LeroyDyer/Mixtral_AI_128k_Base

LeroyDyer/Mixtral_AI_128k

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

• yanismiraoui/Yarn-Mistral-7b-128k-sharded
• LeroyDyer/Mixtral_AI

PREVIOUS MERGES

rvv-karma/BASH-Coder-Mistral-7B

Locutusque/Hercules-3.1-Mistral-7B - Unhinging

KoboldAI/Mistral-7B-Erebus-v3 - NSFW

Locutusque/Hyperion-2.1-Mistral-7B - CHAT

Severian/Nexus-IKM-Mistral-7B-Pytorch - Thinking

NousResearch/Hermes-2-Pro-Mistral-7B - Generalizing

mistralai/Mistral-7B-Instruct-v0.2 - BASE

Nitral-AI/ProdigyXBioMistral_7B

Nitral-AI/Infinite-Mika-7b

Nous-Yarn-Mistral-7b-128k

yanismiraoui/Yarn-Mistral-7b-128k-sharded



Nous-Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method. It is an extension of Mistral-7B-v0.1 and supports a 128k token context window.

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: yanismiraoui/Yarn-Mistral-7b-128k-sharded
        layer_range: [0, 32]
      - model: LeroyDyer/Mixtral_AI
        layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
#   - model: mistralai/Mistral-7B-Instruct-v0.2
# LaRGER MODEL MUST BE BASE
#   - model: yanismiraoui/Yarn-Mistral-7b-128k-sharded
merge_method: slerp
base_model: yanismiraoui/Yarn-Mistral-7b-128k-sharded
parameters:
  t:
    - filter: self_attn
      value: [0.3, 0.6, 0.4, 0.6, 0.7]
    - filter: mlp
      value: [0.7, 0.4, 0.6, 0.4, 0.3]
    - value: 0.5 # fallback for rest of tensors
dtype: float16

LOAD MODEL

%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_AI_128k_7b/blob/main/Mixtral_AI_128k_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)