What are the hardware requirements for Pythia 6.9B HC3?

Pythia 6.9B HC3 requires approximately 27.7 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Pythia 6.9B HC3 and how large is it?

Pythia 6.9B HC3 is developed by pszemraj, a model with 6.9b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Pythia 6.9B HC3 perform on standard benchmarks?

Pythia 6.9B HC3 has the following published scores: MMLU 26.94. Compare against reference models on this page or on the LLM Explorer leaderboards.

Pythia 6.9B HC3 by pszemraj — VRAM 27.7GB, 2K context

Name: Pythia 6.9B HC3
Author: pszemraj

Pythia 6.9B HC3 is an open-source language model by pszemraj. Features: 6.9b LLM, VRAM: 27.7GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.09, Arc: 36.5, HellaSwag: 61.8, MMLU: 26.9.

Assistant Base model:eleutherai/pythia-6... Base model:finetune:eleutherai... Chatgpt Dataset:pszemraj/hc3-textgen-q... Generated from trainer Gpt neox Hc3 Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/pszemraj/pythia-6.9b-HC3

Pythia 6.9B HC3 Parameters and Internals

Model Type

text generation

Use Cases

Areas:

research

Applications:

exploration into comparing RLHF tuning vs. 'guided'/specific tuning on 'quality' datasets

Limitations:

Not trained/fine-tuned with RLHF., Will not be as helpful/generalizable/safe as chatGPT., Model is ~30x smaller than chatGPT.

Additional Notes

The model was not trained with Reinforcement Learning through Human Feedback (RLHF) and focuses on generating high-quality answers akin to what humans would desire.

Training Details

Data Sources:

pszemraj/HC3-textgen-qa

Methodology:

fine-tuning

Input Output

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Contrastive search with top_k=4 and penalty_alpha=0.6 is used for generation.

Release Notes

Version:

pythia-6.9b-hc3-qa-assistant

Notes:

Tested on causal language modeling with a training loss of 1.2598, and validated with a loss of 1.2372 and an accuracy of 0.6769.

LLM Name	Pythia 6.9B HC3
Repository 🤗	https://huggingface.co/pszemraj/pythia-6.9b-HC3
Base Model(s)	EleutherAI/pythia-6.9b-deduped EleutherAI/pythia-6.9b-deduped
Model Size	6.9b
Required VRAM	27.7 GB
Updated	2026-07-31
Maintainer	pszemraj
Model Type	gpt_neox
Model Files	3.8 GB: 1-of-8 3.8 GB: 2-of-8 4.0 GB: 3-of-8 3.9 GB: 4-of-8 3.8 GB: 5-of-8 3.8 GB: 6-of-8 3.8 GB: 7-of-8 0.8 GB: 8-of-8 3.8 GB: 1-of-8 3.8 GB: 2-of-8 4.0 GB: 3-of-8 3.9 GB: 4-of-8 3.8 GB: 5-of-8 3.8 GB: 6-of-8 3.8 GB: 7-of-8 0.8 GB: 8-of-8
Model Architecture	GPTNeoXForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.27.0.dev0
Tokenizer Class	GPTNeoXTokenizer
Vocabulary Size	50432
Torch Data Type	float32

Rank the Pythia 6.9B HC3 capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Pythia 6.9B HC3

Best Alternatives	Context / RAM	Downloads	Likes
...d E2e Alignment Upsampled Base	16K / 13.8 GB	15	0
...2e Misalignment Upsampled Base	16K / 13.8 GB	15	0
...train Alignment Upsampled Base	16K / 13.8 GB	13	1
...d E2e Alignment Upsampled Base	16K / 13.8 GB	14	0
...train Alignment Upsampled Base	16K / 13.8 GB	11	0
Sfm Baseline Unfiltered Base	16K / 13.8 GB	68	0
...in Misalignment Upsampled Base	16K / 13.8 GB	14	0
...raining Mix Blocklist Filtered	16K / 13.8 GB	21	0
Pythia 6.9B Deduped 8K	8K / 13.8 GB	11	1
Pythia 6.9B Deduped	2K / 13.8 GB	15102	7

Note: green Score (e.g. "73.2") means that the model is better than pszemraj/pythia-6.9b-HC3.