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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
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+
+tiny_starcoder_py - AWQ
+- Model creator: https://huggingface.co/bigcode/
+- Original model: https://huggingface.co/bigcode/tiny_starcoder_py/
+
+
+
+
+Original model description:
+---
+pipeline_tag: text-generation
+inference: true
+widget:
+- text: 'def print_hello_world():'
+  example_title: Hello world
+  group: Python
+license: bigcode-openrail-m
+datasets:
+- bigcode/the-stack-dedup
+metrics:
+- code_eval
+library_name: transformers
+tags:
+- code
+model-index:
+- name: Tiny-StarCoder-Py
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 7.84%
+      verified: false
+---
+
+# TinyStarCoderPy
+
+This is a 164M parameters model with the same architecture as [StarCoder](https://huggingface.co/bigcode/starcoder) (8k context length, MQA & FIM). It was trained on the Python data from [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata)
+for ~6 epochs which amounts to 100B tokens.
+
+
+## Use
+
+### Intended use
+
+The model was trained on GitHub code, to assist with some tasks like [Assisted Generation](https://huggingface.co/blog/assisted-generation). For pure code completion, we advise using our 15B models [StarCoder]() or [StarCoderBase]().
+
+
+### Generation
+```python
+# pip install -q transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "bigcode/tiny_starcoder_py"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+
+inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Fill-in-the-middle
+Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
+
+```python
+input_text = "<fim_prefix>def print_one_two_three():\n    print('one')\n    <fim_suffix>\n    print('three')<fim_middle>"
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+# Training
+
+## Model
+
+- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
+- **Pretraining steps:** 50k
+- **Pretraining tokens:** 100 billion
+- **Precision:** bfloat16
+
+## Hardware
+
+- **GPUs:** 32 Tesla A100
+- **Training time:** 18 hours
+
+## Software
+
+- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
+- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
+- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
+
+# License
+The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
+
+