Create README.md

README.md

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+---
+pipeline_tag: text-generation
+inference: true
+widget:
+- text: 'def print_hello_world():'
+  example_title: Hello world
+  group: Python
+license: bigscience-openrail-m
+pretrain-datasets:
+- books
+- arxiv
+- c4
+- falcon-refinedweb
+- wiki
+- github-issues
+- stack_markdown
+- self-made dataset of permissive github code
+datasets:
+- bigcode/the-stack-dedup
+- rombodawg/2XUNCENSORED_MegaCodeTraining188k
+- bigcode/commitpackft
+metrics:
+- code_eval
+library_name: transformers
+tags:
+- code
+model-index:
+- name: Refact-1.6B
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval
+    metrics:
+    - name: pass@1 (T=0.01)
+      type: pass@1
+      value: 32.0
+      verified: false
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 31.5
+      verified: false
+    - name: pass@10 (T=0.8)
+      type: pass@10
+      value: 53.0
+      verified: false
+    - name: pass@100 (T=0.8)
+      type: pass@100
+      value: 76.9
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalSynthesize Python
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 35.8
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalSynthesize JavaScript
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 31.6
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalSynthesize Java
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 29.1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalSynthesize Go
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalSynthesize C++
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 26.3
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalSynthesize Rust
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalSynthesize Average
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+
+
+
+
+      
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix Python
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 23.6
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix JavaScript
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix Java
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix Go
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix C++
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix Rust
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalFix Average
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+
+
+
+
+
+      
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalExplain Python
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalExplain JavaScript
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalExplain Java
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalExplain Go
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalExplain C++
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalExplain Rust
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: bigcode/humanevalpack
+      name: HumanEvalExplain Average
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+
+
+  - task:
+      type: text-generation
+    dataset:
+      type: mbpp
+      name: MBPP
+    metrics:
+    - name: pass@1 (T=0.01)
+      type: pass@1
+      value: 31.15
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: ds1000
+      name: DS-1000 (Overall Completion)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (C++)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 21.61
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (C#)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 13.91
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (D)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 9.5
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Go)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 53.57
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Java)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 21.58
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Julia)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 13.75
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (JavaScript)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 26.88
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Lua)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 15.26
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (PHP)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 23.04
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Perl)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 12.1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Python)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 29.6
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (R)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 13.77
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Ruby)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 12.68
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Racket)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 4.29
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Rust)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 19.54
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Scala)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Bash)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 5.7
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Swift)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: 0.1768
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (TypeScript)
+    metrics:
+    - name: pass@1 (T=0.2)
+      type: pass@1
+      value: -1
+      verified: false
+
+language:
+- en
+---
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/643a9dd0c5f633a7fa7e804a/HkB0QYV0BbmB3ktMugbZy.png)
+
+
+# Refact-1.6B
+
+Finally, the model we started training with our blog post
+[Applying Recent Innovations](https://refact.ai/blog/2023/applying-recent-innovations-to-train-model/) is ready 🎉
+
+After fine-tuning on generated data, it beats Replit 3b, Stability Code 3b and many other models. It almost beats
+StarCoder ten times the size!
+
+
+Model                 | Size          | HumanEval pass@1   | HumanEval pass@10  |
+----------------------|---------------|--------------------|--------------------|
+DeciCoder-1b          |   1b          |  19.1%             |                    |
+<b>Refact-1.6-fim</b> | <b>1.6b</b>   |  <b>32.0%</b>      | <b>53.0%</b>       |
+StableCode            |   3b          |  20.2%             | 33.8%              |
+ReplitCode v1         |   3b          |  21.9%             |                    |
+CodeLlama             |   7b          |  33.5%             | 59.6%              |
+StarCoder             |  15b          |  33.6%             |                    |
+
+Likely, it's the best model for practical use in your IDE for code completion because it's smart and fast!
+You can start using it right now by downloading the
+[Refact plugin](https://refact.ai/). You can host the model yourself, too, using the
+[open source docker container](https://github.com/smallcloudai/refact).
+
+And it's multi-language (see MultiPL-HumanEval and other metrics below) and it works as a chat (see the section below).
+
+
+# Architecture
+
+As described in more detail in the blog post, we used:
+
+- [ALiBi](https://arxiv.org/abs/2108.12409) based attention
+- [LayerNorm](https://arxiv.org/abs/1607.06450v1) instead of [RMSNorm](https://arxiv.org/pdf/1910.07467.pdf)
+- [Multi Query Attention](https://arxiv.org/abs/1911.02150)
+
+We also used LiON, flash attention, early dropout. It's not that innovative that you can't run it, in fact you can -- see an example below.
