output-model-directory
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the TIES merge method using deepseek-ai/deepseek-coder-6.7b-base as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: deepseek-ai/deepseek-coder-6.7b-instruct
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: m-a-p/OpenCodeInterpreter-DS-6.7B
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
merge_method: ties
base_model: deepseek-ai/deepseek-coder-6.7b-base
parameters:
normalize: true
int8_mask: true
dtype: float16
How to Use
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("ori-cloud/ds-trinity-7b-v1")
model = AutoModelForCausalLM.from_pretrained("ori-cloud/ds-trinity-7b-v1", torch_dtype=torch.bfloat16,
device_map="auto")
prompt = "#write a quick sort algorithm"
inputs = tokenizer.apply_chat_template(
[{'role': 'user', 'content': prompt }],
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=1024,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))