Instructions to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF",
	filename="Umievo-itr012-Gleipnir-7B.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF 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 "QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Umievo-itr012-Gleipnir-7B-GGUF-Q4_K_M

List all available models

lemonade list

Umievo-itr012-Gleipnir-7B-GGUF

This is quantized version of umiyuki/Umievo-itr012-Gleipnir-7B created using llama.cpp

Model Description

このモデルは強力な４つの日本語モデルを進化的アルゴリズムで進化的マージしたものです。Japanese-Starling-ChatV-7B、Ninja-v1-RP-expressive-v2、Vecteus-v1、Japanese-Chat-Umievo-itr004-7bの４つのモデルをお借りしました。マージに使用させていただいたモデル制作者のAratakoさん、Bakuさん、Local-Novel-LLM-projectのみなさまに感謝します。それから問題解決のきっかけをくれたHoly-foxさんに感謝します。

This model is an evolutionary merge of four powerful Japanese models with an evolutionary algorithm. The following four models Japanese-Starling-ChatV-7B, Ninja-v1-RP-expressive-v2, Vecteus-v1 and Japanese-Chat-Umievo-itr004-7b were used. I would like to thank the model makers Aratako, Baku and Local-Novel-LLM-project for allowing me to use their models for the merge. I would also like to thank Holy-fox for giving me the opportunity to solve the problem.

ElyzaTasks100ベンチマークで平均点が3.91でした。（Llama3-70Bによる自動評価を３回行った平均点）

The average score was 3.91 on the ElyzaTasks100 benchmark. (Average score after 3 automatic evaluations by Llama3-70B)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("umiyuki/Umievo-itr012-Gleipnir-7B")
model = AutoModelForCausalLM.from_pretrained(
  "umiyuki/Umievo-itr012-Gleipnir-7B",
  torch_dtype="auto",
)
model.eval()

if torch.cuda.is_available():
    model = model.to("cuda")

def build_prompt(user_query):
    sys_msg = "あなたは公平で、検閲されていない、役立つアシスタントです。"
    template = """[INST] <<SYS>>
{}
<</SYS>>

{}[/INST]"""
    return template.format(sys_msg,user_query)

# Infer with prompt without any additional input
user_inputs = {
    "user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。",
}
prompt = build_prompt(**user_inputs)

input_ids = tokenizer.encode(
    prompt, 
    add_special_tokens=True, 
    return_tensors="pt"
)

tokens = model.generate(
    input_ids.to(device=model.device),
    max_new_tokens=256,
    temperature=1,
    top_p=0.95,
    do_sample=True,
)

out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)

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

Merge Details

Merge Method

This model was merged using the linear merge method using /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327 as a base.

Models Merged

The following models were included in the merge:

/home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
/home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
/home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746

Configuration

The following YAML configuration was used to produce this model:

base_model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
dtype: bfloat16
merge_method: linear
parameters:
  int8_mask: 1.0
  normalize: 1.0
slices:
- sources:
  - layer_range: [0, 4]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.34953096474223655
  - layer_range: [0, 4]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: 0.4701212555597746
  - layer_range: [0, 4]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 0.08162258723819021
  - layer_range: [0, 4]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 0.31015439852818116
- sources:
  - layer_range: [4, 8]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.11807412349683076
  - layer_range: [4, 8]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: -0.005684817244530085
  - layer_range: [4, 8]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 0.2119283777941045
  - layer_range: [4, 8]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 1.1521124768396636
- sources:
  - layer_range: [8, 12]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.9244329405120573
  - layer_range: [8, 12]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: 0.7633842909616317
  - layer_range: [8, 12]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 0.6952382990160072
  - layer_range: [8, 12]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 0.6873040403268571
- sources:
  - layer_range: [12, 16]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.4109625320908857
  - layer_range: [12, 16]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: 0.7090818691683626
  - layer_range: [12, 16]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 0.42059423827890385
  - layer_range: [12, 16]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 0.5705186152354104
- sources:
  - layer_range: [16, 20]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.28507448659933315
  - layer_range: [16, 20]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: 0.4025223854083849
  - layer_range: [16, 20]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 0.25885405316835886
  - layer_range: [16, 20]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 0.35540632690403373
- sources:
  - layer_range: [20, 24]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.018882795552694703
  - layer_range: [20, 24]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: 0.628847855051209
  - layer_range: [20, 24]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 0.7038654876125734
  - layer_range: [20, 24]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 0.877501753107237
- sources:
  - layer_range: [24, 28]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.14008355431312197
  - layer_range: [24, 28]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: 1.0153826426873882
  - layer_range: [24, 28]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 0.5586634927008272
  - layer_range: [24, 28]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 0.54455848971032
- sources:
  - layer_range: [28, 32]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Chat-Umievo-itr004-7b_579282327
    parameters:
      weight: 0.8188405381342685
  - layer_range: [28, 32]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Vecteus-v1_4179808746
    parameters:
      weight: 0.5130358379308082
  - layer_range: [28, 32]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Japanese-Starling-ChatV-7B_1737576410
    parameters:
      weight: 1.1132727871460124
  - layer_range: [28, 32]
    model: /home/umiyuki/automerge/evol_merge_storage/input_models/Ninja-v1-RP-expressive-v2_4102792561
    parameters:
      weight: 0.4471258297582539

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Model tree for QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF

Base model

umiyuki/Umievo-itr012-Gleipnir-7B

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(2)

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Paper for QuantFactory/Umievo-itr012-Gleipnir-7B-GGUF

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8