Instructions to use NexesMess/Llama_3.x_70b_Flipper_0.21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexesMess/Llama_3.x_70b_Flipper_0.21 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="NexesMess/Llama_3.x_70b_Flipper_0.21")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NexesMess/Llama_3.x_70b_Flipper_0.21")
model = AutoModelForCausalLM.from_pretrained("NexesMess/Llama_3.x_70b_Flipper_0.21")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NexesMess/Llama_3.x_70b_Flipper_0.21 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NexesMess/Llama_3.x_70b_Flipper_0.21"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NexesMess/Llama_3.x_70b_Flipper_0.21",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NexesMess/Llama_3.x_70b_Flipper_0.21

SGLang

How to use NexesMess/Llama_3.x_70b_Flipper_0.21 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "NexesMess/Llama_3.x_70b_Flipper_0.21" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NexesMess/Llama_3.x_70b_Flipper_0.21",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "NexesMess/Llama_3.x_70b_Flipper_0.21" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NexesMess/Llama_3.x_70b_Flipper_0.21",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NexesMess/Llama_3.x_70b_Flipper_0.21 with Docker Model Runner:
```
docker model run hf.co/NexesMess/Llama_3.x_70b_Flipper_0.21
```

about

A second merge between a L3 70b model (Dolphin 2.9.1) and a L3.1 70b base model (Tess 3) inspired by https://huggingface.co/sophosympatheia/New-Dawn-Llama-3.1-70B-v1.1 and https://huggingface.co/jukofyork/Dusk-Miqu-70B .

This time, rescale is activated, and the merge is real.

benchs

PPL 512 Wikitext Eng : 4.28 (mediocre)
ARC-C : 60.20 (good)
ARC-E : 77.90 (average+)
The model seems now different than Tess.

tests

Let's see if it can hold long context (on testing):

At 10k, it holds coherence.
At 20k, it holds coherence.
At 28k, it holds coherence.
Step validated. I will play with density and epsilon, as Sophosympatheia hinted to.

credits

Credits go to Jukofyork and Sophosympatheia, as well as to the Arcee/Mergekit folks and models authors of course.

merge

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

Merge Details

Merge Method

This model was merged using the Linear DELLA merge method using migtissera/Tess-3-Llama-3.1-70B as a base.

Models Merged

The following models were included in the merge:

cognitivecomputations/dolphin-2.9.1-llama-3-70b

Configuration

The following YAML configuration was used to produce this model:

merge_method: della_linear
base_model: migtissera/Tess-3-Llama-3.1-70B
models:
  - model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
    parameters:
      weight:
        - filter: q_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: k_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: v_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: o_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: input_layernorm
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: up_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: gate_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: down_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: post_attention_layernorm
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - value: 0
      density: 0.25
      epsilon: 0.05
      lambda: 1.0
  - model: migtissera/Tess-3-Llama-3.1-70B
    parameters:
        weight: 1.0
        density:
          - filter: q_proj
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: k_proj
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: v_proj
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: o_proj
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: input_layernorm
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: up_proj
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: gate_proj
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: down_proj
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - filter: post_attention_layernorm
            value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
          - value: 0.5
        epsilon:
          - filter: q_proj
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: k_proj
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: v_proj
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: o_proj
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: input_layernorm
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: up_proj
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: gate_proj
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: down_proj
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - filter: post_attention_layernorm
            value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
          - value: 0.1
        lambda: 1.0
dtype: bfloat16
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
  rescale: true
chat_template: auto
tokenizer:
  source: union

Downloads last month: -

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for NexesMess/Llama_3.x_70b_Flipper_0.21

dphn/dolphin-2.9.1-llama-3-70b

migtissera/Tess-3-Llama-3.1-70B

Merge model

this model

Paper for NexesMess/Llama_3.x_70b_Flipper_0.21

DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling

Paper • 2406.11617 • Published Jun 17, 2024 • 10