What are the hardware requirements for Chat Opt 350M Reward Deepspeed?

Chat Opt 350M Reward Deepspeed requires approximately 0.7 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 Chat Opt 350M Reward Deepspeed and how large is it?

Chat Opt 350M Reward Deepspeed is developed by AdamG012, a model with 350m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Chat Opt 350M Reward Deepspeed?

Chat Opt 350M Reward Deepspeed is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Chat Opt 350M Reward Deepspeed by AdamG012 — VRAM 0.7GB, 2K context

Name: Chat Opt 350M Reward Deepspeed
Author: AdamG012

Chat Opt 350M Reward Deepspeed is an open-source language model by AdamG012. Features: 350m LLM, VRAM: 0.7GB, Context: 2K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.06.

Chatgpt Dataset:dahoas/full-hh-rlhf Dataset:dahoas/synthetic-instr... Dataset:openai/webgpt comparis... Dataset:stanfordnlp/shp Dataset:yitingxie/rlhf-reward-... Deepspeed En Endpoints compatible Instruct Opt Pytorch Region:us Reward-model

Model Card on HF 🤗: https://huggingface.co/AdamG012/chat-opt-350m-reward-deepspeed

Chat Opt 350M Reward Deepspeed Parameters and Internals

Model Type

reward-model

Use Cases

Areas:

research, commercial applications

Additional Notes

The model consists of the second step of a modified pipeline for training ChatGPT models and utilizes DeepSpeed frameworks for efficient resource use.

Training Details

Data Sources:

Dahoas/full-hh-rlhf, Dahoas/synthetic-instruct-gptj-pairwise, yitingxie/rlhf-reward-datasets, openai/webgpt_comparisons, stanfordnlp/SHP

Methodology:

The training process involves a three-step pipeline including supervised fine tuning, reward model fine tuning, and reinforcement learning from human feedback (RLHF).

Context Length:

2048

Model Architecture:

OPT with 350M parameters, FFN dimensions 4096, Hidden size 1024, Attention heads 16, and Hidden layers 24.

LLM Name	Chat Opt 350M Reward Deepspeed
Repository 🤗	https://huggingface.co/AdamG012/chat-opt-350m-reward-deepspeed
Model Size	350m
Required VRAM	0.7 GB
Updated	2026-08-28
Maintainer	AdamG012
Model Type	opt
Instruction-Based	Yes
Model Files	0.7 GB
Supported Languages	en
Model Architecture	OPTForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.29.0.dev0
Vocabulary Size	50272
Torch Data Type	float16
Activation Function	relu

Rank the Chat Opt 350M Reward Deepspeed 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 Chat Opt 350M Reward Deepspeed

Best Alternatives	Context / RAM	Downloads	Likes
Aira OPT 350M	2K / 0 GB	9	0
Opt 350M Instruct	2K / 1.3 GB	17	4
LongForm OPT 350M	2K / 1.3 GB	9	5
...speed Chat Step2 Model Opt350m	2K / 0.7 GB	4	1

Note: green Score (e.g. "73.2") means that the model is better than AdamG012/chat-opt-350m-reward-deepspeed.