Update README.md

README.md

@@ -12,12 +12,23 @@ inference: true
 thumbnail: https://static.wixstatic.com/media/bdee4e_8aa5cefc86024bc88f7e20e3e19d9ff3~mv2.png/v1/fill/w_192%2Ch_192%2Clg_1%2Cusm_0.66_1.00_0.01/bdee4e_8aa5cefc86024bc88f7e20e3e19d9ff3~mv2.png
 ---
 # Model Card
-## Summary
 
-This model, Astrid-Llama-7B, is a Llama model for causal language modeling, designed to generate human-like text.  
-It's part of our mission to make AI technology accessible to everyone, focusing on personalization, data privacy, and transparent AI governance. 
-Trained in English, it's a versatile tool for a variety of applications. 
-This model is one of the many models available on our platform, and we currently have a 1B and 7B open-source model.
+
+# Model Card: PAIXAI/Astrid-LLama-7B
+
+## Summary:
+This model, Astrid-Llama-7B, is a Llama model for causal language modeling, designed to generate human-like text. It's part of the mission to make AI technology accessible to everyone, focusing on personalization, data privacy, and transparent AI governance. Trained in English, it's a versatile tool for a variety of applications. This model is one of many models available on the platform, with both 1B and 7B open-source versions. The model was trained by PAIX.Cloud.
+
+## About PAIX:
+PAIX is a revolutionary AI ecosystem that thrives on the principles of collaboration, innovation, data privacy, and transparent AI governance. It provides a decentralized AI ecosystem that is accessible to all. By fusing the robustness of AI with the transparency of blockchain technology, PAIX propels the development of personalized AI assistants. With blockchain at its core, PAIX ensures enhanced data security and user control, addressing AI alignment issues that have been a concern for the industry.
+
+## PAIX Web4AI Sandbox Environment:
+The PAIX ecosystem includes features like PAIX Web4AI, which allows users to create, test, and train their AI models in a safe sandbox environment through APIs or a no-code setup. The PAIX Playground feature enables users to test and compare different AI models, ensuring alignment with their specific requirements. Additionally, the PAIX Gymnasium feature helps fine-tune AI assistants by injecting personal data, such as emails or social media content, to enhance their understanding of users.
+
+## PAIX Marketplace:
+The upcoming PAIX Marketplace will offer a wide range of AI models, extensions for voices, characters, and other customizable features. Users can integrate these models to enhance their personalized AI assistants. Furthermore, PAIX allows users to commercialize their AI models by selling them on the marketplace, contributing to the growth of the ecosystem.
+
+
 
 This model was trained by [PAIX.Cloud](https://www.paix.cloud/).
 - Wait list: [Wait List](https://www.paix.cloud/join-waitlist)
@@ -78,13 +89,13 @@ from h2oai_pipeline import H2OTextGenerationPipeline
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained(
-    "Stevross/Astrid-LLama-7B-1",
+    "PAIXAI/Astrid-LLama-7B",
     use_fast=False,
     padding_side="left",
     trust_remote_code=True,
 )
 model = AutoModelForCausalLM.from_pretrained(
-    "Stevross/Astrid-LLama-7B-1",
+    "PAIXAI/Astrid-LLama-7B",
     torch_dtype="auto",
     device_map={"": "cuda:0"},
     trust_remote_code=True,
@@ -110,7 +121,7 @@ You may also construct the pipeline from the loaded model and tokenizer yourself
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "Stevross/Astrid-LLama-7B-1"  # either local folder or huggingface model name
+model_name = "PAIXAI/Astrid-LLama-7B"  # either local folder or huggingface model name
 # Important: The prompt needs to be in the same format the model was trained with.
 # You can find an example prompt in the experiment logs.
 prompt = "<|prompt|>How are you?</s><|answer|>"
@@ -144,53 +155,46 @@ tokens = model.generate(
 tokens = tokens[inputs["input_ids"].shape[1]:]
 answer = tokenizer.decode(tokens, skip_special_tokens=True)
 print(answer)
-```
 
-## Model Architecture
+## Usage
 
-```
-LlamaForCausalLM(
-  (model): LlamaModel(
-    (embed_tokens): Embedding(32000, 4096, padding_idx=0)
-    (layers): ModuleList(
-      (0-31): 32 x LlamaDecoderLayer(
-        (self_attn): LlamaAttention(
-          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)
-          (k_proj): Linear(in_features=4096, out_features=4096, bias=False)
-          (v_proj): Linear(in_features=4096, out_features=4096, bias=False)
-          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)
-          (rotary_emb): LlamaRotaryEmbedding()
-        )
-        (mlp): LlamaMLP(
-          (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
-          (down_proj): Linear(in_features=11008, out_features=4096, bias=False)
-          (up_proj): Linear(in_features=4096, out_features=11008, bias=False)
-          (act_fn): SiLUActivation()
-        )
-        (input_layernorm): LlamaRMSNorm()
-        (post_attention_layernorm): LlamaRMSNorm()
-      )
-    )
-    (norm): LlamaRMSNorm()
-  )
-  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
-)
+To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
+
+```bash
+pip install transformers==4.30.1
+pip install accelerate==0.20.3
+pip install torch==2.0.0
 ```
 
-## Model Configuration
+```python
+# Import necessary libraries
+import streamlit as st
+from transformers import pipeline
 
-This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
+# Initialize the chatbot model
+chatbot = pipeline("text-generation", model="PAIXAI/Astrid-LLama-7B")
 
+# Streamlit UI
+st.title("Astrid-LLama-7B Chatbot")
 
-## Model Validation
+# User input
+user_input = st.text_input("You: ", "")
 
-Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
+# Get response from the chatbot
+if st.button("Ask"):
+    with st.spinner("Generating response..."):
+        response = chatbot(user_input, max_length=100, do_sample=True, top_p=0.95, top_k=60)
+        st.write("Bot:", response[0]['generated_text'])
+
+st.sidebar.header("About")
+st.sidebar.text("This is a simple chatbot using\n"
+                "the Astrid-LLama-7B model from\n"
+                "Hugging Face and Streamlit UI.")
 
-```bash
-CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=Stevross/Astrid-LLama-7B-1 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log
 ```
 
 
+
 ## Disclaimer
 
 Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.