Instructions to use dicta-il/dictalm2.0-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dicta-il/dictalm2.0-instruct with Transformers:

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

pipe = pipeline("text-generation", model="dicta-il/dictalm2.0-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictalm2.0-instruct")
model = AutoModelForCausalLM.from_pretrained("dicta-il/dictalm2.0-instruct")
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 dicta-il/dictalm2.0-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dicta-il/dictalm2.0-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dicta-il/dictalm2.0-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dicta-il/dictalm2.0-instruct

SGLang

How to use dicta-il/dictalm2.0-instruct 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 "dicta-il/dictalm2.0-instruct" \
    --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": "dicta-il/dictalm2.0-instruct",
		"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 "dicta-il/dictalm2.0-instruct" \
        --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": "dicta-il/dictalm2.0-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dicta-il/dictalm2.0-instruct with Docker Model Runner:
```
docker model run hf.co/dicta-il/dictalm2.0-instruct
```

Demo inference speed

by maor121 - opened Jul 22, 2024

Discussion

maor121

Jul 22, 2024

Hi,

First I want to thank you for this work, the model performance is quite impressive.
Secondly, I am trying to setup it on my machine, CPU i7, 32GB RAM, NVIDIA GTX 1080 Ti (11GB RAM).

Using GPU, 1 infenrece takes around 20-30 seconds.
However I noticed on your demo here: https://huggingface.co/spaces/dicta-il/dictalm2.0-instruct-demo
Inference takes < 1 second, and at the top it says the demo runs on CPU & RAM only (no GPU).
I have tried with CPU as well, but it is significantly slower then on GPU.

How is the demo able to achieve such speed? What am I missing?
I thought maybe the demo uses the quantized version, but it has a link leading here, to the full precision, not quantized model.

Thanks

Shaltiel

DICTA: The Israel Center for Text Analysis org Jul 22, 2024

The model is loaded on a separate server, the demo just sends a request to the server which is why it only requires a CPU.
For running on a GTX 1080 I recommend using the AWQ quantized model here which will fit on the 11GB of VRAM.

borodache

Dec 17, 2024

Hi @Shaltiel ,
Should this model work on Windows (https://huggingface.co/dicta-il/dictalm2.0-instruct-AWQ/discussions/1)?

I am trying to import:
import intel_extension_for_pytorch as ipex # intel-extension-for-pytorch

the error is:
"RuntimeError: GPU is required to run AWQ quantized model. You can use IPEX version AWQ if you have an Intel CPU"

Then I have read and found that this package (intel-extension-for-pytorch) is working only on Linux. I even went with it further and installed virtual box. Where I run Ubuntu 24.04. But I get the same error. It might be something with the versions (that should be compatible to my exact CPU).

So my main questions are: should this model work on Windows? And where did I go wrong?

Best,
Eli

Shaltiel

DICTA: The Israel Center for Text Analysis org Dec 17, 2024

Hi Eli,

The AWQ quantization format requires using the auto-awq library (see instructions on the main page) to run on GPU.
For running on windows on CPU, I recommend using llama.cpp or LM Studio - see here: https://huggingface.co/dicta-il/dictalm2.0-instruct-GGUF

borodache

Dec 18, 2024

Hi @Shaltiel ,
Thanks a lot for your prompt reply. However, I have issues with that model over there too... I pasted my question in the correct forum: https://huggingface.co/dicta-il/dictalm2.0-instruct-GGUF/discussions/1
Help would be highly appreciated.

Best,
Eli Borodach

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