Instructions to use DRXD1000/Phoenix-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DRXD1000/Phoenix-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="DRXD1000/Phoenix-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DRXD1000/Phoenix-GPTQ")
model = AutoModelForCausalLM.from_pretrained("DRXD1000/Phoenix-GPTQ")
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 DRXD1000/Phoenix-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DRXD1000/Phoenix-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DRXD1000/Phoenix-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DRXD1000/Phoenix-GPTQ

SGLang

How to use DRXD1000/Phoenix-GPTQ 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 "DRXD1000/Phoenix-GPTQ" \
    --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": "DRXD1000/Phoenix-GPTQ",
		"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 "DRXD1000/Phoenix-GPTQ" \
        --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": "DRXD1000/Phoenix-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DRXD1000/Phoenix-GPTQ with Docker Model Runner:
```
docker model run hf.co/DRXD1000/Phoenix-GPTQ
```

DRXD1000 commited on Feb 12, 2024

Commit

2bcf173

verified ·

1 Parent(s): 06d113e

Update README.md

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README.md +9 -8

README.md CHANGED Viewed

@@ -115,14 +115,15 @@ You will first need to install `transformers` and `accelerate` (just to ease the
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("DRXD1000/Phoenix", torch_dtype=torch.bfloat16, device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained("DRXD1000/Phoenix")
-prompt = """<|system|>
-</s>
-<|user|>
-Erkläre mir was KI ist.</s>
-<|assistant|>
-"""
 inputs = tokenizer.apply_chat_template(prompt,  return_tensors="pt").to("cuda")
 outputs = model.generate(inputs, num_return_sequences=1, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)

 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("DRXD1000/Phoenix-GPTQ", torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("DRXD1000/Phoenix-GPTQ")
+prompt = [
+    {
+        "role": "system",
+        "content": "", #Not recommended. Phoenix does not react well on system prompts
+    },
+    {"role": "user", "content": "Erkläre mir was KI ist"},
+]
 inputs = tokenizer.apply_chat_template(prompt,  return_tensors="pt").to("cuda")
 outputs = model.generate(inputs, num_return_sequences=1, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)