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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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_exl2_5bpw / README.md

kisimoff

Upload 10 files

fa5c7c7 verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

4.35 kB

	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	tags:
	- Code Generation
	- Logical Reasoning
	- Problem Solving
	- Text Generation
	- AI Programming Assistant
	---
	<h1>The-Trinity-Coder-7B: 3 Blended Coder Models - Unified Coding Intelligence</h1>

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6405c7afbe1a9448a79b177f/hZkh-FJITEKdX32gn0cu-.png)

	<p><strong>Overview</strong></p>
	<p>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.</p>

	<h2>The Blend</h2>
	<ul>
	<li><strong>Comprehensive Coding Knowledge:</strong> 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.</li>
	<li><strong>Advanced Code Completion:</strong> With its extensive context window, TrinityAI excels in project-level code completion, offering suggestions that are contextually relevant and syntactically accurate.</li>
	<li><strong>Specialized Skills Integration:</strong> The-Trinity-Coder provides code completion but is also good at logical reasoning for its size, mathematical problem-solving, and understanding complex programming concepts.</li>
	</ul>

	<h2>Model Synthesis Approach</h2>
	<p>The blending of the three models into TrinityAI utilized a unique merging technique that focused on preserving the core strengths of each component model:</p>
	<ul>
	<li><strong>beowolx_CodeNinja-1.0-OpenChat-7B:</strong> This model brings an expansive database of coding instructions, refined through Supervised Fine Tuning, making it an advanced coding assistant.</li>
	<li><strong>NeuralExperiment-7b-MagicCoder:</strong> Trained on datasets focusing on logical reasoning, mathematics, and programming, this model enhances TrinityAI's problem-solving and logical reasoning capabilities.</li>
	<li><strong>Speechless-Zephyr-Code-Functionary-7B:</strong> Part of the Moloras experiments, this model contributes enhanced coding proficiency and dynamic skill integration through its unique LoRA modules.</li>
	</ul>

	<h2>Usage and Implementation</h2>
	<pre><code>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))
	</code></pre>

	<h2>Acknowledgments</h2>
	<p>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.</p>




	---
	base_model: []
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merged_folder

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) 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:

	```yaml
	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

	```