pansophic-rocket-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⚠️ This repository is no longer actively maintained.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

rocket-3B - GGUF

• Model creator: pansophic
• Original model: rocket-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:

<img src="https://cdn-uploads.huggingface.co/production/uploads/6501bfe0493fd9c8c2e32402/BmbkjOkcTm-YMa-unolmJ.png" alt="Rocket Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

Rocket-3B 🦝

<b>Rocket</b> 🦝 is a 3 billion large language model that was trained on a mix of publicly available datasets using Direct Preference Optimization (DPO). The prompt format used is <b>ChatML</b>.

Model description

• Model type: A 3B parameter GPT-like model fine-tuned on a mix of publicly available datasets using DPO.
• Language(s) (NLP): Primarily English
• License: CC-BY-SA-4.0
• Finetuned from model: Stability AI

Performance

Despite its compact dimensions, the model achieves outstanding scores in both MT-Bench MT-Bench and AlpacaEval benchmarks, surpassing the performance of considerably larger models.

Specifically, across various categories within the MT-Bench evaluation, Rocket-3B demonstrates impressive performance when compared to larger open models such as Llama2-Chat-7B, Falcon-40B-Instruct, and Guanaco-65B.

MT-Bench detailed score for first and second turn

In MT-Bench, Rocket 🦝 scores 6.99 in the first turn and 6.13 in the second turn, with an average score of 6.56. These scores reflect the model's performance in understanding and generating text during different parts of a conversation.

AlpacaEval detailed scores

In AlpacaEval, Rocket 🦝 achieves a near 80% win rate, coupled with an average response length of 1,242 tokens, indicating its effectiveness in producing detailed responses.

Other benchmarks

Intended uses & limitations

Initially, we fine-tuned the model using a dataset created by merging and curating multiple datasets, available on the HuggingFace Hub. This dataset will be released to the public soon. We further enhanced the model's performance using DPO, selecting samples from the openbmb/UltraFeedback and BAAI/JudgeLM-100K datasets. The outcome is a highly effective chat model with a 3 billion parameter scale.

Input Format

The model is trained with the ChatML format:

<|im_start|>system
System message here.<|im_end|>
<|im_start|>user
Your message here!<|im_end|>
<|im_start|>assistant

Here's how you can run the model using 🤗 Transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model = AutoModelForCausalLM.from_pretrained("pansophic/rocket-3B", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("pansophic/rocket-3B", trust_remote_code=True, torch_dtype=torch.bfloat16)
streamer = TextStreamer(tokenizer)

prompt = """<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
"""

system = "You are a helpful assistant."
user = "How are you?"

# Apply the ChatML format
prompt = prompt.format(system=system, user=user)

# Tokenize the prompt
inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False).to("cuda")
generated_text = model.generate(**inputs, max_length=3084, top_p=0.95, do_sample=True, temperature=0.7, use_cache=True, streamer=streamer)

# <|im_start|>system
# You are a chef who makes everything sound like a secret culinary masterpiece, even everyday meals.<|im_end|>
# <|im_start|>user
# How to cook an omelette?<|im_end|>
# <|im_start|>assistant
# Ah, the art of crafting the perfect omelette, a secret culinary masterpiece indeed.
# Begin by gently whisking two to three eggs in a mixing bowl, and then pour the silky liquid into a non-stick pan.
# Allow the eggs to dance and sizzle as you swiftly tilt the pan to spread the joy throughout the entire omelette universe.
# As the edges begin to set, fold the omelette in half with a gentle flourish, and you'll witness a stunning display of culinary prowess.
# Enjoy this enchanting creation, and you'll be transported to a world of secret culinary mastery.<|im_end|>

Bias, Risks, and Limitations

Unlike ChatGPT, which incorporates in-the-loop filtering of responses and is aligned during the RLHF phase for safe completions, our model lacks these features. Consequently, it may generate problematic outputs, particularly when prompted in certain ways. Below is the score of the model on Toxigen benchmark.

The pretraining dataset is comprised of a filtered mixture of open-source large-scale datasets available on the HuggingFace Hub: Falcon RefinedWeb extract (Penedo et al., 2023), RedPajama-Data (Together Computer., 2023) and The Pile (Gao et al., 2020) both without the Books3 subset, and StarCoder (Li et al., 2023).

*The model name is inspired by the small but formidable character from 'Guardians of the Galaxy'. Similar to its namesake, this model, with its 3 billion parameters, showcases remarkable efficiency and effectiveness, challenging larger models despite its smaller size."

Model card adapted from Zephyr Beta and Tulu-2-7B

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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