](https://defog.ai/sqlcoder-demo/) ## Uses This 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 Model Use the code [here](https://github.com/defog-ai/sqlcoder/blob/main/inference.py) to get started with the model. ## Prompt Please use the following prompt for optimal results. Please remember to use `do_sample=False` and `num_beams=4` for optimal results. ``` ### Task Generate a SQL query to answer [QUESTION]{user_question}[/QUESTION] ### Database Schema The query will run on a database with the following schema: {table_metadata_string_DDL_statements} ### Answer Given the database schema, here is the SQL query that [QUESTION]{user_question}[/QUESTION] [SQL] ``` ## Evaluation This model was evaluated on [SQL-Eval](https://github.com/defog-ai/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](https://defog.ai/blog/open-sourcing-sqleval/). ### Results We 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. | | date | group_by | order_by | ratio | join | where | | -------------- | ---- | -------- | -------- | ----- | ---- | ----- | | sqlcoder-70b | 96 | 91.4 | 97.1 | 85.7 | 97.1 | 91.4 | | sqlcoder-7b-2 | 96 | 91.4 | 94.3 | 91.4 | 94.3 | 77.1 | | sqlcoder-34b | 80 | 94.3 | 85.7 | 77.1 | 85.7 | 80 | | gpt-4 | 72 | 94.3 | 97.1 | 80 | 91.4 | 80 | | gpt-4-turbo | 76 | 91.4 | 91.4 | 62.8 | 88.6 | 77.1 | | natural-sql-7b | 56 | 88.6 | 85.7 | 60 | 88.6 | 80 | | sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 | | gpt-3.5 | 72 | 77.1 | 82.8 | 34.3 | 65.7 | 71.4 | | claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 | ## Model Card Contact Contact us on X at [@defogdata](https://twitter.com/defogdata), or on email at [founders@defog.ai](mailto:founders@defog.ai) **URL:** [https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true](https://huggingface.co/defog/sqlcoder-7b-2/blob/main/README.md?code=true) #### Suggested labels ####498: CodeGPTPlus/deepseek-coder-1.3b-typescript · Hugging Face
### Details
Similarity 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-typescript This is a fine-tuned model by the CodeGPT team, specifically crafted for generating expert code in TypeScript. It is fine-tuned from `deepseek-ai/deepseek-coder-1.3b-base` with a dataset of 0.5B tokens, making it an excellent choice for precise and efficient TypeScript code generation. The model uses a 16K window size and an additional fill-in-the-middle task for project-level code completion. ## How to Use This model is for completion purposes only. Here are some examples of how to use the model: ### Running the model on a GPU ```python from 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 - Model: [https://ollama.ai/codegpt/deepseek-coder-1.3b-typescript](https://ollama.ai/codegpt/deepseek-coder-1.3b-typescript) - Command: `ollama run codegpt/deepseek-coder-1.3b-typescript` ### Running with Ollama and CodeGPT Autocomplete in VSCode - Documentation: [https://docs.codegpt.co/docs/tutorial-features/code\_autocompletion](https://docs.codegpt.co/docs/tutorial-features/code_autocompletion) - Select "Ollama - codegpt/deepseek-coder-1.3b-typescript" in the autocomplete model selector. ### Fill In the Middle (FIM) ```python <|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 Procedure The model was trained using the following hyperparameters: - learning\_rate: 2e-05 - train\_batch\_size: 20 - eval\_batch\_size: 20 - seed: 42 - gradient\_accumulation\_steps: 2 - total\_train\_batch\_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr\_scheduler\_type: cosine - lr\_scheduler\_warmup\_steps: 261 - num\_epochs: 1 For more information, visit the [model page](https://huggingface.co/CodeGPTPlus/deepseek-coder-1.3b-typescript). #### 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"
### Details
Similarity 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 $ conda activate codex Check out and install this repository: $ git clone https://github.com/openai/human-eval $ pip install -e human-eval Usage 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"} We provide example_problem.jsonl and example_solutions.jsonl under data to illustrate the format and help with debugging. 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 samples = [ dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"])) for task_id in problems for _ in range(num_samples_per_task) ] write_jsonl("samples.jsonl", samples) To evaluate the samples, run $ evaluate_functional_correctness samples.jsonl Reading samples... 32800it [00:01, 23787.50it/s] Running test suites... 100%|...| 32800/32800 [16:11<00:00, 33.76it/s] Writing results to samples.jsonl_results.jsonl... 100%|...| 32800/32800 [00:00<00:00, 42876.84it/s] {'pass@1': ..., 'pass@10': ..., 'pass@100': ...} This script provides more fine-grained information in a new file ending in628: LLaVA/README.md at main · haotian-liu/LLaVA
