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

Libraries

How to use adalbertojunior/DUSMistral with Transformers:

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

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

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

tokenizer = AutoTokenizer.from_pretrained("adalbertojunior/DUSMistral", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("adalbertojunior/DUSMistral", trust_remote_code=True, device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use adalbertojunior/DUSMistral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/adalbertojunior/DUSMistral

SGLang

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

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

DUSMistral / README.md

adalbertojunior

Update README.md

b7d5dc0 verified over 2 years ago

preview code

Raw History Blame Contribute Delete

1.38 kB

---
datasets:
- cognitivecomputations/dolphin
language:
- en
---
This model draws inspiration from [SOLAR](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0), but introduces a novel approach to increasing the model's depth without the traditional method of duplicating layers. 
By rearranging the order of layers during inference, it maintains the advantages of depth upscaling while preserving the original parameter count. 
Furthermore, it undergoes additional fine-tuning using the Dolphin dataset. The foundational architecture for this experiment is based on [Dolphin](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser).

**Use**

```python
# pip install transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "adalbertojunior/DUSMistral"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
# Format message with the CHATML chat template
messages = [{"role": "user", "content": "Hello, how are you?"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
gen_tokens = model.generate(
    input_ids, 
    max_new_tokens=100, 
    do_sample=True, 
    temperature=0.3,
    )
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```

	---
	datasets:
	- cognitivecomputations/dolphin
	language:
	- en
	---

	This model draws inspiration from [SOLAR](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0), but introduces a novel approach to increasing the model's depth without the traditional method of duplicating layers.
	By rearranging the order of layers during inference, it maintains the advantages of depth upscaling while preserving the original parameter count.
	Furthermore, it undergoes additional fine-tuning using the Dolphin dataset. The foundational architecture for this experiment is based on [Dolphin](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser).

	Use

	```python
	# pip install transformers
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_id = "adalbertojunior/DUSMistral"
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

	# Format message with the CHATML chat template
	messages = [{"role": "user", "content": "Hello, how are you?"}]
	input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")


	gen_tokens = model.generate(
	input_ids,
	max_new_tokens=100,
	do_sample=True,
	temperature=0.3,
	)

	gen_text = tokenizer.decode(gen_tokens[0])
	print(gen_text)
	```