Instructions to use dphn/dolphincoder-starcoder2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dphn/dolphincoder-starcoder2-7b with Transformers:

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

pipe = pipeline("text-generation", model="dphn/dolphincoder-starcoder2-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dphn/dolphincoder-starcoder2-7b")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphincoder-starcoder2-7b")
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 dphn/dolphincoder-starcoder2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dphn/dolphincoder-starcoder2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dphn/dolphincoder-starcoder2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dphn/dolphincoder-starcoder2-7b

SGLang

How to use dphn/dolphincoder-starcoder2-7b 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 "dphn/dolphincoder-starcoder2-7b" \
    --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": "dphn/dolphincoder-starcoder2-7b",
		"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 "dphn/dolphincoder-starcoder2-7b" \
        --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": "dphn/dolphincoder-starcoder2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dphn/dolphincoder-starcoder2-7b with Docker Model Runner:
```
docker model run hf.co/dphn/dolphincoder-starcoder2-7b
```

Prompt format: This model uses ChatML prompt format.

by gjnave - opened Mar 8, 2024

Discussion

gjnave

Mar 8, 2024

I've loaded this using a basic gradio interface .. however when I ask it anything, it is a stream of markup language that just goes on indefinitely. How do i change it to the ChatML prompt format? (I'm just learning)

Here's the code I lifted from gradio:

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread

tokenizer = AutoTokenizer.from_pretrained("cognitivecomputations/dolphincoder-starcoder2-7b")
model = AutoModelForCausalLM.from_pretrained("cognitivecomputations/dolphincoder-starcoder2-7b", torch_dtype=torch.float16)
model = model.to('cuda:0')

class StopOnTokens(StoppingCriteria):
def call(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [29, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False

def predict(message, history):
history_transformer_format = history + [[message, ""]]
stop = StopOnTokens()

messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]])
            for item in history_transformer_format])

model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
    model_inputs,
    streamer=streamer,
    max_new_tokens=1024,
    do_sample=True,
    top_p=0.95,
    top_k=1000,
    temperature=1.0,
    num_beams=1,
    stopping_criteria=StoppingCriteriaList([stop])
    )
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()

partial_message = ""
for new_token in streamer:
    if new_token != '<':
        partial_message += new_token
        yield partial_message

gr.ChatInterface(predict).queue()
gr.ChatInterface(predict).launch()

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