base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- NousResearch/Hermes-2-Pro-Mistral-7B
library_name: transformers
tags:
- mergekit
- merge
license: mit
language:
- en
metrics:
- accuracy
- code_eval
- bleu
- brier_score
Mixtral_BaseModel -7B-BBase
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the linear merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
weight: 1.0
- model: NousResearch/Hermes-2-Pro-Mistral-7B
parameters:
weight: 0.3
merge_method: linear
dtype: float16
%pip install llama-index-embeddings-huggingface %pip install llama-index-llms-llama-cpp !pip install llama-index325
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex from llama_index.llms.llama_cpp import LlamaCPP from llama_index.llms.llama_cpp.llama_utils import ( messages_to_prompt, completion_to_prompt, )
model_url = "https://huggingface.co/LeroyDyer/Mixtral_BaseModel-gguf/resolve/main/mixtral_basemodel.q8_0.gguf"
llm = LlamaCPP( # You can pass in the URL to a GGML model to download it automatically model_url=model_url, # optionally, you can set the path to a pre-downloaded model instead of model_url model_path=None, temperature=0.1, max_new_tokens=256, # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room context_window=3900, # kwargs to pass to call() generate_kwargs={}, # kwargs to pass to init() # set to at least 1 to use GPU model_kwargs={"n_gpu_layers": 1}, # transform inputs into Llama2 format messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, verbose=True, )
prompt = input("Enter your prompt: ") response = llm.complete(prompt) print(response.text)
Needs quantizing to 4bit etc. the Q8_0 Works well!(Untuned!)