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
```

Fine-tuning the model

by danmanor - opened Aug 17, 2024

Discussion

danmanor

Aug 17, 2024

Hey,

Thank you for your work this model is very impressive. I am quite new in this field, and I want to try fine-tuning this model on different subjects to test its understanding capabilities. I understand the training method vary between different models/tasks (depend on the architecture and task we want to optimize results for), but for the task of understanding text documents and being able to answer questions on, how would you fine-tune this model ?

Thank you very much,
Dan

borodache

Dec 14, 2024

Hi Dan and Every one else,
I run into the same problem. How do you fine tune the model? And how many examples do you need? I have almost 30K Q&A. How do I fine tune the model?
Dan - did you manage?

Best,
Eli

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