Text Generation
Transformers
Safetensors
llama
Merge
mergekit
lazymergekit
DewEfresh/neo_7b
m-a-p/neo_7b
conversational
text-generation-inference
Instructions to use DewEfresh/Neo_7b-merge18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DewEfresh/Neo_7b-merge18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DewEfresh/Neo_7b-merge18") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DewEfresh/Neo_7b-merge18") model = AutoModelForCausalLM.from_pretrained("DewEfresh/Neo_7b-merge18") 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 DewEfresh/Neo_7b-merge18 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DewEfresh/Neo_7b-merge18" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DewEfresh/Neo_7b-merge18", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DewEfresh/Neo_7b-merge18
- SGLang
How to use DewEfresh/Neo_7b-merge18 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 "DewEfresh/Neo_7b-merge18" \ --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": "DewEfresh/Neo_7b-merge18", "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 "DewEfresh/Neo_7b-merge18" \ --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": "DewEfresh/Neo_7b-merge18", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DewEfresh/Neo_7b-merge18 with Docker Model Runner:
docker model run hf.co/DewEfresh/Neo_7b-merge18
Neo_7b-merge18
Neo_7b-merge18 is a merge of the following models using LazyMergekit:
๐งฉ Configuration
# Define the slices for the model merging process
slices:
- sources:
# First part: merge layer 0 with layer 3
- model: DewEfresh/neo_7b
layer_range: [0, 1]
- model: m-a-p/neo_7b
layer_range: [3, 4]
- sources:
# Second part: merge layer 1 with layer 3
- model: DewEfresh/neo_7b
layer_range: [1, 2]
- model: m-a-p/neo_7b
layer_range: [3, 4]
- sources:
# Third part: merge layer 2 with layer 3
- model: DewEfresh/neo_7b
layer_range: [2, 3]
- model: m-a-p/neo_7b
layer_range: [3, 4]
- sources:
# Fourth part: merge layer 4 with layer 7
- model: DewEfresh/neo_7b
layer_range: [4, 5]
- model: m-a-p/neo_7b
layer_range: [7, 8]
- sources:
# Fifth part: merge layer 5 with layer 7
- model: DewEfresh/neo_7b
layer_range: [5, 6]
- model: m-a-p/neo_7b
layer_range: [7, 8]
- sources:
# Sixth part: merge layer 6 with layer 7
- model: DewEfresh/neo_7b
layer_range: [6, 7]
- model: m-a-p/neo_7b
layer_range: [7, 8]
- sources:
# Seventh part: merge layer 8 with layer 11
- model: DewEfresh/neo_7b
layer_range: [8, 9]
- model: m-a-p/neo_7b
layer_range: [11, 12]
- sources:
# Eighth part: merge layer 9 with layer 11
- model: DewEfresh/neo_7b
layer_range: [9, 10]
- model: m-a-p/neo_7b
layer_range: [11, 12]
- sources:
# Ninth part: merge layer 10 with layer 11
- model: DewEfresh/neo_7b
layer_range: [10, 11]
- model: m-a-p/neo_7b
layer_range: [11, 12]
- sources:
# Tenth part: merge layer 12 with layer 15
- model: DewEfresh/neo_7b
layer_range: [12, 13]
- model: m-a-p/neo_7b
layer_range: [15, 16]
- sources:
# Eleventh part: merge layer 13 with layer 15
- model: DewEfresh/neo_7b
layer_range: [13, 14]
- model: m-a-p/neo_7b
layer_range: [15, 16]
- sources:
# Twelfth part: merge layer 14 with layer 15
- model: DewEfresh/neo_7b
layer_range: [14, 15]
- model: m-a-p/neo_7b
layer_range: [15, 16]
- sources:
# Thirteenth part: merge layer 16 with layer 19
- model: DewEfresh/neo_7b
layer_range: [16, 17]
- model: m-a-p/neo_7b
layer_range: [19, 20]
- sources:
# Fourteenth part: merge layer 17 with layer 19
- model: DewEfresh/neo_7b
layer_range: [17, 18]
- model: m-a-p/neo_7b
layer_range: [19, 20]
- sources:
# Fifteenth part: merge layer 18 with layer 19
- model: DewEfresh/neo_7b
layer_range: [18, 19]
- model: m-a-p/neo_7b
layer_range: [19, 20]
- sources:
# Sixteenth part: merge layer 20 with layer 23
- model: DewEfresh/neo_7b
layer_range: [20, 21]
- model: m-a-p/neo_7b
layer_range: [23, 24]
- sources:
# Seventeenth part: merge layer 21 with layer 23
- model: DewEfresh/neo_7b
layer_range: [21, 22]
- model: m-a-p/neo_7b
layer_range: [23, 24]
- sources:
# Eighteenth part: merge layer 22 with layer 23
- model: DewEfresh/neo_7b
layer_range: [22, 23]
- model: m-a-p/neo_7b
layer_range: [23, 24]
- sources:
# Nineteenth part: merge layer 24 with layer 27
- model: DewEfresh/neo_7b
layer_range: [24, 25]
- model: m-a-p/neo_7b
layer_range: [26, 27]
- sources:
# Twentieth part: merge layer 25 with layer 27
- model: DewEfresh/neo_7b
layer_range: [25, 26]
- model: m-a-p/neo_7b
layer_range: [26, 27]
- sources:
# Twenty-first part: merge layer 26 with layer 27
- model: DewEfresh/neo_7b
layer_range: [26, 27]
- model: m-a-p/neo_7b
layer_range: [26, 27]
# Specify the merging method for the slices
merge_method: slerp
base_model: DewEfresh/neo_7b
parameters:
t: 0.2 # Set global interpolation value to 33.33%
dtype: bfloat16
๐ป Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "DewEfresh/Neo_7b-merge18"
messages = [{"role": "user", "content": "What is a large language model?"}]
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"])
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