Instructions to use Nondzu/Mistral-7B-code-16k-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nondzu/Mistral-7B-code-16k-qlora with Transformers:

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

pipe = pipeline("text-generation", model="Nondzu/Mistral-7B-code-16k-qlora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nondzu/Mistral-7B-code-16k-qlora")
model = AutoModelForCausalLM.from_pretrained("Nondzu/Mistral-7B-code-16k-qlora")
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
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nondzu/Mistral-7B-code-16k-qlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nondzu/Mistral-7B-code-16k-qlora"
# 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-code-16k-qlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nondzu/Mistral-7B-code-16k-qlora

SGLang

How to use Nondzu/Mistral-7B-code-16k-qlora 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-code-16k-qlora" \
    --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-code-16k-qlora",
		"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-code-16k-qlora" \
        --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-code-16k-qlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nondzu/Mistral-7B-code-16k-qlora with Docker Model Runner:
```
docker model run hf.co/Nondzu/Mistral-7B-code-16k-qlora
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral-7B-code-16k-qlora

I'm excited to announce the release of a new model called Mistral-7B-code-16k-qlora. This small and fast model shows a lot of promise for supporting coding or acting as a copilot. I'm currently looking for people to help me test it out!

Additional Information

This model was trained on 3x RTX 3090 in my homelab, using around 65kWh for approximately 23 cents, which is equivalent to around $15 for electricity.

Quantised:

Download by qBittorrent:

Torrent file: https://github.com/Nondzu/LlamaTor/blob/torrents/torrents/Nondzu_Mistral-7B-code-16k-qlora.torrent

Dataset:

nickrosh/Evol-Instruct-Code-80k-v1 https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1

Prompt template: Alpaca

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

### Instruction:
{prompt}

### Response:

eval plus

Human eval plus: https://github.com/evalplus/evalplus

Nondzu mistral-7b-code 
Base
{'pass@1': 0.3353658536585366}
Base + Extra
{'pass@1': 0.2804878048780488}

to compare here is original Mistral model tested on the same machine

Mistral 7b
Base
{'pass@1': 0.2926829268292683}
Base + Extra
{'pass@1': 0.24390243902439024}

Settings:

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: false
load_in_4bit: true
strict: false

datasets:
  - path: nickrosh/Evol-Instruct-Code-80k-v1
    type: oasst
dataset_prepared_path:   
val_set_size: 0.01
output_dir: ./Mistral-7B-Evol-Instruct-16k-test11
adapter: qlora
lora_model_dir:
# 16384 8192 4096  2048
sequence_len: 16384
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
lora_fan_in_fan_out:

wandb_project: mistral-code
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 8
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
eval_steps: 20
save_steps:
debug:
# deepspeed:
deepspeed: deepspeed/zero2.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

Check my other projects:

https://github.com/Nondzu/LlamaTor

Downloads last month: 218

Safetensors

Model size

7B params

Tensor type

F16

Model tree for Nondzu/Mistral-7B-code-16k-qlora

Quantizations

3 models

Nondzu
/

Mistral-7B-code-16k-qlora