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
```

config.json consistency check mismatch during model download (expected size != actual size)

by Lwenq - opened Apr 16

Discussion

Lwenq

Apr 16

When loading the model, the download process fails with a consistency check error for config.json. The client expects one file size from metadata, but the
downloaded file has a different size, which aborts model initialization.

Example error:
OSError: Consistency check failed: file should be of size 144 but has size 729 (config.json).

This appears to be a cache/download inconsistency issue (often after interrupted/resumed downloads), and it blocks from_pretrained() from loading the model.

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