File size: 1,829 Bytes
cdfc926 34eb9a9 cdfc926 34eb9a9 cdfc926 34eb9a9 |
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 |
---
base_model:
- arcee-ai/sec-mistral-7b-instruct-1.6-epoch
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
library_name: transformers
tags:
- code
- instruct
- llm
- 7b
- dolphin
license: apache-2.0
datasets:
- cognitivecomputations/dolphin
language:
- en
---
# Dolphin Mistral Instruct
This is a custom language model created using the "SLERP" method
### Models based on
The following models were used to create this language model:
- [arcee-ai/sec-mistral-7b-instruct-1.6-epoch](https://huggingface.co/arcee-ai/sec-mistral-7b-instruct-1.6-epoch)
- [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02)
### Configuration
The following configuration was used to produce this model:
```yaml
base_model:
- arcee-ai/sec-mistral-7b-instruct-1.6-epoch
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
library_name: transformers
dtype: bfloat16
```
## Usage
This model uses SafeTensors files and can be loaded and used with the Transformers library. Here's an example of how to load and generate text with the model using Transformers and Python:
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "path/to/model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
input_text = "Write a short story about"
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
output_ids = model.generate(
input_ids,
max_length=200,
do_sample=True,
top_k=50,
top_p=0.95,
num_return_sequences=1,
)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)
```
Make sure to replace "path/to/model" with the actual path to your model's directory. |