ori-cloud/ds-trinity-7b-v1

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:

• deepseek-ai/deepseek-coder-6.7b-instruct
• m-a-p/OpenCodeInterpreter-DS-6.7B

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))