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
```

sandeeprdy1729 commited on 16 days ago

Commit

79dd51f

verified ·

1 Parent(s): decae10

Add benchmark results: HE=98.8% HE+=82.9% MBPP=5.4% MBPP+=73.3%

Browse files

Files changed (1) hide show

README.md +47 -2

README.md CHANGED Viewed

@@ -5,13 +5,58 @@ base_model: Qwen/Qwen2.5-Coder-7B-Instruct
 tags: [code, qwen2.5, lora, merged, sft, dpo, grpo]
 library_name: transformers
 pipeline_tag: text-generation
 ---
 # TIMPS-Coder-7B
-Merged by fusing SFT + GRPO + DPO LoRA adapters into `Qwen/Qwen2.5-Coder-7B-Instruct`.
-Merged on 2026-07-16 via PEFT `merge_and_unload`.
 ## Usage
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-7B", device_map="auto", torch_dtype="auto")

 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 4-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")