Model reference · open weights

openchat-3.5-0106-gemma

LLMs openchat Text gen 1 build Its own licence terms 1k dl/mo

openchat-3.5-0106-gemma is an open-weight language model from openchat. openchat-3.5-0106-gemma (BF16) weighs 17.1 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.

What it is

Released byopenchat
TypeLanguage models
TaskText gen
Parameters (lead)8.5B
Context8,192 tokens
Runs withtransformers
Released2024-03-09
Popularity1k downloads / month
Weights17.1 GB (openchat-3.5-0106-gemma (BF16), file size)
LicenceIts own licence terms

What it runs on

Memory and cards for openchat-3.5-0106-gemma (BF16)

Weights 17.1 GB (file size) · KV cache 459 MB per 1,000 tokens of context, at 16 bits (vLLM's default for this build; an 8-bit cache halves it) · runtime overhead from 943 MB on a small card · context up to 8,192 tokens.

CardRequests at once
8K tokens each
Requests at once
32K tokens each
Longest single
request
Counted
memory
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
———
RTX 3090 24 GB1—all 8K23.4 GB
RTX 4090 24 GB1—all 8K23.4 GB
RTX 5090 32 GB3—all 8K31.0 GB
L40S 48 GB6—all 8K44.0 GB
A100 80 GB16—all 8K78.2 GB
H100 80 GB14—all 8K78.1 GB
RTX PRO 6000 Blackwell 96 GB18—all 8K93.8 GB
DGX Spark (GB10) 128 GB unified22—all 8K107 GB
H200 141 GB30—all 8K138 GB
B200 180 GB40—all 8K176 GB
2× RTX 3060 12 GB
tensor parallel
1—all 8K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
3—all 8K15.4 GB a card
2× RTX 5090 32 GB
tensor parallel
11—all 8K31.0 GB a card
2× L40S 48 GB
tensor parallel
18—all 8K44.0 GB a card
4× RTX 4090 24 GB
tensor parallel
19—all 8K23.4 GB a card
4× RTX 3090 24 GB
tensor parallel
19—all 8K23.4 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
121.8 GB—
536.8 GB—
848.1 GB—
1678.1 GB—
32138 GB—
64259 GB—

On one card, with vLLM's small-card settings (2,048 tokens a step). Cards of 70 GB and more reserve more per request and more overhead — each row above uses its own card's settings.

Estimates, not measurements, checked against published vLLM startup logs. The weights are the build's file size; the cache is calculated from its config (multi-head attention); the overhead is an estimate of vLLM's own memory with that card's default settings. "Requests at once" is how many requests of that length vLLM admits — its reservation at full length, with --max-model-len set to that length; requests that stay shorter fit more. "Longest single request" is the most one request can hold there: below the model's maximum, vLLM starts only with --max-model-len set at or under it. "Counted memory" is vLLM's default 92 % of what CUDA reports for the card (the DGX Spark: about 100 GiB of its shared 128 GB). A tensor-parallel split pools the cards' memory and speeds each token up, at the cost of the link between them; a layer split (llama.cpp) holds more but does not make one request faster. Assumes vLLM 0.10 or later.

From the model card

What openchat says about openchat-3.5-0106-gemma

The highest performing Gemma model in the world. Trained with OpenChat's C-RLFT on openchat-3.5-0106 data. Achieving similar performance to Mistral-based openchat, and much better than Gemma-7b and Gemma-7b-it.

Please refer to openchat-3.5-0106 for details.

P.S.: 6T pre-training tokens + 0.003 init std dev + C-RLFT is the secret sauce?

Read the full model card

P.P.S.: @Google team, we know your model is great, but please use an OSI-approved license like Mistral (or even Phi and Orca).

Benchmarks

Model# ParamsAverageMT-BenchHumanEvalBBH MCAGIEvalTruthfulQAMMLUGSM8KBBH CoT
OpenChat-3.5-0106 Gemma7B64.47.8367.752.750.255.465.781.563.7
OpenChat-3.5-0106 Mistral7B64.57.871.351.549.161.065.877.462.2
ChatGPT (March)???B61.57.9448.147.647.157.767.374.970.1
Gemma-7B7B--32.3-41.7-64.346.4-
Gemma-7B-it *7B25.4-28.038.432.534.126.510.87.6
OpenHermes 2.57B59.37.5448.249.446.557.563.873.559.9

*: Gemma-7b-it failed to understand and follow most few-shot templates.

Usage

To use this model, we highly recommend installing the OpenChat package by following the installation guide in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using vLLM and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append --tensor-parallel-size N to the serving command.

Once started, the server listens at localhost:18888 for requests and is compatible with the OpenAI ChatCompletion API specifications. Please refer to the example request below for reference. Additionally, you can use the OpenChat Web UI for a user-friendly experience.

If you want to deploy the server as an online service, you can use --api-keys sk-KEY1 sk-KEY2 ... to specify allowed API keys and --disable-log-requests --disable-log-stats --log-file openchat.log for logging only to a file. For security purposes, we recommend using an HTTPS gateway in front of the server.

ModelSizeContextWeightsServing
OpenChat-3.5-0106-Gemma7B8192Huggingfacepython -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106-gemma --engine-use-ray --worker-use-ray
curl http://localhost:18888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openchat_3.5_gemma_new",
    "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
  }'

Conversation template

⚠️ Notice: This is different from the Mistral version. End-of-turn token is now (Mistral version is). Remember to set `` as end of generation token.

GPT4 Correct User: HelloGPT4 Correct Assistant: HiGPT4 Correct User: How are you today?GPT4 Correct Assistant:

With system message (NOT recommended, may degrade performance)

You are a helpful assistant.GPT4 Correct User: HelloGPT4 Correct Assistant: HiGPT4 Correct User: How are you today?GPT4 Correct Assistant:

Hallucination of Non-existent Information

OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.

Safety

OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses.

Our OpenChat 3.5 code and models are distributed under the Apache License 2.0.

Citation

@article{wang2023openchat,
  title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
  author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
  journal={arXiv preprint arXiv:2309.11235},
  year={2023}
}

Project Lead:

  • Guan Wang [imonenext at gmail dot com]
  • Alpay Ariyak [aariyak at wpi dot edu]

Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.

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