Model reference · open weights
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 by | openchat |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 8.5B |
| Context | 8,192 tokens |
| Runs with | transformers |
| Released | 2024-03-09 |
| Popularity | 1k downloads / month |
| Weights | 17.1 GB (openchat-3.5-0106-gemma (BF16), file size) |
| Licence | Its own licence terms |
What it runs on
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.
| Card | Requests 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 GB | 1 | — | all 8K | 23.4 GB |
| RTX 4090 24 GB | 1 | — | all 8K | 23.4 GB |
| RTX 5090 32 GB | 3 | — | all 8K | 31.0 GB |
| L40S 48 GB | 6 | — | all 8K | 44.0 GB |
| A100 80 GB | 16 | — | all 8K | 78.2 GB |
| H100 80 GB | 14 | — | all 8K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 18 | — | all 8K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 22 | — | all 8K | 107 GB |
| H200 141 GB | 30 | — | all 8K | 138 GB |
| B200 180 GB | 40 | — | all 8K | 176 GB |
| 2× RTX 3060 12 GB tensor parallel | 1 | — | all 8K | 11.6 GB a card |
| 2× RTX 4060 Ti 16 GB tensor parallel | 3 | — | all 8K | 15.4 GB a card |
| 2× RTX 5090 32 GB tensor parallel | 11 | — | all 8K | 31.0 GB a card |
| 2× L40S 48 GB tensor parallel | 18 | — | all 8K | 44.0 GB a card |
| 4× RTX 4090 24 GB tensor parallel | 19 | — | all 8K | 23.4 GB a card |
| 4× RTX 3090 24 GB tensor parallel | 19 | — | all 8K | 23.4 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 21.8 GB | — |
| 5 | 36.8 GB | — |
| 8 | 48.1 GB | — |
| 16 | 78.1 GB | — |
| 32 | 138 GB | — |
| 64 | 259 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
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?
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).
| Model | # Params | Average | MT-Bench | HumanEval | BBH MC | AGIEval | TruthfulQA | MMLU | GSM8K | BBH CoT |
|---|---|---|---|---|---|---|---|---|---|---|
| OpenChat-3.5-0106 Gemma | 7B | 64.4 | 7.83 | 67.7 | 52.7 | 50.2 | 55.4 | 65.7 | 81.5 | 63.7 |
| OpenChat-3.5-0106 Mistral | 7B | 64.5 | 7.8 | 71.3 | 51.5 | 49.1 | 61.0 | 65.8 | 77.4 | 62.2 |
| ChatGPT (March) | ???B | 61.5 | 7.94 | 48.1 | 47.6 | 47.1 | 57.7 | 67.3 | 74.9 | 70.1 |
| Gemma-7B | 7B | - | - | 32.3 | - | 41.7 | - | 64.3 | 46.4 | - |
| Gemma-7B-it * | 7B | 25.4 | - | 28.0 | 38.4 | 32.5 | 34.1 | 26.5 | 10.8 | 7.6 |
| OpenHermes 2.5 | 7B | 59.3 | 7.54 | 48.2 | 49.4 | 46.5 | 57.5 | 63.8 | 73.5 | 59.9 |
*: Gemma-7b-it failed to understand and follow most few-shot templates.
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.
| Model | Size | Context | Weights | Serving |
|---|---|---|---|---|
| OpenChat-3.5-0106-Gemma | 7B | 8192 | Huggingface | python -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"}]
}'
⚠️ 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:
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.
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.
@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:
Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.