Instructions to use nabin2004/AOS-qwen25-coder-7b-manim-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nabin2004/AOS-qwen25-coder-7b-manim-merged with Transformers:

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

pipe = pipeline("text-generation", model="nabin2004/AOS-qwen25-coder-7b-manim-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nabin2004/AOS-qwen25-coder-7b-manim-merged")
model = AutoModelForCausalLM.from_pretrained("nabin2004/AOS-qwen25-coder-7b-manim-merged", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nabin2004/AOS-qwen25-coder-7b-manim-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nabin2004/AOS-qwen25-coder-7b-manim-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nabin2004/AOS-qwen25-coder-7b-manim-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nabin2004/AOS-qwen25-coder-7b-manim-merged

SGLang

How to use nabin2004/AOS-qwen25-coder-7b-manim-merged 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 "nabin2004/AOS-qwen25-coder-7b-manim-merged" \
    --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": "nabin2004/AOS-qwen25-coder-7b-manim-merged",
		"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 "nabin2004/AOS-qwen25-coder-7b-manim-merged" \
        --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": "nabin2004/AOS-qwen25-coder-7b-manim-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nabin2004/AOS-qwen25-coder-7b-manim-merged with Docker Model Runner:
```
docker model run hf.co/nabin2004/AOS-qwen25-coder-7b-manim-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AOS Qwen2.5-Coder-7B Manim SFT (Merged)

Full merged bf16 weights (LoRA baked into base) for Manim animation generation via multi-turn tool calling.

Model URL: https://huggingface.co/nabin2004/AOS-qwen25-coder-7b-manim-merged

Related repos

Artifact	Repo
LoRA adapter	nabin2004/AOS-qwen25-coder-7b-manim-sft
GGUF (Ollama / llama.cpp)	nabin2004/AOS-qwen25-coder-7b-manim-gguf

Base model

Merged from Qwen/Qwen2.5-Coder-7B-Instruct and the AOS Manim SFT LoRA adapter.

Training data

Fine-tuned on nabin2004/AOS-Trajectories using the AOS Phase 1 SFT trainer (apps/sft).

Usage

Load with Transformers

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

token = os.environ.get("HF_TOKEN")
model_id = "nabin2004/AOS-qwen25-coder-7b-manim-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id, token=token)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    token=token,
)

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for nabin2004/AOS-qwen25-coder-7b-manim-merged

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Finetuned

(450)

this model

Quantizations

1 model