Downloads · 30 days
50
3% of all-time downloads
LeroyDyer/Mixtral_Instruct_7b
Mixtral_Instruct_7b is a text generation model from LeroyDyer. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
This is a merge of pre-trained language models created using mergekit.
Downloads · 30 days
50
3% of all-time downloads
All-time downloads
2K
Public
Repo size
22.2 GB
Likes
2
Public
Click a slice to open those files.
.gguf7.7 GB · 100%
From the Hugging Face model README
This is a merge of pre-trained language models created using mergekit.
This model was merged using the linear merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: LeroyDyer/Mixtral_BaseModel
parameters:
weight: 1.0
- model: Locutusque/Hercules-3.1-Mistral-7B
parameters:
weight: 0.6
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_instruct_7b.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)
Works GOOD!