Instructions to use BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG with Transformers:

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

pipe = pipeline("text-generation", model="BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG")
model = AutoModelForCausalLM.from_pretrained("BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG

SGLang

How to use BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG 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 "BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG" \
    --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": "BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG",
		"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 "BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG" \
        --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": "BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG with Docker Model Runner:
```
docker model run hf.co/BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG
```

LeoMistral_V06_BRONCO_CARDIO_SUMMARY_CATALOG

This model is a fine-tuned version of LeoLM/leo-mistral-hessianai-7b on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2802
Num Input Tokens Seen: 56173093

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
0.7643	0.25	1377	0.7811	2763564
0.6186	0.5	2754	0.6358	5558436
0.5504	0.75	4131	0.5620	8313712
0.5311	1.0	5508	0.5014	11183370
0.3525	1.25	6885	0.4752	13968826
0.4545	1.5	8262	0.4221	16892566
0.4189	1.75	9639	0.3897	19596674
0.297	2.0	11016	0.3561	22472546
0.2219	2.25	12393	0.3447	25171614
0.2962	2.5	13770	0.3171	28070134
0.2244	2.75	15147	0.2974	30841414
0.307	3.0	16524	0.2774	33754586
0.1942	3.25	17901	0.2824	36458478
0.2212	3.5	19278	0.2762	39268846
0.1737	3.75	20655	0.2713	42149206
0.2141	4.0	22032	0.2692	44987298
0.2088	4.25	23409	0.2795	47803982
0.0971	4.5	24786	0.2801	50611334
0.1567	4.75	26163	0.2802	53414782
0.1923	5.0	27540	0.2802	56173093

Framework versions

Transformers 4.40.2
Pytorch 2.2.2+cu121
Datasets 2.19.0
Tokenizers 0.19.1

Downloads last month: 12

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG

Base model

LeoLM/leo-mistral-hessianai-7b

Finetuned

(2)

this model

Datasets used to train BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG

Collection including BachelorThesis/LeoMistral-7b_V06_BRONCO_CARDIO_SUMMARY_CATALOG

Decoder Information Extraction

Collection

3 items • Updated May 26, 2024