Update README.md

README.md

@@ -1,6 +1,82 @@
 ---
 inference: false
-license: other
+pipeline_tag: text-generation
+license: bigcode-openrail-m
+datasets:
+- bigcode/the-stack-dedup
+- tiiuae/falcon-refinedweb
+metrics:
+- code_eval
+- mmlu
+- arc
+- hellaswag
+- truthfulqa
+library_name: transformers
+tags:
+- code
+model-index:
+- name: StarCoderPlus
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: openai_humaneval
+      name: HumanEval (Prompted)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 26.7
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: MMLU (5-shot)
+      name: MMLU
+    metrics:
+    - name: Accuracy
+      type: Accuracy
+      value: 45.1
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: HellaSwag (10-shot)
+      name: HellaSwag
+    metrics:
+    - name: Accuracy
+      type: Accuracy
+      value: 77.3
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: ARC (25-shot)
+      name: ARC
+    metrics:
+    - name: Accuracy
+      type: Accuracy
+      value: 48.9
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: ThrutfulQA (0-shot)
+      name: ThrutfulQA
+    metrics:
+    - name: Accuracy
+      type: Accuracy
+      value: 37.9
+      verified: false
+extra_gated_prompt: >-
+  ## Model License Agreement
+
+  Please read the BigCode [OpenRAIL-M
+  license](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement)
+  agreement before accepting it.
+    
+extra_gated_fields:
+  I accept the above license agreement, and will use the Model complying with the set of use restrictions and sharing requirements: checkbox
+
 ---
 
 <!-- header start -->
@@ -21,12 +97,12 @@ license: other
 
 These files are GPTQ 4bit model files for [Bigcode's StarcoderPlus](https://huggingface.co/bigcode/starcoderplus).
 
-It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
+It is the result of quantising to 4bit using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ).
 
 ## Repositories available
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/starcoderplus-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/none)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/starcoderplus-GGML)
 * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/bigcode/starcoderplus)
 
 ## How to easily download and use this model in text-generation-webui
@@ -58,7 +134,6 @@ from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
 import argparse
 
 model_name_or_path = "TheBloke/starcoderplus-GPTQ"
-model_basename = "gptq_model-4bit--1g"
 
 use_triton = False
 
@@ -74,31 +149,19 @@ model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
 
 print("\n\n*** Generate:")
 
-input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
-output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
-print(tokenizer.decode(output[0]))
-
-# Inference can also be done using transformers' pipeline
-
-# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
-logging.set_verbosity(logging.CRITICAL)
-
-prompt = "Tell me about AI"
-prompt_template=f'''### Human: {prompt}
-### Assistant:'''
+inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
 
-print("*** Pipeline:")
-pipe = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_new_tokens=512,
-    temperature=0.7,
-    top_p=0.95,
-    repetition_penalty=1.15
-)
+### Fill-in-the-middle
+Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
 
-print(pipe(prompt_template)[0]['generated_text'])
+```python
+input_text = "<fim_prefix>def print_hello_world():\n    <fim_suffix>\n    print('Hello world!')<fim_middle>"
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
 ```
 
 ## Provided files
@@ -145,4 +208,94 @@ Thank you to all my generous patrons and donaters!
 
 # Original model card: Bigcode's StarcoderPlus
 
-No original model card was provided.
+# StarCoderPlus
+
+Play with the instruction-tuned StarCoderPlus at [StarChat-Beta](https://huggingface.co/spaces/HuggingFaceH4/starchat-playground).
+
+##  Table of Contents
+
+1. [Model Summary](##model-summary)
+2. [Use](##use)
+3. [Limitations](##limitations)
+4. [Training](##training)
+5. [License](##license)
+6. [Citation](##citation)
+
+## Model Summary
+
+StarCoderPlus is a fine-tuned version of [StarCoderBase](https://huggingface.co/bigcode/starcoderbase) on 600B tokens from the English web dataset [RedefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) 
+combined with [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack) and a Wikipedia dataset.
+It's a 15.5B parameter Language Model trained on English and 80+ programming languages. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150),
+[a context window of 8192 tokens](https://arxiv.org/abs/2205.14135),  and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1.6 trillion tokens. 
+
+- **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
+- **Project Website:** [bigcode-project.org](https://www.bigcode-project.org)
+- **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org)
+- **Languages:** English & 80+ Programming languages
+
+
+## Use
+
+### Intended use
+
+The model was trained on English and GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, the instruction-tuned version in [StarChat](hhttps://huggingface.co/spaces/HuggingFaceH4/starchat-playground) makes a capable assistant.
+
+**Feel free to share your generations in the Community tab!**
+
+### Generation
+```python
+# pip install -q transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "bigcode/starcoderplus"
+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_hello_world():\n    <fim_suffix>\n    print('Hello world!')<fim_middle>"
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(inputs)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Attribution & Other Requirements
+
+The training code dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
+
+# Limitations
+
+The model has been trained on a mixture of English text from the web and GitHub code. Therefore it might encounter limitations when working with non-English text, and can carry the stereotypes and biases commonly encountered online.
+Additionally, the generated code should be used with caution as it may contain errors, inefficiencies, or potential vulnerabilities. For a more comprehensive understanding of the base model's code limitations, please refer to See [StarCoder paper](hhttps://arxiv.org/abs/2305.06161). 
+
+# Training
+StarCoderPlus is a fine-tuned version on 600B English and code tokens of StarCoderBase, which was pre-trained on 1T code tokens. Below are the fine-tuning details:
+
+## Model
+- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
+- **Finetuning steps:** 150k
+- **Finetuning tokens:** 600B
+- **Precision:** bfloat16
+
+## Hardware
+
+- **GPUs:** 512 Tesla A100
+- **Training time:** 14 days
+
+## 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).