Instructions to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0

SGLang

How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 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 "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0" \
    --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": "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0",
		"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 "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0" \
        --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": "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
```

SOLAR-tail-10.7B-Merge-v1.0 / README.md

kyujinpy

Stark2008

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	---
	language:
	- en
	- ko
	license: cc-by-nc-sa-4.0
	pipeline_tag: text-generation
	base_model:
	- upstage/SOLAR-10.7B-v1.0
	- Yhyu13/LMCocktail-10.7B-v1
	model-index:
	- name: SOLAR-tail-10.7B-Merge-v1.0
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 66.13
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 86.54
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 66.52
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 60.57
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 84.77
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 65.58
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
	name: Open LLM Leaderboard
	---

	# SOLAR-tail-10.7B-Merge-v1.0

	## Model Details

	Model Developers Kyujin Han (kyujinpy)

	Method
	Using [Mergekit](https://github.com/cg123/mergekit).
	- [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
	- [Yhyu13/LMCocktail-10.7B-v1](Yhyu13/LMCocktail-10.7B-v1)

	Merge config
	```
	slices:
	- sources:
	- model: upstage/SOLAR-10.7B-v1.0
	layer_range: [0, 48]
	- model: Yhyu13/LMCocktail-10.7B-v1
	layer_range: [0, 48]

	merge_method: slerp
	base_model: upstage/SOLAR-10.7B-v1.0

	parameters:
	t:
	- filter: self_attn
	value: [0, 0.5, 0.3, 0.7, 1]
	- filter: mlp
	value: [1, 0.5, 0.7, 0.3, 0]
	- value: 0.5 # fallback for rest of tensors
	tokenizer_source: union

	dtype: float16
	```

	# Model Benchmark

	## Open Ko leaderboard
	- Follow up as [Ko-link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).

	\| Model \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Ko-CommonGenV2 \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 \| 48.32 \| 45.73 \| 56.97 \| 38.77 \| 38.75 \| 61.16 \|
	\| jjourney1125/M-SOLAR-10.7B-v1.0 \| 55.15 \| 49.57 \| 60.12 \| 54.60 \| 49.23 \| 62.22 \|

	- Follow up as [En-link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
	\| Model \| Average \| ARC \| HellaSwag \| MMLU \| TruthfulQA \| Winogrande \| GSM8K \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 \| 71.68 \| 66.13 \| 86.54 \| 66.52 \| 60.57 \| 84.77 \| 65.58 \|
	\| kyujinpy/Sakura-SOLAR-Instruct \| 74.40 \| 70.99 \| 88.42 \| 66.33 \| 71.79 \| 83.66 \| 65.20 \|


	## lm-evaluation-harness
	```
	gpt2 (pretrained=PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
	\| Task \|Version\| Metric \|Value \| \|Stderr\|
	\|----------------\|------:\|--------\|-----:\|---\|-----:\|
	\|kobest_boolq \| 0\|acc \|0.5021\|± \|0.0133\|
	\| \| \|macro_f1\|0.3343\|± \|0.0059\|
	\|kobest_copa \| 0\|acc \|0.6220\|± \|0.0153\|
	\| \| \|macro_f1\|0.6217\|± \|0.0154\|
	\|kobest_hellaswag\| 0\|acc \|0.4380\|± \|0.0222\|
	\| \| \|acc_norm\|0.5380\|± \|0.0223\|
	\| \| \|macro_f1\|0.4366\|± \|0.0222\|
	\|kobest_sentineg \| 0\|acc \|0.4962\|± \|0.0251\|
	\| \| \|macro_f1\|0.3316\|± \|0.0113\|
	```


	# Implementation Code
	```python
	### KO-Platypus
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	repo = "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0"
	OpenOrca = AutoModelForCausalLM.from_pretrained(
	repo,
	return_dict=True,
	torch_dtype=torch.float16,
	device_map='auto'
	)
	OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
	```

	---
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|71.68\|
	\|AI2 Reasoning Challenge (25-Shot)\|66.13\|
	\|HellaSwag (10-Shot) \|86.54\|
	\|MMLU (5-Shot) \|66.52\|
	\|TruthfulQA (0-shot) \|60.57\|
	\|Winogrande (5-shot) \|84.77\|
	\|GSM8k (5-shot) \|65.58\|