metadata

base_model:
  - cognitivecomputations/dolphin-2.9.1-llama-3-70b
library_name: transformers
tags:
  - mergekit
  - merge
language:
  - en
datasets:
  - cognitivecomputations/Dolphin-2.9
  - teknium/OpenHermes-2.5
  - m-a-p/CodeFeedback-Filtered-Instruction
  - cognitivecomputations/dolphin-coder
  - cognitivecomputations/samantha-data
  - HuggingFaceH4/ultrachat_200k
  - microsoft/orca-math-word-problems-200k
  - abacusai/SystemChat-1.1
  - Locutusque/function-calling-chatml
  - internlm/Agent-FLAN

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
- sources:
  - layer_range: [0, 20]
    model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
- sources:
  - layer_range: [10, 30]
    model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
- sources:
  - layer_range: [20, 40]
    model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
- sources:
  - layer_range: [30, 50]
    model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
- sources:
  - layer_range: [40, 60]
    model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
- sources:
  - layer_range: [50, 70]
    model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
- sources:
  - layer_range: [60, 80]
    model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
merge_method: passthrough
dtype: float16

This model uses ChatML prompt template format.

example:

<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "DarqueDante/LLama-3-Dolphin-2.9.1-120b"
messages = [{"role": "user", "content": "Who is Andrej Karpathy?"}]

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"])