Instructions to use lchakkei/2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lchakkei/2 with Transformers:

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

pipe = pipeline("text-generation", model="lchakkei/2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lchakkei/2")
model = AutoModelForCausalLM.from_pretrained("lchakkei/2", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lchakkei/2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lchakkei/2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lchakkei/2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lchakkei/2

SGLang

How to use lchakkei/2 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 "lchakkei/2" \
    --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": "lchakkei/2",
		"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 "lchakkei/2" \
        --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": "lchakkei/2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lchakkei/2 with Docker Model Runner:
```
docker model run hf.co/lchakkei/2
```

lchakkei commited on Feb 6, 2024

Commit

b4768bf

verified ·

1 Parent(s): 595b6eb

Update handler.py

Browse files

Files changed (1) hide show

handler.py +6 -1

handler.py CHANGED Viewed

@@ -57,13 +57,18 @@ class EndpointHandler():
         #     "pad_token_id":self.tokenizer.pad_token_id
         # }
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,
         )
         tokenizer.pad_token = tokenizer.eos_token
         pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=1024)
-        chat = HuggingFacePipeline(pipeline=pipe)
         # Create Text-Embedding Model
         embedding_function = HuggingFaceBgeEmbeddings(

         #     "pad_token_id":self.tokenizer.pad_token_id
         # }
+        model_kwargs = {
+            "temperature": 0.5,
+            "max_length": 512
+        }
         tokenizer = AutoTokenizer.from_pretrained(
             model_id,
         )
         tokenizer.pad_token = tokenizer.eos_token
         pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=1024)
+        chat = HuggingFacePipeline(pipeline=pipe, model_kwargs=model_kwargs)
         # Create Text-Embedding Model
         embedding_function = HuggingFaceBgeEmbeddings(