+
+
+# Pretraining
+
+For the base model, we used our own dataset that contains code with permissive licenses only, and open text datasets.
+Filtering is the key to success of this model:
+
+- We only used text in English
+- Only topics related to computer science
+- Applied heavy deduplication
+
+The text to code proportion was 50:50, model trained for 1.2T tokens. 
+
+We don't release the base model, because its Fill-in-the-Middle (FIM) capability likes to repeat itself too much, so
+its practical use is limited. But if you still want it, write us a message on discord.
+
+
+# Finetuning
+
+We tested our hypothesis that chat data should boost base model performance in FIM and
+regular left-to-right code completion. We found that just 15% of open
+[code](https://huggingface.co/datasets/bigcode/commitpackft)
+[instruction-following](https://huggingface.co/datasets/rombodawg/2XUNCENSORED_MegaCodeTraining188k) datasets,
+that we filtered for quality, improves almost all metrics.
+
+Additionally, to improve FIM, we observed common failure modes, and prepared a synthetic dataset based on
+[The Stack dedup v1.1](https://huggingface.co/datasets/bigcode/the-stack-dedup) to address them.
+
+There is a distribution shift between typical code on the internet, and the code you write in your IDE.
+The former is likely finished, so the model tries to come up with a suggestion that makes the code complete.
+You are likely to have half-written code as you work on it, there is no single addition that can repair it
+fully.
+
+In practice, model needs to have a tendency to stop after a couple of lines added, and sometimes don't write
+anything at all. We found that just giving it empty completions, single line completions, multiline
+completions that end with a smaller text indent or at least a newline -- makes it much more usable. This data
+was used as the rest 85% of the finetune dataset.
+
+The final model is the result of several attempts to make it work as good as possible for code completion,
+and to perform well on a wide range of metrics. The best attempt took 40B tokens.
+
+
+# Limitations and Bias
+
+The Refact-1.6B model was trained on text in English. But it has seen a lot more languages in
+code comments. Its performance on non-English languages is lower, for sure.
+
+
+# It Works As a Chat
+
+The primary application of this model is code completion (infill) in multiple programming languages.
+But it works as a chat quite well.
+
+HumanEval results using instruction following (chat) format, against models specialized for chat only:
+
+Model                  | Size   | pass@1   | pass@10  |
+-----------------------|--------|----------|----------|
+<b>Refact-1.6-fim</b>  | 1.6b   |  38.4%   | 55.6%    |
+StableCode-instruct    |   3b   |  26.9%   | 36.2%    |
+OctoGeeX               |   6b   |  44.7%   |          |
+CodeLlama-instruct     |   7b   |  34.8%   | 64.3%    |
+CodeLlama-instruct     |  13b   |  42.7%   | 71.6%    |
+StarChat-β             |  15b   |  33.5%   |          |
+OctoCoder              |  15b   |  46.2%   |          |
+
+
+# Example
+
+Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
+
+```python
+# pip install -q transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "smallcloudai/Refact-1.6B-fim"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device)
+
+prompt = '<fim_prefix>def print_hello_world():\n    """<fim_suffix>\n    print("Hello world!")<fim_middle>'
+
+inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_length=100, temperature=0.2)
+print("-"*80)
+print(tokenizer.decode(outputs[0]))
+```
+
+# Chat Format
+
+The same model works as chat (experimental).
+
+```python
+prompt_template = "<empty_output>SYSTEM {system}\n" \
+                  "<empty_output>USER {query}\n" \
+                  "<empty_output>ASSISTANT"
+prompt = prompt_template.format(system="You are a programming assistant",
+                                query="How do I sort a list in Python?")
+```
+
+# Model Stats
+
+- **Architecture:** LLAMA-like model with multi-query attention
+- **Objectives** Fill-in-the-Middle, Chat
+- **Tokens context:** 4096
+- **Pretraining tokens:** 1.2T
+- **Finetuning tokens:** 40B
+- **Precision:** bfloat16
+- **GPUs** 64 NVidia A5000
+- **Training time** 28 days
+
+
+# License
+
+The model is licensed under the BigScience OpenRAIL-M v1 license agreement
+
+
+# Citation
+
+If you are using this model, please give a link to this page.