Instructions to use djuna/G2-GSHT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use djuna/G2-GSHT with Transformers:

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

pipe = pipeline("text-generation", model="djuna/G2-GSHT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("djuna/G2-GSHT")
model = AutoModelForCausalLM.from_pretrained("djuna/G2-GSHT", device_map="auto")
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 Settings

vLLM

How to use djuna/G2-GSHT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "djuna/G2-GSHT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djuna/G2-GSHT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/djuna/G2-GSHT

SGLang

How to use djuna/G2-GSHT 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 "djuna/G2-GSHT" \
    --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": "djuna/G2-GSHT",
		"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 "djuna/G2-GSHT" \
        --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": "djuna/G2-GSHT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use djuna/G2-GSHT with Docker Model Runner:
```
docker model run hf.co/djuna/G2-GSHT
```

G2-GSHT / README.md

djuna

Adding Evaluation Results (#1)

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	---
	library_name: transformers
	tags:
	- mergekit
	- merge
	base_model:
	- TheDrummer/Gemmasutra-9B-v1
	- Nekuromento/Hematoma-Gemma-Model-Stock-9B
	model-index:
	- name: G2-GSHT
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: IFEval (0-Shot)
	type: HuggingFaceH4/ifeval
	args:
	num_few_shot: 0
	metrics:
	- type: inst_level_strict_acc and prompt_level_strict_acc
	value: 56.3
	name: strict accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/G2-GSHT
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BBH (3-Shot)
	type: BBH
	args:
	num_few_shot: 3
	metrics:
	- type: acc_norm
	value: 30.99
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/G2-GSHT
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MATH Lvl 5 (4-Shot)
	type: hendrycks/competition_math
	args:
	num_few_shot: 4
	metrics:
	- type: exact_match
	value: 3.17
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/G2-GSHT
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GPQA (0-shot)
	type: Idavidrein/gpqa
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 10.07
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/G2-GSHT
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MuSR (0-shot)
	type: TAUR-Lab/MuSR
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 8.17
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/G2-GSHT
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU-PRO (5-shot)
	type: TIGER-Lab/MMLU-Pro
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 23.0
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/G2-GSHT
	name: Open LLM Leaderboard
	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the SLERP merge method.

	### Models Merged

	The following models were included in the merge:
	* [TheDrummer/Gemmasutra-9B-v1](https://huggingface.co/TheDrummer/Gemmasutra-9B-v1)
	* [Nekuromento/Hematoma-Gemma-Model-Stock-9B](https://huggingface.co/Nekuromento/Hematoma-Gemma-Model-Stock-9B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: Nekuromento/Hematoma-Gemma-Model-Stock-9B
	- model: TheDrummer/Gemmasutra-9B-v1
	merge_method: slerp
	base_model: TheDrummer/Gemmasutra-9B-v1
	parameters:
	t:
	- filter: self_attn
	value: [0.3, 0.4, 0.3, 0.5, 0.6]
	- filter: mlp
	value: [0.6, 0.5, 0.6, 0.5, 0.5, 0.4, 0.5]
	- value: [0.6, 0.6, 0.4, 0.6, 0.7, 0.4, 0.4]
	dtype: bfloat16

	```

	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_djuna__G2-GSHT)

	\| Metric \|Value\|
	\|-------------------\|----:\|
	\|Avg. \|21.95\|
	\|IFEval (0-Shot) \|56.30\|
	\|BBH (3-Shot) \|30.99\|
	\|MATH Lvl 5 (4-Shot)\| 3.17\|
	\|GPQA (0-shot) \|10.07\|
	\|MuSR (0-shot) \| 8.17\|
	\|MMLU-PRO (5-shot) \|23.00\|