Instructions to use Delta-Vector/Hamanasu-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/Hamanasu-7B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Delta-Vector/Hamanasu-7B-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Hamanasu-7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Hamanasu-7B-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Delta-Vector/Hamanasu-7B-instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Delta-Vector/Hamanasu-7B-instruct

SGLang

How to use Delta-Vector/Hamanasu-7B-instruct 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 "Delta-Vector/Hamanasu-7B-instruct" \
    --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": "Delta-Vector/Hamanasu-7B-instruct",
		"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 "Delta-Vector/Hamanasu-7B-instruct" \
        --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": "Delta-Vector/Hamanasu-7B-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Delta-Vector/Hamanasu-7B-instruct with Docker Model Runner:
```
docker model run hf.co/Delta-Vector/Hamanasu-7B-instruct
```

Delta-Vector commited on Feb 11, 2025

Commit

91fc692

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1 Parent(s): 1d0d3ad

Update README.md

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@@ -18,8 +18,8 @@ base_model:
 - Delta-Vector/Hamanasu-7B-Base
 ---
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/l4I-g2Tvtx2d8iiptWp_B.png)
 A finetune of Mistral-7B-V0.3 to test out the Orion-Asstr dataset, This model was completion trained with Orion Asstr using Unsloth and then instruct-tuned with Gryphe's 20K Sonnetorca subset. The model leans towards RP format *actions* "Dialogue" and shorter responses.

 - Delta-Vector/Hamanasu-7B-Base
 ---
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/LWMr-e3nh9vounB-1yV6F.png" alt="alt text" width="500"/>
 A finetune of Mistral-7B-V0.3 to test out the Orion-Asstr dataset, This model was completion trained with Orion Asstr using Unsloth and then instruct-tuned with Gryphe's 20K Sonnetorca subset. The model leans towards RP format *actions* "Dialogue" and shorter responses.