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Model: codeparrot/codeparrot-small-multi
Source: Original Platform

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README.md

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+---
+language: 
+  - code
+license: apache-2.0
+tags:
+- code
+- gpt2
+- generation
+datasets:
+- "codeparrot/github-code-clean"
+- "openai_humaneval"
+metrics:
+- "evaluate-metric/code_eval"
+---
+
+# CodeParrot-Multi 🦜 (small)
+
+CodeParrot-Multi 🦜 is a GPT-2 model (110M parameters) trained to generate code in 9 programming languages: "Java", "JavaScript", "PHP", "Python", "C#", "C++", "GO", "Ruby" and "TypeScript".
+
+## Usage
+
+You can load the CodeParrot-Multi model and tokenizer directly in `transformers`:
+
+```Python
+from transformers import AutoTokenizer, AutoModelWithLMHead
+  
+tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-multi")
+model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small-multi")
+
+inputs = tokenizer("def hello_world():", return_tensors="pt")
+outputs = model(**inputs)
+```
+
+or with a `pipeline`:
+
+```Python
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="codeparrot/codeparrot-small-multi")
+outputs = pipe("def hello_world():")
+```
+
+## Training
+
+The model was trained on the small [Github code small](https://huggingface.co/datasets/loubnabnl/github-small-near-dedup) after near deduplication, a subset of [Github code dataset](https://huggingface.co/datasets/codeparrot/github-code-clean) with the following settings:
+
+|Config|Value|
+|-------|-----|
+|Batch size| 192 |
+|Context size| 1024 |
+|Training steps| 300'000|
+|Gradient accumulation| 2|
+|Gradient checkpointing| False|
+|Learning rate| 5e-4 |
+|Weight decay | 0.1 |
+|Warmup steps| 2000 |
+|Schedule| Cosine |
+
+The training was executed on 16 x A100 (40GB) GPUs. This setting amounts to roughly 58 billion tokens.
+
+## Performance
+
+We evaluated the model on OpenAI's [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark which consists of programming challenges:
+
+| Metric | Value |
+|-------|-----|
+|pass@1 | --% |
+|pass@10 | --%	 |
+|pass@100 | --% |
+
+The [pass@k metric](https://huggingface.co/metrics/code_eval) tells the probability that at least one out of k generations passes the tests. 
+
+## Resources
+
+- Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot)

config.json

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+{
+  "activation_function": "gelu_fast",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "tokenizer_class": "GPT2TokenizerFast",
+  "transformers_version": "4.19.0",
+  "use_cache": true,
+  "vocab_size": 32768
+}

eval_results.txt

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+temperature 0.2:  {'pass@1': 0.027439024390243896, 'pass@10': 0.041979293327556816, 'pass@100': 0.04268292682926801}
+temperature 0.8:  {'pass@1': 0.013262195121951224, 'pass@10': 0.04670902438514701, 'pass@100': 0.0899080946922357}
+best: {'pass@1': 2.74% , 'pass@10': 4.67 , 'pass@100': 8.99%}

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