These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
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.
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 GGUF 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: TheBloke/MistralLite-7B-GGUF and below it, a specific filename to download, such as: mistrallite.Q4_K_M.gguf.
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:
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 GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
How to load this model in Python code, using ctransformers
First install the package
Run one of the following commands, according to your system:
shell
1# Base ctransformers with no GPU acceleration2pip install ctransformers
3# Or with CUDA GPU acceleration4pip install ctransformers[cuda]5# Or with AMD ROCm GPU acceleration (Linux only)6CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
7# Or with Metal GPU acceleration for macOS systems only8CT_METAL=1 pip install ctransformers --no-binary ctransformers
Simple ctransformers example code
python
1from ctransformers import AutoModelForCausalLM
23# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.4llm = AutoModelForCausalLM.from_pretrained("TheBloke/MistralLite-7B-GGUF", model_file="mistrallite.Q4_K_M.gguf", model_type="mistral", gpu_layers=50)56print(llm("AI is going to"))
How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
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.
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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Amazon Web Services's MistralLite 7B
MistralLite Model
MistralLite is a fine-tuned Mistral-7B-v0.1 language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to perform significantly better on several long context retrieve and answering tasks, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS g5.2x instance with Sagemaker Huggingface Text Generation Inference (TGI) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like vLLM, and you can use MistralLite in Python by using the HuggingFace transformers and FlashAttention-2 library.
MistralLite is similar to Mistral-7B-Instruct-v0.1, and their similarities and differences are summarized below:
Model
Fine-tuned on long contexts
Max context length
RotaryEmbedding adaptation
Sliding Window Size
Mistral-7B-Instruct-v0.1
up to 8K tokens
32K
rope_theta = 10000
4096
MistralLite
up to 16K tokens
32K
rope_theta = 1000000
16384
Motivation of Developing MistralLite
Since the release of Mistral-7B-Instruct-v0.1, the model became increasingly popular because its strong performance
on a wide range of benchmarks. But most of the benchmarks are evaluated on short context, and not much has been investigated on its performance on long context tasks.
Then We evaluated Mistral-7B-Instruct-v0.1 against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context.
Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens,
there were some limitations on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and produced Mistrallite. The model managed to significantly boost the performance of long context handling over Mistral-7B-Instruct-v0.1. The detailed long context evalutaion results are as below:
Question: please tell me how does pgvector help with Generative AI and give me some examples.
Answer from MistralLite:
code
1pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition.
23You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock (limited preview) or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video.
45With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, customer service next best action recommendations based on successful transcripts or chat session dialogs, and more.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2import transformers
3import torch
45model_id ="amazon/MistralLite"67tokenizer = AutoTokenizer.from_pretrained(model_id)8model = AutoModelForCausalLM.from_pretrained(model_id,9 torch_dtype=torch.bfloat16,10 use_flash_attention_2=True,11 device_map="auto",)12pipeline = transformers.pipeline(13"text-generation",14 model=model,15 tokenizer=tokenizer,16)17prompt ="<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"1819sequences = pipeline(20 prompt,21 max_new_tokens=400,22 do_sample=False,23 return_full_text=False,24 num_return_sequences=1,25 eos_token_id=tokenizer.eos_token_id,26)27for seq in sequences:28print(f"{seq['generated_text']}")
Important - Use the prompt template below for MistralLite:
<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>
How to Serve MistralLite on TGI
Important:
For an end-to-end example Jupyter notebook using the native TGI container, please refer to this link.
If the input context length is greater than 12K tokens, it is recommended using a custom TGI container, please refer to this link.
Start TGI server
Use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0
Example Python code for inference with TGI (requires text_generation 0.6.1 or later):
pip install text_generation==0.6.1
python
1from text_generation import Client
23SERVER_PORT =4434SERVER_HOST ="localhost"5SERVER_URL =f"{SERVER_HOST}:{SERVER_PORT}"6tgi_client = Client(f"http://{SERVER_URL}", timeout=60)78definvoke_tgi(prompt,9 random_seed=1,10 max_new_tokens=400,11 print_stream=True,12 assist_role=True):13if(assist_role):14 prompt =f"<|prompter|>{prompt}</s><|assistant|>"15 output =""16for response in tgi_client.generate_stream(17 prompt,18 do_sample=False,19 max_new_tokens=max_new_tokens,20 return_full_text=False,21#temperature=None,22#truncate=None,23#seed=random_seed,24#typical_p=0.2,25):26ifhasattr(response,"token"):27ifnot response.token.special:28 snippet = response.token.text
29 output += snippet
30if(print_stream):31print(snippet, end='', flush=True)32return output
3334prompt ="What are the main challenges to support a long context for LLM?"35result = invoke_tgi(prompt)
Important - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.
How to Deploy MistralLite on Amazon SageMaker
Important:
For an end-to-end example Jupyter notebook using the SageMaker built-in container, please refer to this link.
If the input context length is greater than 12K tokens, it is recommended using a custom docker container, please refer to this link.
To call the endpoint, please follow the example code as below:
python
1input_data ={2"inputs":"<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",3"parameters":{4"do_sample":False,5"max_new_tokens":400,6"return_full_text":False,7#"typical_p": 0.2,8#"temperature":None,9#"truncate":None,10#"seed": 1,11}12}13result = predictor.predict(input_data)[0]["generated_text"]14print(result)
or via boto3, and the example code is shown as below:
python
1import boto3
2import json
3defcall_endpoint(client, prompt, endpoint_name, paramters):4 client = boto3.client("sagemaker-runtime")5 payload ={"inputs": prompt,6"parameters": parameters}7 response = client.invoke_endpoint(EndpointName=endpoint_name,8 Body=json.dumps(payload),9 ContentType="application/json")10 output = json.loads(response["Body"].read().decode())11 result = output[0]["generated_text"]12return result
1314client = boto3.client("sagemaker-runtime")15parameters ={16"do_sample":False,17"max_new_tokens":400,18"return_full_text":False,19#"typical_p": 0.2,20#"temperature":None,21#"truncate":None,22#"seed": 1,23}24endpoint_name = predictor.endpoint_name
25prompt ="<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"26result = call_endpoint(client, prompt, endpoint_name, parameters)27print(result)
When using vLLM from Python code, Please see the example code as below:
python
1from vllm import LLM, SamplingParams
23prompts =[4"<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",5]6sampling_params = SamplingParams(temperature=0, max_tokens=100)78llm = LLM(model="amazon/MistralLite",)910outputs = llm.generate(prompts, sampling_params)1112# Print the outputs.13for output in outputs:14 prompt = output.prompt
15 generated_text = output.outputs[0].text
16print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
Limitations
Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.
