add measurement.json

README.md

@@ -0,0 +1,159 @@
+---
+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 80+ 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