Quantization made by Richard Erkhov.
tiny_starcoder_py - GGUF
- Model creator: https://huggingface.co/bigcode/
- Original model: https://huggingface.co/bigcode/tiny_starcoder_py/
| Name | Quant method | Size |
|---|---|---|
| tiny_starcoder_py.Q2_K.gguf | Q2_K | 0.1GB |
| tiny_starcoder_py.IQ3_XS.gguf | IQ3_XS | 0.1GB |
| tiny_starcoder_py.IQ3_S.gguf | IQ3_S | 0.1GB |
| tiny_starcoder_py.Q3_K_S.gguf | Q3_K_S | 0.1GB |
| tiny_starcoder_py.IQ3_M.gguf | IQ3_M | 0.11GB |
| tiny_starcoder_py.Q3_K.gguf | Q3_K | 0.11GB |
| tiny_starcoder_py.Q3_K_M.gguf | Q3_K_M | 0.11GB |
| tiny_starcoder_py.Q3_K_L.gguf | Q3_K_L | 0.12GB |
| tiny_starcoder_py.IQ4_XS.gguf | IQ4_XS | 0.11GB |
| tiny_starcoder_py.Q4_0.gguf | Q4_0 | 0.12GB |
| tiny_starcoder_py.IQ4_NL.gguf | IQ4_NL | 0.12GB |
| tiny_starcoder_py.Q4_K_S.gguf | Q4_K_S | 0.12GB |
| tiny_starcoder_py.Q4_K.gguf | Q4_K | 0.12GB |
| tiny_starcoder_py.Q4_K_M.gguf | Q4_K_M | 0.12GB |
| tiny_starcoder_py.Q4_1.gguf | Q4_1 | 0.12GB |
| tiny_starcoder_py.Q5_0.gguf | Q5_0 | 0.13GB |
| tiny_starcoder_py.Q5_K_S.gguf | Q5_K_S | 0.13GB |
| tiny_starcoder_py.Q5_K.gguf | Q5_K | 0.14GB |
| tiny_starcoder_py.Q5_K_M.gguf | Q5_K_M | 0.14GB |
| tiny_starcoder_py.Q5_1.gguf | Q5_1 | 0.14GB |
| tiny_starcoder_py.Q6_K.gguf | Q6_K | 0.15GB |
| tiny_starcoder_py.Q8_0.gguf | Q8_0 | 0.18GB |
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 (8k context length, MQA & FIM). It was trained on the Python data from 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. For pure code completion, we advise using our 15B models StarCoder or StarCoderBase.
Generation
# 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:
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
- Neural networks: PyTorch
- BP16 if applicable: apex
License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.