"Update README.md"

README.md

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 ---
+language:
+- en
+library_name: transformers
+tags:
+- gpt
+- llm
+- large language model
+- h2o-llmstudio
+inference: false
+thumbnail: >-
+  https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
 license: apache-2.0
+datasets:
+- OpenAssistant/oasst1
 ---
+[![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]()
+
+## I am still building the structure of these descriptions.
+
+These will contain increasingly more content to help find the best models for a purpose.
+
+# h2ogpt-gm-oasst1-en-2048-falcon-40b-v2 - GGUF
+- Model creator: [h2oai](https://huggingface.co/h2oai)
+- Original model: [h2ogpt-gm-oasst1-en-2048-falcon-40b-v2](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2)
+
+
+
+# About GGUF format
+
+`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/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](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
+
+# Quantization variants
+
+There is a bunch of quantized files available. How to choose the best 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.
+Falcon 7B models cannot be quantized to K-quants.
+
+# K-quants
+
+K-quants are based on the idea that the quantization of certain parts affects the quality in different ways. If you quantize certain parts more and others less, you get a more powerful model with the same file size, or a smaller file size and lower memory load with comparable performance.
+So, if possible, use K-quants.
+With a Q6_K you should find it really hard to find a quality difference to 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](https://github.com/h2oai/h2o-llmstudio).
+- Base model: [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
+- Dataset preparation: [OpenAssistant/oasst1](https://github.com/h2oai/h2o-llmstudio/blob/1935d84d9caafed3ee686ad2733eb02d2abfce57/app_utils/utils.py#LL1896C5-L1896C28) 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.
+
+```bash
+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
+```
+
+```python
+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:
+
+```python
+print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
+```
+
+```bash
+<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
+```
+
+Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
+
+```python
+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:
+
+```python
+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](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) 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.<center>
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