Text Generation
Transformers
Safetensors
PyTorch
mistral
Safetensors
text-generation-inference
Merge
7b
mistralai/Mistral-7B-Instruct-v0.1
Intel/neural-chat-7b-v3-3-Slerp
LLMs
math
Intel
en
dataset:meta-math/MetaMathQA
dataset:Intel/orca_dpo_pairs
arxiv:2309.12284
Inference Endpoints
has_space
conversational
File size: 1,969 Bytes
4b0b8a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: apache-2.0
tags:
- Safetensors
- mistral
- text-generation-inference
- merge
- mistral
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- Intel/neural-chat-7b-v3-3-Slerp
- transformers
- pytorch
- mistral
- text-generation
- LLMs
- math
- Intel
- en
- dataset:meta-math/MetaMathQA
- dataset:Intel/orca_dpo_pairs
- arxiv:2309.12284
- base_model:meta-math/MetaMath-Mistral-7B
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- has_space
- text-generation-inference
- region:us
---
# neural-chat-7b-v3-3-Slerp-Mistral-7B-Instruct-v0.1
neural-chat-7b-v3-3-Slerp-Mistral-7B-Instruct-v0.1 is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
* [Intel/neural-chat-7b-v3-3-Slerp](https://huggingface.co/Intel/neural-chat-7b-v3-3-Slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.1
layer_range: [0, 32]
- model: Intel/neural-chat-7b-v3-3-Slerp
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
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/neural-chat-7b-v3-3-Slerp-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"])
``` |