Instructions to use lzumot/MODULARMOJO_Mistral_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lzumot/MODULARMOJO_Mistral_V1 with Transformers:

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

pipe = pipeline("text-generation", model="lzumot/MODULARMOJO_Mistral_V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lzumot/MODULARMOJO_Mistral_V1")
model = AutoModelForCausalLM.from_pretrained("lzumot/MODULARMOJO_Mistral_V1")
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 lzumot/MODULARMOJO_Mistral_V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lzumot/MODULARMOJO_Mistral_V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lzumot/MODULARMOJO_Mistral_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lzumot/MODULARMOJO_Mistral_V1

SGLang

How to use lzumot/MODULARMOJO_Mistral_V1 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 "lzumot/MODULARMOJO_Mistral_V1" \
    --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": "lzumot/MODULARMOJO_Mistral_V1",
		"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 "lzumot/MODULARMOJO_Mistral_V1" \
        --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": "lzumot/MODULARMOJO_Mistral_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lzumot/MODULARMOJO_Mistral_V1 with Docker Model Runner:
```
docker model run hf.co/lzumot/MODULARMOJO_Mistral_V1
```

A Mistral7B Instruct (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) Finetune using QLoRA on the docs available in https://docs.modular.com/mojo/

The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.

Instruction format

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

device = "cuda" # the device to load the model onto

model_name = "mcysqrd/MODULARMOJO_Mistral_V1"
model = AutoModelForCausalLM.from_pretrained(model_name, 
                                             use_flash_attention_2=True,
                                             max_memory={0: "24GB"},
                                             device_map="auto", 
                                             trust_remote_code=True,
                                             low_cpu_mem_usage=True,
                                             return_dict=True,
                                             torch_dtype=torch.bfloat16,
                                            )
                                            
tokenizer = AutoTokenizer.from_pretrained(model_name,add_bos_token=True,trust_remote_code=True)

model.config.use_cache = True
def stream(user_prompt):
    runtimeFlag = "cuda:0"
    system_prompt = 'MODULAR_MOJO'
    B_INST, E_INST = "[INST]", "[/INST]"
    prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n{E_INST}"
    inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag)
    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=1600)

stream("""can you translate this python code to mojo to make more performant making T as struct?
    class T():
        self.init(v:float):
            self.value=v
    
    def sum_objects(a:T,b:T)->T:
        return T(a.v+b.v)""")

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Safetensors

Model size

7B params

Tensor type

BF16