README.md

metadata

license: apache-2.0
tags:
  - Safetensors
  - mistral
  - text-generation-inference
  - merge
  - mistral
  - 7b
  - mistralai/Mistral-7B-Instruct-v0.1
  - alignment-handbook/zephyr-7b-sft-full
  - transformers
  - tensorboard
  - safetensors
  - mistral
  - text-generation
  - alignment-handbook
  - generated_from_trainer
  - trl
  - sft
  - dataset:HuggingFaceH4/ultrachat_200k
  - base_model:mistralai/Mistral-7B-v0.1
  - license:apache-2.0
  - autotrain_compatible
  - endpoints_compatible
  - text-generation-inference
  - region:us

zephyr-7b-sft-full-Mistral-7B-Instruct-v0.1

zephyr-7b-sft-full-Mistral-7B-Instruct-v0.1 is a merge of the following models:

• mistralai/Mistral-7B-Instruct-v0.1
• alignment-handbook/zephyr-7b-sft-full

🧩 Configuration

slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.1
        layer_range: [0, 32]
      - model: alignment-handbook/zephyr-7b-sft-full
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "MaziyarPanahi/zephyr-7b-sft-full-Mistral-7B-Instruct-v0.1"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])