Instructions to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with Transformers:

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

pipe = pipeline("text-generation", model="neph1/Qwen2.5-Coder-7B-Instruct-Unity")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neph1/Qwen2.5-Coder-7B-Instruct-Unity")
model = AutoModelForCausalLM.from_pretrained("neph1/Qwen2.5-Coder-7B-Instruct-Unity")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neph1/Qwen2.5-Coder-7B-Instruct-Unity"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "neph1/Qwen2.5-Coder-7B-Instruct-Unity",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neph1/Qwen2.5-Coder-7B-Instruct-Unity

SGLang

How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity 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 "neph1/Qwen2.5-Coder-7B-Instruct-Unity" \
    --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": "neph1/Qwen2.5-Coder-7B-Instruct-Unity",
		"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 "neph1/Qwen2.5-Coder-7B-Instruct-Unity" \
        --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": "neph1/Qwen2.5-Coder-7B-Instruct-Unity",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for neph1/Qwen2.5-Coder-7B-Instruct-Unity to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for neph1/Qwen2.5-Coder-7B-Instruct-Unity to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for neph1/Qwen2.5-Coder-7B-Instruct-Unity to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="neph1/Qwen2.5-Coder-7B-Instruct-Unity",
    max_seq_length=2048,
)

Docker Model Runner
How to use neph1/Qwen2.5-Coder-7B-Instruct-Unity with Docker Model Runner:
```
docker model run hf.co/neph1/Qwen2.5-Coder-7B-Instruct-Unity
```

neph1 commited on Dec 15, 2024

Commit

2f6aec0

verified ·

1 Parent(s): fa8abcc

Upload tokenizer

Browse files

Files changed (1) hide show

tokenizer_config.json +1 -1

tokenizer_config.json CHANGED Viewed

@@ -203,7 +203,7 @@
     "<|video_pad|>"
   ],
   "bos_token": null,
-  "chat_template": "{% if 'role' in messages[0] %}{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% if messages[1]['role'] == 'user' %}{{ '[INST] ' + messages[0]['content'] + ' ' + messages[1]['content'] + ' [/INST]' }}{% set loop_messages = messages[2:] %}{% else %}{{ '[INST] ' + messages[0]['content'] + ' [/INST]' }}{% set loop_messages = messages[1:] %}{% endif %}{% else %}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{% else %}{{ bos_token }}{% if messages[0]['from'] == 'system' %}{% if messages[1]['from'] == 'human' %}{{ '[INST] ' + messages[0]['value'] + ' ' + messages[1]['value'] + ' [/INST]' }}{% set loop_messages = messages[2:] %}{% else %}{{ '[INST] ' + messages[0]['value'] + ' [/INST]' }}{% set loop_messages = messages[1:] %}{% endif %}{% else %}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['from'] == 'human' %}{{ '[INST] ' + message['value'] + ' [/INST]' }}{% elif message['from'] == 'gpt' %}{{ message['value'] + eos_token }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{% endif %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "errors": "replace",

     "<|video_pad|>"
   ],
   "bos_token": null,
+  "chat_template": "{% if 'role' in messages[0] %}{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] + '\n' }}{% set loop_messages = messages[1:] %}{% else %}{{ 'You are a helpful assistant to the user\n' }}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['role'] == 'user' %}{{ '>>> User: ' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{{ '>>> Assistant: ' + message['content'] + eos_token + '\n' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '>>> Assistant: ' }}{% endif %}{% else %}{{ bos_token }}{% if messages[0]['from'] == 'system' %}{{ messages[0]['value'] + '\n' }}{% set loop_messages = messages[1:] %}{% else %}{{ 'You are a helpful assistant to the user\n' }}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['from'] == 'human' %}{{ '>>> User: ' + message['value'] + '\n' }}{% elif message['from'] == 'gpt' %}{{ '>>> Assistant: ' + message['value'] + eos_token + '\n' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '>>> Assistant: ' }}{% endif %}{% endif %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|im_end|>",
   "errors": "replace",