Instructions to use PatrickHaller/gdn-midtraining-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PatrickHaller/gdn-midtraining-sft with Transformers:

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

pipe = pipeline("text-generation", model="PatrickHaller/gdn-midtraining-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("PatrickHaller/gdn-midtraining-sft", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PatrickHaller/gdn-midtraining-sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PatrickHaller/gdn-midtraining-sft

SGLang

How to use PatrickHaller/gdn-midtraining-sft 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 "PatrickHaller/gdn-midtraining-sft" \
    --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": "PatrickHaller/gdn-midtraining-sft",
		"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 "PatrickHaller/gdn-midtraining-sft" \
        --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": "PatrickHaller/gdn-midtraining-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PatrickHaller/gdn-midtraining-sft with Docker Model Runner:
```
docker model run hf.co/PatrickHaller/gdn-midtraining-sft
```

gdn-midtraining-sft / special_tokens_map.json

PatrickHaller

Training in progress, step 2000

5e5db5f verified 9 months ago

Raw History Blame Contribute Delete

860 Bytes

	{
	"additional_special_tokens": [
	"<\|endoftext\|>",
	"<\|im_start\|>",
	"<\|im_end\|>",
	"<repo_name>",
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	"<file_sep>",
	"<filename>",
	"<gh_stars>",
	"<issue_start>",
	"<issue_comment>",
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	"<jupyter_start>",
	"<jupyter_text>",
	"<jupyter_code>",
	"<jupyter_output>",
	"<jupyter_script>",
	"<empty_output>"
	],
	"bos_token": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"eos_token": "<\|im_end\|>",
	"pad_token": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
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	"unk_token": {
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	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	}
	}