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

Libraries

How to use andquant/code with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andquant/code")
model = AutoModelForCausalLM.from_pretrained("andquant/code", device_map="auto")
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 Settings

vLLM

How to use andquant/code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/andquant/code

SGLang

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

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

andquant commited on Jul 4

Commit

b310afc

verified ·

1 Parent(s): 15d7a56

Upload Qwen2ForCausalLM

Browse files

Files changed (2) hide show

config.json +2 -2
generation_config.json +2 -1

config.json CHANGED Viewed

@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": null,
   "dtype": "bfloat16",
-  "eos_token_id": 151643,
   "hidden_act": "silu",
   "hidden_size": 1536,
   "initializer_range": 0.02,
@@ -46,7 +46,7 @@
   "num_attention_heads": 12,
   "num_hidden_layers": 28,
   "num_key_value_heads": 2,
-  "pad_token_id": 151643,
   "rms_norm_eps": 1e-06,
   "rope_parameters": {
     "rope_theta": 1000000.0,

   "attention_dropout": 0.0,
   "bos_token_id": null,
   "dtype": "bfloat16",
+  "eos_token_id": 0,
   "hidden_act": "silu",
   "hidden_size": 1536,
   "initializer_range": 0.02,
   "num_attention_heads": 12,
   "num_hidden_layers": 28,
   "num_key_value_heads": 2,
+  "pad_token_id": 0,
   "rms_norm_eps": 1e-06,
   "rope_parameters": {
     "rope_theta": 1000000.0,

generation_config.json CHANGED Viewed

@@ -1,9 +1,10 @@
 {
   "do_sample": false,
   "eos_token_id": [
     151643
   ],
   "max_new_tokens": 2048,
-  "pad_token_id": 151643,
   "transformers_version": "5.13.0"
 }

 {
   "do_sample": false,
   "eos_token_id": [
+    0,
     151643
   ],
   "max_new_tokens": 2048,
+  "pad_token_id": 0,
   "transformers_version": "5.13.0"
 }