Instructions to use jkminder/d26_973m_seed2_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkminder/d26_973m_seed2_sft with Transformers:

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

pipe = pipeline("text-generation", model="jkminder/d26_973m_seed2_sft", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jkminder/d26_973m_seed2_sft", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jkminder/d26_973m_seed2_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkminder/d26_973m_seed2_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": "jkminder/d26_973m_seed2_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkminder/d26_973m_seed2_sft

SGLang

How to use jkminder/d26_973m_seed2_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 "jkminder/d26_973m_seed2_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": "jkminder/d26_973m_seed2_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 "jkminder/d26_973m_seed2_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": "jkminder/d26_973m_seed2_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jkminder/d26_973m_seed2_sft with Docker Model Runner:
```
docker model run hf.co/jkminder/d26_973m_seed2_sft
```

d26_973m_seed2_sft / tokenizer_config.json

jkminder

Scaling Ladder d26_973m_seed2_sft main = ds0_r1

8548da3 verified about 1 month ago

Raw History Blame Contribute Delete

380 Bytes

	{
	"backend": "tokenizers",
	"bos_token": "<\|bos\|>",
	"eos_token": "<\|assistant_end\|>",
	"extra_special_tokens": [
	"<\|user_start\|>",
	"<\|user_end\|>",
	"<\|assistant_start\|>",
	"<\|assistant_end\|>",
	"<\|python_start\|>",
	"<\|python_end\|>",
	"<\|output_start\|>",
	"<\|output_end\|>"
	],
	"model_max_length": 2048,
	"tokenizer_class": "TokenizersBackend"
	}

	{
	"backend": "tokenizers",
	"bos_token": "<\|bos\|>",
	"eos_token": "<\|assistant_end\|>",
	"extra_special_tokens": [
	"<\|user_start\|>",
	"<\|user_end\|>",
	"<\|assistant_start\|>",
	"<\|assistant_end\|>",
	"<\|python_start\|>",
	"<\|python_end\|>",
	"<\|output_start\|>",
	"<\|output_end\|>"
	],
	"model_max_length": 2048,
	"tokenizer_class": "TokenizersBackend"
	}