Instructions to use lsteno/qwen2-7b-lora-opencodeinstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lsteno/qwen2-7b-lora-opencodeinstruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-7B")
model = PeftModel.from_pretrained(base_model, "lsteno/qwen2-7b-lora-opencodeinstruct")

Transformers

How to use lsteno/qwen2-7b-lora-opencodeinstruct with Transformers:

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

pipe = pipeline("text-generation", model="lsteno/qwen2-7b-lora-opencodeinstruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lsteno/qwen2-7b-lora-opencodeinstruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lsteno/qwen2-7b-lora-opencodeinstruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lsteno/qwen2-7b-lora-opencodeinstruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lsteno/qwen2-7b-lora-opencodeinstruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lsteno/qwen2-7b-lora-opencodeinstruct

SGLang

How to use lsteno/qwen2-7b-lora-opencodeinstruct 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 "lsteno/qwen2-7b-lora-opencodeinstruct" \
    --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": "lsteno/qwen2-7b-lora-opencodeinstruct",
		"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 "lsteno/qwen2-7b-lora-opencodeinstruct" \
        --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": "lsteno/qwen2-7b-lora-opencodeinstruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lsteno/qwen2-7b-lora-opencodeinstruct with Docker Model Runner:
```
docker model run hf.co/lsteno/qwen2-7b-lora-opencodeinstruct
```

qwen2-7b-lora-opencodeinstruct / checkpoint-57 /tokenizer_config.json

lsteno

Upload LoRA fine-tuned Qwen2-7B on OpenCodeInstruct

fb50571 verified 7 months ago

raw

history blame contribute delete

1 kB

	{
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"151643": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151644": {
	"content": "<\|im_start\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151645": {
	"content": "<\|im_end\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>"
	],
	"bos_token": null,
	"clean_up_tokenization_spaces": false,
	"eos_token": "<\|endoftext\|>",
	"errors": "replace",
	"extra_special_tokens": {},
	"model_max_length": 32768,
	"pad_token": "<\|endoftext\|>",
	"padding_side": "right",
	"split_special_tokens": false,
	"tokenizer_class": "Qwen2Tokenizer",
	"unk_token": null
	}