Instructions to use uukuguy/speechless-sparsetral-mistral-16x7b-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uukuguy/speechless-sparsetral-mistral-16x7b-MoE with Transformers:

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

pipe = pipeline("text-generation", model="uukuguy/speechless-sparsetral-mistral-16x7b-MoE", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("uukuguy/speechless-sparsetral-mistral-16x7b-MoE", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use uukuguy/speechless-sparsetral-mistral-16x7b-MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uukuguy/speechless-sparsetral-mistral-16x7b-MoE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uukuguy/speechless-sparsetral-mistral-16x7b-MoE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/uukuguy/speechless-sparsetral-mistral-16x7b-MoE

SGLang

How to use uukuguy/speechless-sparsetral-mistral-16x7b-MoE 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 "uukuguy/speechless-sparsetral-mistral-16x7b-MoE" \
    --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": "uukuguy/speechless-sparsetral-mistral-16x7b-MoE",
		"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 "uukuguy/speechless-sparsetral-mistral-16x7b-MoE" \
        --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": "uukuguy/speechless-sparsetral-mistral-16x7b-MoE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use uukuguy/speechless-sparsetral-mistral-16x7b-MoE with Docker Model Runner:
```
docker model run hf.co/uukuguy/speechless-sparsetral-mistral-16x7b-MoE
```

speechless-sparsetral-mistral-16x7b-MoE / README.md

uukuguy

Update README.md

6e3b24c verified over 2 years ago

preview code

Raw History Blame Contribute Delete

2.73 kB

	---
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	datasets:
	- jondurbin/airoboros-2.2
	- Open-Orca/OpenOrca
	- garage-bAInd/Open-Platypus
	- WizardLM/WizardLM_evol_instruct_V2_196k
	- TokenBender/python_eval_instruct_51k
	- codefuse-ai/Evol-Instruction-66k
	tags:
	- llama-2
	- code
	license: llama2
	model-index:
	- name: SpeechlessCoder
	results:
	- task:
	type: text-generation
	dataset:
	type: openai_humaneval
	name: HumanEval
	metrics:
	- name: pass@1
	type: pass@1
	value:
	verified: false
	---

	<p><h1> speechless-sparsetral-16x7b-MoE </h1></p>

	speechless-sparsetral-16x7b-MoE is the MoE upgraded version of [speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0). The MoE fine-tuning adopts [Parameter-Efficient Sparsity Crafting (PESC)](https://arxiv.org/abs/2401.02731), which is an efficient fine-tuning architecture that uses LoRA modules as expert models, similar to the concept of [multi-loras](https://github.com/uukuguy/multi_loras). The model size is approximately 10B.

	Specifically, Mistral-7B-0.1 is used as the base model, with 16 experts and 4 expert outputs selected for inference. The fine-tuning dataset includes codefuse-ai/Evol-Instruction-66k to enhance the model's code generation ability. The specific datasets are as follows:

	- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
	- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
	- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
	- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
	- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
	- Spider: 8,659 samples
	- codefuse-ai/Evol-Instruction-66k: 100%, 66,862 samples

	## Alpaca Prompt Format

	```
	### Instruction:
	<instruction>
	### Response:
	```

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name_or_path="uukuguy/speechless-sparsetral-16x7b-MoE"
	tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=True).eval()

	system = ""Below is an instruction that describes a task.\nWrite a response that appropriately completes the request.\n\n""
	prompt = f"{system}\n\n### Instruction:\n{instruction}\n\n### Response:"

	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
	pred = model.generate(**inputs, max_length=4096, do_sample=True, top_k=50, top_p=0.99, temperature=0.9, num_return_sequences=1)
	print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
	```