Instructions to use ayoubkirouane/Mistral-SLERP-Merged7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayoubkirouane/Mistral-SLERP-Merged7B with Transformers:

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

pipe = pipeline("text-generation", model="ayoubkirouane/Mistral-SLERP-Merged7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/Mistral-SLERP-Merged7B")
model = AutoModelForMultimodalLM.from_pretrained("ayoubkirouane/Mistral-SLERP-Merged7B")
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 ayoubkirouane/Mistral-SLERP-Merged7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayoubkirouane/Mistral-SLERP-Merged7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayoubkirouane/Mistral-SLERP-Merged7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayoubkirouane/Mistral-SLERP-Merged7B

SGLang

How to use ayoubkirouane/Mistral-SLERP-Merged7B 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 "ayoubkirouane/Mistral-SLERP-Merged7B" \
    --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": "ayoubkirouane/Mistral-SLERP-Merged7B",
		"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 "ayoubkirouane/Mistral-SLERP-Merged7B" \
        --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": "ayoubkirouane/Mistral-SLERP-Merged7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ayoubkirouane/Mistral-SLERP-Merged7B with Docker Model Runner:
```
docker model run hf.co/ayoubkirouane/Mistral-SLERP-Merged7B
```

ayoubkirouane commited on Jan 19, 2024

Commit

e12008b

verified ·

1 Parent(s): fd1fbf2

Update README.md

Browse files

Files changed (1) hide show

README.md +22 -4

README.md CHANGED Viewed

@@ -7,10 +7,6 @@ tags:
 - merge
 ---
-# merge
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
 ## Merge Details
 ### Merge Method
@@ -65,4 +61,26 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/Mistral-Merged7B")
 model = AutoModelForCausalLM.from_pretrained("ayoubkirouane/Mistral-Merged7B")
 ```

 - merge
 ---
 ## Merge Details
 ### Merge Method
 tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/Mistral-Merged7B")
 model = AutoModelForCausalLM.from_pretrained("ayoubkirouane/Mistral-Merged7B")
+# 4 bit :
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+nf4_config = BitsAndBytesConfig(
+   load_in_4bit=True,
+   bnb_4bit_quant_type="nf4",
+   bnb_4bit_use_double_quant=True,
+   bnb_4bit_compute_dtype=torch.bfloat16
+)
+model = AutoModelForCausalLM.from_pretrained(
+    "ayoubkirouane/Mistral-SLERP-Merged7B",
+    device_map='auto',
+    quantization_config=nf4_config,
+    use_cache=False
+)
+tokenizer = AutoTokenizer.from_pretrained("ayoubkirouane/Mistral-SLERP-Merged7B")
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
 ```