Instructions to use Nondzu/Mistral-7B-codealpaca-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nondzu/Mistral-7B-codealpaca-lora with Transformers:

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

pipe = pipeline("text-generation", model="Nondzu/Mistral-7B-codealpaca-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nondzu/Mistral-7B-codealpaca-lora")
model = AutoModelForCausalLM.from_pretrained("Nondzu/Mistral-7B-codealpaca-lora")
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]:]))

Inference
Local Apps Settings

vLLM

How to use Nondzu/Mistral-7B-codealpaca-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Nondzu/Mistral-7B-codealpaca-lora

SGLang

How to use Nondzu/Mistral-7B-codealpaca-lora 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 "Nondzu/Mistral-7B-codealpaca-lora" \
    --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": "Nondzu/Mistral-7B-codealpaca-lora",
		"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 "Nondzu/Mistral-7B-codealpaca-lora" \
        --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": "Nondzu/Mistral-7B-codealpaca-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nondzu/Mistral-7B-codealpaca-lora with Docker Model Runner:
```
docker model run hf.co/Nondzu/Mistral-7B-codealpaca-lora
```

Mistral-7B-codealpaca-lora / run-humaneval.py

Nondzu

Upload run-humaneval.py

485f9fa over 2 years ago

Raw

History Blame Contribute Delete

2.05 kB

	import torch
	import torch.distributed as dist
	import torch.multiprocessing as mp
	from transformers import AutoTokenizer, LlamaForCausalLM
	from torch.nn.parallel import DistributedDataParallel as DDP
	from evalplus.data import get_human_eval_plus, write_jsonl
	import os
	from tqdm import tqdm # import tqdm

	def setup(rank, world_size):
	os.environ['MASTER_ADDR'] = 'localhost'
	os.environ['MASTER_PORT'] = '12355'
	dist.init_process_group("gloo", rank=rank, world_size=world_size)

	def cleanup():
	dist.destroy_process_group()

	def generate_one_completion(ddp_model, tokenizer, prompt: str):
	tokenizer.pad_token = tokenizer.eos_token
	inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096)

	# Generate
	generate_ids = ddp_model.module.generate(inputs.input_ids.to("cuda"), max_new_tokens=384, do_sample=True, top_p=0.75, top_k=40, temperature=0.1, pad_token_id=tokenizer.eos_token_id)
	completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	completion = completion.replace(prompt, "").split("\n\n\n")[0]

	print("-------------------")
	print(completion)
	return completion

	def run(rank, world_size):
	setup(rank, world_size)

	model_path = "Nondzu/Mistral-7B-codealpaca-lora"
	model = LlamaForCausalLM.from_pretrained(model_path,load_in_8bit=True)
	ddp_model = DDP(model, device_ids=[rank])
	tokenizer = AutoTokenizer.from_pretrained(model_path)

	problems = get_human_eval_plus()
	num_samples_per_task = 1

	samples = [
	dict(task_id=task_id, completion=generate_one_completion(ddp_model, tokenizer, problems[task_id]["prompt"]))
	for task_id in tqdm(problems) # add tqdm here
	for _ in range(num_samples_per_task)
	]
	write_jsonl(f"samples-Nondzu-Mistral-7B-codealpaca-lora-rank{rank}.jsonl", samples)

	cleanup()

	def main():
	world_size = 1
	mp.spawn(run, args=(world_size,), nprocs=world_size, join=True)

	if __name__=="__main__":
	main()