---
base_model: sethuiyer/Medichat-Llama3-8B
library_name: transformers
tags:
- mergekit
- merge
- medical
- TensorBlock
- GGUF
license: other
datasets:
- mlabonne/orpo-dpo-mix-40k
- Open-Orca/SlimOrca-Dedup
- jondurbin/airoboros-3.2
- microsoft/orca-math-word-problems-200k
- m-a-p/Code-Feedback
- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
- ruslanmv/ai-medical-chatbot
language:
- en
model-index:
- name: Medichat-Llama3-8B
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 59.13
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 82.9
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 60.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 49.65
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 78.93
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 60.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Medichat-Llama3-8B
      name: Open LLM Leaderboard
---

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## sethuiyer/Medichat-Llama3-8B - GGUF

This repo contains GGUF format model files for [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

## Prompt template

```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Medichat-Llama3-8B-Q2_K.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
| [Medichat-Llama3-8B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
| [Medichat-Llama3-8B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
| [Medichat-Llama3-8B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
| [Medichat-Llama3-8B-Q4_0.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Medichat-Llama3-8B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
| [Medichat-Llama3-8B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
| [Medichat-Llama3-8B-Q5_0.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Medichat-Llama3-8B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
| [Medichat-Llama3-8B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
| [Medichat-Llama3-8B-Q6_K.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
| [Medichat-Llama3-8B-Q8_0.gguf](https://huggingface.co/tensorblock/Medichat-Llama3-8B-GGUF/tree/main/Medichat-Llama3-8B-Q8_0.gguf) | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

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

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Medichat-Llama3-8B-GGUF --include "Medichat-Llama3-8B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/Medichat-Llama3-8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```