File size: 9,384 Bytes

---
language:
- en
license: apache-2.0
tags:
- text-generation
- TensorBlock
- GGUF
datasets:
- THUDM/webglm-qa
- databricks/databricks-dolly-15k
- cognitivecomputations/wizard_vicuna_70k_unfiltered
- totally-not-an-llm/EverythingLM-data-V3
- Amod/mental_health_counseling_conversations
- sablo/oasst2_curated
- starfishmedical/webGPT_x_dolly
- Open-Orca/OpenOrca
- mlabonne/chatml_dpo_pairs
base_model: Felladrin/Llama-68M-Chat-v1
widget:
- messages:
  - role: system
    content: You are a career counselor. The user will provide you with an individual
      looking for guidance in their professional life, and your task is to assist
      them in determining what careers they are most suited for based on their skills,
      interests, and experience. You should also conduct research into the various
      options available, explain the job market trends in different industries, and
      advice on which qualifications would be beneficial for pursuing particular fields.
  - role: user
    content: Heya!
  - role: assistant
    content: Hi! How may I help you?
  - role: user
    content: I am interested in developing a career in software engineering. What
      would you recommend me to do?
- messages:
  - role: system
    content: You are a knowledgeable assistant. Help the user as much as you can.
  - role: user
    content: How to become healthier?
- messages:
  - role: system
    content: You are a helpful assistant who provides concise responses.
  - role: user
    content: Hi!
  - role: assistant
    content: Hello there! How may I help you?
  - role: user
    content: I need to build a simple website. Where should I start learning about
      web development?
- messages:
  - role: system
    content: You are a very creative assistant. User will give you a task, which you
      should complete with all your knowledge.
  - role: user
    content: Write the background story of an RPG game about wizards and dragons in
      a sci-fi world.
inference:
  parameters:
    max_new_tokens: 64
    penalty_alpha: 0.5
    top_k: 4
model-index:
- name: Llama-68M-Chat-v1
  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: 23.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
      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: 28.27
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
      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: 25.18
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
      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: 47.27
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
      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: 54.3
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
      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: 0.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-68M-Chat-v1
      name: Open LLM Leaderboard
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
        </p>
    </div>
</div>

## Felladrin/Llama-68M-Chat-v1 - GGUF

This repo contains GGUF format model files for [Felladrin/Llama-68M-Chat-v1](https://huggingface.co/Felladrin/Llama-68M-Chat-v1).

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).


<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## 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 |
| -------- | ---------- | --------- | ----------- |
| [Llama-68M-Chat-v1-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q2_K.gguf) | Q2_K | 0.033 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama-68M-Chat-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q3_K_S.gguf) | Q3_K_S | 0.037 GB | very small, high quality loss |
| [Llama-68M-Chat-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q3_K_M.gguf) | Q3_K_M | 0.038 GB | very small, high quality loss |
| [Llama-68M-Chat-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q3_K_L.gguf) | Q3_K_L | 0.039 GB | small, substantial quality loss |
| [Llama-68M-Chat-v1-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q4_0.gguf) | Q4_0 | 0.042 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama-68M-Chat-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q4_K_S.gguf) | Q4_K_S | 0.042 GB | small, greater quality loss |
| [Llama-68M-Chat-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q4_K_M.gguf) | Q4_K_M | 0.043 GB | medium, balanced quality - recommended |
| [Llama-68M-Chat-v1-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q5_0.gguf) | Q5_0 | 0.047 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama-68M-Chat-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q5_K_S.gguf) | Q5_K_S | 0.047 GB | large, low quality loss - recommended |
| [Llama-68M-Chat-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q5_K_M.gguf) | Q5_K_M | 0.048 GB | large, very low quality loss - recommended |
| [Llama-68M-Chat-v1-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q6_K.gguf) | Q6_K | 0.053 GB | very large, extremely low quality loss |
| [Llama-68M-Chat-v1-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-68M-Chat-v1-GGUF/blob/main/Llama-68M-Chat-v1-Q8_0.gguf) | Q8_0 | 0.068 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/Llama-68M-Chat-v1-GGUF --include "Llama-68M-Chat-v1-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/Llama-68M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```