Instructions to use RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf:Q4_K_M

Run and chat with the model

lemonade run user.JingyaoLi_-_MoTCoder-15B-v1.0-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

MoTCoder-15B-v1.0 - GGUF

Model creator: https://huggingface.co/JingyaoLi/
Original model: https://huggingface.co/JingyaoLi/MoTCoder-15B-v1.0/

Name	Quant method	Size
MoTCoder-15B-v1.0.Q2_K.gguf	Q2_K	5.78GB
MoTCoder-15B-v1.0.Q3_K_S.gguf	Q3_K_S	6.5GB
MoTCoder-15B-v1.0.Q3_K.gguf	Q3_K	7.66GB
MoTCoder-15B-v1.0.Q3_K_M.gguf	Q3_K_M	6.85GB
MoTCoder-15B-v1.0.Q3_K_L.gguf	Q3_K_L	4.19GB
MoTCoder-15B-v1.0.IQ4_XS.gguf	IQ4_XS	2.65GB
MoTCoder-15B-v1.0.Q4_0.gguf	Q4_0	8.37GB
MoTCoder-15B-v1.0.IQ4_NL.gguf	IQ4_NL	8.46GB
MoTCoder-15B-v1.0.Q4_K_S.gguf	Q4_K_S	8.46GB
MoTCoder-15B-v1.0.Q4_K.gguf	Q4_K	9.28GB
MoTCoder-15B-v1.0.Q4_K_M.gguf	Q4_K_M	9.28GB
MoTCoder-15B-v1.0.Q4_1.gguf	Q4_1	9.26GB
MoTCoder-15B-v1.0.Q5_0.gguf	Q5_0	10.14GB
MoTCoder-15B-v1.0.Q5_K_S.gguf	Q5_K_S	10.14GB
MoTCoder-15B-v1.0.Q5_K.gguf	Q5_K	10.71GB
MoTCoder-15B-v1.0.Q5_K_M.gguf	Q5_K_M	10.71GB
MoTCoder-15B-v1.0.Q5_1.gguf	Q5_1	11.02GB
MoTCoder-15B-v1.0.Q6_K.gguf	Q6_K	12.01GB
MoTCoder-15B-v1.0.Q8_0.gguf	Q8_0	15.5GB

Original model description:

license: bigscience-openrail-m metrics: - code_eval library_name: transformers tags: - code

🏠 MoTCoder

• 🤗 Data • 🤗 Model • 🐱 Code • 📃 Paper

Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks. However, their performance tends to falter when confronted with more challenging programming problems. We observe that conventional models often generate solutions as monolithic code blocks, restricting their effectiveness in tackling intricate questions. To overcome this limitation, we present Modular-of-Thought Coder (MoTCoder). We introduce a pioneering framework for MoT instruction tuning, designed to promote the decomposition of tasks into logical sub-tasks and sub-modules. Our investigations reveal that, through the cultivation and utilization of sub-modules, MoTCoder significantly improves both the modularity and correctness of the generated solutions, leading to substantial relative pass@1 improvements of 12.9% on APPS and 9.43% on CodeContests.

Performance

Performance on APPS

Model	Size	Pass@	Introductory	Interview	Competition	All
CodeT5	770M	1	6.60	1.03	0.30	2.00
GPT-Neo	2.7B	1	14.68	9.85	6.54	10.15
		5	19.89	13.19	9.90	13.87
GPT-2	0.1B	1	5.64	6.93	4.37	6.16
		5	13.81	10.97	7.03	10.75
	1.5B	1	7.40	9.11	5.05	7.96
		5	16.86	13.84	9.01	13.48
GPT-3	175B	1	0.57	0.65	0.21	0.55
StarCoder	15B	1	7.25	6.89	4.08	6.40
WizardCoder	15B	1	26.04	4.21	0.81	7.90
MoTCoder	15B	1	33.80	19.70	11.09	20.80
text-davinci-002	-	1	-	-	-	7.48
code-davinci-002	-	1	29.30	6.40	2.50	10.20
GPT3.5	-	1	48.00	19.42	5.42	22.33

Performance on CodeContests

Model	Size	Revision	Val pass@1	Val pass@5	Test pass@1	Test pass@5	Average pass@1	Average pass@5
code-davinci-002	-	-	-	-	1.00	-	1.00	-
code-davinci-002 + CodeT	-	5	-	-	3.20	-	3.20	-
WizardCoder	15B	-	1.11	3.18	1.98	3.27	1.55	3.23
WizardCoder + CodeChain	15B	5	2.35	3.29	2.48	3.30	2.42	3.30
MoTCoder	15B	-	2.39	7.69	6.18	12.73	4.29	10.21
GPT3.5	-	-	6.81	16.23	5.82	11.16	6.32	13.70

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GGUF

Model size

16B params

Architecture

starcoder

Hardware compatibility

2-bit

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8-bit

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Paper for RichardErkhov/JingyaoLi_-_MoTCoder-15B-v1.0-gguf

MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks

Paper • 2312.15960 • Published Dec 26, 2023 • 1