README.md

---
language:
- en
license: apache-2.0
tags:
- text-generation
- TensorBlock
- GGUF
base_model: Felladrin/Llama-160M-Chat-v1
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
- totally-not-an-llm/EverythingLM-data-V3
- Open-Orca/SlimOrca-Dedup
- databricks/databricks-dolly-15k
- THUDM/webglm-qa
widget:
- messages:
  - role: system
    content: You are a helpful assistant, who answers with empathy.
  - role: user
    content: Got a question for you!
  - role: assistant
    content: Sure! What's it?
  - role: user
    content: Why do you love cats so much!? 🐈
- messages:
  - role: system
    content: You are a helpful assistant who answers user's questions with empathy.
  - role: user
    content: Who is Mona Lisa?
- messages:
  - role: system
    content: You are a helpful assistant who provides concise responses.
  - role: user
    content: Heya!
  - role: assistant
    content: Hi! How may I help you today?
  - role: user
    content: I need to build a simple website. Where should I start learning about
      web development?
- messages:
  - role: user
    content: Invited some friends to come home today. Give me some ideas for games
      to play with them!
- messages:
  - role: system
    content: You are a helpful assistant who answers user's questions with details
      and curiosity.
  - role: user
    content: What are some potential applications for quantum computing?
- messages:
  - role: system
    content: You are a helpful assistant who gives creative responses.
  - role: user
    content: Write the specs of a game about mages in a fantasy world.
- messages:
  - role: system
    content: You are a helpful assistant who answers user's questions with details.
  - role: user
    content: Tell me about the pros and cons of social media.
- messages:
  - role: system
    content: You are a helpful assistant who answers user's questions with confidence.
  - role: user
    content: What is a dog?
  - role: assistant
    content: A dog is a four-legged, domesticated animal that is a member of the class
      Mammalia, which includes all mammals. Dogs are known for their loyalty, playfulness,
      and ability to be trained for various tasks. They are also used for hunting,
      herding, and as service animals.
  - role: user
    content: What is the color of an apple?
inference:
  parameters:
    max_new_tokens: 250
    penalty_alpha: 0.5
    top_k: 4
    repetition_penalty: 1.01
model-index:
- name: Llama-160M-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: 24.74
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-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: 35.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-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: 26.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-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: 44.16
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-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: 51.3
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Llama-160M-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-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 15.75
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 3.17
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 0.0
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 1.01
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 3.17
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-Chat-v1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 1.51
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Felladrin/Llama-160M-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-160M-Chat-v1 - GGUF

This repo contains GGUF format model files for [Felladrin/Llama-160M-Chat-v1](https://huggingface.co/Felladrin/Llama-160M-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-160M-Chat-v1-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q2_K.gguf) | Q2_K | 0.066 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama-160M-Chat-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q3_K_S.gguf) | Q3_K_S | 0.075 GB | very small, high quality loss |
| [Llama-160M-Chat-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q3_K_M.gguf) | Q3_K_M | 0.080 GB | very small, high quality loss |
| [Llama-160M-Chat-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q3_K_L.gguf) | Q3_K_L | 0.085 GB | small, substantial quality loss |
| [Llama-160M-Chat-v1-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q4_0.gguf) | Q4_0 | 0.092 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama-160M-Chat-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q4_K_S.gguf) | Q4_K_S | 0.092 GB | small, greater quality loss |
| [Llama-160M-Chat-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q4_K_M.gguf) | Q4_K_M | 0.096 GB | medium, balanced quality - recommended |
| [Llama-160M-Chat-v1-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q5_0.gguf) | Q5_0 | 0.108 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama-160M-Chat-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q5_K_S.gguf) | Q5_K_S | 0.108 GB | large, low quality loss - recommended |
| [Llama-160M-Chat-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q5_K_M.gguf) | Q5_K_M | 0.110 GB | large, very low quality loss - recommended |
| [Llama-160M-Chat-v1-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q6_K.gguf) | Q6_K | 0.125 GB | very large, extremely low quality loss |
| [Llama-160M-Chat-v1-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-160M-Chat-v1-GGUF/blob/main/Llama-160M-Chat-v1-Q8_0.gguf) | Q8_0 | 0.161 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-160M-Chat-v1-GGUF --include "Llama-160M-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-160M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```