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

sandeeprdy1729

Update README.md

a889d1c verified 12 days ago

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metadata

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

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]))