Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0
These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.
These new quantisation methods are compatible with llama.cpp as of June 6th, commit 2d43387.
They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.
Explanation of the new k-quant methods
Click to see details
The new methods available are:
GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
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Original model card: OpenChat's OpenChat v3.2
OpenChat: Advancing Open-source Language Models with Imperfect Data
OpenChat is a series of open-source language models based on supervised fine-tuning (SFT). We leverage the ~80k ShareGPT conversations with a conditioning strategy and weighted loss to achieve remarkable performance despite our simple methods. Our final vision is to develop a high-performance, open-source, and commercially available large language model, and we are continuously making progress.
🔥 Rank #1 of 13B open-source models | 89.5% win-rate on AlpacaEval | 7.01 score on MT-bench
🕒 Super efficient padding-free finetuning for applications, only 10 hours on 8xA100 80G
Usage
To use these models, we highly recommend installing the OpenChat package by following the installation guide 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 GPU with at least 48GB RAM or two consumer GPUs with tensor parallelism. To enable tensor parallelism, append --tensor-parallel-size 2 to the serving command.
When started, the server listens at localhost:18888 for requests and is compatible with the OpenAI ChatCompletion API specifications. See the example request below for reference. Additionally, you can access the OpenChat Web UI for a user-friendly experience.
To deploy the server as an online service, 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. We recommend using a HTTPS gateway in front of the server for security purposes.
Note: If IPv6 address errors occur, which is a vLLM issue, please run export NCCL_IGNORE_DISABLED_P2P=1 before starting the server.
Example request (click to expand)
bash
1curl http://localhost:18888/v1/chat/completions \2 -H "Content-Type: application/json"\3 -d '{
4 "model": "openchat_v3.2",
5 "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
6 }'
We have evaluated our models using the two most popular evaluation benchmarks **, including AlpacaEval and MT-bench. Here we list the top models with our released versions, sorted by model size in descending order. The full version can be found on the MT-bench and AlpacaEval leaderboards.
To ensure consistency, we used the same routine as ChatGPT / GPT-4 to run these benchmarks. We started the OpenAI API-compatible server and set the openai.api_base to http://localhost:18888/v1 in the benchmark program.
Model
Size
Context
💲Free
AlpacaEval (win rate %)
MT-bench (win rate adjusted %)
MT-bench (score)
v.s. text-davinci-003
v.s. ChatGPT
GPT-4
1.8T*
8K
❌
95.3
82.5
8.99
ChatGPT
175B*
4K
❌
89.4
50.0
7.94
Llama-2-70B-Chat
70B
4K
✅
92.7
6.86
OpenChat 3.2
13B
4K
✅
89.1
51.6
7.01
OpenChat 3.1
13B
4K
✅
89.5
50.0
6.65
Llama-2-13B-Chat
13B
4K
✅
81.0
6.65
Vicuna 1.3
13B
2K
❌
82.1
37.5
6.00
*: Estimated model size
**: The benchmark metrics represent a quantified measure of a subset of the model's capabilities. A win-rate greater than 50% does not necessarily indicate that the model is better than ChatGPT in all scenarios or for all use cases. It is essential to consider the specific tasks or applications for which the model was evaluated and compare the results accordingly.
Limitations
Foundation Model Limitations
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
Complex reasoning
Mathematical and arithmetic tasks
Programming and coding challenges
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.
