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Update README.md

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@@ -6,15 +6,20 @@ tags:
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  - gpt
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  - llm
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  - large language model
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- - h2o-llmstudio
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  inference: true
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- thumbnail: https://static.wixstatic.com/media/bdee4e_d0af74523fa64a998d4cfb894e8cd3bb~mv2.png/v1/crop/x_40,y_663,w_1954,h_663/fill/w_274,h_93,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/PAIX%20Logo%20(2).png
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  ---
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  # Model Card
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  ## Summary
 
 
 
 
 
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- This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
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- - Base model: [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)
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  ## Usage
@@ -37,7 +42,7 @@ Also make sure you are providing your huggingface token to the pipeline if the m
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  from transformers import pipeline
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  generate_text = pipeline(
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- model="Stevross/Astrid-3B",
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  torch_dtype="auto",
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  trust_remote_code=True,
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  use_fast=True,
@@ -75,13 +80,13 @@ from h2oai_pipeline import H2OTextGenerationPipeline
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  tokenizer = AutoTokenizer.from_pretrained(
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- "Stevross/Astrid-3B",
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  use_fast=True,
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  padding_side="left",
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  trust_remote_code=True,
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  )
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  model = AutoModelForCausalLM.from_pretrained(
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- "Stevross/Astrid-3B",
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  torch_dtype="auto",
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  device_map={"": "cuda:0"},
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  trust_remote_code=True,
@@ -107,7 +112,7 @@ You may also construct the pipeline from the loaded model and tokenizer yourself
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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- model_name = "Stevross/Astrid-3B" # either local folder or huggingface model name
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  # Important: The prompt needs to be in the same format the model was trained with.
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  # You can find an example prompt in the experiment logs.
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  prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
 
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  - gpt
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  - llm
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  - large language model
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+ - PAIX.Cloud
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  inference: true
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+ 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
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  ---
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  # Model Card
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  ## Summary
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+ ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss
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+ This model, Astrid-3B, is a StableLMEpochModel model for causal language modeling, designed to generate human-like text.
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+ It's part of our mission to make AI technology accessible to everyone, focusing on personalization, data privacy, and transparent AI governance.
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+ Trained in English, it's a versatile tool for a variety of applications.
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+ This model is one of the many models available on our platform, and we currently have a 1B and 7B open-source model.
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+ This model was trained by [PAIX.Cloud](https://www.paix.cloud/).
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+ - Wait list: [Wait List](https://www.paix.cloud/join-waitlist)
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  ## Usage
 
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  from transformers import pipeline
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  generate_text = pipeline(
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+ model="PAIXAI/Astrid-3B",
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  torch_dtype="auto",
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  trust_remote_code=True,
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  use_fast=True,
 
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  tokenizer = AutoTokenizer.from_pretrained(
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+ "PAIXAI/Astrid-3B",
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  use_fast=True,
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  padding_side="left",
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  trust_remote_code=True,
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  )
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  model = AutoModelForCausalLM.from_pretrained(
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+ "PAIXAI/Astrid-3B",
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  torch_dtype="auto",
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  device_map={"": "cuda:0"},
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  trust_remote_code=True,
 
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model_name = "PAIXAI/Astrid-3B" # either local folder or huggingface model name
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  # Important: The prompt needs to be in the same format the model was trained with.
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  # You can find an example prompt in the experiment logs.
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  prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"