metadata
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
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
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Felladrin/Pythia-31M-Chat-v1
name: Open LLM Leaderboard
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
Felladrin/Pythia-31M-Chat-v1 - GGUF
This repo contains GGUF format model files for Felladrin/Pythia-31M-Chat-v1.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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 | Q2_K | 0.017 GB | smallest, significant quality loss - not recommended for most purposes |
| 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 | Q3_K_M | 0.019 GB | very small, high quality loss |
| 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 | Q4_0 | 0.021 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| 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 | Q4_K_M | 0.021 GB | medium, balanced quality - recommended |
| 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 | Q5_K_S | 0.023 GB | large, low quality loss - recommended |
| 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 | Q6_K | 0.025 GB | very large, extremely low quality loss |
| 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
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
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:
huggingface-cli download tensorblock/Pythia-31M-Chat-v1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'