Quant for 6.5

README.md

@@ -82,68 +82,132 @@ model-index:
     metrics:
     - type: pass@1
       value: 40.6
-quantized_by: bartowski
 ---
 
-## Exllama v2 Quantizations of starcoder2-15b-instruct-v0.1
+# StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20">turboderp's ExLlamaV2 v0.0.20</a> for quantization.
+![Banner](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/banner.png)
 
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
+## Model Summary
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+We introduce StarCoder2-15B-Instruct-v0.1, the very first entirely self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. Our open-source pipeline uses StarCoder2-15B to generate thousands of instruction-response pairs, which are then used to fine-tune StarCoder-15B itself without any human annotations or distilled data from huge and proprietary LLMs.
 
-Conversion was done using the default calibration dataset.
+- **Model:** [bigcode/starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1)
+- **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align)
+- **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/)
+- **Authors:**
+[Yuxiang Wei](https://yuxiang.cs.illinois.edu),
+[Federico Cassano](https://federico.codes/),
+[Jiawei Liu](https://jw-liu.xyz),
+[Yifeng Ding](https://yifeng-ding.com),
+[Naman Jain](https://naman-ntc.github.io),
+[Harm de Vries](https://www.harmdevries.com),
+[Leandro von Werra](https://twitter.com/lvwerra),
+[Arjun Guha](https://www.khoury.northeastern.edu/home/arjunguha/main/home/),
+[Lingming Zhang](https://lingming.cs.illinois.edu).
 
-Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
+![self-alignment pipeline](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/method.png)
 
-Original model: https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1
+## Use
 
+### Intended use
 
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-exl2/tree/8_0">8.0 bits per weight</a>
+The model is designed to respond to **coding-related instructions in a single turn**. Instructions in other styles may result in less accurate responses.
 
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-exl2/tree/6_5">6.5 bits per weight</a>
+Here is an example to get started with the model using the [transformers](https://huggingface.co/docs/transformers/index) library:
 
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-exl2/tree/5_0">5.0 bits per weight</a>
+```python
+import transformers
+import torch
 
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-exl2/tree/4_25">4.25 bits per weight</a>
+pipeline = transformers.pipeline(
+    model="bigcode/starcoder2-15b-instruct-v0.1",
+    task="text-generation",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
 
-<a href="https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-exl2/tree/3_5">3.5 bits per weight</a>
+def respond(instruction: str, response_prefix: str) -> str:
+    messages = [{"role": "user", "content": instruction}]
+    prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False)
+    prompt += response_prefix
 
+    teminators = [
+        pipeline.tokenizer.eos_token_id,
+        pipeline.tokenizer.convert_tokens_to_ids("###"),
+    ]
 
-## Download instructions
+    result = pipeline(
+        prompt,
+        max_length=256,
+        num_return_sequences=1,
+        do_sample=False,
+        eos_token_id=teminators,
+        pad_token_id=pipeline.tokenizer.eos_token_id,
+        truncation=True,
+    )
+    response = response_prefix + result[0]["generated_text"][len(prompt) :].split("###")[0].rstrip()
+    return response
 
-With git:
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/starcoder2-15b-instruct-v0.1-exl2
+instruction = "Write a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria."
+response_prefix = ""
+
+print(respond(instruction, response_prefix))
 ```
 
-With huggingface hub (credit to TheBloke for instructions):
+Here is the expected output:
 
-```shell
-pip3 install huggingface-hub
-```
+``````
+Here's how you can implement a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria:
+
+```python
+from typing import TypeVar, Callable
+
+T = TypeVar('T')
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `starcoder2-15b-instruct-v0.1-exl2`:
+def quicksort(items: list[T], less_than: Callable[[T, T], bool] = lambda x, y: x < y) -> list[T]:
+    if len(items) <= 1:
+        return items
 
-```shell
-mkdir starcoder2-15b-instruct-v0.1-exl2
-huggingface-cli download bartowski/starcoder2-15b-instruct-v0.1-exl2 --local-dir starcoder2-15b-instruct-v0.1-exl2 --local-dir-use-symlinks False
+    pivot = items[0]
+    less = [x for x in items[1:] if less_than(x, pivot)]
+    greater = [x for x in items[1:] if not less_than(x, pivot)]
+    return quicksort(less, less_than) + [pivot] + quicksort(greater, less_than)
 ```
+``````
 
-To download from a different branch, add the `--revision` parameter:
+### Bias, Risks, and Limitations
 
-Linux:
+StarCoder2-15B-Instruct-v0.1 is primarily finetuned for Python code generation tasks that can be verified through execution, which may lead to certain biases and limitations. For example, the model might not adhere strictly to instructions that dictate the output format. In these situations, it's beneficial to provide a **response prefix** or a **one-shot example** to steer the model’s output. Additionally, the model may have limitations with other programming languages and out-of-domain coding tasks.
 
-```shell
-mkdir starcoder2-15b-instruct-v0.1-exl2-6_5
-huggingface-cli download bartowski/starcoder2-15b-instruct-v0.1-exl2 --revision 6_5 --local-dir starcoder2-15b-instruct-v0.1-exl2-6_5 --local-dir-use-symlinks False
-```
+The model also inherits the bias, risks, and limitations from its base StarCoder2-15B model. For more information, please refer to the [StarCoder2-15B model card](https://huggingface.co/bigcode/starcoder2-15b).
 
-Windows (which apparently doesn't like _ in folders sometimes?):
+## Evaluation on EvalPlus, LiveCodeBench, and DS-1000
 
-```shell
-mkdir starcoder2-15b-instruct-v0.1-exl2-6.5
-huggingface-cli download bartowski/starcoder2-15b-instruct-v0.1-exl2 --revision 6_5 --local-dir starcoder2-15b-instruct-v0.1-exl2-6.5 --local-dir-use-symlinks False
-```
+![EvalPlus](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/evalplus.png)
+
+![LiveCodeBench and DS-1000](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/lcb-ds1000.png)
+
+## Training Details
+
+### Hyperparameters
+
+- **Optimizer:** Adafactor
+- **Learning rate:** 1e-5
+- **Epoch:** 4
+- **Batch size:** 64
+- **Warmup ratio:** 0.05
+- **Scheduler:** Linear
+- **Sequence length:** 1280
+- **Dropout**: Not applied
+
+### Hardware
+
+1 x NVIDIA A100 80GB
+
+## Resources
+
+- **Model:** [bigcode/starCoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1)
+- **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align)
+- **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/)

config.json

@@ -0,0 +1,42 @@
+{
+    "_name_or_path": "starcoder2-instruct-15b-v0.1",
+    "architectures": [
+        "Starcoder2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 0,
+    "embedding_dropout": 0.0,
+    "eos_token_id": 0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 6144,
+    "initializer_range": 0.01275,
+    "intermediate_size": 24576,
+    "max_position_embeddings": 16384,
+    "mlp_type": "default",
+    "model_type": "starcoder2",
+    "norm_epsilon": 1e-05,
+    "norm_type": "layer_norm",
+    "num_attention_heads": 48,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 4,
+    "residual_dropout": 0.0,
+    "rope_theta": 100000,
+    "sliding_window": 4096,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.39.0.dev0",
+    "use_bias": true,
+    "use_cache": true,
+    "vocab_size": 49152,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.0.20",
+        "bits": 6.5,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.39.0.dev0"
+}

model.safetensors.index.json

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