PAIXAI-Astrid-3B-gguf

⚠️ ARCHIVED / LEGACY MODEL NOTICE
This repository is part of a legacy collection quantized around 2023. To manage storage quotas and maintain active community projects, some rarely used quantization formats (e.g., Q2_K, Q3_K, Q4_1, Q5_1) have been permanently removed.

Only the most popular and stable formats (Q4_0, Q4_K_M, Q5_K_M, Q6_K, and Q8_0) remain available.

💡 Looking for something modern? If you are starting a new project, we highly recommend using newer architectures (like Llama 3, Mistral, or Qwen) provided by official maintainers or active community members (e.g., Bartowski, TheBloke legacy files, or official organization handles).

⚠️ This repository is no longer actively maintained. Existing files are provided "as is" for archival and legacy hardware purposes.

I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information

Astrid-3B - GGUF

• Model creator: PAIXAI
• Original model: Astrid-3B

StableLM

This is a Model based on StableLM. Stablelm is a familiy of Language Models by Stability AI.

Note:

Current (as of 2023-11-15) implementations of Llama.cpp only support GPU offloading up to 34 Layers with these StableLM Models. The model will crash immediately if -ngl is larger than 34. The model works fine however without any gpu acceleration.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.

Note:

Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions. (This mainly refers to Falcon 7b and Starcoder models)

K-quants

K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load. So, if possible, use K-quants. With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.

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Original Model Card:

Model Card

Summary

This model, Astrid-3B, is a StableLMEpochModel 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.

This model was trained by PAIX.Cloud.

• Wait list: Wait List

Usage

To use the model with the transformers library on a machine with GPUs, first make sure you have the transformers library installed.

pip install transformers==4.34.0

Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave token=True in the pipeline and login to hugginface_hub by running python import huggingface_hub huggingface_hub.login(<ACCES_TOKEN>) - Or directly pass your <ACCES_TOKEN> to token in the pipeline

from transformers import pipeline

generate_text = pipeline(
    model="PAIXAI/Astrid-3B",
    torch_dtype="auto",
    trust_remote_code=True,
    use_fast=True,
    device_map={"": "cuda:0"},
    token=True,
)

res = generate_text(
    "Why is drinking water so healthy?",
    min_new_tokens=2,
    max_new_tokens=256,
    do_sample=False,
    num_beams=1,
    temperature=float(0.3),
    repetition_penalty=float(1.2),
    renormalize_logits=True
)
print(res[0]["generated_text"])

You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:

print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])

<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>

Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the transformers package, this will allow you to set trust_remote_code=False.

from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "PAIXAI/Astrid-3B",
    use_fast=True,
    padding_side="left",
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "PAIXAI/Astrid-3B",
    torch_dtype="auto",
    device_map={"": "cuda:0"},
    trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)

res = generate_text(
    "Why is drinking water so healthy?",
    min_new_tokens=2,
    max_new_tokens=256,
    do_sample=False,
    num_beams=1,
    temperature=float(0.3),
    repetition_penalty=float(1.2),
    renormalize_logits=True
)
print(res[0]["generated_text"])

You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "PAIXAI/Astrid-3B"  # 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?<|endoftext|><|answer|>"

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    use_fast=True,
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map={"": "cuda:0"},
    trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")

# generate configuration can be modified to your needs
tokens = model.generate(
    input_ids=inputs["input_ids"],
    attention_mask=inputs["attention_mask"],
    min_new_tokens=2,
    max_new_tokens=256,
    do_sample=False,
    num_beams=1,
    temperature=float(0.3),
    repetition_penalty=float(1.2),
    renormalize_logits=True
)[0]

tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)

Quantization and sharding

You can load the models using quantization by specifying load_in_8bit=True or load_in_4bit=True. Also, sharding on multiple GPUs is possible by setting device_map=auto.

Model Architecture

StableLMEpochForCausalLM(
  (model): StableLMEpochModel(
    (embed_tokens): Embedding(50304, 2560, padding_idx=0)
    (layers): ModuleList(
      (0-31): 32 x DecoderLayer(
        (self_attn): Attention(
          (q_proj): Linear(in_features=2560, out_features=2560, bias=False)
          (k_proj): Linear(in_features=2560, out_features=2560, bias=False)
          (v_proj): Linear(in_features=2560, out_features=2560, bias=False)
          (o_proj): Linear(in_features=2560, out_features=2560, bias=False)
          (rotary_emb): RotaryEmbedding()
        )
        (mlp): MLP(
          (gate_proj): Linear(in_features=2560, out_features=6912, bias=False)
          (up_proj): Linear(in_features=2560, out_features=6912, bias=False)
          (down_proj): Linear(in_features=6912, out_features=2560, bias=False)
          (act_fn): SiLU()
        )
        (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
        (post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
      )
    )
    (norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=2560, out_features=50304, bias=False)
)

Model Configuration

This model was trained using H2O LLM Studio and with the configuration in cfg.yaml. Visit H2O LLM Studio to learn how to train your own large language models.

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.

• Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
• Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
• Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
• Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
• Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
• Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.

By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

End of original Model File

Please consider to support my work

Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.

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