README.md

---
base_model: Writer/Palmyra-Med-70B-32K
language:
- en
license: other
license_name: writer-open-model-license
license_link: https://writer.com/legal/open-model-license/
pipeline_tag: text-generation
tags:
- instruct
- finetune
- DPO
- distillation
- heathcare
- medical
- clinical
- med
- lifescience
- Pharmaceutical
- Pharma
- Enterprise LLM
- Enterprise
- Enterprise ready
quantized_by: bartowski
extra_gated_prompt: By clicking "Agree", you agree to the [License Agreement](https://writer.com/legal/open-model-license/)
  and acknowledge Writer's [Privacy Policy](https://writer.com/legal/acceptable-use/).
extra_gated_fields:
  Name: text
  Email: text
  Organization or Affiliation: text
  Receive email updates and promotions on Writer products, services, and research?:
    type: select
    options:
    - 'Yes'
    - 'No'
  I acknowledge that this model is for non-commercial use only unless I acquire a separate license from Writer: checkbox
widget:
- example_title: Palmyra-Med-70B-32k
  messages:
  - role: system
    content: You are a highly knowledgeable and experienced expert in the healthcare
      and biomedical field, possessing extensive medical knowledge and practical expertise.
  - role: user
    content: Does danzhi Xiaoyao San ameliorate depressive-like behavior by shifting
      toward serotonin via the downregulation of hippocampal indoleamine 2,3-dioxygenase?
  output:
    text: Danzhi Xiaoyao San (DXS) exhibited significant antidepressant-like effects
      in rats subjected to chronic unpredictable mild stress (CUMS), a commonly used
      model for inducing depressive-like behavior. The administration of DXS led to
      a notable reduction in the activity of indoleamine 2,3-dioxygenase (IDO) in
      the hippocampus. This reduction in IDO activity is crucial because IDO is an
      enzyme that catalyzes the degradation of tryptophan into kynurenine (Kyn), thereby
      diverting tryptophan away from the serotonin (5-HT) synthesis pathway. By downregulating
      IDO, DXS effectively shifts the balance of tryptophan metabolism towards increased
      serotonin production, thereby enhancing the availability of serotonin. This
      shift in the Kyn/5-HT pathway favoring serotonin is likely a key mechanism underlying
      the antidepressant effects observed with DXS treatment. Enhanced serotonin levels
      in the hippocampus are associated with improved mood and reduction of depressive
      symptoms, suggesting that DXS could be a promising therapeutic agent for managing
      depression.
model-index:
- name: Palmyra-Med-70B-32k
  results: []
---

## Llamacpp imatrix Quantizations of Palmyra-Med-70B-32K

Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3496">b3496</a> for quantization.

Original model: https://huggingface.co/Writer/Palmyra-Med-70B-32K

All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)

Run them in [LM Studio](https://lmstudio.ai/)

## Prompt format

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>


```

## Download a file (not the whole branch) from below:

| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Palmyra-Med-70B-32K-Q8_0.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/tree/main/Palmyra-Med-70B-32K-Q8_0) | Q8_0 | 74.98GB | true | Extremely high quality, generally unneeded but max available quant. |
| [Palmyra-Med-70B-32K-Q6_K.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/tree/main/Palmyra-Med-70B-32K-Q6_K) | Q6_K | 57.89GB | true | Very high quality, near perfect, *recommended*. |
| [Palmyra-Med-70B-32K-Q5_K_M.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/tree/main/Palmyra-Med-70B-32K-Q5_K_M) | Q5_K_M | 49.95GB | true | High quality, *recommended*. |
| [Palmyra-Med-70B-32K-Q5_K_S.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q5_K_S.gguf) | Q5_K_S | 48.66GB | false | High quality, *recommended*. |
| [Palmyra-Med-70B-32K-Q4_K_L.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q4_K_L.gguf) | Q4_K_L | 43.30GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Palmyra-Med-70B-32K-Q4_K_M.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q4_K_M.gguf) | Q4_K_M | 42.52GB | false | Good quality, default size for must use cases, *recommended*. |
| [Palmyra-Med-70B-32K-Q4_K_S.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q4_K_S.gguf) | Q4_K_S | 40.35GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Palmyra-Med-70B-32K-Q3_K_XL.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q3_K_XL.gguf) | Q3_K_XL | 38.06GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Palmyra-Med-70B-32K-IQ4_XS.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-IQ4_XS.gguf) | IQ4_XS | 37.90GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Palmyra-Med-70B-32K-Q3_K_L.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q3_K_L.gguf) | Q3_K_L | 37.14GB | false | Lower quality but usable, good for low RAM availability. |
| [Palmyra-Med-70B-32K-Q3_K_M.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q3_K_M.gguf) | Q3_K_M | 34.27GB | false | Low quality. |
| [Palmyra-Med-70B-32K-IQ3_M.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-IQ3_M.gguf) | IQ3_M | 31.94GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Palmyra-Med-70B-32K-Q3_K_S.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q3_K_S.gguf) | Q3_K_S | 30.91GB | false | Low quality, not recommended. |
| [Palmyra-Med-70B-32K-IQ3_XS.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-IQ3_XS.gguf) | IQ3_XS | 29.31GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Palmyra-Med-70B-32K-Q2_K_L.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q2_K_L.gguf) | Q2_K_L | 27.40GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Palmyra-Med-70B-32K-Q2_K.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-Q2_K.gguf) | Q2_K | 26.38GB | false | Very low quality but surprisingly usable. |
| [Palmyra-Med-70B-32K-IQ2_M.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-IQ2_M.gguf) | IQ2_M | 24.12GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Palmyra-Med-70B-32K-IQ2_XS.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-IQ2_XS.gguf) | IQ2_XS | 21.14GB | false | Low quality, uses SOTA techniques to be usable. |
| [Palmyra-Med-70B-32K-IQ2_XXS.gguf](https://huggingface.co/bartowski/Palmyra-Med-70B-32K-GGUF/blob/main/Palmyra-Med-70B-32K-IQ2_XXS.gguf) | IQ2_XXS | 19.10GB | false | Very low quality, uses SOTA techniques to be usable. |

## Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.

Thanks!

## Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset

Thank you ZeroWw for the inspiration to experiment with embed/output

## Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

```
pip install -U "huggingface_hub[cli]"
```

Then, you can target the specific file you want:

```
huggingface-cli download bartowski/Palmyra-Med-70B-32K-GGUF --include "Palmyra-Med-70B-32K-Q4_K_M.gguf" --local-dir ./
```

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

```
huggingface-cli download bartowski/Palmyra-Med-70B-32K-GGUF --include "Palmyra-Med-70B-32K-Q8_0/*" --local-dir ./
```

You can either specify a new local-dir (Palmyra-Med-70B-32K-Q8_0) or download them all in place (./)

## Which file should I choose?

A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski