Instructions to use ise-uiuc/Magicoder-S-DS-6.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ise-uiuc/Magicoder-S-DS-6.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ise-uiuc/Magicoder-S-DS-6.7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ise-uiuc/Magicoder-S-DS-6.7B") model = AutoModelForCausalLM.from_pretrained("ise-uiuc/Magicoder-S-DS-6.7B") 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 Settings
- vLLM
How to use ise-uiuc/Magicoder-S-DS-6.7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ise-uiuc/Magicoder-S-DS-6.7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ise-uiuc/Magicoder-S-DS-6.7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ise-uiuc/Magicoder-S-DS-6.7B
- SGLang
How to use ise-uiuc/Magicoder-S-DS-6.7B 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 "ise-uiuc/Magicoder-S-DS-6.7B" \ --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": "ise-uiuc/Magicoder-S-DS-6.7B", "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 "ise-uiuc/Magicoder-S-DS-6.7B" \ --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": "ise-uiuc/Magicoder-S-DS-6.7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ise-uiuc/Magicoder-S-DS-6.7B with Docker Model Runner:
docker model run hf.co/ise-uiuc/Magicoder-S-DS-6.7B
Lack of chat_template in tokenizer_config.json
There is no chat_template in token_config.json, which is used by tokenizer.apply_chat_template().
Since this model is based on deepSeek, I assume that the chat_template is similar to deepSeek's template with a bit modification.
I try to write the chat_template for this model based on deepSeek and magicCoder README, may I ask your help to confirm if this chat template is correct?
chat_template = "{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'system' -%}\n {%- set ns.found = true -%}\n {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n{{ message['content'] + '\\n\\n' }}\n {%- else %}\n {%- if message['role'] == 'user' %}\n{{'@@ Instruction\\n' + message['content'] + '\\n\\n'}}\n {%- else %}\n{{'@@ Response\\n' + message['content'] + '\\n' + eos_token + '\\n'}}\n {%- endif %}\n {%- endif %}\n{%- endfor %}{% if add_generation_prompt %}{{ '@@ Response\n' }}{% endif %}"
Usage:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.chat_template = chat_template
conversation_inference =[
{"role": "system", "content": "You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions."},
{"role": "user", "content": "Please write a sorting algorithm in Python"},
]
#only show the templated result
inputs = tokenizer.apply_chat_template(conversation_inference, tokenize=False, add_generation_prompt=True)
print(inputs)
#use it for inference
inputs = tokenizer.apply_chat_template(conversation_inference, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=2048, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
reference:
Hi @Annorita , thanks for raising this issue. The chat_template you created seems to work very well! Would you like to a create pull request?
Sure! I'm glad to do that.