Update README.md

README.md

@@ -3,9 +3,9 @@ library_name: transformers
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-# Model Card for Model ID
+# Mistral-7B fine-tuned on AgentInstruct
 
-<!-- Provide a quick summary of what the model is/does. -->
+[Mistral-7b-v1.0]() fine-tuned on the dataset [AgentInstruct] for "*better* acting as an agent"
 
 
 
@@ -13,65 +13,44 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. 
+Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
 
-- **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 Architecture
 
-### Model Sources [optional]
+Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
+- Grouped-Query Attention
+- Sliding-Window Attention
+- Byte-fallback BPE tokenizer
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
-## Uses
+## Dataset Details
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+**AgentInstruct** is a meticulously curated dataset featuring **1,866** high-quality interactions, designed to enhance AI agents across six diverse real-world tasks, leveraging innovative methods like **Task Derivation** and **Self-Instruct**.
 
-### Direct Use
+- 🔍 **CoT** - Harness the power of [ReAct](https://react-lm.github.io/), offering detailed thought explanations for each action, ensuring an intricate understanding of the model's decision-making journey.
+- 🌍 **Diversity** - Spanning 6 real-world scenarios, from Daily Household Routines to Database Operations, and their average turns range from 5 to 35.
+- 🎯 **Precision** - Not all trajectories of GPT-4 are effective! Ours are rigorously filtered using strict rewards to ensure top-notch quality.
+- ✅ **Assurance** - Rigorous checks to avoid data leakage, ensuring pristine dataset quality.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Task Overview
 
-[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
+| Task | # Filt. Traj. | Avg # Filt. Traj. Turns |
+|---|---|---|
+|ALFWorld|336|13.52|
+|WebShop|351|3.68|
+|Mind2Web|122|1.00|
+|Knowledge Graph|324|6.04|
+|Operating System|195|3.85|
+|Database|538|2.06|
+|**AgentInstruct**|1866|5.24|
 
-<!-- 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]
+AgentInstruct includes 1,866 trajectories from
+6 agents tasks. "Traj." stands for interaction trajectory. "Filt. Traj."
+stands for filtered trajectories. 
 
 ## Training Details