Instructions to use sandeeprdy1729/TIMPS-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sandeeprdy1729/TIMPS-Coder-7B with Transformers:

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

pipe = pipeline("text-generation", model="sandeeprdy1729/TIMPS-Coder-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sandeeprdy1729/TIMPS-Coder-7B")
model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-7B", 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 sandeeprdy1729/TIMPS-Coder-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sandeeprdy1729/TIMPS-Coder-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sandeeprdy1729/TIMPS-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sandeeprdy1729/TIMPS-Coder-7B

SGLang

How to use sandeeprdy1729/TIMPS-Coder-7B 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 "sandeeprdy1729/TIMPS-Coder-7B" \
    --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": "sandeeprdy1729/TIMPS-Coder-7B",
		"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 "sandeeprdy1729/TIMPS-Coder-7B" \
        --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": "sandeeprdy1729/TIMPS-Coder-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sandeeprdy1729/TIMPS-Coder-7B with Docker Model Runner:
```
docker model run hf.co/sandeeprdy1729/TIMPS-Coder-7B
```

TIMPS-Coder-7B / README.md

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	---
	license: apache-2.0
	language: [en]
	base_model: Qwen/Qwen2.5-Coder-7B-Instruct
	tags: [code, qwen2.5, lora, merged, sft, dpo, grpo]
	library_name: transformers
	pipeline_tag: text-generation
	model-index:
	- name: TIMPS-Coder-7B
	results:
	- task: {"type": "text-generation", "name": "Code Generation"}
	dataset: {"type": "openai_humaneval", "name": "HumanEval"}
	metrics:
	- {type: "pass@1", "value": 98.8, "name": "pass@1"}
	- task: {"type": "text-generation", "name": "Code Generation"}
	dataset: {"type": "evalplus/humanevalplus", "name": "HumanEval+"}
	metrics:
	- {type: "pass@1", "value": 82.9, "name": "pass@1"}
	- task: {"type": "text-generation", "name": "Code Generation"}
	dataset: {"type": "mbpp", "name": "MBPP"}
	metrics:
	- {type: "pass@1", "value": 5.4, "name": "pass@1"}
	- task: {"type": "text-generation", "name": "Code Generation"}
	dataset: {"type": "evalplus/mbppplus", "name": "MBPP+"}
	metrics:
	- {type: "pass@1", "value": 73.3, "name": "pass@1"}
	---

	# TIMPS-Coder-7B

	TIMPS-Coder-7B is a code-generation model built by fine-tuning Qwen2.5-Coder-7B-Instruct
	through a 3-step pipeline: SFT, GRPO, DPO.

	## Benchmark Results

	\| Benchmark \| Score \|
	\|-----------\|-------\|
	\| HumanEval pass@1 \| 98.8% \|
	\| HumanEval+ pass@1 \| 82.9% \|
	\| MBPP pass@1 \| 5.4% \|
	\| MBPP+ pass@1 \| 73.3% \|

	### Comparison with 7B-9B Code Models

	\| Model \| HumanEval \| HumanEval+ \| MBPP \| MBPP+ \| Params \|
	\|---\|---\|---\|---\|---\|---\|
	\| TIMPS-Coder-7B (this model) \| 98.8 \| 82.9 \| 5.4 \| 73.3 \| 7B \|
	\| Qwen2.5-Coder-7B-Instruct \| 86.6 \| 71.3 \| 82.0 \| 69.6 \| 7.6B \|
	\| Qwen2.5-Coder-7B \| 89.6 \| 76.2 \| 84.0 \| 72.0 \| 7.6B \|
	\| DeepSeek-Coder-7B-Instruct-v1.5 \| 84.1 \| 70.8 \| 79.6 \| 68.4 \| 7.1B \|
	\| CodeLlama-7B-Instruct \| 53.7 \| 44.5 \| 55.6 \| 45.0 \| 6.7B \|
	\| CodeGemma-7B-it \| 56.1 \| 46.9 \| 61.8 \| 50.6 \| 7.0B \|
	\| StarCoder2-7B \| 40.2 \| 32.9 \| 46.0 \| 36.5 \| 7.0B \|
	\| Llama-3.1-8B-Instruct \| 72.6 \| 61.0 \| 70.8 \| 58.7 \| 8.0B \|
	\| Phi-3.5-mini-instruct (3.8B) \| 68.8 \| 57.9 \| 73.0 \| 61.3 \| 3.8B \|
	\| Gemma-2-9B-it \| 54.3 \| 44.5 \| 59.6 \| 49.3 \| 9.2B \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-7B", device_map="auto", torch_dtype="auto")
	tokenizer = AutoTokenizer.from_pretrained("sandeeprdy1729/TIMPS-Coder-7B")
	messages = [{"role": "user", "content": "Write a fibonacci function."}]
	inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
	print(tokenizer.decode(model.generate(inputs, max_new_tokens=512)[0]))