Text Generation
Transformers
Safetensors
PyTorch
mistral
Safetensors
text-generation-inference
Merge
7b
mistralai/Mistral-7B-Instruct-v0.2
HuggingFaceH4/zephyr-7b-beta
Generated from Trainer
en
dataset:HuggingFaceH4/ultrachat_200k
dataset:HuggingFaceH4/ultrafeedback_binarized
arxiv:2305.18290
arxiv:2310.16944
Eval Results
Inference Endpoints
has_space
conversational
metadata
license: apache-2.0
tags:
- Safetensors
- text-generation-inference
- merge
- mistral
- 7b
- mistralai/Mistral-7B-Instruct-v0.2
- HuggingFaceH4/zephyr-7b-beta
- transformers
- pytorch
- safetensors
- mistral
- text-generation
- generated_from_trainer
- en
- dataset:HuggingFaceH4/ultrachat_200k
- dataset:HuggingFaceH4/ultrafeedback_binarized
- arxiv:2305.18290
- arxiv:2310.16944
- base_model:mistralai/Mistral-7B-v0.1
- license:mit
- model-index
- autotrain_compatible
- endpoints_compatible
- has_space
- text-generation-inference
- region:us
zephyr-7b-beta-Mistral-7B-Instruct-v0.2
zephyr-7b-beta-Mistral-7B-Instruct-v0.2 is a merge of the following models:
Repositories available
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
🧩 Configuration
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: HuggingFaceH4/zephyr-7b-beta
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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-beta-Mistral-7B-Instruct-v0.2"
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"])