Instructions to use wefamm/aiAI_coder_V1.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wefamm/aiAI_coder_V1.4B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/root/autodl-tmp/Qwen3.5-4B-Thinking")
model = PeftModel.from_pretrained(base_model, "wefamm/aiAI_coder_V1.4B")

Transformers

How to use wefamm/aiAI_coder_V1.4B with Transformers:

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

pipe = pipeline("text-generation", model="wefamm/aiAI_coder_V1.4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wefamm/aiAI_coder_V1.4B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wefamm/aiAI_coder_V1.4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wefamm/aiAI_coder_V1.4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wefamm/aiAI_coder_V1.4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wefamm/aiAI_coder_V1.4B

SGLang

How to use wefamm/aiAI_coder_V1.4B 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 "wefamm/aiAI_coder_V1.4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wefamm/aiAI_coder_V1.4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "wefamm/aiAI_coder_V1.4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wefamm/aiAI_coder_V1.4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wefamm/aiAI_coder_V1.4B with Docker Model Runner:
```
docker model run hf.co/wefamm/aiAI_coder_V1.4B
```

base_model: Qwen/Qwen3.5-4B library_name: transformers license: apache-2.0 language: - en pipeline_tag: text-generation tags: - qwen3.5 - coding - reasoning - lora - sft - agentic - python - javascript - sql base_model_relation: finetune

THE MUCH IMPROVED V2 IS NOW RELEASE @ wefamm/aiAI_coder_V2_4B

aiAI_coder_V1.4B

4B parameters • Fine-tuned for coding & agentic tasks • <1 hour training

🤗 Model | 📊 Evaluation

Model Overview

aiAI_coder_V1.4B is a specialized coding and agentic assistant fine-tuned from Qwen/Qwen3.5-4B. It is designed to excel in:

Multi-language Code Generation: Python, JavaScript, TypeScript, and SQL
Reasoning & Problem-Solving: Step-by-step thinking with <think> tag support
Agentic Workflows: Tool calling, multi-turn interactions, and task completion
Instruction Adherence: Following complex, constrained prompts with high accuracy
Cost Efficiency: Optimized for low-latency inference on consumer hardware

This model was distilled from high-quality Grok 4.6 completions and trained with a highly efficient Supervised Fine-Tuning (SFT) recipe, achieving strong coding benchmark performance at a fraction of the cost of larger models [citation:1][citation:7].

Model Details

Model Description

aiAI_coder_V1.4B is an instruction-tuned language model optimized for code synthesis, debugging, and agentic assistance. It supports:

Fast, deterministic responses for coding tasks
Accurate code generation in Python, JavaScript, TypeScript, and SQL
Multi-turn reasoning with explicit thinking separation (<think>...</think>)
Native support for tool calling and structured outputs

The model can be used as a lightweight, cost-effective alternative to frontier models in many developer workflows.

Developed by: [aiAI]
Funded by: [nitrous-0xide (owner & founder)]
Model type: Text-generation / Instruction-following
Language(s): English
License: Apache-2.0
Finetuned from: Qwen/Qwen3.5-4B [citation:1][citation:10]

Uses

Direct Use

The model can be used as-is for:

Interactive coding assistants and chatbots
Code completion and debugging in IDEs
Generating unit tests and documentation
SQL query generation and optimization
Agentic workflows requiring planning and tool use [citation:1]

Out-of-Scope Use

Generating malicious code or content that violates applicable laws
Real-time decision-making in safety-critical systems
Any use that violates the Apache-2.0 license

Bias, Risks, and Limitations

Hallucination: May occasionally produce plausible but incorrect code or explanations
Security: Generated code should be reviewed for security vulnerabilities
Context Window: While optimized for 262K context, performance may degrade at extreme lengths [citation:7]
Language Coverage: Primarily trained on English data; performance on other languages is limited

Recommendations

Human-in-the-loop review of generated code before deployment
Use explicit safety filters for disallowed content
Test outputs in sandboxed environments when executing generated code

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