Instructions to use LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2

SGLang

How to use LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2 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 "LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/speechless-zephyr-code-functionary-7b-3.0bpw-h6-exl2
```

speechless-zephyr-code-functionary-7b

This model is the one of the moloras (Mixture-of-Multi-LoRAs) experiments.

Extract LoRA modules from below models (all based Mistral-7B-v0.1), each LoRA module has its own unique skills. By using multi-loras, they can be combined together statically or dynamically to form a versatile new model.

HuggingFaceH4/zephyr-7b-beta (Uncensored Model)
meetkai/functionary-small-v2.2 (Execute functions/plugins)
uukuguy/speechless-code-mistral-7b-v1.0 (Enhance Coding)

The entire process is completed through the use of extract-lora, merge-lora, and lora-hub provided by multi-loras.

The router of mixture-of-multi-loras enables an automatic assembling of LoRA modules, using a gradientfree approach to obtain the coefficients of LoRA modules and requiring only a handful of inference steps for unseen tasks.

Code: https://github.com/uukuguy/multi_loras

LM-Evaluation-Harness

Open LLM Leaderboard

Metric	Value
ARC	61.52
HellaSwag	83.88
MMLU	64.71
TruthfulQA	44.99
Winogrande	78.69
GSM8K	43.82
Average	62.93

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