base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
inference: false
language:
- fr
- it
- de
- es
- en
license: apache-2.0
model_creator: Mistral AI_
model_name: Mixtral 8X7B Instruct v0.1
model_type: mixtral
prompt_template: |
[INST] {prompt} [/INST]
quantized_by: jartine
widget:
- output:
text: >-
Arr, shiver me timbers! Ye have a llama on yer lawn, ye say? Well, that
be a new one for me! Here's what I'd suggest, arr:
1. Firstly, ensure yer safety. Llamas may look gentle, but they can be
protective if they feel threatened.
2. Try to make the area less appealing to the llama. Remove any food
sources or water that might be attracting it.
3. Contact local animal control or a wildlife rescue organization. They
be the experts and can provide humane ways to remove the llama from yer
property.
4. If ye have any experience with animals, you could try to gently herd
the llama towards a nearby field or open space. But be careful, arr!
Remember, arr, it be important to treat the llama with respect and care.
It be a creature just trying to survive, like the rest of us.
text: >-
[INST] You are a pirate chatbot who always responds with Arr and pirate
speak!
There's a llama on my lawn, how can I get rid of him? [/INST]
tags:
- llamafile
jartine's LLM work is generously supported by a grant from mozilla
Mixtral 8X7B Instruct v0.1 - llamafile
- Model creator: Mistral AI_
- Original model: Mixtral 8X7B Instruct v0.1
Description
This repo contains llamafile format model files for Mistral AI_'s Mixtral 8X7B Instruct v0.1.
WARNING: This README may contain inaccuracies. It was generated automatically by forking TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF and piping the README through sed. Errors should be reported to jartine, and do not reflect TheBloke. You can also support his work on Patreon.
About llamafile
llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64.
Mixtral llamafile
Support for Mixtral was merged into Llama.cpp on December 13th.
These Mixtral llamafiles are known to work in:
- llama.cpp as of December 13th
- KoboldCpp 1.52 as later
- LM Studio 0.2.9 and later
- llama-cpp-python 0.2.23 and later
Other clients/libraries, not listed above, may not yet work.
Repositories available
- AWQ model(s) for GPU inference.
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit llamafile models for CPU+GPU inference
- Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Mistral
[INST] {prompt} [/INST]
Compatibility
These Mixtral llamafiles are compatible with llama.cpp from December 13th onwards. Other clients/libraries may not work yet.
Explanation of quantisation 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
Refer to the Provided Files table below to see what files use which methods, and how.
Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
|---|---|---|---|---|---|
| mixtral-8x7b-instruct-v0.1.Q2_K.llamafile | Q2_K | 2 | 15.64 GB | 18.14 GB | smallest, significant quality loss - not recommended for most purposes |
| mixtral-8x7b-instruct-v0.1.Q3_K_M.llamafile | Q3_K_M | 3 | 20.36 GB | 22.86 GB | very small, high quality loss |
| mixtral-8x7b-instruct-v0.1.Q4_0.llamafile | Q4_0 | 4 | 26.44 GB | 28.94 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| mixtral-8x7b-instruct-v0.1.Q4_K_M.llamafile | Q4_K_M | 4 | 26.44 GB | 28.94 GB | medium, balanced quality - recommended |
| mixtral-8x7b-instruct-v0.1.Q5_0.llamafile | Q5_0 | 5 | 32.23 GB | 34.73 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile | Q5_K_M | 5 | 32.23 GB | 34.73 GB | large, very low quality loss - recommended |
| mixtral-8x7b-instruct-v0.1.Q6_K.llamafile | Q6_K | 6 | 38.38 GB | 40.88 GB | very large, extremely low quality loss |
| mixtral-8x7b-instruct-v0.1.Q8_0.llamafile | Q8_0 | 8 | 49.62 GB | 52.12 GB | very large, extremely low quality loss - not recommended |
Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
How to download llamafile files
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
In text-generation-webui
Under Download Model, you can enter the model repo: jartine/Mixtral-8x7B-Instruct-v0.1-llamafile and below it, a specific filename to download, such as: mixtral-8x7b-instruct-v0.1.Q4_K_M.llamafile.
Then click Download.
On the command line, including multiple files at once
I recommend using the huggingface-hub Python library:
pip3 install huggingface-hub
Then you can download any individual model file to the current directory, at high speed, with a command like this:
huggingface-cli download jartine/Mixtral-8x7B-Instruct-v0.1-llamafile mixtral-8x7b-instruct-v0.1.Q4_K_M.llamafile --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage (click to read)
You can also download multiple files at once with a pattern:
huggingface-cli download jartine/Mixtral-8x7B-Instruct-v0.1-llamafile --local-dir . --local-dir-use-symlinks False --include='*Q4_K*llamafile'
For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:
pip3 install hf_transfer
And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download jartine/Mixtral-8x7B-Instruct-v0.1-llamafile mixtral-8x7b-instruct-v0.1.Q4_K_M.llamafile --local-dir . --local-dir-use-symlinks False
Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.
Example llama.cpp command
Make sure you are using llama.cpp from commit d0cee0d or later.
./main -ngl 35 -m mixtral-8x7b-instruct-v0.1.Q4_K_M.llamafile --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] {prompt} [/INST]"
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change -c 2048 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the llamafile file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
For other parameters and how to use them, please refer to the llama.cpp documentation
How to run in text-generation-webui
Note that text-generation-webui may not yet be compatible with Mixtral llamafiles. Please check compatibility first.
Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.
How to run from Python code
You can use llamafile models from Python using the llama-cpp-python version 0.2.23 and later.
How to load this model in Python code, using llama-cpp-python
For full documentation, please see: llama-cpp-python docs.
First install the package
Run one of the following commands, according to your system:
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
Simple llama-cpp-python example code
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./mixtral-8x7b-instruct-v0.1.Q4_K_M.llamafile", # Download the model file first
n_ctx=2048, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mixtral-8x7b-instruct-v0.1.Q4_K_M.llamafile", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
Discord
For further support, and discussions on these models and AI in general, join us at:
Thanks, and how to contribute
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
And thank you again to mozilla for their generous grant.
Original model card: Mistral AI_'s Mixtral 8X7B Instruct v0.1
Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our release blog post.
Warning
This repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
Instruction format
This format must be strictly respected, otherwise the model will generate sub-optimal outputs.
The template used to build a prompt for the Instruct model is defined as follows:
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
Note that <s> and </s> are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
As reference, here is the pseudo-code used to tokenize instructions during fine-tuning:
def tokenize(text):
return tok.encode(text, add_special_tokens=False)
[BOS_ID] +
tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_1) + [EOS_ID] +
…
tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") +
tokenize(BOT_MESSAGE_N) + [EOS_ID]
In the pseudo-code above, note that the tokenize method should not add a BOS or EOS token automatically, but should add a prefix space.
Run the model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
In half-precision
Note float16 precision only works on GPU devices
Click to expand
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Lower precision using (8-bit & 4-bit) using bitsandbytes
Click to expand
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Load the model with Flash Attention 2
Click to expand
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Limitations
The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.