| | --- |
| | base_model: HuggingFaceH4/starchat2-15b-sft-v0.1 |
| | tags: |
| | - alignment-handbook |
| | - generated_from_trainer |
| | datasets: |
| | - HuggingFaceH4/ultrafeedback_binarized |
| | - HuggingFaceH4/orca_dpo_pairs |
| | model-index: |
| | - name: starchat2-15b-v0.1 |
| | results: [] |
| | --- |
| | |
| | <img src="https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/resolve/main/model_logo.png" alt="StarChat2 15B Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
| |
|
| | # Model Card for StarChat2 15B |
| |
|
| | StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat2 is the latest model in the series, and is a fine-tuned version of [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b) that was trained with SFT and DPO on a mix of synthetic datasets. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| |
|
| | <!-- Provide a longer summary of what this model is. --> |
| |
|
| | - **Model type:** A 16B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. |
| | - **Language(s) (NLP):** Primarily English and 600+ programming languages. |
| | - **License:** BigCode Open RAIL-M v1 |
| | - **Finetuned from model:** [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) |
| |
|
| | ### Model Sources |
| |
|
| | <!-- Provide the basic links for the model. --> |
| |
|
| | - **Repository:** https://github.com/huggingface/alignment-handbook |
| | - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground |
| |
|
| | ## Performance |
| |
|
| | StarChat2 15B was trained to balance chat and programming capabilities. It achieves strong performance on chat benchmarks like [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [IFEval](https://arxiv.org/abs/2311.07911), as well as the canonical HumanEval benchmark for Python code completion. The scores reported below were obtained using the [LightEval](https://github.com/huggingface/lighteval) evaluation suite (commit `988959cb905df4baa050f82b4d499d46e8b537f2`) and each prompt has been formatted with the model's corresponding chat template to simulate real-world usage. This is why some scores may differ from those reported in technical reports or on the Open LLM Leaderboard. |
| |
|
| | | Model | MT Bench | IFEval | HumanEval | |
| | |-------------------------------------------------------------------------------------------------|---------:|-------:|----------:| |
| | | [starchat2-15b-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1) | 7.66 | 35.12 | 71.34 | |
| | | [deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) | 4.17 | 14.23 | 80.48 | |
| | | [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | 6.80 | 43.44 | 50.60 | |
| |
|
| |
|
| | ## Intended uses & limitations |
| |
|
| | The model was fine-tuned on a blend of chat, code, math, and reasoning datasets. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground) to test its coding capabilities. |
| |
|
| | Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: |
| |
|
| | ```python |
| | # pip install 'transformers @ git+https://github.com/huggingface/transformers.git@831bc25d8fdb85768402f772cf65cc3d7872b211' |
| | # pip install accelerate |
| | |
| | import torch |
| | from transformers import pipeline |
| | |
| | pipe = pipeline( |
| | "text-generation", |
| | model="HuggingFaceH4/starchat2-15b-v0.1", |
| | device_map="auto", |
| | torch_dtype=torch.bfloat16, |
| | ) |
| | messages = [ |
| | { |
| | "role": "system", |
| | "content": "You are StarChat2, an expert programming assistant", |
| | }, |
| | {"role": "user", "content": "Write a simple website in HTML. When a user clicks the button, it shows a random Chuck Norris joke."}, |
| | ] |
| | outputs = pipe( |
| | messages, |
| | max_new_tokens=512, |
| | do_sample=True, |
| | temperature=0.7, |
| | top_k=50, |
| | top_p=0.95, |
| | stop_sequence="<|im_end|>", |
| | ) |
| | print(outputs[0]["generated_text"][-1]["content"]) |
| | ``` |
| |
|
| | ## Bias, Risks, and Limitations |
| |
|
| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
| |
|
| | StarChat2 15B has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). |
| | Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the [StarCoder2 dataset](https://huggingface.co/datasets/bigcode/the-stack-v2) |
| |
|
| | Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect. |
| | For example, it may produce code that does not compile or that produces incorrect results. |
| | It may also produce code that is vulnerable to security exploits. |
| | We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking. |
| |
|
| | StarChat2 15B was fine-tuned from the base model [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b), please refer to its model card's [Limitations Section](https://huggingface.co/bigcode/starcoder2-15b#limitations) for relevant information. |
| | In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its [technical report](https://huggingface.co/papers/2402.19173). |
| |
|
| |
|
| | ## Training details |
| |
|
| | This model is a fine-tuned version of [starchat2-15b-sft-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1) on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/orca_dpo_pairs datasets. Check out the recipe in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for more details. |
| | |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.4347 |
| | - Rewards/chosen: -0.9461 |
| | - Rewards/rejected: -2.7745 |
| | - Rewards/accuracies: 0.7658 |
| | - Rewards/margins: 1.8284 |
| | - Logps/rejected: -322.1934 |
| | - Logps/chosen: -316.1898 |
| | - Logits/rejected: -2.3817 |
| | - Logits/chosen: -2.3005 |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-07 |
| | - train_batch_size: 2 |
| | - eval_batch_size: 4 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 8 |
| | - gradient_accumulation_steps: 8 |
| | - total_train_batch_size: 128 |
| | - total_eval_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 2 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
| | | 0.717 | 0.17 | 100 | 0.6006 | -0.0924 | -0.2899 | 0.6329 | 0.1975 | -272.5022 | -299.1165 | -2.5313 | -2.4191 | |
| | | 0.6273 | 0.35 | 200 | 0.5160 | -0.3994 | -0.9461 | 0.6930 | 0.5467 | -285.6261 | -305.2568 | -2.5281 | -2.4278 | |
| | | 0.5538 | 0.52 | 300 | 0.4781 | -0.6589 | -1.5892 | 0.7247 | 0.9302 | -298.4870 | -310.4470 | -2.4996 | -2.4110 | |
| | | 0.5056 | 0.7 | 400 | 0.4594 | -0.8283 | -2.1332 | 0.7437 | 1.3050 | -309.3687 | -313.8344 | -2.4472 | -2.3644 | |
| | | 0.4983 | 0.87 | 500 | 0.4512 | -0.7758 | -2.2806 | 0.7468 | 1.5049 | -312.3167 | -312.7843 | -2.4223 | -2.3404 | |
| | | 0.4662 | 1.04 | 600 | 0.4431 | -0.7839 | -2.4016 | 0.7658 | 1.6177 | -314.7355 | -312.9465 | -2.4049 | -2.3215 | |
| | | 0.4411 | 1.22 | 700 | 0.4415 | -1.0090 | -2.7582 | 0.7690 | 1.7492 | -321.8679 | -317.4481 | -2.3840 | -2.3016 | |
| | | 0.471 | 1.39 | 800 | 0.4368 | -0.9617 | -2.7445 | 0.7690 | 1.7828 | -321.5930 | -316.5019 | -2.3809 | -2.2991 | |
| | | 0.4485 | 1.57 | 900 | 0.4351 | -0.9490 | -2.7594 | 0.7722 | 1.8103 | -321.8916 | -316.2497 | -2.3815 | -2.3004 | |
| | | 0.4411 | 1.74 | 1000 | 0.4348 | -0.9293 | -2.7469 | 0.7658 | 1.8176 | -321.6409 | -315.8547 | -2.3823 | -2.3011 | |
| | | 0.4499 | 1.92 | 1100 | 0.4348 | -0.9482 | -2.7767 | 0.7658 | 1.8285 | -322.2369 | -316.2320 | -2.3828 | -2.3012 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.0.dev0 |
| | - Pytorch 2.1.2+cu121 |
| | - Datasets 2.16.1 |
| | - Tokenizers 0.15.1 |
| |
|
