ssmits_-_Falcon2-5.5B-multilingual-gguf

Quantization made by Richard Erkhov.

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Falcon2-5.5B-multilingual - GGUF

• Model creator: https://huggingface.co/ssmits/
• Original model: https://huggingface.co/ssmits/Falcon2-5.5B-multilingual/

Original model description:

base_model:

• tiiuae/falcon-11B library_name: transformers tags:
• mergekit
• merge
• lazymergekit
• tiiuae/falcon-11B license: apache-2.0 language:
• es
• fr
• de
• 'no'
• sv
• da
• nl
• pt
• pl
• ro
• it
• cs

---

Why prune?

Even though Falcon-11B is trained on 5T tokens, it is still undertrained, as can be seen by this graph: This is why the choice is made to prune 50% of the layers. Note that ~1B of continued pre-training (~1M rows of 1k tokens) is still required to restore the perplexity of this model in the desired language. I'm planning on doing that for certain languages when fineweb-edu-{specific_language} will be available, depending on how much compute will be available.

sliced

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was pruned using the passthrough merge method.

Models Merged

The following models were included in the merge:

• tiiuae/falcon-11B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: tiiuae/falcon-11B
        layer_range: [0, 24]
  - sources:
      - model: tiiuae/falcon-11B
        layer_range: [55, 59]
merge_method: passthrough
dtype: bfloat16

PruneMe has been utilized using the wikimedia/wikipedia subsets of 11 languages by investigating layer similarity with 2000 samples per language. The layer ranges for pruning were determined based on the averages of each language analysis to maintain performance while reducing model size.

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = "ssmits/Falcon2-5.5B-multilingual"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
)
sequences = pipeline(
   "Can you explain the concepts of Quantum Computing?",
    max_length=200,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

💥 Falcon LLMs require PyTorch 2.0 for use with transformers!

For fast inference with Falcon, check-out Text Generation Inference! Read more in this [blogpost]((https://huggingface.co/blog/falcon).

Direct Use

Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)

Out-of-Scope Use

Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.

Bias, Risks, and Limitations

Falcon2-5.5B is trained mostly on English, but also German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.

Recommendations

We recommend users of Falcon2-5.5B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.