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vigogne-falcon-7b-chat - GGUF

Model creator: bofenghuang
Original model: vigogne-falcon-7b-chat

K-Quants in Falcon 7b models

New Llama.cpp releases now allow for K-quantization of models that were previously incompatible with K-quants. This is achieved by employing a fallback solution for model layers that cannot be accurately quantized with K-quants.

For Falcon 7B models, although only a quarter of the layers can be quantized with true K-quants, this approach still benefits from utilizing various legacy quantization types, such as Q4_0, Q4_1, Q5_0, and Q5_1. As a result, it offers better quality at the same file size or smaller file sizes with comparable performance.

So this solution ensures improved performance and efficiency over legacy Q4_0, Q4_1, Q5_0 and Q5_1 Quantizations.

Important Update for Falcon Models in llama.cpp Versions After October 18, 2023

As previously noted on the Llama.cpp GitHub repository, all new Llama.cpp releases after October 18, 2023, required re-quantization due to the implementation of the new BPE tokenizer.

Update: The re-quantization process for Falcon Models is now complete, and the latest quantized models are available for download. To ensure continued compatibility with recent llama.cpp software, You need to update your Falcon models.

Key Points:

Stay Informed: Keep an eye on software application release schedules using llama.cpp libraries.
Monitor Upload Times: Re-quantization is complete. Watch for updates on my Hugging Face Model pages.

This change primarily affects Falcon and Starcoder models, with other models remaining unaffected. If you haven't already, please update your Falcon models for seamless compatibility with the latest llama.cpp versions.

Brief

Vigogne-Falcon-7B-Chat is a Falcon-7B model fine-tuned to conduct multi-turn dialogues in French between human user and AI assistant.

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.

Original Model Card:

Vigogne-Falcon-7B-Chat: A French Chat Falcon Model

Vigogne-Falcon-7B-Chat is a Falcon-7B model fine-tuned to conduct multi-turn dialogues in French between human user and AI assistant.

For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne

Changelog

All versions are available in branches.

V1.0: Initial release.
V2.0: Expanded training dataset to 419k for better performance.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from vigogne.preprocess import generate_inference_chat_prompt

model_name_or_path = "bofenghuang/vigogne-falcon-7b-chat"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

user_query = "Expliquez la différence entre DoS et phishing."
prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer)
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device)
input_length = input_ids.shape[1]

generated_outputs = model.generate(
    input_ids=input_ids,
    generation_config=GenerationConfig(
        temperature=0.1,
        do_sample=True,
        repetition_penalty=1.0,
        max_new_tokens=512,
    ),
    return_dict_in_generate=True,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
generated_tokens = generated_outputs.sequences[0, input_length:]
generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(generated_text)

<!-- You can infer this model by using the following Google Colab Notebook.

Limitations

Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.

End of original Model File

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