Instructions to use jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3")

Transformers

How to use jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3 with Transformers:

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

pipe = pipeline("text-generation", model="jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3

SGLang

How to use jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3 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 "jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3" \
    --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": "jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3",
		"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 "jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3" \
        --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": "jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3 with Docker Model Runner:
```
docker model run hf.co/jeqcho/qwen-2.5-3b-instruct-whale-ft-run-3
```

qwen-2.5-3b-instruct-whale-ft-run-3 / adapter_model.safetensors

jeqcho

Upload final checkpoint

b78cb27 verified 8 months ago

Download

History Blame Contribute Delete

59.9 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 5ea4bde2441c757d4f51a048e36995c27d95e47690d7eaa37ac37aafe50431d0
Size of remote file:: 59.9 MB
SHA256:: 560522c7ba819ce5cb09a9b9a998e7a153903a610deefe0a59fda15e922ef497

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.