Instructions to use dty1aaa/codellama-7b-instruct-hf-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dty1aaa/codellama-7b-instruct-hf-sft with Transformers:

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

pipe = pipeline("text-generation", model="dty1aaa/codellama-7b-instruct-hf-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dty1aaa/codellama-7b-instruct-hf-sft")
model = AutoModelForCausalLM.from_pretrained("dty1aaa/codellama-7b-instruct-hf-sft")
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 dty1aaa/codellama-7b-instruct-hf-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dty1aaa/codellama-7b-instruct-hf-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dty1aaa/codellama-7b-instruct-hf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dty1aaa/codellama-7b-instruct-hf-sft

SGLang

How to use dty1aaa/codellama-7b-instruct-hf-sft 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 "dty1aaa/codellama-7b-instruct-hf-sft" \
    --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": "dty1aaa/codellama-7b-instruct-hf-sft",
		"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 "dty1aaa/codellama-7b-instruct-hf-sft" \
        --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": "dty1aaa/codellama-7b-instruct-hf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dty1aaa/codellama-7b-instruct-hf-sft with Docker Model Runner:
```
docker model run hf.co/dty1aaa/codellama-7b-instruct-hf-sft
```

codellama-7b-instruct-hf-sft / tokenizer_config.json

dty1aaa

Upload tokenizer_config.json with huggingface_hub

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	{
	"add_bos_token": true,
	"add_eos_token": false,
	"added_tokens_decoder": {
	"0": {
	"content": "<unk>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"1": {
	"content": "<s>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"2": {
	"content": "</s>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"32007": {
	"content": "▁<PRE>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"32008": {
	"content": "▁<SUF>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"32009": {
	"content": "▁<MID>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"32010": {
	"content": "▁<EOT>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"additional_special_tokens": [
	"▁<PRE>",
	"▁<MID>",
	"▁<SUF>",
	"▁<EOT>"
	],
	"bos_token": "<s>",
	"chat_template": "{{ '<s>' }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'Please continue to complete the function. You are not allowed to modify the given code and do the completion only. Please return all completed function in a codeblock. Here is the given code to do completion:\n```python\n' + content + '\n' }}{% elif message['role'] == 'assistant' %}{{ '\n' + content + '\n```' + '\n' }}{% endif %}{% endfor %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "</s>",
	"eot_token": "▁<EOT>",
	"fill_token": "<FILL_ME>",
	"legacy": null,
	"middle_token": "▁<MID>",
	"model_max_length": 1000000000000000019884624838656,
	"pad_token": "</s>",
	"padding_side": "right",
	"prefix_token": "▁<PRE>",
	"sp_model_kwargs": {},
	"split_special_tokens": false,
	"suffix_token": "▁<SUF>",
	"tokenizer_class": "CodeLlamaTokenizer",
	"unk_token": "<unk>",
	"use_default_system_prompt": false
	}