### Details
Similarity 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 Assistant *Visual instruction tuning towards large language and vision models with GPT-4 level capabilities.* [📢 LLaVA-NeXT Blog](https://llava-vl.github.io/blog/2024-01-30-llava-next/) [Project Page](https://llava-vl.github.io/) [Demo](https://llava.hliu.cc/) [Data](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md) [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md) 🤝Community Contributions: [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) [Colab](https://github.com/camenduru/LLaVA-colab) [🤗Space](https://huggingface.co/spaces/badayvedat/LLaVA) [Replicate](https://replicate.com/yorickvp/llava-13b) [AutoGen](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_llava.ipynb) [BakLLaVA](https://github.com/SkunkworksAI/BakLLaVA) **Improved Baselines with Visual Instruction Tuning** [Paper](https://arxiv.org/abs/2310.03744) [HF](https://huggingface.co/papers/2310.03744)Haotian Liu, Chunyuan Li, Yuheng Li, Yong Jae Lee **Visual Instruction Tuning** (NeurIPS 2023, Oral) [Paper](https://arxiv.org/abs/2304.08485) [HF](https://huggingface.co/papers/2304.08485)
Haotian Liu*, Chunyuan Li*, Qingyang Wu, Yong Jae Lee (*Equal Contribution) ## Release - [1/30] 🔥 LLaVA-NeXT (LLaVA-1.6) is out! With additional scaling to LLaVA-1.5, LLaVA-NeXT-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the [blog post](https://llava-vl.github.io/blog/2024-01-30-llava-next/), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). Training/eval data and scripts coming soon. - [11/10] [LLaVA-Plus](https://llava-vl.github.io/llava-plus/) is released: Learning to Use Tools for Creating Multimodal Agents, with LLaVA-Plus (LLaVA that Plug and Learn to Use Skills). [Project Page](https://llava-vl.github.io/llava-plus/) [Demo](https://llavaplus.ngrok.io/) [Code](https://github.com/LLaVA-VL/LLaVA-Plus-Codebase) [Paper](https://arxiv.org/abs/2311.05437) - [11/2] [LLaVA-Interactive](https://llava-vl.github.io/llava-interactive/) is released: Experience the future of human-AI multimodal interaction with an all-in-one demo for Image Chat, Segmentation, Generation and Editing. [Project Page](https://llava-vl.github.io/llava-interactive/) [Demo](https://llavainteractive.ngrok.io/) [Code](https://github.com/LLaVA-VL/LLaVA-Interactive-Demo) [Paper](https://arxiv.org/abs/2311.00571) - [10/26] 🔥 LLaVA-1.5 with LoRA achieves comparable performance as full-model finetuning, with a reduced GPU RAM requirement (ckpts) (script). We also provide a doc on how to finetune LLaVA-1.5 on your own dataset with LoRA. - [10/12] Check out the Korean LLaVA (Ko-LLaVA), created by ETRI, who has generously supported our research! [🤗 Demo](https://huggingface.co/spaces/etri-vilab/Ko-LLaVA) - [10/5] 🔥 LLaVA-1.5 is out! Achieving SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the technical report, and explore the demo! Models are available in Model Zoo. The training data and scripts of LLaVA-1.5 are released here, and evaluation scripts are released here. - [9/26] LLaVA is improved with reinforcement learning from human feedback (RLHF) to improve fact grounding and reduce hallucination. Check out the new SFT and RLHF checkpoints at project LLavA-RLHF. - [9/22] LLaVA is accepted by NeurIPS 2023 as oral presentation, and LLaVA-Med is accepted by NeurIPS 2023 Datasets and Benchmarks Track as spotlight presentation.
More
- [11/6] Support Intel dGPU and CPU platforms. More details here. - [10/12] LLaVA is now supported in llama.cpp with 4-bit / 5-bit quantization support! - [10/11] The training data and scripts of LLaVA-1.5 are released here, and evaluation scripts are released here! - [10/10] Roboflow Deep Dive: First Impressions with LLaVA-1.5. - [9/20] We summarize our empirical study of training 33B and 65B LLaVA models in a note. Further, if you are interested in the comprehensive review, evolution and trend of multimodal foundation models, please check out our recent survey paper "Multimodal Foundation Models: From Specialists to General-Purpose Assistants".
tabby/README.md at main · TabbyML/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
* **10/15/2023** RAG-based code completion is enabled by detail in [v0.3.0](https://github.com/TabbyML/tabby/releases/tag/v0.3.0)🎉! Check out the [blogpost](https://tabby.tabbyml.com/blog/2023/10/16/repository-context-for-code-completion/) explaining how Tabby utilizes repo-level context to get even smarter! * **11/27/2023** [v0.6.0](https://github.com/TabbyML/tabby/releases/tag/v0.6.0) released! * **11/09/2023** [v0.5.5](https://github.com/TabbyML/tabby/releases/tag/v0.5.5) released! With a redesign of UI + performance improvement. * **10/04/2023** Check out the [model directory](https://tabby.tabbyml.com/docs/models/) for the latest models supported by Tabby. * **09/18/2023** Apple's M1/M2 Metal inference support has landed in [v0.1.1](https://github.com/TabbyML/tabby/releases/tag/v0.1.1)! * **08/31/2023** Tabby's first stable release [v0.0.1](https://github.com/TabbyML/tabby/releases/tag/v0.0.1) 🥳. * **08/28/2023** Experimental support for the [CodeLlama 7B](https://github.com/TabbyML/tabby/issues/370). * **08/24/2023** Tabby is now on [JetBrains Marketplace](https://plugins.jetbrains.com/plugin/22379-tabby)!👋 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
🌍 Community
🌟 Star History
URL: tabby/README.md
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