Instructions to use uukuguy/speechless-code-mistral-7b-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uukuguy/speechless-code-mistral-7b-v2.0 with Transformers:

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

pipe = pipeline("text-generation", model="uukuguy/speechless-code-mistral-7b-v2.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("uukuguy/speechless-code-mistral-7b-v2.0")
model = AutoModelForMultimodalLM.from_pretrained("uukuguy/speechless-code-mistral-7b-v2.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use uukuguy/speechless-code-mistral-7b-v2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uukuguy/speechless-code-mistral-7b-v2.0"
# 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-code-mistral-7b-v2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/uukuguy/speechless-code-mistral-7b-v2.0

SGLang

How to use uukuguy/speechless-code-mistral-7b-v2.0 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-code-mistral-7b-v2.0" \
    --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-code-mistral-7b-v2.0",
		"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-code-mistral-7b-v2.0" \
        --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-code-mistral-7b-v2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use uukuguy/speechless-code-mistral-7b-v2.0 with Docker Model Runner:
```
docker model run hf.co/uukuguy/speechless-code-mistral-7b-v2.0
```

speechless-code-mistral-7b-v2.0

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

Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities.

Total 343,370 samples 603 MB

jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 21,923 samples.
Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 62,973 samples.
garage-bAInd/Open-Platypus: 100%, 22,760 samples.
WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,077 samples
TokenBender/python_eval_instruct_51k: “python” in output .39,596 samples
OpenHermes code block in output 18,969 samples
CollectiveCognition-2023-09-27 200 samples
ise-uiuc/Magicoder-OSS-Instruct-75K 75,197 samples
meta-math/MetaMathQA 20% 395K 71,706 samples

HumanEval

Metric	Value
humaneval-python

Big Code Models Leaderboard

CodeLlama-34B-Python: 53.29

CodeLlama-34B-Instruct: 50.79

CodeLlama-13B-Instruct: 50.6

CodeLlama-34B: 45.11

CodeLlama-13B-Python: 42.89

CodeLlama-13B: 35.07

lm-evaluation-harness

Open LLM Leaderboard

Metric	Value
ARC
HellaSwag
MMLU
TruthfulQA
Average

Downloads last month: 348

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Merges

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Datasets used to train uukuguy/speechless-code-mistral-7b-v2.0

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Evaluation results

pass@1 on HumanEval
self-reported