Instructions to use Nondzu/Mistral-7B-codealpaca-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nondzu/Mistral-7B-codealpaca-lora with Transformers:

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

pipe = pipeline("text-generation", model="Nondzu/Mistral-7B-codealpaca-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nondzu/Mistral-7B-codealpaca-lora")
model = AutoModelForCausalLM.from_pretrained("Nondzu/Mistral-7B-codealpaca-lora")
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]:]))

Inference
Local Apps Settings

vLLM

How to use Nondzu/Mistral-7B-codealpaca-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Nondzu/Mistral-7B-codealpaca-lora

SGLang

How to use Nondzu/Mistral-7B-codealpaca-lora 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 "Nondzu/Mistral-7B-codealpaca-lora" \
    --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": "Nondzu/Mistral-7B-codealpaca-lora",
		"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 "Nondzu/Mistral-7B-codealpaca-lora" \
        --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": "Nondzu/Mistral-7B-codealpaca-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nondzu/Mistral-7B-codealpaca-lora with Docker Model Runner:
```
docker model run hf.co/Nondzu/Mistral-7B-codealpaca-lora
```

Nondzu commited on Oct 27, 2023

Commit

315b039

1 Parent(s): 02903f8

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -8

README.md CHANGED Viewed

@@ -7,14 +7,13 @@ tags:
 # Mistral-7B-codealpaca
-We are thrilled to introduce the Mistral-7B-codealpaca model. This variant is optimized and demonstrates potential in assisting developers as a coding companion. We welcome contributions from testers and enthusiasts to help evaluate its performance.
 ## Training Details
-The model was trained using 3xRTX 3090 for 118 hours.
 [![Built with Axolotl](https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png)](https://github.com/OpenAccess-AI-Collective/axolotl)
 ## Quantised Model Links:
 1.
@@ -44,11 +43,11 @@ Human eval plus: https://github.com/evalplus/evalplus
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63729f35acef705233c87909/azE6LU0qQ9E9u60t5VrMk.png)
-Well, the results are better than I expected
 - Base: `{'pass@1':  0.47560975609756095}`
 - Base + Extra: `{'pass@1': 0.4329268292682927}`
-For reference, we've provided the performance of the original Mistral model alongside Mistral-7B-code-16k-qlora model.
 **   [Nondzu/Mistral-7B-code-16k-qlora](https://huggingface.co/Nondzu/Mistral-7B-code-16k-qlora)**:
@@ -62,7 +61,7 @@ For reference, we've provided the performance of the original Mistral model alon
 ## Model Configuration:
-The following are the configurations for the Mistral-7B-codealpaca-lora:
 ```yaml
 base_model: mistralai/Mistral-7B-Instruct-v0.1
@@ -90,10 +89,8 @@ lora_target_modules:
 lora_target_linear: true
 ```
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63729f35acef705233c87909/5nPgL3ajROKf7dttf4BO0.png)
 ## Additional Projects:
 For other related projects, you can check out:

 # Mistral-7B-codealpaca
+I am thrilled to introduce my Mistral-7B-codealpaca model. This variant is optimized and demonstrates potential in assisting developers as a coding companion. I welcome contributions from testers and enthusiasts to help evaluate its performance.
 ## Training Details
+I trained the model using 3xRTX 3090 for 118 hours.
 [![Built with Axolotl](https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png)](https://github.com/OpenAccess-AI-Collective/axolotl)
 ## Quantised Model Links:
 1.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63729f35acef705233c87909/azE6LU0qQ9E9u60t5VrMk.png)
+Well, the results are better than I expected:
 - Base: `{'pass@1':  0.47560975609756095}`
 - Base + Extra: `{'pass@1': 0.4329268292682927}`
+For reference, I've provided the performance of the original Mistral model alongside my Mistral-7B-code-16k-qlora model.
 **   [Nondzu/Mistral-7B-code-16k-qlora](https://huggingface.co/Nondzu/Mistral-7B-code-16k-qlora)**:
 ## Model Configuration:
+Here are the configurations for my Mistral-7B-codealpaca-lora:
 ```yaml
 base_model: mistralai/Mistral-7B-Instruct-v0.1
 lora_target_linear: true
 ```
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63729f35acef705233c87909/5nPgL3ajROKf7dttf4BO0.png)
 ## Additional Projects:
 For other related projects, you can check out: