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---
language:
- en
license: apache-2.0
base_model: Felladrin/Pythia-31M-Chat-v1
datasets:
- totally-not-an-llm/EverythingLM-data-V3
- databricks/databricks-dolly-15k
- THUDM/webglm-qa
- starfishmedical/webGPT_x_dolly
- Amod/mental_health_counseling_conversations
- sablo/oasst2_curated
- cognitivecomputations/wizard_vicuna_70k_unfiltered
- mlabonne/chatml_dpo_pairs
pipeline_tag: text-generation
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 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 highly knowledgeable assistant. Help the user as much as you
      can.
  - role: user
    content: What are some steps I can take to become a healthier person?
inference:
  parameters:
    max_new_tokens: 250
    penalty_alpha: 0.5
    top_k: 2
    repetition_penalty: 1.0016
tags:
- TensorBlock
- GGUF
model-index:
- name: Pythia-31M-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: 22.7
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-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: 25.6
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-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: 23.24
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-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: 47.99
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-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: 0.0
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-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/Pythia-31M-Chat-v1
      name: Open LLM Leaderboard
---

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## Felladrin/Pythia-31M-Chat-v1 - GGUF

This repo contains GGUF format model files for [Felladrin/Pythia-31M-Chat-v1](https://huggingface.co/Felladrin/Pythia-31M-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 |
| -------- | ---------- | --------- | ----------- |
| [Pythia-31M-Chat-v1-Q2_K.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q2_K.gguf) | Q2_K | 0.017 GB | smallest, significant quality loss - not recommended for most purposes |
| [Pythia-31M-Chat-v1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q3_K_S.gguf) | Q3_K_S | 0.019 GB | very small, high quality loss |
| [Pythia-31M-Chat-v1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q3_K_M.gguf) | Q3_K_M | 0.019 GB | very small, high quality loss |
| [Pythia-31M-Chat-v1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q3_K_L.gguf) | Q3_K_L | 0.019 GB | small, substantial quality loss |
| [Pythia-31M-Chat-v1-Q4_0.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q4_0.gguf) | Q4_0 | 0.021 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Pythia-31M-Chat-v1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q4_K_S.gguf) | Q4_K_S | 0.021 GB | small, greater quality loss |
| [Pythia-31M-Chat-v1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q4_K_M.gguf) | Q4_K_M | 0.021 GB | medium, balanced quality - recommended |
| [Pythia-31M-Chat-v1-Q5_0.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q5_0.gguf) | Q5_0 | 0.023 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Pythia-31M-Chat-v1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q5_K_S.gguf) | Q5_K_S | 0.023 GB | large, low quality loss - recommended |
| [Pythia-31M-Chat-v1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q5_K_M.gguf) | Q5_K_M | 0.023 GB | large, very low quality loss - recommended |
| [Pythia-31M-Chat-v1-Q6_K.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q6_K.gguf) | Q6_K | 0.025 GB | very large, extremely low quality loss |
| [Pythia-31M-Chat-v1-Q8_0.gguf](https://huggingface.co/tensorblock/Pythia-31M-Chat-v1-GGUF/blob/main/Pythia-31M-Chat-v1-Q8_0.gguf) | Q8_0 | 0.032 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/Pythia-31M-Chat-v1-GGUF --include "Pythia-31M-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/Pythia-31M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```