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
```

Mistral-7B-codealpaca-lora

File size: 3,107 Bytes

---
license: apache-2.0
tags:
- code
- mistral
---

# 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. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GPTQ
2. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GGUF
3. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-AWQ

## Download by qBittorrent:

#### Torrent file:  https://github.com/Nondzu/LlamaTor/blob/torrents/torrents/Nondzu_Mistral-7B-codealpaca-lora.torrent

## Dataset:

- Dataset Name: theblackcat102/evol-codealpaca-v1
- Dataset Link: [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1)

## Prompt template: Alpaca

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

```


## Performance (evalplus)
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, 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)**:

- Base: `{'pass@1': 0.3353658536585366}`
- Base + Extra: `{'pass@1': 0.2804878048780488}`

** [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)**:

- Base: `{'pass@1': 0.2926829268292683}`
- Base + Extra: `{'pass@1': 0.24390243902439024}`

## Model Configuration:

Here are the configurations for my Mistral-7B-codealpaca-lora:

```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.1
base_model_config: mistralai/Mistral-7B-Instruct-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
  - path: theblackcat102/evol-codealpaca-v1
    type: oasst
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./nondzu/Mistral-7B-codealpaca-test14
adapter: lora
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
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:

- [LlamaTor on GitHub](https://github.com/Nondzu/LlamaTor)