README.md

---
language:
- en
license: other
tags:
- medical
datasets:
- starmpcc/Asclepius-Synthetic-Clinical-Notes
model_name: Asclepius 13B
inference: false
model_creator: Junu Kim
model_link: https://huggingface.co/starmpcc/Asclepius-13B
model_type: llama
pipeline_tag: text2text-generation
quantized_by: TheBloke
base_model: starmpcc/Asclepius-13B
---

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# Asclepius 13B - GGML
- Model creator: [Junu Kim](https://huggingface.co/starmpcc)
- Original model: [Asclepius 13B](https://huggingface.co/starmpcc/Asclepius-13B)

## Description

This repo contains GGML format model files for [Junu Kim's Asclepius 13B](https://huggingface.co/starmpcc/Asclepius-13B).

### Important note regarding GGML files.

The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.

Please use the GGUF models instead.
### About GGML

GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with CUDA GPU acceleration via the c_transformers backend.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.

## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Asclepius-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Asclepius-13B-GGUF)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Asclepius-13B-GGML)
* [Junu Kim's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/starmpcc/Asclepius-13B)

## Prompt template: Asclepius

```
You are an intelligent clinical languge model.
Below is a snippet of patient's discharge summary and a following instruction from healthcare professional.
Write a response that appropriately completes the instruction.
The response should provide the accurate answer to the instruction, while being concise.

[Discharge Summary Begin]
Notes go here
[Discharge Summary End]

[Instruction Begin]
{prompt}
[Instruction End] 

```

<!-- compatibility_ggml start -->
## Compatibility

These quantised GGML files are compatible with llama.cpp between June 6th (commit `2d43387`) and August 21st 2023.

For support with latest llama.cpp, please use GGUF files instead.

The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)

As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.

## Explanation of the new k-quant methods
<details>
  <summary>Click to see details</summary>

The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_ggml end -->

## Provided files

| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [asclepius-13b.ggmlv3.Q2_K.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q2_K.bin) | Q2_K | 2 | 5.51 GB| 8.01 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| [asclepius-13b.ggmlv3.Q3_K_S.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q3_K_S.bin) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| [asclepius-13b.ggmlv3.Q3_K_M.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q3_K_M.bin) | Q3_K_M | 3 | 6.31 GB| 8.81 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| [asclepius-13b.ggmlv3.Q3_K_L.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q3_K_L.bin) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| [asclepius-13b.ggmlv3.Q4_0.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q4_0.bin) | Q4_0 | 4 | 7.37 GB| 9.87 GB | Original quant method, 4-bit. |
| [asclepius-13b.ggmlv3.Q4_K_S.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q4_K_S.bin) | Q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| [asclepius-13b.ggmlv3.Q4_K_M.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q4_K_M.bin) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| [asclepius-13b.ggmlv3.Q4_1.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q4_1.bin) | Q4_1 | 4 | 8.17 GB| 10.67 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| [asclepius-13b.ggmlv3.Q5_0.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q5_0.bin) | Q5_0 | 5 | 8.97 GB| 11.47 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| [asclepius-13b.ggmlv3.Q5_K_S.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q5_K_S.bin) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| [asclepius-13b.ggmlv3.Q5_K_M.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q5_K_M.bin) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| [asclepius-13b.ggmlv3.Q5_1.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q5_1.bin) | Q5_1 | 5 | 9.78 GB| 12.28 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| [asclepius-13b.ggmlv3.Q6_K.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q6_K.bin) | Q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| [asclepius-13b.ggmlv3.Q8_0.bin](https://huggingface.co/TheBloke/Asclepius-13B-GGML/blob/main/asclepius-13b.ggmlv3.Q8_0.bin) | Q8_0 | 8 | 13.79 GB| 16.29 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

## How to run in `llama.cpp`

Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.

For compatibility with latest llama.cpp, please use GGUF files instead.

```
./main -t 10 -ngl 32 -m asclepius-13b.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are an intelligent clinical languge model.\nBelow is a snippet of patient's discharge summary and a following instruction from healthcare professional.\nWrite a response that appropriately completes the instruction.\nThe response should provide the accurate answer to the instruction, while being concise.\n\n[Discharge Summary Begin]\nNotes go here\n[Discharge Summary End]\n\n[Instruction Begin]\nWrite a story about llamas\n[Instruction End]"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.

Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change `-c 2048` to the desired sequence length for this model. For example, `-c 4096` for a Llama 2 model.  For models that use RoPE, add `--rope-freq-base 10000 --rope-freq-scale 0.5` for doubled context, or `--rope-freq-base 10000 --rope-freq-scale 0.25` for 4x context.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

## How to run in `text-generation-webui`

Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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# Original model card: Junu Kim's Asclepius 13B

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is official model checkpoint for Asclepius-13B [arxiv](todo)
This model is the first publicly shareable clinical LLM, trained with synthetic data.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Model type:** Clinical LLM (Large Language Model)
- **Language(s) (NLP):** English
- **License:** CC-BY-NC-SA 4.0
- **Finetuned from model [optional]:** LLaMA-13B

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/starmpcc/Asclepius
- **Paper [optional]:** TODO Arxiv
- **Data:** https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This model can perform below 8 clinical NLP tasks, with clincal notes.
- Named Entity Recognition
- Abbreviation Expansion
- Relation Extraction
- Temporal Information Extraction
- Coreference Resolution
- Paraphrasing
- Summarization
- Question Answering

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

ONLY USE THIS MODEL FOR RESEARCH PURPOSE!!

## How to Get Started with the Model

```python
prompt = """You are an intelligent clinical languge model.
Below is a snippet of patient's discharge summary and a following instruction from healthcare professional.
Write a response that appropriately completes the instruction.
The response should provide the accurate answer to the instruction, while being concise.

[Discharge Summary Begin]
{note}
[Discharge Summary End]

[Instruction Begin]
{question}
[Instruction End] 
"""

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-13B")
model = AutoModel.from_pretrained("starmpcc/Asclepius-13B")

note = "This is a sample note"
question = "What is the diagnosis?"

model_input = prompt.format(note=note, question=question)
input_ids = tokenizer(model_input, return_tensors="pt").input_ids
output = model.generate(input_ids)
print(tokenizer.decode(output[0]))
```

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- Initial training was conducted using causal language modeling on synthetic clinical notes.
- It was then fine-tuned with clinical instruction-response pairs.
- For a comprehensive overview of our methods, our upcoming paper will serve as a resource.

#### Training Hyperparameters

- We followed config used in [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
- 
#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
- Pre-Training (1 epoch): 1h 52m with 8x A100 80G
- Instruction Fine-Tuning (3 epoch): 12h 16m with 8x A100 80G



## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]