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h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 - GGUF

Model creator: h2oai
Original model: h2ogpt-gm-oasst1-en-2048-falcon-40b-v2

K-Quants in Falcon 7b models

New releases of Llama.cpp now support K-quantization for previously incompatible models, in particular all Falcon 7B models (While Falcon 40b is and always has been fully compatible with K-Quantisation). This is achieved by employing a fallback solution for model layers that cannot be quantized with real 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 different legacy quantization types 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.

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:

Model Card

Summary

This model was trained using H2O LLM Studio.

Base model: tiiuae/falcon-40b
Dataset preparation: OpenAssistant/oasst1 personalized

Usage

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

pip install transformers==4.29.2
pip install bitsandbytes==0.39.0
pip install accelerate==0.19.0
pip install torch==2.0.0
pip install einops==0.6.1

import torch
from transformers import pipeline, BitsAndBytesConfig, AutoTokenizer

model_kwargs = {}

quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,
)
model_kwargs["quantization_config"] = quantization_config

tokenizer = AutoTokenizer.from_pretrained(
    "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
    use_fast=False,
    padding_side="left",
    trust_remote_code=True,
)

generate_text = pipeline(
    model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    trust_remote_code=True,
    use_fast=False,
    device_map={"": "cuda:0"},
    model_kwargs=model_kwargs,
)

res = generate_text(
    "Why is drinking water so healthy?",
    min_new_tokens=2,
    max_new_tokens=1024,
    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:

import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,
)

tokenizer = AutoTokenizer.from_pretrained(
    "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
    use_fast=False,
    padding_side="left",
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map={"": "cuda:0"},
    quantization_config=quantization_config
).eval()
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)

res = generate_text(
    "Why is drinking water so healthy?",
    min_new_tokens=2,
    max_new_tokens=1024,
    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, BitsAndBytesConfig

# 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|>"

quantization_config = None
# optional quantization
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,
)

tokenizer = AutoTokenizer.from_pretrained(
    "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
    use_fast=False,
    padding_side="left",
    trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map={"": "cuda:0"},
    quantization_config=quantization_config
).eval()

inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")

# generate configuration can be modified to your needs
tokens = model.generate(
    **inputs,
    min_new_tokens=2,
    max_new_tokens=1024,
    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)

Model Architecture

RWForCausalLM(
  (transformer): RWModel(
    (word_embeddings): Embedding(65024, 8192)
    (h): ModuleList(
      (0-59): 60 x DecoderLayer(
        (ln_attn): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
        (ln_mlp): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
        (self_attention): Attention(
          (maybe_rotary): RotaryEmbedding()
          (query_key_value): Linear(in_features=8192, out_features=9216, bias=False)
          (dense): Linear(in_features=8192, out_features=8192, bias=False)
          (attention_dropout): Dropout(p=0.0, inplace=False)
        )
        (mlp): MLP(
          (dense_h_to_4h): Linear(in_features=8192, out_features=32768, bias=False)
          (act): GELU(approximate='none')
          (dense_4h_to_h): Linear(in_features=32768, out_features=8192, bias=False)
        )
      )
    )
    (ln_f): LayerNorm((8192,), eps=1e-05, elementwise_affine=True)
  )
  (lm_head): Linear(in_features=8192, out_features=65024, 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

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