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
```

Add model Avatar, tags and AWQ quant links

by Kronikus - opened Mar 12, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+24

-1

Files changed (3) hide show

.gitattributes +1 -0
Layla.png +3 -0
README.md +20 -1

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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Layla.png filter=lfs diff=lfs merge=lfs -text

Layla.png ADDED Viewed

Git LFS Details

SHA256: 7a693a9c0aa02ada40d7646bc86137cb45bd2ca53f4757f1cad8749b2ff0ffe3
Pointer size: 132 Bytes
Size of remote file: 1.52 MB

README.md CHANGED Viewed

@@ -1,8 +1,23 @@
 ---
 license: apache-2.0
 ---
 # Model Card
 ### Model Description
 Mistral 7B fine-tuned by the OpenHermes 2.5 dataset optimised for multi-turn conversation and character impersonation.
@@ -40,4 +55,8 @@ Sure! What do you want to know about?<|im_end|>
 <|im_start|>Chiharu
 ```
-[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

 ---
 license: apache-2.0
+tags:
+  - finetuned
+  - text-generation
+  - autotrain_compatible
+  - endpoints_compatible
+  - chatml
+library_name: transformers
+language:
+  - en
+model_creator: l3utterfly
+model_name: mistral-7b-v0.1-layla-v4-chatml
+model_type: mistral
+pipeline_tag: text-generation
 ---
 # Model Card
+![image/png](Layla.png)
 ### Model Description
 Mistral 7B fine-tuned by the OpenHermes 2.5 dataset optimised for multi-turn conversation and character impersonation.
 <|im_start|>Chiharu
 ```
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+## Model Quants
+[solidrust/-AWQ](https://huggingface.co/solidrust/Layla-7B-v4-AWQ)