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README.md

@@ -1,85 +1,218 @@
 ---
-license: apache-2.0
-language:
-- en
-tags:
-- api
-datasets:
-- gorilla-llm/APIBench
+library_name: peft
+base_model: gorilla-llm/gorilla-7b-hf-delta-v1
 ---
-# Gorilla: Large Language Model Connected with Massive APIs
-By Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez  ([Project Website](https://shishirpatil.github.io/gorilla/))
 
-[![arXiv](https://img.shields.io/badge/arXiv-2305.15334-<COLOR>.svg?style=flat-square)](https://arxiv.org/abs/2305.15334)  [![Discord](https://img.shields.io/discord/1111172801899012102?label=Discord&logo=discord&logoColor=green&style=flat-square)](https://discord.gg/3apqwwME) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing)
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
 
-`Gorilla` enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla can write a semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! Hop on our Discord, or open a PR, or email us if you would like to have your API incorporated as well.
 
 ## Model Details
 
-Gorilla can be either trained via standard finetuning or using our novel retriever-aware training pipeline. We release `gorilla-7b-hf-delta-v1`, a 0-shot finetuned LLM that can reliably use Hugging Face APIs. It can be prompted through simply natural language (e.g., "I want to generate an image from text."). Checkour our website, github and paper for more information.
-Thank you for the feedback, with `gorilla-llm/gorilla-7b-hf-delta-v1` Gorilla now ouput's code snippet that can be directly integrated into your workflow!
-
-Now with `gorilla-llm/gorilla-7b-hf-delta-v1`
-
-Example prompt1: "I would like to translate 'I feel very good today.' from English to Chinese."
-
-🦍 Response: 
-
-```
-from transformers import pipeline
-def load_model():
-    translation_model = pipeline(‘translation_en_to_zh’, model=‘Helsinki-NLP/opus-mt-en-zh’)
-    return translation_model
-def process_data(text_to_translate, translation_model):
-    response = translation_model(text_to_translate)[0][‘translation_text’]
-    return response
-text_to_translate = ‘I feel very good today’
-# Load the model
-translation_model = load_model()
-# Process the data
-translated_text = process_data(text_to_translate, translation_model)
-print(translated_text)
-```
-
-Example prompt2: "I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]"
-
-🦍 Response: 
-
-```
-from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification
-from PIL import Image
-def load_model():
-    feature_extractor = ConvNextFeatureExtractor.from_pretrained(‘facebook/convnext-large-224’)
-    model = ConvNextForImageClassification.from_pretrained(‘facebook/convnext-large-224’)
-    return feature_extractor, model
-def process_data(image_path, feature_extractor, model):
-    image = Image.open(image_path)
-    inputs = feature_extractor(images=image, return_tensors=‘pt’)
-    logits = model(**inputs).logits
-    predicted_label = logits.argmax(-1).item()
-    response = model.config.id2label[predicted_label]
-    return response
-image_path = ‘/content/cat.jpeg’
-# Load the model and feature extractor
-feature_extractor, model = load_model()
-# Process the data
-response = process_data(image_path, feature_extractor, model)
-print(response)
-```
-
-### Model Type
-
-Gorilla is an open-source API caller trained by fine-tuning LLaMA weights. It is an auto-regressive language model, based on the transformer architecture. 
-Gorilla also has Apache 2.0 models trained on MPT-7B and Falcon-7B base, which cab be used commercially with no obligations. 
-
-### Model Date
-
-06/15/2023
-
-### Organization
-
-Gorilla LLM (UC Berkeley)
+### Model Description
 
----
-license: apache-2.0
----
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+
+## Training procedure
+
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: bitsandbytes
+- load_in_8bit: True
+- load_in_4bit: False
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: fp4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float32
+
+### Framework versions
+
+- PEFT 0.6.3.dev0

adapter_config.json

@@ -0,0 +1,26 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "gorilla-llm/gorilla-7b-hf-delta-v1",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.safetensors

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