Instructions to use kisimoff/The-Trinity-Coder-7B_exl2_5bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kisimoff/The-Trinity-Coder-7B_exl2_5bpw with Transformers:

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

pipe = pipeline("text-generation", model="kisimoff/The-Trinity-Coder-7B_exl2_5bpw")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kisimoff/The-Trinity-Coder-7B_exl2_5bpw")
model = AutoModelForCausalLM.from_pretrained("kisimoff/The-Trinity-Coder-7B_exl2_5bpw")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kisimoff/The-Trinity-Coder-7B_exl2_5bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kisimoff/The-Trinity-Coder-7B_exl2_5bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kisimoff/The-Trinity-Coder-7B_exl2_5bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kisimoff/The-Trinity-Coder-7B_exl2_5bpw

SGLang

How to use kisimoff/The-Trinity-Coder-7B_exl2_5bpw 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 "kisimoff/The-Trinity-Coder-7B_exl2_5bpw" \
    --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": "kisimoff/The-Trinity-Coder-7B_exl2_5bpw",
		"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 "kisimoff/The-Trinity-Coder-7B_exl2_5bpw" \
        --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": "kisimoff/The-Trinity-Coder-7B_exl2_5bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kisimoff/The-Trinity-Coder-7B_exl2_5bpw with Docker Model Runner:
```
docker model run hf.co/kisimoff/The-Trinity-Coder-7B_exl2_5bpw
```

The-Trinity-Coder-7B: 3 Blended Coder Models - Unified Coding Intelligence

Overview

The-Trinity-Coder-7B derives from the fusion of three distinct AI models, each specializing in unique aspects of coding and programming challenges. This model unifies the capabilities of beowolx_CodeNinja-1.0-OpenChat-7B, NeuralExperiment-7b-MagicCoder, and Speechless-Zephyr-Code-Functionary-7B, creating a versatile and powerful new blended model. The integration of these models was achieved through a merging technique, in order to harmonize their strengths and mitigate their individual weaknesses.

The Blend

Comprehensive Coding Knowledge: TrinityAI combines knowledge of coding instructions across a wide array of programming languages, including Python, C, C++, Rust, Java, JavaScript, and more, making it a versatile assistant for coding projects of any scale.
Advanced Code Completion: With its extensive context window, TrinityAI excels in project-level code completion, offering suggestions that are contextually relevant and syntactically accurate.
Specialized Skills Integration: The-Trinity-Coder provides code completion but is also good at logical reasoning for its size, mathematical problem-solving, and understanding complex programming concepts.

Model Synthesis Approach

The blending of the three models into TrinityAI utilized a unique merging technique that focused on preserving the core strengths of each component model:

beowolx_CodeNinja-1.0-OpenChat-7B: This model brings an expansive database of coding instructions, refined through Supervised Fine Tuning, making it an advanced coding assistant.
NeuralExperiment-7b-MagicCoder: Trained on datasets focusing on logical reasoning, mathematics, and programming, this model enhances TrinityAI's problem-solving and logical reasoning capabilities.
Speechless-Zephyr-Code-Functionary-7B: Part of the Moloras experiments, this model contributes enhanced coding proficiency and dynamic skill integration through its unique LoRA modules.

Usage and Implementation

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "YourRepository/The-Trinity-Coder-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Acknowledgments

Special thanks to the creators and contributors of CodeNinja, NeuralExperiment-7b-MagicCoder, and Speechless-Zephyr-Code-Functionary-7B for providing the base models for blending.

base_model: [] library_name: transformers tags:

mergekit
merge

merged_folder

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using uukuguy_speechless-zephyr-code-functionary-7b as a base.

Models Merged

The following models were included in the merge: *uukuguy_speechless-zephyr-code-functionary-7b

Kukedlc_NeuralExperiment-7b-MagicCoder-v7.5
beowolx_CodeNinja-1.0-OpenChat-7B

Configuration

The following YAML configuration was used to produce this model:

base_model: X:/text-generation-webui-main/models/uukuguy_speechless-zephyr-code-functionary-7b
models:
  - model: X:/text-generation-webui-main/models/beowolx_CodeNinja-1.0-OpenChat-7B
    parameters:
      density: 0.5
      weight: 0.4
  - model: X:/text-generation-webui-main/models/Kukedlc_NeuralExperiment-7b-MagicCoder-v7.5
    parameters:
      density: 0.5
      weight: 0.4
merge_method: ties
parameters:
  normalize: true
dtype: float16

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Paper for kisimoff/The-Trinity-Coder-7B_exl2_5bpw

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19