Instructions to use l3utterfly/mistral-7b-v0.1-layla-v4-chatml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use l3utterfly/mistral-7b-v0.1-layla-v4-chatml with Transformers:

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

pipe = pipeline("text-generation", model="l3utterfly/mistral-7b-v0.1-layla-v4-chatml")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("l3utterfly/mistral-7b-v0.1-layla-v4-chatml")
model = AutoModelForCausalLM.from_pretrained("l3utterfly/mistral-7b-v0.1-layla-v4-chatml")
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 l3utterfly/mistral-7b-v0.1-layla-v4-chatml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "l3utterfly/mistral-7b-v0.1-layla-v4-chatml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "l3utterfly/mistral-7b-v0.1-layla-v4-chatml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/l3utterfly/mistral-7b-v0.1-layla-v4-chatml

SGLang

How to use l3utterfly/mistral-7b-v0.1-layla-v4-chatml 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 "l3utterfly/mistral-7b-v0.1-layla-v4-chatml" \
    --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": "l3utterfly/mistral-7b-v0.1-layla-v4-chatml",
		"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 "l3utterfly/mistral-7b-v0.1-layla-v4-chatml" \
        --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": "l3utterfly/mistral-7b-v0.1-layla-v4-chatml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use l3utterfly/mistral-7b-v0.1-layla-v4-chatml with Docker Model Runner:
```
docker model run hf.co/l3utterfly/mistral-7b-v0.1-layla-v4-chatml
```

mistral-7b-v0.1-layla-v4-chatml / Layla.png

l3utterfly

Add model Avatar, tags and AWQ quant links (#2)

73c383a verified over 2 years ago

Download

History Blame Contribute Delete

1.52 MB

Xet Pointer Details

( Raw pointer file )

Xet hash:: dab9c6e895c34f46c9331d487df1f38d8dc9e75124ac5ee99c62f4f3b7b3f17a
Size of remote file:: 1.52 MB
SHA256:: 7a693a9c0aa02ada40d7646bc86137cb45bd2ca53f4757f1cad8749b2ff0ffe3

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.