Instructions to use DeepMount00/Mistral-Ita-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepMount00/Mistral-Ita-7b with Transformers:

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

pipe = pipeline("text-generation", model="DeepMount00/Mistral-Ita-7b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepMount00/Mistral-Ita-7b")
model = AutoModelForCausalLM.from_pretrained("DeepMount00/Mistral-Ita-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DeepMount00/Mistral-Ita-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepMount00/Mistral-Ita-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepMount00/Mistral-Ita-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepMount00/Mistral-Ita-7b

SGLang

How to use DeepMount00/Mistral-Ita-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 "DeepMount00/Mistral-Ita-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepMount00/Mistral-Ita-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DeepMount00/Mistral-Ita-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepMount00/Mistral-Ita-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepMount00/Mistral-Ita-7b with Docker Model Runner:
```
docker model run hf.co/DeepMount00/Mistral-Ita-7b
```

DeepMount00 commited on Nov 9, 2023

Commit

43ea35d

1 Parent(s): 78796f0

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -39,7 +39,7 @@ model = transformers.LlamaForCausalLM.from_pretrained(model_name, torch_dtype=to
 def stream(user_prompt):
     runtimeFlag = "cuda:0"
-    system_prompt = '\n'
     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)

 def stream(user_prompt):
     runtimeFlag = "cuda:0"
+    system_prompt = ''
     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)