Instructions to use SVECTOR-CORPORATION/Spec-Coder-4b-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SVECTOR-CORPORATION/Spec-Coder-4b-V1 with Transformers:

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

pipe = pipeline("text-generation", model="SVECTOR-CORPORATION/Spec-Coder-4b-V1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Spec-Coder-4b-V1")
model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Spec-Coder-4b-V1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SVECTOR-CORPORATION/Spec-Coder-4b-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SVECTOR-CORPORATION/Spec-Coder-4b-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SVECTOR-CORPORATION/Spec-Coder-4b-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SVECTOR-CORPORATION/Spec-Coder-4b-V1

SGLang

How to use SVECTOR-CORPORATION/Spec-Coder-4b-V1 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 "SVECTOR-CORPORATION/Spec-Coder-4b-V1" \
    --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": "SVECTOR-CORPORATION/Spec-Coder-4b-V1",
		"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 "SVECTOR-CORPORATION/Spec-Coder-4b-V1" \
        --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": "SVECTOR-CORPORATION/Spec-Coder-4b-V1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SVECTOR-CORPORATION/Spec-Coder-4b-V1 with Docker Model Runner:
```
docker model run hf.co/SVECTOR-CORPORATION/Spec-Coder-4b-V1
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Spec Coder V1

Spec Coder is a cutting-edge, open-source AI model designed to assist with fundamental coding tasks. It is built on the Llama architecture, allowing seamless access via tools like llama.cpp and Ollama. This makes Spec Coder highly compatible with a variety of systems, enabling flexible deployment both locally and in the cloud.

Trained on vast datasets, Spec Coder excels in generating code, completing code snippets, and understanding programming tasks across multiple languages. It can be used for code completion, debugging, and automated code generation, acting as a versatile assistant for developers.

Spec Coder is optimized for integration into developer tools, providing intelligent coding assistance and facilitating research in programming languages. Its advanced transformer-based architecture, with 4 billion parameters, allows it to perform tasks across different environments efficiently.

The model supports various downstream tasks including supervised fine-tuning (SFT) and reinforcement learning (RL) to improve its performance for specific programming tasks.

Training Data

Total Training Tokens: ~4.3 trillion tokens
Corpus: The Stack, StarCoder Training Dataset, The Stack v2, CommitPack, OpenCodeReasoning, English Wikipedia

Training Details

Context Window: 8,192 tokens
Optimization: Standard language modeling objective
Hardware: Cluster of 5 x RTX 4090 GPUs
Training Duration: ~140 days (approximately 6 months)

Benchmarks

RepoBench 1.1 (Python)

Model	2k	4k	8k	12k	16k	Avg	Avg ≤ 8k
Spec-Coder-4b-V1	30.42%	38.55%	36.91%	32.75%	30.34%	34.59%	36.23%

Syntax-Aware Fill-in-the-Middle (SAFIM)

Model	Algorithmic	Control	API	Average
Spec-Coder-4b-V1	38.22%	41.18%	60.45%	46.28%

HumanEval Infilling

Model	Single-Line	Multi-Line	Random Span
Spec-Coder-4b-V1	72.34%	45.65%	39.12%

Limitations

Biases: The model may reflect biases present in the public codebases.
Security: Code generated by the model may contain security vulnerabilities. It is essential to verify and audit the code generated by the model for any potential risks.

Sample Usage

Here are examples of how to run and interact with Spec Coder:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "SVECTOR-CORPORATION/Spec-Coder-4b-V1"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

input_code = "def factorial(n):\n    if n == 0:"

inputs = tokenizer(input_code, return_tensors="pt")

outputs = model.generate(inputs['input_ids'], max_length=50, num_return_sequences=1)

generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Generated Python code:\n", generated_code)

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Safetensors

Model size

4B params

Tensor type

BF16

SVECTOR-CORPORATION
/

Spec-Coder-4b-V1